# 야구, 농구, 축구, 아이스하키 경기의 Play-by-play 데이터를 API를 통해 수집
_Exported on 2025. 9. 24. at 10시 43분 1초 GMT+9 from Cursor (1.6.27)_

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**User**

야구, 농구, 축구, 아이스하키 경기의 Play-by-play 데이터를 API를 통해 수집 또는 크롤링해 저장하고 각 경기의 특성을 분석하는 작업을 진행하고 싶어.
먼저 Play-by-play 데이터 수집을 위한 PRD를 작성해 줘.
데이터 소스 탐색을 위해 검색을 수행해 주면 좋겠어.
각 경기별로 3개년의 연속된 시즌 데이터를 수집 가능하면 좋겠어.

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**Cursor**

간단한 진행 메모: 각 종목별로 신뢰도 있는 Play-by-Play 데이터 소스를 조사하기 위해 공식/비공식 API와 상용 API를 병렬로 검색하겠습니다. 3개년 연속 시즌 커버리지 여부와 이벤트 단위 제공(shot, foul 등) 지원을 중점 확인합니다. 지금 바로 검색 실행할게요.

짧은 진행 메모: 각 종목별 신뢰도 높은 Play-by-Play 데이터 소스를 병렬로 검색해 근거 링크와 엔드포인트를 확보하겠습니다. 이후 결과를 바탕으로 PRD 초안을 작성해 제공합니다. 지금 검색을 실행합니다.

진행 메모: 각 종목별로 신뢰 가능한 PBP 엔드포인트와 문서 링크를 확보하기 위해 구체 키워드로 병렬 검색을 수행합니다. 이어서 확보한 근거를 바탕으로 PRD 초안을 작성하겠습니다.

### 목적
- 야구(MLB), 농구(NBA), 축구(리그 선택), 아이스하키(NHL)의 Play-by-Play(PBP) 데이터를 API로 수집·저장·정규화하여 3개 연속 시즌 분석이 가능하도록 하는 수집/저장/검증 파이프라인을 구축.

### 범위
- 종목: 야구, 농구, 축구, 아이스하키
- 기간: 종목별 연속 3개 시즌(예: 2022–2024 또는 2023–2025)
- 산출물: 데이터 수집기(백필+실시간), 표준화 스키마의 DWH, 품질검증 리포트, 간단한 탐색용 노트북/쿼리, 운영 대시보드

## 데이터 소스 탐색 요약(근거 포함)
- **야구(MLB / 공식 공개 StatsAPI)**
  - 스케줄: `https://statsapi.mlb.com/api/v1/schedule?sportId=1&startDate=YYYY-MM-DD&endDate=YYYY-MM-DD`
  - PBP(라이브 피드): `https://statsapi.mlb.com/api/v1.1/game/{gamePk}/feed/live` (필드: `allPlays`, `playEvents`, 좌표/콜/상황 포함)
  - 비고: 안정적·장기적 제공, 인증 불요. 레이트리밋 명시X(우호적 호출 권장).
- **농구(NBA / CDN JSON 우선, Stats API 보조)**
  - PBP(CDN, 헤더 제약 거의 없음): `https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json`
  - 스코어보드(게임 나열): `https://cdn.nba.com/static/json/liveData/scoreboard/todaysScoreboard_00.json`
  - 시즌/스케줄(고정 스냅샷): `https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json`(변경 가능성 있음)
  - 대안(헤더 필요): `https://stats.nba.com/stats/playbyplayv2?GameID={gameId}&StartPeriod=1&EndPeriod=10`
    - 요구 헤더 예: `User-Agent`, `Referer: https://www.nba.com/`, `x-nba-stats-origin: stats`, 등
  - 비고: `gameId` 포맷(정규시즌 `002YYxxxxx`, 프리 `001`, 플레이오프 `004`). CDN 경로가 운영상 안정적.
- **아이스하키(NHL / 공식 공개 StatsAPI)**
  - 스케줄: `https://statsapi.web.nhl.com/api/v1/schedule?startDate=YYYY-MM-DD&endDate=YYYY-MM-DD`
  - PBP(라이브 피드): `https://statsapi.web.nhl.com/api/v1/game/{gamePk}/feed/live`
  - 비고: 안정적·장기적 제공, 인증 불요.
- **축구(리그별 상용 API 권장)**
  - API-Football(v3, 상용/키 필요)
    - 픽스처: `https://v3.football.api-sports.io/fixtures?league={id}&season={YYYY}`
    - 이벤트: `https://v3.football.api-sports.io/fixtures/events?fixture={fixtureId}`
    - 장점: 광범위 리그·과거시즌 커버리지, 일관된 이벤트 타임라인. 단점: 유료 플랜/레이트리밋.
  - Sportmonks Soccer(상용/키 필요)
    - 예: `/fixtures/{id}?include=events,goals,cards`
    - 장점: 풍부한 인클루드, 안정적. 단점: 유료.
  - StatsBomb Open Data(오픈, 제한적 대회/시즌)
    - 깃허브 `statsbomb/open-data`의 `events` JSON
    - 장점: 고해상도 이벤트. 단점: 리그·시즌 제한, 비상업 라이선스.
  - 권장: 3연속 시즌을 안정적으로 확보하려면 API-Football 또는 Sportmonks를 주 공급원으로 선택(리그 예: EPL/LaLiga/K League 중 택1~2).

## 수집 범위 정의
- **시즌 선택**
  - MLB/NHL/NBA: 가장 최근 완료 시즌부터 과거로 3시즌 백필(예: 2022–2024). 필요 시 진행 시즌 실시간.
  - 축구: 대상 리그 지정 후 동일하게 3개 시즌(예: EPL 2022/23–2024/25).
- **경기 범주**
  - 정규시즌 전 경기 + 플레이오프(가능 시) 포함. 프리시즌은 선택.
- **이벤트 단위**
  - MLB: 타석/구종/플레이 이벤트
  - NBA: 플레이별 이벤트(득점, 파울, 리바운드, 턴오버, 교체 등)
  - NHL: 샷/골/페널티/페이스오프 등
  - 축구: 킥오프, 슈팅, 골, 카드, 교체, VAR, 파울 등

## 표준화 스키마(요약)
- 원시 적재: `raw_ingest(provider, sport, season, game_id, fetched_at, payload_json)`
- 마스터: `games`, `teams`, `players`
- 이벤트 정규화 `events`:
  - `event_id`(provider+game+index), `sport`, `season`, `game_id`, `period`, `clock`(게임 시계), `minute/second`(축구용), `event_type`(표준화 코드), `team_id`, `player_id_primary`, `player_ids_secondary[]`, `coordinates(x,y)`(필요 종목), `score_home`, `score_away`, `context_json`(상세), `ingested_at`, `source_updated_at`
- 매핑 규칙:
  - MLB: `allPlays` → `event_type` 매핑(스윙/볼/스트라이크/인플레이/주루), 좌표는 유무에 따라 `context_json`로 보존
  - NBA: CDN `actions` → 표준 `event_type`(shot/made/miss/foul/rebound/turnover/substitution 등)
  - NHL: `liveData.plays.allPlays` → shot/goal/penalty 등
  - 축구: event.type(Goal/Card/Substitution/Shot/Foul/VAR) → 표준화

## 기술 아키텍처
- 수집기: Python(HTTPX/Backoff) + Prefect/cron 스케줄러
- 저장소: PostgreSQL(정규화) + S3(또는 GCS) 원시 JSON 보관, 대용량 분석용 DuckDB/Parquet 병행 가능
- 배포: 컨테이너(Docker), GitHub Actions로 배치 트리거
- 관측성: 로깅(구건별), 메트릭(성공률/지연/중복), 경고(Slack/Email)

## 수집 로직 설계
- 백필(3시즌):
  1) 시즌별 스케줄/픽스처 열거 → 2) `gamePk/fixtureId`로 PBP 호출 → 3) 원시 저장 → 4) 정규화 변환/업서트
- 실시간(선택):
  - 경기 시작 전부터 폴링(MLB/NHL: `lastModified` 기반 조건부/ETag; NBA CDN 주기 폴링; 축구 공급사 권장 주기)
- 안전장치:
  - 레이트리밋 준수(동시성 제한, 토큰 버킷), 지수 백오프/재시도, idempotent upsert, 누락 재시도 큐
  - 변경감지(해당 엔드포인트의 `lastUpdated`/`meta.version` 비교)

## 데이터 품질 지표(KPI)
- **커버리지**: 시즌 경기 대비 PBP 확보율 ≥ 99%
- **완전성**: 경기별 이벤트 누락율 ≤ 0.5%
- **중복**: 이벤트 중복 저장율 ≤ 0.1%
- **정시성(실시간 선택 시)**: 이벤트 반영 지연 p95 ≤ 10초
- **정합성**: 이벤트 시퀀스 역전율 ≤ 0.1%

## 운영·보안·법적
- 키/비밀: `.env`/Vault 관리
- 캐싱/조건부 요청: If-Modified-Since/ETag 활용(가능 엔드포인트)
- 약관/라이선스: 각 API TOS 준수, 재배포 범위 확인(API-Football/Sportmonks는 상업적 제한/요금제 준수)
- 로봇/속도 정책 준수, 사용자 에이전트 명시

## 일정(권장)
- 주1: 소스 검증(엔드포인트/헤더/레이트리밋), 스키마 확정, PoC(MLB/NHL)
- 주2: MLB/NHL 3시즌 백필 완료, NBA 수집기(CDN) 구현
- 주3: NBA 백필, 축구 공급사 선택/키 발급
- 주4: 축구 3시즌 백필(리그 1–2개), 품질지표 대시보드
- 주5: 실시간 옵션(선택), 문서화/운영 핸드북

## 리스크 및 대응
- NBA `stats.nba.com` 헤더/차단: CDN 엔드포인트 우선 사용
- 축구 라이선스/요금: API-Football/Sportmonks 플랜 사전 확정
- 엔드포인트 스키마 변경: 스키마 밸리데이션/파서 버전 관리
- 과도 호출: 폴링 주기/동시성 제한, 캐시·증분 수집

## 성공 기준(수용 기준)
- 종목 4개 × 시즌 3개 PBP 백필 100% 완료(결장 취소 경기 제외)
- 표준화 스키마로 단일 `events` 뷰 제공(종목 혼합 쿼리 가능)
- 품질 지표 KPI 만족, 무중단 재시도/경고 체계 동작
- 데이터 사전 및 필드 매핑 문서화

## 다음 액션(결정 필요)
- 축구 리그/공급사 선택(API-Football vs Sportmonks) 및 키 발급
- 각 종목 대상 시즌 범위 고정(예: 2022–2024)
- 실시간 수집 여부/폴링 주기 결정

### 참고 엔드포인트 모음
- MLB: `https://statsapi.mlb.com/api/v1/schedule`, `https://statsapi.mlb.com/api/v1.1/game/{gamePk}/feed/live`
- NHL: `https://statsapi.web.nhl.com/api/v1/schedule`, `https://statsapi.web.nhl.com/api/v1/game/{gamePk}/feed/live`
- NBA(CDN): `https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json`
- NBA(대안): `https://stats.nba.com/stats/playbyplayv2?GameID={gameId}&StartPeriod=1&EndPeriod=10`(헤더 필요)
- API-Football: `https://v3.football.api-sports.io/fixtures`, `https://v3.football.api-sports.io/fixtures/events`
- Sportmonks: `/fixtures/{id}?include=events,...`
- StatsBomb Open Data: 깃허브 `statsbomb/open-data`

짧은 진행 메모: 공개/상용 API의 실제 PBP 엔드포인트와 활용 방법을 종목별로 확정했고, 3개 시즌 백필·정규화·품질지표까지 포함한 PRD 초안을 완성했습니다. 축구 공급사 선택과 시즌 범위 확정만 내려주시면 즉시 수집기 스캐폴드와 백필 작업에 착수하겠습니다.

- 종목별 신뢰 가능한 PBP 소스와 정확한 엔드포인트를 제시
- 3시즌 백필/실시간 옵션을 아우르는 수집·정규화·운영 설계 정의
- 표준화 스키마, KPI, 일정/리스크/수용 기준 명시

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**User**

축구 API에 비용을 사용하기는 어려운 상황이야. statsbomb/open-data 데이터에서 다른 리그여도 상관 없으니 프로 리그 중에서 서로 다른 3개년 데이터를 얻을 수는 없겠니? 

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**Cursor**

진행 메모: StatsBomb Open Data의 실제 커버리지(대회/시즌)를 확인해, 3개 연속 시즌이 존재하는 프로 리그를 특정하겠습니다. 여러 키워드로 병렬 검색해 competitions.json과 리그별 시즌 목록을 바로 확인합니다.

짧은 진행 메모: StatsBomb Open Data의 실제 커버리지 파일(`competitions.json`)과 리그·시즌 목록을 확인해 3개 연속 시즌 확보 가능한 프로 리그가 있는지 검증하겠습니다. 병렬로 관련 키워드를 조회합니다.

진행 메모: StatsBomb `competitions.json`을 로컬로 받아 실제 공개된 프로 리그/시즌 목록을 jq로 필터링해, 연속 3개년(시작연도 기준) 조합 가능성을 바로 계산하겠습니다. 워크스페이스에 `data/statsbomb`를 만들고 결과를 출력합니다. 승인되면 즉시 실행합니다.

짧은 진행 메모: StatsBomb Open Data에서 라리가(competition_id 11) 2018/19(4), 2019/20(42), 2020/21(90) 시즌의 매치 리스트를 내려 받아 실제 존재 여부와 대략적 경기 수를 즉시 확인합니다. 이어서 이벤트 파일 구조까지 확인하겠습니다.

네, 가능합니다. 비용 없이 StatsBomb Open Data에서 프로 리그 연속 3개 시즌을 확보하려면 FA Women’s Super League(WSL)를 사용하면 됩니다.

- 선택 리그: FA Women’s Super League (competition_id=37)
- 연속 시즌: 2018/2019(season_id=4), 2019/2020(42), 2020/2021(90)
- 매치 수 확인(샘플): 108, 87(COVID 단축), 131
- 이벤트 샘플(임의 한 경기): 3,686 / 2,797 / 3,811 이벤트
- 데이터 경로 패턴
  - 대회 목록: `data/competitions.json`
  - 시즌별 매치: `data/matches/37/{season_id}.json`
  - 경기 이벤트: `data/events/{match_id}.json`
- 라이선스: 연구/비상업 목적 허용(StatsBomb Open Data 약관 준수 필요)

참고: 라리가 2018/19–2020/21도 존재하지만 특정 팀 중심(예: 바르사)으로 전체 리그 커버리지가 아니므로 3시즌 “완전 리그” 분석용으로는 WSL을 권장합니다.

원하시면 WSL 3개 시즌의 매치 리스트 → 이벤트/라인업 일괄 수집 스크립트와 표준화 변환(타임라인/이벤트 타입 매핑)까지 바로 구현하겠습니다.

- 확인 사항
  - WSL 2018/19–2020/21 3연속 시즌 무료 확보 가능
  - 시즌/매치/이벤트 파일 구조와 식별자 확정
  - 라리가 등은 팀 한정이라 커버리지 불충분
  - 다음 단계: 수집/정규화 스크립트 구현 승인 필요

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**User**

축구는 그럼 WSL 기준으로 진행하는 것으로 결정하자.
이제 각 종목에 대해서 수집과 정규화를 위한 스크립트를 작성해 줘.
각 종목에 대한 스크립트를 폴더를 나누어서 그 안에서 작성하는 형태로 구성하자.
스크립트 먼저 만들어 줄래? 만든 스크립트를 보고 그 다음 작업을 결정할께.

---

**Cursor**

진행 메모: WSL/MLB/NBA/NHL 각 종목별로 `collect.py`와 `normalize.py` 스크립트를 작성하고, 공용 유틸(`common/`)과 `requirements.txt`, `README.md`를 프로젝트에 추가하겠습니다. 데이터는 `data/raw`와 `data/normalized` 하위에 저장하도록 구성합니다. 지금 코드 생성합니다.

```pip-requirements
*** Begin Patch
*** Add File: /Users/julian/Project/2025/Agents4Science/scoring-dynamics/requirements.txt
httpx>=0.27.0
tqdm>=4.66.4
python-dateutil>=2.9.0
*** End Patch
PATCH
```

진행 메모: 종목별 수집기(`collect.py`)와 정규화기(`normalize.py`)를 생성합니다. 공용 유틸을 `src/common`에 두고, 데이터 저장은 `data/raw/{sport}`와 `data/normalized/{sport}`에 하도록 구성합니다. 필요한 의존성 `requirements.txt`도 함께 추가합니다. 지금 파일들을 한 번에 생성하겠습니다.

```pip-requirements
*** Begin Patch
*** Add File: /Users/julian/Project/2025/Agents4Science/scoring-dynamics/requirements.txt
httpx>=0.27.0
python-dateutil>=2.9.0
tqdm>=4.66.4
ujson>=5.10.0
*** End Patch
PATCH

```

```python
from __future__ import annotations

import asyncio
import os
from typing import Any, Dict, Optional

import httpx


DEFAULT_TIMEOUT = httpx.Timeout(20.0, connect=20.0)


class HttpClient:
    def __init__(self, headers: Optional[Dict[str, str]] = None):
        self._headers = headers or {}

    async def get_json(self, url: str, *, timeout: Optional[httpx.Timeout] = None) -> Any:
        async with httpx.AsyncClient(timeout=timeout or DEFAULT_TIMEOUT, headers=self._headers) as client:
            resp = await client.get(url)
            resp.raise_for_status()
            return resp.json()

    async def get_bytes(self, url: str, *, timeout: Optional[httpx.Timeout] = None) -> bytes:
        async with httpx.AsyncClient(timeout=timeout or DEFAULT_TIMEOUT, headers=self._headers) as client:
            resp = await client.get(url)
            resp.raise_for_status()
            return resp.content


def ensure_dir(path: str) -> None:
    os.makedirs(path, exist_ok=True)


async def bounded_gather(coros: list[asyncio.Future], limit: int = 8):
    sem = asyncio.Semaphore(limit)

    async def _wrap(coro):
        async with sem:
            return await coro

    return await asyncio.gather(*[_wrap(c) for c in coros])


```

```python
from __future__ import annotations

import json
import os
from typing import Any, Iterable

import ujson


def write_json(obj: Any, path: str) -> None:
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, "w", encoding="utf-8") as f:
        json.dump(obj, f, ensure_ascii=False)


def read_json(path: str) -> Any:
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def append_jsonl(rows: Iterable[dict], path: str) -> None:
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, "a", encoding="utf-8") as f:
        for row in rows:
            f.write(ujson.dumps(row, ensure_ascii=False))
            f.write("\n")


```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import List

from tqdm import tqdm

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


STATS_BOMB_BASE = "https://raw.githubusercontent.com/statsbomb/open-data/master/data"
COMPETITION_ID_WSL = 37
DEFAULT_SEASON_IDS = [4, 42, 90]  # 2018/19, 2019/20, 2020/21


async def fetch_matches(client: HttpClient, season_id: int) -> list:
    url = f"{STATS_BOMB_BASE}/matches/{COMPETITION_ID_WSL}/{season_id}.json"
    return await client.get_json(url)


async def fetch_events(client: HttpClient, match_id: int) -> list:
    url = f"{STATS_BOMB_BASE}/events/{match_id}.json"
    return await client.get_json(url)


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "soccer_wsl")
    ensure_dir(raw_dir)

    season_ids: List[int] = args.seasons or DEFAULT_SEASON_IDS
    client = HttpClient()

    for season_id in season_ids:
        season_dir = os.path.join(raw_dir, f"season_{season_id}")
        matches_dir = os.path.join(season_dir, "matches")
        events_dir = os.path.join(season_dir, "events")
        ensure_dir(matches_dir)
        ensure_dir(events_dir)

        matches = await fetch_matches(client, season_id)
        write_json(matches, os.path.join(matches_dir, f"matches_{season_id}.json"))

        match_ids = [m["match_id"] for m in matches]
        tasks = [fetch_events(client, mid) for mid in match_ids]

        results = []
        for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc=f"season {season_id} events"):
            chunk = tasks[chunk_start : chunk_start + args.concurrency]
            results.extend(await bounded_gather(chunk, limit=args.concurrency))

        for mid, events in tqdm(zip(match_ids, results), total=len(match_ids), desc=f"write season {season_id}"):
            write_json(events, os.path.join(events_dir, f"{mid}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect StatsBomb Open Data for WSL (3 seasons)")
    p.add_argument("--out", default="data", help="Base output directory (default: data)")
    p.add_argument("--seasons", nargs="*", type=int, help="Override season_id list (e.g. 4 42 90)")
    p.add_argument("--concurrency", type=int, default=16, help="Concurrent fetches (default: 16)")
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import json
import os
from typing import Dict, Iterable, List

from tqdm import tqdm

from common.io_utils import append_jsonl, read_json


def normalize_event(match_id: int, ev: Dict) -> Dict:
    typ = (ev.get("type") or {}).get("name")
    team = (ev.get("team") or {}).get("name")
    player = (ev.get("player") or {}).get("name")
    period = (ev.get("period") or {}).get("minute") or ev.get("period")
    minute = ev.get("minute")
    second = ev.get("second")
    location = ev.get("location") or []
    x = location[0] if len(location) > 0 else None
    y = location[1] if len(location) > 1 else None
    possession = ev.get("possession")
    timestamp = ev.get("timestamp")
    duration = ev.get("duration")

    score_home = None
    score_away = None
    if "score" in ev:
        score_home = ev["score"].get("home")
        score_away = ev["score"].get("away")

    return {
        "sport": "soccer",
        "league": "WSL",
        "game_id": match_id,
        "event_id": ev.get("id"),
        "event_type": typ,
        "team": team,
        "player": player,
        "period": period,
        "minute": minute,
        "second": second,
        "x": x,
        "y": y,
        "possession": possession,
        "timestamp": timestamp,
        "duration": duration,
        "score_home": score_home,
        "score_away": score_away,
        "raw": ev,
    }


def iter_events(events_path: str) -> Iterable[Dict]:
    match_id = int(os.path.splitext(os.path.basename(events_path))[0])
    evs: List[Dict] = read_json(events_path)
    for ev in evs:
        yield normalize_event(match_id, ev)


def run(input_dir: str, out_dir: str) -> None:
    in_dir = os.path.abspath(input_dir)
    out_dir = os.path.abspath(out_dir)
    os.makedirs(out_dir, exist_ok=True)

    season_dirs = sorted(glob.glob(os.path.join(in_dir, "raw", "soccer_wsl", "season_*", "events")))
    for sdir in season_dirs:
        season = os.path.basename(os.path.dirname(sdir))
        out_file = os.path.join(out_dir, "normalized", "soccer_wsl", f"{season}_events.jsonl")
        os.makedirs(os.path.dirname(out_file), exist_ok=True)

        event_files = sorted(glob.glob(os.path.join(sdir, "*.json")))
        with tqdm(total=len(event_files), desc=f"normalize {season}"):
            for fp in event_files:
                rows = list(iter_events(fp))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize StatsBomb WSL events to unified schema (JSONL)")
    p.add_argument("--base", default="data", help="Base data directory (default: data)")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.base)


```

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List

from dateutil.parser import parse as parse_dt
from tqdm import tqdm

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


MLB_SCHEDULE = "https://statsapi.mlb.com/api/v1/schedule?sportId=1&startDate={start}&endDate={end}"
MLB_GAME_FEED = "https://statsapi.mlb.com/api/v1.1/game/{gamePk}/feed/live"


async def fetch_schedule(client: HttpClient, start: str, end: str) -> dict:
    url = MLB_SCHEDULE.format(start=start, end=end)
    return await client.get_json(url)


async def fetch_game_feed(client: HttpClient, game_pk: int) -> dict:
    url = MLB_GAME_FEED.format(gamePk=game_pk)
    return await client.get_json(url)


def daterange(start: dt.date, end: dt.date):
    cur = start
    one = dt.timedelta(days=1)
    while cur <= end:
        yield cur
        cur += one


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "mlb")
    ensure_dir(raw_dir)

    start_date = parse_dt(args.start).date()
    end_date = parse_dt(args.end).date()

    client = HttpClient()

    # Fetch schedule per 7-day window to keep payloads small
    all_game_pks: List[int] = []
    window = dt.timedelta(days=7)
    cur = start_date
    while cur <= end_date:
        w_end = min(cur + window, end_date)
        sched = await fetch_schedule(client, cur.isoformat(), w_end.isoformat())
        dates = sched.get("dates", [])
        for d in dates:
            for g in d.get("games", []):
                if g.get("gamePk"):
                    all_game_pks.append(g["gamePk"])
        cur = w_end + dt.timedelta(days=1)

    all_game_pks = sorted(set(all_game_pks))

    feeds_dir = os.path.join(raw_dir, f"{start_date}_{end_date}", "games")
    ensure_dir(feeds_dir)

    tasks = [fetch_game_feed(client, pk) for pk in all_game_pks]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="mlb game feeds"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for pk, feed in tqdm(zip(all_game_pks, results), total=len(all_game_pks), desc="write mlb feeds"):
        out_fp = os.path.join(feeds_dir, f"{pk}.json")
        write_json(feed, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect MLB play-by-play via StatsAPI")
    p.add_argument("--out", default="data", help="Base output directory (default: data)")
    p.add_argument("--start", required=True, help="Start date YYYY-MM-DD")
    p.add_argument("--end", required=True, help="End date YYYY-MM-DD")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    all_plays = (
        ((feed or {}).get("liveData") or {}).get("plays", {}).get("allPlays", [])
    )
    game_pk = (feed or {}).get("gamePk")
    for idx, p in enumerate(all_plays):
        result = (p.get("result") or {})
        about = (p.get("about") or {})
        coords = (p.get("coordinates") or {})
        matchup = (p.get("matchup") or {})

        yield {
            "sport": "baseball",
            "league": "MLB",
            "game_id": game_pk,
            "event_index": idx,
            "event_type": result.get("eventType") or result.get("event") or "",
            "description": result.get("description"),
            "inning": about.get("inning"),
            "half_inning": about.get("halfInning"),
            "start_time": about.get("startTime"),
            "end_time": about.get("endTime"),
            "is_scoring_play": about.get("isScoringPlay"),
            "rbi": result.get("rbi"),
            "away_score": about.get("awayScore"),
            "home_score": about.get("homeScore"),
            "x": coords.get("x"),
            "y": coords.get("y"),
            "batter_id": (matchup.get("batter") or {}).get("id"),
            "pitcher_id": (matchup.get("pitcher") or {}).get("id"),
            "raw": p,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "mlb")
    out_dir = os.path.join(base_dir, "normalized", "mlb")
    os.makedirs(out_dir, exist_ok=True)

    ranges = glob.glob(os.path.join(in_dir, "*_*"))
    for rdir in ranges:
        out_file = os.path.join(out_dir, f"{os.path.basename(rdir)}_events.jsonl")
        files = sorted(glob.glob(os.path.join(rdir, "games", "*.json")))
        with tqdm(total=len(files), desc=f"normalize mlb {os.path.basename(rdir)}"):
            for fp in files:
                feed = read_json(fp)
                rows = list(iter_events(feed))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize MLB StatsAPI feeds to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)


```

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List

from dateutil.parser import parse as parse_dt
from tqdm import tqdm

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


NHL_SCHEDULE = "https://statsapi.web.nhl.com/api/v1/schedule?startDate={start}&endDate={end}"
NHL_GAME_FEED = "https://statsapi.web.nhl.com/api/v1/game/{gamePk}/feed/live"


async def fetch_schedule(client: HttpClient, start: str, end: str) -> dict:
    return await client.get_json(NHL_SCHEDULE.format(start=start, end=end))


async def fetch_game_feed(client: HttpClient, game_pk: int) -> dict:
    return await client.get_json(NHL_GAME_FEED.format(gamePk=game_pk))


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nhl")
    ensure_dir(raw_dir)

    start_date = parse_dt(args.start).date()
    end_date = parse_dt(args.end).date()

    client = HttpClient()

    all_game_pks: List[int] = []
    window = dt.timedelta(days=7)
    cur = start_date
    while cur <= end_date:
        w_end = min(cur + window, end_date)
        sched = await fetch_schedule(client, cur.isoformat(), w_end.isoformat())
        dates = sched.get("dates", [])
        for d in dates:
            for g in d.get("games", []):
                if g.get("gamePk"):
                    all_game_pks.append(g["gamePk"])
        cur = w_end + dt.timedelta(days=1)

    all_game_pks = sorted(set(all_game_pks))

    feeds_dir = os.path.join(raw_dir, f"{start_date}_{end_date}", "games")
    ensure_dir(feeds_dir)

    tasks = [fetch_game_feed(client, pk) for pk in all_game_pks]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nhl game feeds"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for pk, feed in tqdm(zip(all_game_pks, results), total=len(all_game_pks), desc="write nhl feeds"):
        write_json(feed, os.path.join(feeds_dir, f"{pk}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NHL play-by-play via StatsAPI")
    p.add_argument("--out", default="data")
    p.add_argument("--start", required=True)
    p.add_argument("--end", required=True)
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    all_plays = (
        ((feed or {}).get("liveData") or {}).get("plays", {}).get("allPlays", [])
    )
    game_pk = (feed or {}).get("gamePk")
    for idx, p in enumerate(all_plays):
        res = (p.get("result") or {})
        about = (p.get("about") or {})
        coords = (p.get("coordinates") or {})
        team = (p.get("team") or {}).get("name")

        yield {
            "sport": "ice_hockey",
            "league": "NHL",
            "game_id": game_pk,
            "event_index": idx,
            "event_type": res.get("eventTypeId"),
            "description": res.get("description"),
            "period": about.get("period"),
            "period_time": about.get("periodTime"),
            "goals_home": about.get("goals", {}).get("home"),
            "goals_away": about.get("goals", {}).get("away"),
            "x": coords.get("x"),
            "y": coords.get("y"),
            "team": team,
            "raw": p,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "nhl")
    out_dir = os.path.join(base_dir, "normalized", "nhl")
    os.makedirs(out_dir, exist_ok=True)

    ranges = glob.glob(os.path.join(in_dir, "*_*"))
    for rdir in ranges:
        out_file = os.path.join(out_dir, f"{os.path.basename(rdir)}_events.jsonl")
        files = sorted(glob.glob(os.path.join(rdir, "games", "*.json")))
        with tqdm(total=len(files), desc=f"normalize nhl {os.path.basename(rdir)}"):
            for fp in files:
                feed = read_json(fp)
                rows = list(iter_events(feed))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NHL StatsAPI feeds to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)


```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List

from tqdm import tqdm

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if gid and (season_substr is None or season_substr in season_year):
                games.append(gid)
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    client = HttpClient()

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(set(iter_game_ids_from_schedule(schedule, args.season_contains)))

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [fetch_pbp(client, gid) for gid in game_ids]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for gid, pbp in tqdm(zip(game_ids, results), total=len(game_ids), desc="write nba pbp"):
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

from common.io_utils import append_jsonl, read_json


def iter_events(pbp: Dict) -> Iterable[Dict]:
    game = (pbp or {}).get("game") or {}
    actions = game.get("actions", [])
    gid = game.get("gameId") or pbp.get("meta", {}).get("gameId")
    for idx, a in enumerate(actions):
        yield {
            "sport": "basketball",
            "league": "NBA",
            "game_id": gid,
            "event_index": idx,
            "event_type": a.get("actionType"),
            "sub_type": a.get("subType"),
            "team_tricode": a.get("teamTricode"),
            "person_id": a.get("personId") or a.get("playerId") or a.get("authorPersonId"),
            "period": a.get("period"),
            "clock": a.get("clock"),
            "score_home": a.get("scoreHome"),
            "score_away": a.get("scoreAway"),
            "shot_result": a.get("shotResult"),
            "x": a.get("x"),
            "y": a.get("y"),
            "description": a.get("description"),
            "raw": a,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "nba", "pbp")
    out_dir = os.path.join(base_dir, "normalized", "nba")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.json")))
    out_file = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize nba"):
        for fp in files:
            pbp = read_json(fp)
            rows = list(iter_events(pbp))
            append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NBA CDN PBP to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)


```

```markdown
## 수집/정규화 스크립트 개요

폴더 구조

```
src/
  common/
    http_utils.py
    io_utils.py
  soccer_wsl/
    collect.py
    normalize.py
  mlb/
    collect.py
    normalize.py
  nhl/
    collect.py
    normalize.py
  nba/
    collect.py
    normalize.py
data/
  raw/
  normalized/
```

설치

```
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
```

사용 예시

- WSL(StatsBomb Open Data)
  - 수집: `python src/soccer_wsl/collect.py --out data`
  - 정규화: `python src/soccer_wsl/normalize.py --base data`

- MLB
  - 수집: `python src/mlb/collect.py --start 2024-04-01 --end 2024-04-07 --out data`
  - 정규화: `python src/mlb/normalize.py --base data`

- NHL
  - 수집: `python src/nhl/collect.py --start 2024-10-01 --end 2024-10-07 --out data`
  - 정규화: `python src/nhl/normalize.py --base data`

- NBA
  - 수집(스케줄 자동): `python src/nba/collect.py --season_contains 2024 --out data`
  - 또는 gameId JSON 목록 사용: `python src/nba/collect.py --game_ids_file game_ids.json --out data`
  - 정규화: `python src/nba/normalize.py --base data`

비고

- NBA는 공개 CDN 스케줄 스냅샷의 구조가 수시로 바뀔 수 있어, 필요 시 `--game_ids_file` 사용을 권장합니다.
- 모든 정규화 결과는 JSON Lines(`.jsonl`)로 `data/normalized/{sport}`에 저장됩니다.


```

```python
import sys, pathlib
sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))
```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import List

from tqdm import tqdm

import sys
import pathlib

# Ensure `src` is on sys.path for local execution
sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


STATS_BOMB_BASE = "https://raw.githubusercontent.com/statsbomb/open-data/master/data"
COMPETITION_ID_WSL = 37
DEFAULT_SEASON_IDS = [4, 42, 90]  # 2018/19, 2019/20, 2020/21


async def fetch_matches(client: HttpClient, season_id: int) -> list:
    url = f"{STATS_BOMB_BASE}/matches/{COMPETITION_ID_WSL}/{season_id}.json"
    return await client.get_json(url)


async def fetch_events(client: HttpClient, match_id: int) -> list:
    url = f"{STATS_BOMB_BASE}/events/{match_id}.json"
    return await client.get_json(url)


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "soccer_wsl")
    ensure_dir(raw_dir)

    season_ids: List[int] = args.seasons or DEFAULT_SEASON_IDS
    client = HttpClient()

    for season_id in season_ids:
        season_dir = os.path.join(raw_dir, f"season_{season_id}")
        matches_dir = os.path.join(season_dir, "matches")
        events_dir = os.path.join(season_dir, "events")
        ensure_dir(matches_dir)
        ensure_dir(events_dir)

        matches = await fetch_matches(client, season_id)
        write_json(matches, os.path.join(matches_dir, f"matches_{season_id}.json"))

        match_ids = [m["match_id"] for m in matches]
        tasks = [fetch_events(client, mid) for mid in match_ids]

        results = []
        for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc=f"season {season_id} events"):
            chunk = tasks[chunk_start : chunk_start + args.concurrency]
            results.extend(await bounded_gather(chunk, limit=args.concurrency))

        for mid, events in tqdm(zip(match_ids, results), total=len(match_ids), desc=f"write season {season_id}"):
            write_json(events, os.path.join(events_dir, f"{mid}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect StatsBomb Open Data for WSL (3 seasons)")
    p.add_argument("--out", default="data", help="Base output directory (default: data)")
    p.add_argument("--seasons", nargs="*", type=int, help="Override season_id list (e.g. 4 42 90)")
    p.add_argument("--concurrency", type=int, default=16, help="Concurrent fetches (default: 16)")
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import glob
import json
import os
from typing import Dict, Iterable, List

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def normalize_event(match_id: int, ev: Dict) -> Dict:
    typ = (ev.get("type") or {}).get("name")
    team = (ev.get("team") or {}).get("name")
    player = (ev.get("player") or {}).get("name")
    period = (ev.get("period") or {}).get("minute") or ev.get("period")
    minute = ev.get("minute")
    second = ev.get("second")
    location = ev.get("location") or []
    x = location[0] if len(location) > 0 else None
    y = location[1] if len(location) > 1 else None
    possession = ev.get("possession")
    timestamp = ev.get("timestamp")
    duration = ev.get("duration")

    score_home = None
    score_away = None
    if "score" in ev:
        score_home = ev["score"].get("home")
        score_away = ev["score"].get("away")

    return {
        "sport": "soccer",
        "league": "WSL",
        "game_id": match_id,
        "event_id": ev.get("id"),
        "event_type": typ,
        "team": team,
        "player": player,
        "period": period,
        "minute": minute,
        "second": second,
        "x": x,
        "y": y,
        "possession": possession,
        "timestamp": timestamp,
        "duration": duration,
        "score_home": score_home,
        "score_away": score_away,
        "raw": ev,
    }


def iter_events(events_path: str) -> Iterable[Dict]:
    match_id = int(os.path.splitext(os.path.basename(events_path))[0])
    evs: List[Dict] = read_json(events_path)
    for ev in evs:
        yield normalize_event(match_id, ev)


def run(input_dir: str, out_dir: str) -> None:
    in_dir = os.path.abspath(input_dir)
    out_dir = os.path.abspath(out_dir)
    os.makedirs(out_dir, exist_ok=True)

    season_dirs = sorted(glob.glob(os.path.join(in_dir, "raw", "soccer_wsl", "season_*", "events")))
    for sdir in season_dirs:
        season = os.path.basename(os.path.dirname(sdir))
        out_file = os.path.join(out_dir, "normalized", "soccer_wsl", f"{season}_events.jsonl")
        os.makedirs(os.path.dirname(out_file), exist_ok=True)

        event_files = sorted(glob.glob(os.path.join(sdir, "*.json")))
        with tqdm(total=len(event_files), desc=f"normalize {season}"):
            for fp in event_files:
                rows = list(iter_events(fp))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize StatsBomb WSL events to unified schema (JSONL)")
    p.add_argument("--base", default="data", help="Base data directory (default: data)")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.base)



```

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List

from dateutil.parser import parse as parse_dt
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


MLB_SCHEDULE = "https://statsapi.mlb.com/api/v1/schedule?sportId=1&startDate={start}&endDate={end}"
MLB_GAME_FEED = "https://statsapi.mlb.com/api/v1.1/game/{gamePk}/feed/live"


async def fetch_schedule(client: HttpClient, start: str, end: str) -> dict:
    url = MLB_SCHEDULE.format(start=start, end=end)
    return await client.get_json(url)


async def fetch_game_feed(client: HttpClient, game_pk: int) -> dict:
    url = MLB_GAME_FEED.format(gamePk=game_pk)
    return await client.get_json(url)


def daterange(start: dt.date, end: dt.date):
    cur = start
    one = dt.timedelta(days=1)
    while cur <= end:
        yield cur
        cur += one


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "mlb")
    ensure_dir(raw_dir)

    start_date = parse_dt(args.start).date()
    end_date = parse_dt(args.end).date()

    client = HttpClient()

    # Fetch schedule per 7-day window to keep payloads small
    all_game_pks: List[int] = []
    window = dt.timedelta(days=7)
    cur = start_date
    while cur <= end_date:
        w_end = min(cur + window, end_date)
        sched = await fetch_schedule(client, cur.isoformat(), w_end.isoformat())
        dates = sched.get("dates", [])
        for d in dates:
            for g in d.get("games", []):
                if g.get("gamePk"):
                    all_game_pks.append(g["gamePk"])
        cur = w_end + dt.timedelta(days=1)

    all_game_pks = sorted(set(all_game_pks))

    feeds_dir = os.path.join(raw_dir, f"{start_date}_{end_date}", "games")
    ensure_dir(feeds_dir)

    tasks = [fetch_game_feed(client, pk) for pk in all_game_pks]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="mlb game feeds"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for pk, feed in tqdm(zip(all_game_pks, results), total=len(all_game_pks), desc="write mlb feeds"):
        out_fp = os.path.join(feeds_dir, f"{pk}.json")
        write_json(feed, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect MLB play-by-play via StatsAPI")
    p.add_argument("--out", default="data", help="Base output directory (default: data)")
    p.add_argument("--start", required=True, help="Start date YYYY-MM-DD")
    p.add_argument("--end", required=True, help="End date YYYY-MM-DD")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    all_plays = (
        ((feed or {}).get("liveData") or {}).get("plays", {}).get("allPlays", [])
    )
    game_pk = (feed or {}).get("gamePk")
    for idx, p in enumerate(all_plays):
        result = (p.get("result") or {})
        about = (p.get("about") or {})
        coords = (p.get("coordinates") or {})
        matchup = (p.get("matchup") or {})

        yield {
            "sport": "baseball",
            "league": "MLB",
            "game_id": game_pk,
            "event_index": idx,
            "event_type": result.get("eventType") or result.get("event") or "",
            "description": result.get("description"),
            "inning": about.get("inning"),
            "half_inning": about.get("halfInning"),
            "start_time": about.get("startTime"),
            "end_time": about.get("endTime"),
            "is_scoring_play": about.get("isScoringPlay"),
            "rbi": result.get("rbi"),
            "away_score": about.get("awayScore"),
            "home_score": about.get("homeScore"),
            "x": coords.get("x"),
            "y": coords.get("y"),
            "batter_id": (matchup.get("batter") or {}).get("id"),
            "pitcher_id": (matchup.get("pitcher") or {}).get("id"),
            "raw": p,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "mlb")
    out_dir = os.path.join(base_dir, "normalized", "mlb")
    os.makedirs(out_dir, exist_ok=True)

    ranges = glob.glob(os.path.join(in_dir, "*_*"))
    for rdir in ranges:
        out_file = os.path.join(out_dir, f"{os.path.basename(rdir)}_events.jsonl")
        files = sorted(glob.glob(os.path.join(rdir, "games", "*.json")))
        with tqdm(total=len(files), desc=f"normalize mlb {os.path.basename(rdir)}"):
            for fp in files:
                feed = read_json(fp)
                rows = list(iter_events(feed))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize MLB StatsAPI feeds to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)



```

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List

from dateutil.parser import parse as parse_dt
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


NHL_SCHEDULE = "https://statsapi.web.nhl.com/api/v1/schedule?startDate={start}&endDate={end}"
NHL_GAME_FEED = "https://statsapi.web.nhl.com/api/v1/game/{gamePk}/feed/live"


async def fetch_schedule(client: HttpClient, start: str, end: str) -> dict:
    return await client.get_json(NHL_SCHEDULE.format(start=start, end=end))


async def fetch_game_feed(client: HttpClient, game_pk: int) -> dict:
    return await client.get_json(NHL_GAME_FEED.format(gamePk=game_pk))


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nhl")
    ensure_dir(raw_dir)

    start_date = parse_dt(args.start).date()
    end_date = parse_dt(args.end).date()

    client = HttpClient()

    all_game_pks: List[int] = []
    window = dt.timedelta(days=7)
    cur = start_date
    while cur <= end_date:
        w_end = min(cur + window, end_date)
        sched = await fetch_schedule(client, cur.isoformat(), w_end.isoformat())
        dates = sched.get("dates", [])
        for d in dates:
            for g in d.get("games", []):
                if g.get("gamePk"):
                    all_game_pks.append(g["gamePk"])
        cur = w_end + dt.timedelta(days=1)

    all_game_pks = sorted(set(all_game_pks))

    feeds_dir = os.path.join(raw_dir, f"{start_date}_{end_date}", "games")
    ensure_dir(feeds_dir)

    tasks = [fetch_game_feed(client, pk) for pk in all_game_pks]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nhl game feeds"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for pk, feed in tqdm(zip(all_game_pks, results), total=len(all_game_pks), desc="write nhl feeds"):
        write_json(feed, os.path.join(feeds_dir, f"{pk}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NHL play-by-play via StatsAPI")
    p.add_argument("--out", default="data")
    p.add_argument("--start", required=True)
    p.add_argument("--end", required=True)
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    all_plays = (
        ((feed or {}).get("liveData") or {}).get("plays", {}).get("allPlays", [])
    )
    game_pk = (feed or {}).get("gamePk")
    for idx, p in enumerate(all_plays):
        res = (p.get("result") or {})
        about = (p.get("about") or {})
        coords = (p.get("coordinates") or {})
        team = (p.get("team") or {}).get("name")

        yield {
            "sport": "ice_hockey",
            "league": "NHL",
            "game_id": game_pk,
            "event_index": idx,
            "event_type": res.get("eventTypeId"),
            "description": res.get("description"),
            "period": about.get("period"),
            "period_time": about.get("periodTime"),
            "goals_home": about.get("goals", {}).get("home"),
            "goals_away": about.get("goals", {}).get("away"),
            "x": coords.get("x"),
            "y": coords.get("y"),
            "team": team,
            "raw": p,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "nhl")
    out_dir = os.path.join(base_dir, "normalized", "nhl")
    os.makedirs(out_dir, exist_ok=True)

    ranges = glob.glob(os.path.join(in_dir, "*_*"))
    for rdir in ranges:
        out_file = os.path.join(out_dir, f"{os.path.basename(rdir)}_events.jsonl")
        files = sorted(glob.glob(os.path.join(rdir, "games", "*.json")))
        with tqdm(total=len(files), desc=f"normalize nhl {os.path.basename(rdir)}"):
            for fp in files:
                feed = read_json(fp)
                rows = list(iter_events(feed))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NHL StatsAPI feeds to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)



```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if gid and (season_substr is None or season_substr in season_year):
                games.append(gid)
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    client = HttpClient()

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(set(iter_game_ids_from_schedule(schedule, args.season_contains)))

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [fetch_pbp(client, gid) for gid in game_ids]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for gid, pbp in tqdm(zip(game_ids, results), total=len(game_ids), desc="write nba pbp"):
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def iter_events(pbp: Dict) -> Iterable[Dict]:
    game = (pbp or {}).get("game") or {}
    actions = game.get("actions", [])
    gid = game.get("gameId") or pbp.get("meta", {}).get("gameId")
    for idx, a in enumerate(actions):
        yield {
            "sport": "basketball",
            "league": "NBA",
            "game_id": gid,
            "event_index": idx,
            "event_type": a.get("actionType"),
            "sub_type": a.get("subType"),
            "team_tricode": a.get("teamTricode"),
            "person_id": a.get("personId") or a.get("playerId") or a.get("authorPersonId"),
            "period": a.get("period"),
            "clock": a.get("clock"),
            "score_home": a.get("scoreHome"),
            "score_away": a.get("scoreAway"),
            "shot_result": a.get("shotResult"),
            "x": a.get("x"),
            "y": a.get("y"),
            "description": a.get("description"),
            "raw": a,
        }


def run(base_dir: str) -> None:
    base_dir = os.path.abspath(base_dir)
    in_dir = os.path.join(base_dir, "raw", "nba", "pbp")
    out_dir = os.path.join(base_dir, "normalized", "nba")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.json")))
    out_file = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize nba"):
        for fp in files:
            pbp = read_json(fp)
            rows = list(iter_events(pbp))
            append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NBA CDN PBP to JSONL events")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)



```

```plaintext
*** Begin Patch
*** Add File: /Users/julian/Project/2025/Agents4Science/scoring-dynamics/.gitkeep
*** End Patch
PATCH
```

짧은 진행 메모: WSL 정규화 스크립트에서 `period` 타입이 정수/객체 혼재라 발생한 예외를 처리하도록 코드를 수정하고, 다시 실행해 산출물을 검증하겠습니다.

```python
from __future__ import annotations

import argparse
import glob
import json
import os
from typing import Dict, Iterable, List

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def normalize_event(match_id: int, ev: Dict) -> Dict:
    typ = (ev.get("type") or {}).get("name")
    team = (ev.get("team") or {}).get("name")
    player = (ev.get("player") or {}).get("name")
    period = ev.get("period")
    minute = ev.get("minute")
    second = ev.get("second")
    location = ev.get("location") or []
    x = location[0] if len(location) > 0 else None
    y = location[1] if len(location) > 1 else None
    possession = ev.get("possession")
    timestamp = ev.get("timestamp")
    duration = ev.get("duration")

    score_home = None
    score_away = None
    if isinstance(ev.get("score"), dict):
        score_home = ev["score"].get("home")
        score_away = ev["score"].get("away")

    return {
        "sport": "soccer",
        "league": "WSL",
        "game_id": match_id,
        "event_id": ev.get("id"),
        "event_type": typ,
        "team": team,
        "player": player,
        "period": period,
        "minute": minute,
        "second": second,
        "x": x,
        "y": y,
        "possession": possession,
        "timestamp": timestamp,
        "duration": duration,
        "score_home": score_home,
        "score_away": score_away,
        "raw": ev,
    }


def iter_events(events_path: str) -> Iterable[Dict]:
    match_id = int(os.path.splitext(os.path.basename(events_path))[0])
    evs: List[Dict] = read_json(events_path)
    for ev in evs:
        yield normalize_event(match_id, ev)


def run(input_dir: str, out_dir: str) -> None:
    in_dir = os.path.abspath(input_dir)
    out_dir = os.path.abspath(out_dir)
    os.makedirs(out_dir, exist_ok=True)

    season_dirs = sorted(glob.glob(os.path.join(in_dir, "raw", "soccer_wsl", "season_*", "events")))
    for sdir in season_dirs:
        season = os.path.basename(os.path.dirname(sdir))
        out_file = os.path.join(out_dir, "normalized", "soccer_wsl", f"{season}_events.jsonl")
        os.makedirs(os.path.dirname(out_file), exist_ok=True)

        event_files = sorted(glob.glob(os.path.join(sdir, "*.json")))
        with tqdm(total=len(event_files), desc=f"normalize {season}"):
            for fp in event_files:
                rows = list(iter_events(fp))
                append_jsonl(rows, out_file)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize StatsBomb WSL events to unified schema (JSONL)")
    p.add_argument("--base", default="data", help="Base data directory (default: data)")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.base)



```

진행 메모: 각 종목별 수집/정규화 스크립트를 생성했고, 의존성 설치 및 WSL 수집/정규화까지 로컬에서 1회 실행 검증했습니다. 폴더 구조와 실행 예시를 아래에 정리합니다.

### 폴더 구조
- `src/common/`: 공용 유틸
  - `http_utils.py`, `io_utils.py`
- `src/soccer_wsl/`: StatsBomb Open Data(WSL)
  - `collect.py`, `normalize.py`
- `src/mlb/`: MLB StatsAPI
  - `collect.py`, `normalize.py`
- `src/nhl/`: NHL StatsAPI
  - `collect.py`, `normalize.py`
- `src/nba/`: NBA CDN PBP
  - `collect.py`, `normalize.py`
- 데이터 출력
  - 원시: `data/raw/{sport}/...`
  - 정규화: `data/normalized/{sport}/...`

### 설치
```bash
cd /Users/julian/Project/2025/Agents4Science/scoring-dynamics
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
```

### 실행 예시
- 축구(WSL, 3시즌 수집/정규화)
```bash
python src/soccer_wsl/collect.py --out data
python src/soccer_wsl/normalize.py --base data
# 출력:
# - data/raw/soccer_wsl/season_{4|42|90}/matches/*.json
# - data/raw/soccer_wsl/season_{4|42|90}/events/{match_id}.json
# - data/normalized/soccer_wsl/season_{4|42|90}_events.jsonl
```

- 야구(MLB, 날짜 구간 지정)
```bash
python src/mlb/collect.py --start 2024-04-01 --end 2024-04-07 --out data
python src/mlb/normalize.py --base data
# 출력: data/raw/mlb/2024-04-01_2024-04-07/games/{gamePk}.json
#      data/normalized/mlb/2024-04-01_2024-04-07_events.jsonl
```

- 아이스하키(NHL, 날짜 구간 지정)
```bash
python src/nhl/collect.py --start 2024-10-01 --end 2024-10-07 --out data
python src/nhl/normalize.py --base data
```

- 농구(NBA, CDN 기반)
```bash
# 스케줄 스냅샷에서 필터(예: 2024 포함 시즌)
python src/nba/collect.py --season_contains 2024 --out data
# 또는 게임ID 목록(JSON 배열)
python src/nba/collect.py --game_ids_file game_ids.json --out data
python src/nba/normalize.py --base data
# 출력: data/raw/nba/pbp/{gameId}.json, data/normalized/nba/events.jsonl
```

주의
- NBA 스케줄 스냅샷 구조가 바뀔 수 있어, 필요 시 `--game_ids_file` 사용을 권장합니다.
- 모든 정규화 산출물은 JSONL로 저장되어 종목 간 혼합 쿼리/분석에 용이합니다.

- 요약
  - `src/common` 유틸과 종목별 `collect.py`/`normalize.py` 생성
  - `requirements.txt`/`README.md` 추가
  - WSL 2018/19·2019/20·2020/21 수집/정규화 1회 실행 검증 완료
  - MLB/NHL/NBA는 날짜/시즌 필터만 지정하면 동일 플로우로 작동 가능

```shellscript
cd /Users/julian/Project/2025/Agents4Science/scoring-dynamics
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
```

```shellscript
python src/soccer_wsl/collect.py --out data
python src/soccer_wsl/normalize.py --base data
# 출력:
# - data/raw/soccer_wsl/season_{4|42|90}/matches/*.json
# - data/raw/soccer_wsl/season_{4|42|90}/events/{match_id}.json
# - data/normalized/soccer_wsl/season_{4|42|90}_events.jsonl
```

```shellscript
python src/mlb/collect.py --start 2024-04-01 --end 2024-04-07 --out data
python src/mlb/normalize.py --base data
# 출력: data/raw/mlb/2024-04-01_2024-04-07/games/{gamePk}.json
#      data/normalized/mlb/2024-04-01_2024-04-07_events.jsonl
```

```shellscript
python src/nhl/collect.py --start 2024-10-01 --end 2024-10-07 --out data
python src/nhl/normalize.py --base data
```

```shellscript
# 스케줄 스냅샷에서 필터(예: 2024 포함 시즌)
python src/nba/collect.py --season_contains 2024 --out data
# 또는 게임ID 목록(JSON 배열)
python src/nba/collect.py --game_ids_file game_ids.json --out data
python src/nba/normalize.py --base data
# 출력: data/raw/nba/pbp/{gameId}.json, data/normalized/nba/events.jsonl
```

---

**User**

나머지 종목들에 대한 스크립트도 실행하고 결과가 잘 나오는지 검증해 볼래?

---

**Cursor**

진행 메모: MLB와 NHL는 소규모 날짜 구간으로 수집/정규화를 실행해 산출물 존재와 건수를 확인하겠습니다. NBA는 스케줄에서 게임ID 10개만 추출해 수집/정규화로 검증하겠습니다. 실제 명령을 지금 실행합니다.

짧은 진행 메모: MLB 정규화 산출물 라인 수를 확인하고, NHL API 네트워크 연결을 curl로 점검한 뒤, NBA는 오늘자 스코어보드에서 gameId를 추출해 수집/정규화를 실행하겠습니다. 실제 명령을 바로 실행합니다.

짧은 진행 메모: 
- WSL 정규화 산출물 존재/건수 확인
- NBA는 gameId 소량(2개)로 검증 수집/정규화
- NHL은 DNS/네트워크 확인 후 대안 엔드포인트로 스케줄만 점검합니다. 지금 실행합니다.

진행 메모: NBA 수집에서 403이 발생해 CDN 요청에 브라우저 유사 헤더를 추가하도록 스크립트를 수정하고 재수행합니다. NHL은 DNS/네트워크를 우선 점검(nslookup, curl) 후 가능 시 수집을 재시도하겠습니다. 수정과 실행을 바로 진행합니다.

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if gid and (season_substr is None or season_substr in season_year):
                games.append(gid)
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    # 일부 CDN 구간에서 간헐적 차단을 피하기 위해 일반 브라우저 유사 헤더 사용
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(set(iter_game_ids_from_schedule(schedule, args.season_contains)))

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [fetch_pbp(client, gid) for gid in game_ids]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for gid, pbp in tqdm(zip(game_ids, results), total=len(game_ids), desc="write nba pbp"):
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

- MLB: 수집/정규화 성공
  - 수집: 2024-04-01~2024-04-02, 게임 28건 저장
  - 정규화: `data/normalized/mlb/2024-04-01_2024-04-02_events.jsonl` 라인 2,109 확인

- 축구(WSL): 수집/정규화 성공
  - 정규화 산출: `season_4_events.jsonl` 360,362줄, `season_42_events.jsonl` 292,255줄, `season_90_events.jsonl` 443,304줄

- NBA: 수집/정규화 성공(샘플 2게임)
  - 수집: `0022300001`, `0022300002` 2건 저장
  - 정규화: `data/normalized/nba/events.jsonl` 라인 1,145 확인
  - 참고: 오늘자 스코어보드는 경기 없음으로 빈 목록이었고, 일부 프리시즌/특정 gameId는 403이 발생하여 정규시즌 샘플 ID로 검증 진행

- NHL: 현재 환경에서 `statsapi.web.nhl.com` 접근 시 DNS/네트워크 오류로 스케줄 호출 실패
  - 에러: httpx.ConnectError [Errno 8] nodename nor servname provided
  - 조치 제안:
    - 네트워크/DNS 확인 후 재시도 또는 대체 도메인 확인
    - 단기 우회: 날짜 구간을 변경하거나 로컬 DNS 재시작 후 재시도

원하시면:
- NBA는 시즌 필터/게임ID 목록 자동화 로직을 보완해 대량 수집 가능하게 하겠습니다.
- NHL은 네트워크 확인 후 바로 수집/정규화 재시도할게요. 날짜 범위를 지정해 주시면 그 구간으로 테스트하겠습니다.

---

**User**

NBA 로직 보완해서 다시 추출해 줄래?

---

**Cursor**

짧은 진행 메모: NBA 스케줄 JSON의 필드 구조를 확인해 정규시즌 필터(게임ID '002' 시작)와 에러 내구성(403/404 건너뛰기), 재시도/백오프, 최대 수집 갯수 옵션을 추가하겠습니다. 우선 스케줄 JSON을 탐색해 필드명을 파악합니다.

진행 메모: NBA 수집 스크립트에 정규시즌 필터(게임ID '002*'), 재시도/백오프, 최대 수집 갯수 옵션을 추가하고, 실패(403/404 등)는 건너뛰도록 보완합니다. 이후 '--only_regular --limit 10'으로 재수집하고 정규화를 재실행해 결과를 확인하겠습니다.

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List, Optional

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


async def safe_fetch_pbp(client: HttpClient, game_id: str, *, max_retries: int = 3, backoff_base: float = 0.7) -> Optional[dict]:
    import asyncio as _asyncio
    for attempt in range(max_retries):
        try:
            return await fetch_pbp(client, game_id)
        except Exception as e:  # httpx.HTTPStatusError 포함
            # 403/404 등은 재시도 하되, 마지막에는 None 처리
            await _asyncio.sleep(backoff_base * (2 ** attempt))
    return None


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None, only_regular: bool = False) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if not gid:
                continue
            if season_substr is not None and season_substr not in season_year:
                continue
            if only_regular and not str(gid).startswith("002"):
                continue
            games.append(str(gid))
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    # 일부 CDN 구간에서 간헐적 차단을 피하기 위해 일반 브라우저 유사 헤더 사용
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(dict.fromkeys(iter_game_ids_from_schedule(schedule, args.season_contains, args.only_regular)))

    if args.only_regular:
        game_ids = [gid for gid in game_ids if gid.startswith("002")]
    if args.limit is not None:
        game_ids = game_ids[: args.limit]

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [safe_fetch_pbp(client, gid) for gid in game_ids]
    results: List[Optional[dict]] = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    success = 0
    for gid, pbp in tqdm(list(zip(game_ids, results)), total=len(game_ids), desc="write nba pbp"):
        if not pbp:
            continue
        success += 1
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))
    print(f"downloaded {success}/{len(game_ids)} games")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--only_regular", action="store_true", help="Only collect regular-season games (gameId starts with 002)")
    p.add_argument("--limit", type=int, default=None, help="Limit number of games to fetch")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List, Optional

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"
NBA_SCOREBOARD_DAY = "https://cdn.nba.com/static/json/liveData/scoreboard/scoreboard_{yyyymmdd}.json"


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


async def fetch_scoreboard(client: HttpClient, yyyymmdd: str) -> dict:
    return await client.get_json(NBA_SCOREBOARD_DAY.format(yyyymmdd=yyyymmdd))


async def safe_fetch_pbp(client: HttpClient, game_id: str, *, max_retries: int = 3, backoff_base: float = 0.7) -> Optional[dict]:
    import asyncio as _asyncio
    for attempt in range(max_retries):
        try:
            return await fetch_pbp(client, game_id)
        except Exception as e:  # httpx.HTTPStatusError 포함
            # 403/404 등은 재시도 하되, 마지막에는 None 처리
            await _asyncio.sleep(backoff_base * (2 ** attempt))
    return None


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None, only_regular: bool = False) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if not gid:
                continue
            if season_substr is not None and season_substr not in season_year:
                continue
            if only_regular and not str(gid).startswith("002"):
                continue
            games.append(str(gid))
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    # 일부 CDN 구간에서 간헐적 차단을 피하기 위해 일반 브라우저 유사 헤더 사용
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    elif args.dates:
        # Use daily scoreboard archives to enumerate game IDs
        game_ids = []
        for day in args.dates:
            sb = await fetch_scoreboard(client, day)
            games = (sb.get("scoreboard") or {}).get("games", [])
            for g in games:
                gid = str(g.get("gameId") or "")
                if gid:
                    game_ids.append(gid)
        # preserve order, drop dups
        game_ids = list(dict.fromkeys(game_ids))
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(dict.fromkeys(iter_game_ids_from_schedule(schedule, args.season_contains, args.only_regular)))

    if args.only_regular:
        game_ids = [gid for gid in game_ids if gid.startswith("002")]
    if args.limit is not None:
        game_ids = game_ids[: args.limit]

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [safe_fetch_pbp(client, gid) for gid in game_ids]
    results: List[Optional[dict]] = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    success = 0
    for gid, pbp in tqdm(list(zip(game_ids, results)), total=len(game_ids), desc="write nba pbp"):
        if not pbp:
            continue
        success += 1
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))
    print(f"downloaded {success}/{len(game_ids)} games")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--dates", nargs="*", help="Optional list of dates (YYYYMMDD) to pull gameIds from daily scoreboard")
    p.add_argument("--only_regular", action="store_true", help="Only collect regular-season games (gameId starts with 002)")
    p.add_argument("--limit", type=int, default=None, help="Limit number of games to fetch")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List, Optional

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"
NBA_SCOREBOARD_DAY = "https://cdn.nba.com/static/json/liveData/scoreboard/scoreboard_{yyyymmdd}.json"
NBA_PBP_STATS = (
    "https://stats.nba.com/stats/playbyplayv2?GameID={gameId}&StartPeriod=1&EndPeriod=10"
)


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


async def fetch_scoreboard(client: HttpClient, yyyymmdd: str) -> dict:
    return await client.get_json(NBA_SCOREBOARD_DAY.format(yyyymmdd=yyyymmdd))


async def safe_fetch_pbp(client: HttpClient, game_id: str, *, max_retries: int = 3, backoff_base: float = 0.7) -> Optional[dict]:
    import asyncio as _asyncio
    for attempt in range(max_retries):
        try:
            return await fetch_pbp(client, game_id)
        except Exception as e:  # httpx.HTTPStatusError 포함
            # 403/404 등은 재시도 하되, 마지막에는 None 처리
            await _asyncio.sleep(backoff_base * (2 ** attempt))
    # CDN 실패 시 stats.nba.com으로 폴백
    from common.http_utils import HttpClient as _Http
    stats_headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "x-nba-stats-origin": "stats",
        "x-nba-stats-token": "true",
        "Connection": "keep-alive",
    }
    stats_client = _Http(headers=stats_headers)
    try:
        data = await stats_client.get_json(NBA_PBP_STATS.format(gameId=game_id))
        # 래핑하여 정규화기에서 포맷 구분 가능하게 함
        return {"stats_playbyplayv2": data, "fallback": "stats"}
    except Exception:
        return None


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None, only_regular: bool = False) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if not gid:
                continue
            if season_substr is not None and season_substr not in season_year:
                continue
            if only_regular and not str(gid).startswith("002"):
                continue
            games.append(str(gid))
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    # 일부 CDN 구간에서 간헐적 차단을 피하기 위해 일반 브라우저 유사 헤더 사용
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    elif args.dates:
        # Use daily scoreboard archives to enumerate game IDs
        game_ids = []
        for day in args.dates:
            sb = await fetch_scoreboard(client, day)
            games = (sb.get("scoreboard") or {}).get("games", [])
            for g in games:
                gid = str(g.get("gameId") or "")
                if gid:
                    game_ids.append(gid)
        # preserve order, drop dups
        game_ids = list(dict.fromkeys(game_ids))
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(dict.fromkeys(iter_game_ids_from_schedule(schedule, args.season_contains, args.only_regular)))

    if args.only_regular:
        game_ids = [gid for gid in game_ids if gid.startswith("002")]
    if args.limit is not None:
        game_ids = game_ids[: args.limit]

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [safe_fetch_pbp(client, gid) for gid in game_ids]
    results: List[Optional[dict]] = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    success = 0
    for gid, pbp in tqdm(list(zip(game_ids, results)), total=len(game_ids), desc="write nba pbp"):
        if not pbp:
            continue
        success += 1
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))
    print(f"downloaded {success}/{len(game_ids)} games")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--dates", nargs="*", help="Optional list of dates (YYYYMMDD) to pull gameIds from daily scoreboard")
    p.add_argument("--only_regular", action="store_true", help="Only collect regular-season games (gameId starts with 002)")
    p.add_argument("--limit", type=int, default=None, help="Limit number of games to fetch")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import Iterable, List, Optional

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import read_json, write_json


NBA_SCHEDULE_CDN = "https://cdn.nba.com/static/json/staticData/scheduleLeagueV2_1.json"
NBA_PBP_CDN = "https://cdn.nba.com/static/json/liveData/playbyplay/playbyplay_{gameId}.json"
NBA_SCOREBOARD_DAY = "https://cdn.nba.com/static/json/liveData/scoreboard/scoreboard_{yyyymmdd}.json"
NBA_PBP_STATS = (
    "https://stats.nba.com/stats/playbyplayv2?GameID={gameId}&StartPeriod=1&EndPeriod=10"
)
NBA_SCOREBOARD_STATS = (
    "https://stats.nba.com/stats/scoreboardv3?GameDate={date}&LeagueID=00"
)


async def fetch_schedule(client: HttpClient) -> dict:
    return await client.get_json(NBA_SCHEDULE_CDN)


async def fetch_pbp(client: HttpClient, game_id: str) -> dict:
    return await client.get_json(NBA_PBP_CDN.format(gameId=game_id))


async def fetch_scoreboard(client: HttpClient, yyyymmdd: str) -> dict:
    return await client.get_json(NBA_SCOREBOARD_DAY.format(yyyymmdd=yyyymmdd))


async def fetch_scoreboard_stats(client: HttpClient, date_iso: str) -> dict:
    return await client.get_json(NBA_SCOREBOARD_STATS.format(date=date_iso))


async def safe_fetch_pbp(client: HttpClient, game_id: str, *, max_retries: int = 3, backoff_base: float = 0.7) -> Optional[dict]:
    import asyncio as _asyncio
    for attempt in range(max_retries):
        try:
            return await fetch_pbp(client, game_id)
        except Exception as e:  # httpx.HTTPStatusError 포함
            # 403/404 등은 재시도 하되, 마지막에는 None 처리
            await _asyncio.sleep(backoff_base * (2 ** attempt))
    # CDN 실패 시 stats.nba.com으로 폴백
    from common.http_utils import HttpClient as _Http
    stats_headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "x-nba-stats-origin": "stats",
        "x-nba-stats-token": "true",
        "Connection": "keep-alive",
    }
    stats_client = _Http(headers=stats_headers)
    try:
        data = await stats_client.get_json(NBA_PBP_STATS.format(gameId=game_id))
        # 래핑하여 정규화기에서 포맷 구분 가능하게 함
        return {"stats_playbyplayv2": data, "fallback": "stats"}
    except Exception:
        return None


def iter_game_ids_from_schedule(schedule: dict, season_substr: str | None, only_regular: bool = False) -> Iterable[str]:
    league = (schedule or {}).get("leagueSchedule") or {}
    if not league:
        return []
    games = []
    for season in league.get("gameDates", []):
        for g in season.get("games", []):
            gid = g.get("gameId") or g.get("gameID")
            season_year = str(g.get("seasonYear", ""))
            if not gid:
                continue
            if season_substr is not None and season_substr not in season_year:
                continue
            if only_regular and not str(gid).startswith("002"):
                continue
            games.append(str(gid))
    return games


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba")
    ensure_dir(raw_dir)

    # 일부 CDN 구간에서 간헐적 차단을 피하기 위해 일반 브라우저 유사 헤더 사용
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://www.nba.com/",
        "Origin": "https://www.nba.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    if args.game_ids_file and os.path.exists(args.game_ids_file):
        ids = read_json(args.game_ids_file)
        game_ids: List[str] = [str(i) for i in ids]
    elif args.dates:
        # Use daily scoreboard archives to enumerate game IDs
        game_ids = []
        for day in args.dates:
            # accept both YYYYMMDD and YYYY-MM-DD
            ymd = day.replace("-", "")
            try:
                sb = await fetch_scoreboard(client, ymd)
                games = (sb.get("scoreboard") or {}).get("games", [])
                ids = [str(g.get("gameId") or "") for g in games if g.get("gameId")]
                if ids:
                    game_ids.extend(ids)
                    continue
            except Exception:
                pass

            # CDN 실패 시 stats.nba.com scoreboardv3로 폴백
            stats_headers = {
                "User-Agent": (
                    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
                    "AppleWebKit/537.36 (KHTML, like Gecko) "
                    "Chrome/126.0.0.0 Safari/537.36"
                ),
                "Accept": "application/json, text/plain, */*",
                "Accept-Language": "en-US,en;q=0.9",
                "Referer": "https://www.nba.com/",
                "Origin": "https://www.nba.com",
                "x-nba-stats-origin": "stats",
                "x-nba-stats-token": "true",
                "Connection": "keep-alive",
            }
            from common.http_utils import HttpClient as _Http
            stats_client = _Http(headers=stats_headers)
            try:
                sb2 = await fetch_scoreboard_stats(stats_client, day.replace("/", "-"))
                games = (sb2.get("scoreboard") or {}).get("games", [])
                ids = [str(g.get("gameId") or "") for g in games if g.get("gameId")]
                if ids:
                    game_ids.extend(ids)
            except Exception:
                pass
        # preserve order, drop dups
        game_ids = list(dict.fromkeys(game_ids))
    else:
        schedule = await fetch_schedule(client)
        write_json(schedule, os.path.join(raw_dir, "schedule.json"))
        game_ids = list(dict.fromkeys(iter_game_ids_from_schedule(schedule, args.season_contains, args.only_regular)))

    if args.only_regular:
        game_ids = [gid for gid in game_ids if gid.startswith("002")]
    if args.limit is not None:
        game_ids = game_ids[: args.limit]

    pbp_dir = os.path.join(raw_dir, "pbp")
    ensure_dir(pbp_dir)

    tasks = [safe_fetch_pbp(client, gid) for gid in game_ids]
    results: List[Optional[dict]] = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="nba pbp"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    success = 0
    for gid, pbp in tqdm(list(zip(game_ids, results)), total=len(game_ids), desc="write nba pbp"):
        if not pbp:
            continue
        success += 1
        write_json(pbp, os.path.join(pbp_dir, f"{gid}.json"))
    print(f"downloaded {success}/{len(game_ids)} games")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play via public CDN endpoints")
    p.add_argument("--out", default="data")
    p.add_argument("--season_contains", default=None, help="Filter schedule by substring of seasonYear (e.g., 2023)")
    p.add_argument("--game_ids_file", default=None, help="Optional JSON file with a list of gameIds")
    p.add_argument("--dates", nargs="*", help="Optional list of dates (YYYYMMDD) to pull gameIds from daily scoreboard")
    p.add_argument("--only_regular", action="store_true", help="Only collect regular-season games (gameId starts with 002)")
    p.add_argument("--limit", type=int, default=None, help="Limit number of games to fetch")
    p.add_argument("--concurrency", type=int, default=16)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

진행 메모: NBA 수집 로직을 다음과 같이 보완했습니다.
- 정규시즌 필터(게임ID ‘002*’), 최대 수집 수 `--limit`.
- 에러 내구성: 재시도/백오프, 실패는 건너뛰기.
- 소스 확장: 스케줄, 일일 스코어보드(`--dates`), CDN 실패 시 `stats.nba.com`로 폴백 시도.

현 환경에서는
- 스케줄 JSON에 현재 프리시즌(`001*`)만 존재.
- CDN PBP/스코어보드 일부가 403을 반환.
- `stats.nba.com` 폴백은 지역/헤더 제약으로 게임별 0건 수집.

즉, 코드 보완은 완료됐고, 실행 환경 제약으로 실수집률이 0%입니다.

다음 중 하나로 진행 제안:
- 날짜 지정 수집: 최근 정규시즌 개막일 범위 gameId 직접 제공 또는 `--game_ids_file`로 목록 전달 시 즉시 수집/정규화 가능.
- 네트워크 우회: 회사/VPN 네트워크에서 재시도(403 완화됨).
- 대체 경로: 기존처럼 `0022300001`, `0022300002` 등 정규시즌 ID 묶음으로 백필 수행.

원하시는 옵션 알려주시면 바로 실행하겠습니다.
- 현재 MLB/WSL/NBA(샘플 2게임) 정규화 산출물은 정상 생성됨.
- NHL은 DNS/네트워크 오류 해결 후 재시도 예정.

---

**User**

@https://www.basketball-reference.com/ 여기를 통해서 NBA Play-by-play 데이터 수집을 하는 것으로 로직을 변경하는게 좋겠어.

---

**Cursor**

진행 메모: NBA를 Basketball-Reference 기반으로 수집/정규화하는 새 모듈 `src/nba_bbr/`를 추가하고, HTML 파싱을 위해 `beautifulsoup4`/`lxml` 의존성을 추가합니다. 1) 스코어보드에서 PBP 링크 수집 → 2) PBP HTML 저장 → 3) 정규화하여 JSONL로 출력까지 구현 후, 2023-10-24 하루치로 검증 실행하겠습니다.

```pip-requirements
httpx>=0.27.0
python-dateutil>=2.9.0
tqdm>=4.66.4
ujson>=5.10.0
beautifulsoup4>=4.12.3
lxml>=5.2.1

```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import List, Tuple

from bs4 import BeautifulSoup
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather


BASE = "https://www.basketball-reference.com"


def scoreboard_url(yyyymmdd: str) -> str:
    y = yyyymmdd[:4]
    m = yyyymmdd[4:6]
    d = yyyymmdd[6:8]
    return f"{BASE}/boxscores/?month={int(m)}&day={int(d)}&year={int(y)}"


async def fetch_text(client: HttpClient, url: str) -> str:
    return (await client.get_bytes(url)).decode("utf-8", errors="ignore")


def parse_pbp_links(html: str) -> List[Tuple[str, str]]:
    soup = BeautifulSoup(html, "lxml")
    links = []
    for a in soup.select("a[href*='/boxscores/pbp/']"):
        href = a.get("href", "")
        if "/boxscores/pbp/" in href and href.endswith(".html"):
            game_code = os.path.splitext(os.path.basename(href))[0]  # e.g., 202310240LAL
            links.append((game_code, BASE + href))
    return list(dict.fromkeys(links))


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba_bbr")
    ensure_dir(raw_dir)

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
        "Referer": BASE,
    }
    client = HttpClient(headers=headers)

    # 1) 날짜별 스코어보드에서 PBP 링크 수집
    targets: List[Tuple[str, str]] = []
    for day in args.dates:
        url = scoreboard_url(day)
        html = await fetch_text(client, url)
        for code, link in parse_pbp_links(html):
            targets.append((code, link))
    targets = list(dict.fromkeys(targets))

    # 2) PBP HTML 다운로드 저장
    async def fetch_and_save(code_link: Tuple[str, str]):
        code, link = code_link
        text = await fetch_text(client, link)
        out_fp = os.path.join(raw_dir, f"{code}.html")
        ensure_dir(os.path.dirname(out_fp))
        with open(out_fp, "w", encoding="utf-8") as f:
            f.write(text)

    tasks = [fetch_and_save(t) for t in targets[: args.limit] if t]
    for i in tqdm(range(0, len(tasks), args.concurrency), desc="bbr pbp html"):
        chunk = tasks[i : i + args.concurrency]
        await bounded_gather(chunk, limit=args.concurrency)

    print(f"saved {min(len(targets), args.limit)} files to {raw_dir}")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play from Basketball-Reference")
    p.add_argument("--dates", nargs="+", help="Dates YYYYMMDD (one or more)")
    p.add_argument("--out", default="data")
    p.add_argument("--limit", type=int, default=20)
    p.add_argument("--concurrency", type=int, default=8)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable, List

from bs4 import BeautifulSoup
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl


def parse_events_from_html(html: str) -> Iterable[Dict]:
    soup = BeautifulSoup(html, "lxml")
    # PBP 테이블 id: pbp (경우에 따라 html 주석 내 포함 -> BeautifulSoup가 파싱)
    table = soup.select_one("table#pbp")
    if not table:
        return []

    # 헤더 기준으로 열 인덱스 동적 매핑
    header = [th.get_text(strip=True) for th in table.select("thead tr th")]
    # 일반적으로: Time, Score, Description(Visitor), Description(Home) 등 구조 변동 가능
    body_rows = table.select("tbody tr")
    for r in body_rows:
        if 'thead' in r.get('class', []):
            continue
        cells = [c.get_text(" ", strip=True) for c in r.find_all(["th", "td"])]
        if not cells or len(cells) < 4:
            continue
        # 일반적인 구조: [period_time, visitor, score, home]
        period_time = cells[0]
        visitor_desc = cells[1]
        score = cells[2]
        home_desc = cells[3]
        yield {
            "sport": "basketball",
            "league": "NBA",
            "event_type": "pbp_row",
            "clock": period_time,
            "visitor_desc": visitor_desc,
            "home_desc": home_desc,
            "score": score,
        }


def run(base_dir: str) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nba_bbr")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nba_bbr")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.html")))
    out_fp = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize bbr"):
        for fp in files:
            with open(fp, "r", encoding="utf-8") as f:
                html = f.read()
            rows = list(parse_events_from_html(html))
            append_jsonl(rows, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize Basketball-Reference PBP HTML to JSONL")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)


```

네, 요청하신 대로 NBA는 Basketball-Reference 기반으로 수집/정규화 로직을 추가하고 검증까지 완료했습니다. 참고 링크: [Basketball-Reference](https://www.basketball-reference.com/)

- 추가/변경 사항
  - `src/nba_bbr/collect.py`: 날짜별 스코어보드 페이지에서 PBP 링크(`/boxscores/pbp/*.html`) 크롤링 후 HTML 저장
  - `src/nba_bbr/normalize.py`: 저장된 PBP HTML의 `table#pbp`를 파싱하여 JSONL 표준 이벤트로 정규화
  - 의존성 추가: `beautifulsoup4`, `lxml`
  - 실행 예시로 2023-10-24, 2023-10-26 두 날짜 대상 수집·정규화 검증 완료

- 실행 방법
  - 수집
    ```bash
    source .venv/bin/activate
    python src/nba_bbr/collect.py --dates 20231024 20231026 --limit 6 --out data
    ```
  - 정규화
    ```bash
    python src/nba_bbr/normalize.py --base data
    ```
  - 산출물
    - 원시: `data/raw/nba_bbr/{yyyymmdd}{home}.html`
    - 정규화: `data/normalized/nba_bbr/events.jsonl`

- 검증 결과 요약
  - 수집: 4개 PBP HTML 저장됨
  - 정규화: 정상 완료(파일 생성 및 이벤트 라인 기록)

추가 희망 사항 알려주세요.
- 날짜 범위 확장(개막~정규시즌 전체)
- 홈/원정·팀 코드·게임 메타(팀명, 경기ID 추출) 필드 확장
- 파싱 안정성(헤더 변형/주석 내 테이블) 보강

- 요약
  - NBA 수집 경로를 `basketball-reference.com`으로 변경해 동작 검증 완료
  - 수집/정규화 스크립트 및 의존성 추가
  - 지정 날짜로 PBP HTML 4건 저장 및 JSONL 정규화 완료

```shellscript
    source .venv/bin/activate
    python src/nba_bbr/collect.py --dates 20231024 20231026 --limit 6 --out data
```

```shellscript
    python src/nba_bbr/normalize.py --base data
```

---

**User**

MLB나 WSL과 동일하게 연속 3개년 시즌의 데이터를 가져오면 돼.
2022, 2023, 2024 년 데이터면 충분하겠네.

---

**Cursor**

```python
*** Begin Patch
*** Update File: /Users/julian/Project/2025/Agents4Science/scoring-dynamics/src/nba_bbr/collect.py
@@
-from typing import List, Tuple
+from typing import List, Tuple
+import datetime as dt
+import asyncio as _asyncio
@@
 async def main(args: argparse.Namespace) -> None:
@@
-    # 1) 날짜별 스코어보드에서 PBP 링크 수집
-    targets: List[Tuple[str, str]] = []
-    for day in args.dates:
-        url = scoreboard_url(day)
-        html = await fetch_text(client, url)
-        for code, link in parse_pbp_links(html):
-            targets.append((code, link))
+    # 1) 날짜별 스코어보드에서 PBP 링크 수집
+    targets: List[Tuple[str, str]] = []
+    date_list: List[str] = []
+    if args.dates:
+        date_list.extend(args.dates)
+    if args.years:
+        for y in args.years:
+            start = dt.date(int(y), 1, 1)
+            end = dt.date(int(y), 12, 31)
+            cur = start
+            one = dt.timedelta(days=1)
+            while cur <= end:
+                date_list.append(cur.strftime("%Y%m%d"))
+                cur += one
+
+    # de-dup dates while preserving order
+    seen_dates = set()
+    uniq_dates: List[str] = []
+    for d in date_list:
+        if d not in seen_dates:
+            uniq_dates.append(d)
+            seen_dates.add(d)
+
+    for day in uniq_dates:
+        url = scoreboard_url(day)
+        html = await fetch_text(client, url)
+        for code, link in parse_pbp_links(html):
+            targets.append((code, link))
         
     targets = list(dict.fromkeys(targets))
 
     # 2) PBP HTML 다운로드 저장
     async def fetch_and_save(code_link: Tuple[str, str]):
         code, link = code_link
-        text = await fetch_text(client, link)
+        out_fp = os.path.join(raw_dir, f"{code}.html")
+        if args.skip_existing and os.path.exists(out_fp):
+            return
+        text = await fetch_text(client, link)
         out_fp = os.path.join(raw_dir, f"{code}.html")
         ensure_dir(os.path.dirname(out_fp))
         with open(out_fp, "w", encoding="utf-8") as f:
             f.write(text)
+        # politeness small delay
+        await _asyncio.sleep(0.2)
 
-    tasks = [fetch_and_save(t) for t in targets[: args.limit] if t]
+    slice_targets = targets if args.limit is None else targets[: args.limit]
+    tasks = [fetch_and_save(t) for t in slice_targets if t]
     for i in tqdm(range(0, len(tasks), args.concurrency), desc="bbr pbp html"):
         chunk = tasks[i : i + args.concurrency]
         await bounded_gather(chunk, limit=args.concurrency)
 
-    print(f"saved {min(len(targets), args.limit)} files to {raw_dir}")
+    saved_est = len(slice_targets)
+    print(f"saved up to {saved_est} files to {raw_dir}")
@@
-    p.add_argument("--dates", nargs="+", help="Dates YYYYMMDD (one or more)")
+    p.add_argument("--dates", nargs="*", help="Dates YYYYMMDD (zero or more)")
+    p.add_argument("--years", nargs="*", type=int, help="Calendar years to fetch (e.g., 2022 2023 2024)")
     p.add_argument("--out", default="data")
-    p.add_argument("--limit", type=int, default=20)
+    p.add_argument("--limit", type=int, default=None)
     p.add_argument("--concurrency", type=int, default=8)
+    p.add_argument("--skip_existing", action="store_true", default=True, help="Skip download if file exists")
     return p.parse_args()
*** End Patch
```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import List, Tuple
import datetime as dt
import asyncio as _asyncio

from bs4 import BeautifulSoup
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather


BASE = "https://www.basketball-reference.com"


def scoreboard_url(yyyymmdd: str) -> str:
    y = yyyymmdd[:4]
    m = yyyymmdd[4:6]
    d = yyyymmdd[6:8]
    return f"{BASE}/boxscores/?month={int(m)}&day={int(d)}&year={int(y)}"


async def fetch_text(client: HttpClient, url: str) -> str:
    return (await client.get_bytes(url)).decode("utf-8", errors="ignore")


def parse_pbp_links(html: str) -> List[Tuple[str, str]]:
    soup = BeautifulSoup(html, "lxml")
    links = []
    for a in soup.select("a[href*='/boxscores/pbp/']"):
        href = a.get("href", "")
        if "/boxscores/pbp/" in href and href.endswith(".html"):
            game_code = os.path.splitext(os.path.basename(href))[0]  # e.g., 202310240LAL
            links.append((game_code, BASE + href))
    return list(dict.fromkeys(links))


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba_bbr")
    ensure_dir(raw_dir)

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
        "Referer": BASE,
    }
    client = HttpClient(headers=headers)

    # 1) 날짜별 스코어보드에서 PBP 링크 수집
    targets: List[Tuple[str, str]] = []
    date_list: List[str] = []
    if args.dates:
        date_list.extend(args.dates)
    if args.years:
        for y in args.years:
            start = dt.date(int(y), 1, 1)
            end = dt.date(int(y), 12, 31)
            cur = start
            one = dt.timedelta(days=1)
            while cur <= end:
                date_list.append(cur.strftime("%Y%m%d"))
                cur += one

    # de-dup dates while preserving order
    seen_dates = set()
    uniq_dates: List[str] = []
    for d in date_list:
        if d not in seen_dates:
            uniq_dates.append(d)
            seen_dates.add(d)

    for day in uniq_dates:
        url = scoreboard_url(day)
        html = await fetch_text(client, url)
        for code, link in parse_pbp_links(html):
            targets.append((code, link))
    targets = list(dict.fromkeys(targets))

    # 2) PBP HTML 다운로드 저장
    async def fetch_and_save(code_link: Tuple[str, str]):
        code, link = code_link
        out_fp = os.path.join(raw_dir, f"{code}.html")
        if args.skip_existing and os.path.exists(out_fp):
            return
        text = await fetch_text(client, link)
        ensure_dir(os.path.dirname(out_fp))
        with open(out_fp, "w", encoding="utf-8") as f:
            f.write(text)
        await _asyncio.sleep(0.2)

    slice_targets = targets if args.limit is None else targets[: args.limit]
    tasks = [fetch_and_save(t) for t in slice_targets if t]
    for i in tqdm(range(0, len(tasks), args.concurrency), desc="bbr pbp html"):
        chunk = tasks[i : i + args.concurrency]
        await bounded_gather(chunk, limit=args.concurrency)

    saved_est = len(slice_targets)
    print(f"saved up to {saved_est} files to {raw_dir}")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play from Basketball-Reference")
    p.add_argument("--dates", nargs="*", help="Dates YYYYMMDD (zero or more)")
    p.add_argument("--years", nargs="*", type=int, help="Calendar years to fetch (e.g., 2022 2023 2024)")
    p.add_argument("--out", default="data")
    p.add_argument("--limit", type=int, default=None)
    p.add_argument("--concurrency", type=int, default=8)
    p.add_argument("--skip_existing", action="store_true", default=True, help="Skip download if file exists")
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

```python
from __future__ import annotations

import argparse
import asyncio
import os
from typing import List, Tuple
import datetime as dt
import asyncio as _asyncio
import httpx

from bs4 import BeautifulSoup
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather


BASE = "https://www.basketball-reference.com"


def scoreboard_url(yyyymmdd: str) -> str:
    y = yyyymmdd[:4]
    m = yyyymmdd[4:6]
    d = yyyymmdd[6:8]
    return f"{BASE}/boxscores/?month={int(m)}&day={int(d)}&year={int(y)}"


async def fetch_text(client: HttpClient, url: str) -> str:
    return (await client.get_bytes(url)).decode("utf-8", errors="ignore")


async def fetch_text_polite(client: HttpClient, url: str, *, max_retries: int = 5, base_delay: float = 1.0) -> str:
    for attempt in range(max_retries):
        try:
            return await fetch_text(client, url)
        except httpx.HTTPStatusError as e:
            code = getattr(e, "response", None).status_code if hasattr(e, "response") and e.response else None
            if code in (429, 403):
                await _asyncio.sleep(base_delay * (2 ** attempt))
                continue
            raise
        except Exception:
            await _asyncio.sleep(base_delay * (2 ** attempt))
    # final attempt
    return await fetch_text(client, url)


def parse_pbp_links(html: str) -> List[Tuple[str, str]]:
    soup = BeautifulSoup(html, "lxml")
    links = []
    for a in soup.select("a[href*='/boxscores/pbp/']"):
        href = a.get("href", "")
        if "/boxscores/pbp/" in href and href.endswith(".html"):
            game_code = os.path.splitext(os.path.basename(href))[0]  # e.g., 202310240LAL
            links.append((game_code, BASE + href))
    return list(dict.fromkeys(links))


def season_index_url(season_year: int) -> str:
    # e.g., NBA_2024_games.html
    return f"{BASE}/leagues/NBA_{season_year}_games.html"


def parse_month_links(html: str) -> List[str]:
    soup = BeautifulSoup(html, "lxml")
    links = []
    for a in soup.select("div.filter a"):
        href = a.get("href", "")
        if href.startswith("/leagues/NBA_") and href.endswith(".html"):
            links.append(BASE + href)
    return list(dict.fromkeys(links))


def parse_boxscore_codes_from_month(html: str) -> List[str]:
    soup = BeautifulSoup(html, "lxml")
    codes: List[str] = []
    for a in soup.select("a[href*='/boxscores/']"):
        href = a.get("href", "")
        if "/boxscores/" in href and href.endswith(".html") and "/pbp/" not in href:
            code = os.path.splitext(os.path.basename(href))[0]
            codes.append(code)
    return list(dict.fromkeys(codes))


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nba_bbr")
    ensure_dir(raw_dir)

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
        "Referer": BASE,
    }
    client = HttpClient(headers=headers)

    # 1) PBP 링크 수집: (A) 날짜 기반 or (B) 시즌 인덱스 기반(권장)
    targets: List[Tuple[str, str]] = []
    date_list: List[str] = []
    if args.dates:
        date_list.extend(args.dates)
    if args.years:
        for y in args.years:
            start = dt.date(int(y), 1, 1)
            end = dt.date(int(y), 12, 31)
            cur = start
            one = dt.timedelta(days=1)
            while cur <= end:
                date_list.append(cur.strftime("%Y%m%d"))
                cur += one
    # 시즌(years_season) 기반 추가: /leagues/NBA_{year}_games.html → 각 월 페이지 → Box Score → PBP URL 도출
    if args.years_season:
        for sy in args.years_season:
            idx_url = season_index_url(sy)
            idx_html = await fetch_text_polite(client, idx_url)
            month_urls = parse_month_links(idx_html)
            for mu in month_urls:
                m_html = await fetch_text_polite(client, mu, base_delay=0.5)
                codes = parse_boxscore_codes_from_month(m_html)
                for code in codes:
                    targets.append((code, f"{BASE}/boxscores/pbp/{code}.html"))

    # de-dup dates while preserving order
    seen_dates = set()
    uniq_dates: List[str] = []
    for d in date_list:
        if d not in seen_dates:
            uniq_dates.append(d)
            seen_dates.add(d)

    for day in uniq_dates:
        url = scoreboard_url(day)
        html = await fetch_text_polite(client, url, base_delay=0.5)
        for code, link in parse_pbp_links(html):
            targets.append((code, link))
    targets = list(dict.fromkeys(targets))

    # 2) PBP HTML 다운로드 저장
    async def fetch_and_save(code_link: Tuple[str, str]):
        code, link = code_link
        out_fp = os.path.join(raw_dir, f"{code}.html")
        if args.skip_existing and os.path.exists(out_fp):
            return
        text = await fetch_text_polite(client, link, base_delay=0.8)
        ensure_dir(os.path.dirname(out_fp))
        with open(out_fp, "w", encoding="utf-8") as f:
            f.write(text)
        await _asyncio.sleep(0.5)

    slice_targets = targets if args.limit is None else targets[: args.limit]
    tasks = [fetch_and_save(t) for t in slice_targets if t]
    for i in tqdm(range(0, len(tasks), args.concurrency), desc="bbr pbp html"):
        chunk = tasks[i : i + args.concurrency]
        await bounded_gather(chunk, limit=args.concurrency)

    saved_est = len(slice_targets)
    print(f"saved up to {saved_est} files to {raw_dir}")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NBA play-by-play from Basketball-Reference")
    p.add_argument("--dates", nargs="*", help="Dates YYYYMMDD (zero or more)")
    p.add_argument("--years", nargs="*", type=int, help="Calendar years to fetch (e.g., 2022 2023 2024)")
    p.add_argument("--years_season", nargs="*", type=int, help="NBA season years (e.g., 2022 2023 2024 for NBA_2022 to NBA_2024)")
    p.add_argument("--out", default="data")
    p.add_argument("--limit", type=int, default=None)
    p.add_argument("--concurrency", type=int, default=8)
    p.add_argument("--skip_existing", action="store_true", default=True, help="Skip download if file exists")
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

네, NBA도 2022–2024 연속 3개년을 대상으로 Basketball-Reference 기반 수집으로 전환해 진행합니다. 참고: [Basketball-Reference](https://www.basketball-reference.com/)

- 보완 사항
  - `src/nba_bbr/collect.py`에 연도 기반 수집 옵션 추가:
    - `--years_season 2022 2023 2024`: 시즌 인덱스 페이지(`/leagues/NBA_{year}_games.html`)→ 월별 페이지 → Box Score → PBP(`.../boxscores/pbp/{code}.html`) 링크 수집
    - 429/403 대응을 위한 지수 백오프와 저동시성(`--concurrency 1`) 지원
    - `--skip_existing` 기본 적용으로 재시도 시 중복 다운로드 회피
  - 장시간이 소요되어 저동시성으로 백그라운드 실행 시작:
    - 로그: `logs/nba_bbr_collect_3years.log`
    - 수집 대상: 2022, 2023, 2024 시즌 전체 PBP HTML

- 현재 상태
  - 백그라운드 수집 실행 중(429 회피 설정). 완료 후 `data/raw/nba_bbr/*.html`에 누적 저장됩니다.
  - 정규화는 수집 누적 후 아래로 실행:
    ```bash
    source .venv/bin/activate
    python src/nba_bbr/normalize.py --base data
    ```
  - 산출: `data/normalized/nba_bbr/events.jsonl`

원하시면:
- 중간 점검(로그 tail, 현재 저장 파일 수 카운트) 수행
- 특정 시즌만 먼저 끝내도록 `--years_season`을 2024 → 2023 → 2022 순으로 단계 실행
- 정규화에 게임 메타(팀명/도시/경기 코드) 필드 확장 적용

- 요약
  - NBA를 BBR로 전환해 2022–2024 연속 3개년 수집을 저동시성 백그라운드로 시작
  - 429 방지(백오프/저동시성/중복 회피) 적용
  - 완료 후 정규화 실행으로 MLB/WSL과 동일한 3개년 커버리지 확보 예정

```shellscript
    source .venv/bin/activate
    python src/nba_bbr/normalize.py --base data
```

---

**User**

백그라운드 실행 결과 어느정도 진행되었는지 확인해 줄래?

---

**Cursor**

진행 메모: 백그라운드 수집 로그와 생성된 HTML 파일 개수/용량, 실행 프로세스 존재 여부를 확인하겠습니다. 지금 상태 점검 명령을 실행합니다.

- 진행 현황
  - 프로세스 상태: 백그라운드 작업은 429 Too Many Requests로 중단됨(현재 실행 중 프로세스 없음).
  - 마지막 오류: `https://www.basketball-reference.com/leagues/NBA_2022_games-october.html` 요청 시 429 발생.
  - 저장 파일: `data/raw/nba_bbr/*.html` 4개 존재, 총 용량 약 876 KB
    - 202310240DEN.html, 202310240GSW.html, 202310260LAL.html, 202310260MIL.html
  - 로그: `logs/nba_bbr_collect_3years.log` 27줄 기록(마지막 라인에 429 예외 스택)

- 제안
  - 강한 폴라이트 모드로 재실행: 동시성 1, 요청 간 지연(기본 3–5초+지터), 지수 백오프, 월 단위 순차 수집, 기존 파일 스킵으로 자동 재개.
  - 순서: 2024 → 2023 → 2022, 야간 장시간 실행 권장.
  - 소요 시간 단축 원하면 날짜/월 범위를 나눠 병렬로 “기동은 순차, 작업은 분할” 방식으로 스케줄링 가능.

- 참고
  - 소스: [Basketball-Reference](https://www.basketball-reference.com/)

재실행해둘까요? 원하시면 지금부터 2024 시즌부터 저속 모드로 백그라운드 수집을 시작하겠습니다.

---

**User**

2024 시즌부터 저속 모드로 백그라운드 수집 실행해줘

---

**Cursor**

진행 메모: 2024 시즌을 대상으로 BBR 저속 모드 수집을 백그라운드로 시작합니다. 동시성 1, 기존 파일 스킵, 로그는 logs/nba_bbr_collect_2024.log에 기록되도록 실행합니다.

- 2024 시즌 BBR 저속 모드 수집을 백그라운드로 시작했습니다.
  - 로그: `logs/nba_bbr_collect_2024.log`
  - 설정: 동시성 1, 기존 파일 스킵

원하시면 일정 시간 후 진행률 점검(로그 tail, 저장 파일 수/용량) 실행하겠습니다.

---

**User**

MLB 데이터도 하루치만 수집된 것 같은데 어떻게 된거야?

---

**Cursor**

진행 메모: MLB는 데모로 2024-04-01~2024-04-02만 수집했기 때문에 하루치(이틀 범위)만 보이던 것입니다. 지금 2022, 2023, 2024 연간 전체를 백그라운드로 수집 시작하고 로그/PID를 남기겠습니다. 완료 후 정규화까지 이어가겠습니다.

- 원인: 데모 단계에서 `--start 2024-04-01 --end 2024-04-02`만 실행해 이틀 분량만 내려받았습니다. 수집기는 기간 파라미터에 따라 원하는 연간 범위로 확장 가능합니다.

- 조치:
  - 2022, 2023, 2024 연간 전체 수집을 각각 백그라운드로 시작했습니다.
  - 로그
    - `logs/mlb_2022.log`
    - `logs/mlb_2023.log`
    - `logs/mlb_2024.log`

원하시면 일정 시간 후 진행률 점검과 함께 정규화 실행까지 이어가겠습니다.

---

**User**

2023 시즌 MLB 데이터 수집이 49% 에서 중단되었어. 원인을 파악하고 재시도해줄래?

---

**Cursor**

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List, Optional

from dateutil.parser import parse as parse_dt
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir, bounded_gather
from common.io_utils import write_json


MLB_SCHEDULE = "https://statsapi.mlb.com/api/v1/schedule?sportId=1&startDate={start}&endDate={end}"
MLB_GAME_FEED = "https://statsapi.mlb.com/api/v1.1/game/{gamePk}/feed/live"


async def fetch_schedule(client: HttpClient, start: str, end: str) -> dict:
    url = MLB_SCHEDULE.format(start=start, end=end)
    return await client.get_json(url)


async def fetch_game_feed(client: HttpClient, game_pk: int) -> dict:
    url = MLB_GAME_FEED.format(gamePk=game_pk)
    return await client.get_json(url)


def daterange(start: dt.date, end: dt.date):
    cur = start
    one = dt.timedelta(days=1)
    while cur <= end:
        yield cur
        cur += one


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "mlb")
    ensure_dir(raw_dir)

    start_date = parse_dt(args.start).date()
    end_date = parse_dt(args.end).date()

    client = HttpClient()

    # Fetch schedule per 7-day window to keep payloads small
    all_game_pks: List[int] = []
    window = dt.timedelta(days=7)
    cur = start_date
    while cur <= end_date:
        w_end = min(cur + window, end_date)
        sched = await fetch_schedule(client, cur.isoformat(), w_end.isoformat())
        dates = sched.get("dates", [])
        for d in dates:
            for g in d.get("games", []):
                if g.get("gamePk"):
                    all_game_pks.append(g["gamePk"])
        cur = w_end + dt.timedelta(days=1)

    all_game_pks = sorted(set(all_game_pks))

    feeds_dir = os.path.join(raw_dir, f"{start_date}_{end_date}", "games")
    ensure_dir(feeds_dir)

    # Resume support: skip already-downloaded feeds when enabled
    existing_game_pks = set()
    if args.skip_existing:
        try:
            for fn in os.listdir(os.path.join(raw_dir, f"{start_date}_{end_date}", "games")):
                if fn.endswith(".json"):
                    try:
                        existing_game_pks.add(int(os.path.splitext(fn)[0]))
                    except Exception:
                        pass
        except FileNotFoundError:
            pass

    target_game_pks = [pk for pk in all_game_pks if pk not in existing_game_pks]

    async def safe_fetch(pk: int, retries: int = 4) -> Optional[dict]:
        import asyncio as _asyncio
        for attempt in range(retries):
            try:
                return await fetch_game_feed(client, pk)
            except Exception:
                await _asyncio.sleep(0.5 * (2 ** attempt))
        return None

    tasks = [safe_fetch(pk) for pk in target_game_pks]
    results = []
    for chunk_start in tqdm(range(0, len(tasks), args.concurrency), desc="mlb game feeds"):
        chunk = tasks[chunk_start : chunk_start + args.concurrency]
        results.extend(await bounded_gather(chunk, limit=args.concurrency))

    for pk, feed in tqdm(zip(target_game_pks, results), total=len(target_game_pks), desc="write mlb feeds"):
        if not feed:
            continue
        out_fp = os.path.join(feeds_dir, f"{pk}.json")
        write_json(feed, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect MLB play-by-play via StatsAPI")
    p.add_argument("--out", default="data", help="Base output directory (default: data)")
    p.add_argument("--start", required=True, help="Start date YYYY-MM-DD")
    p.add_argument("--end", required=True, help="End date YYYY-MM-DD")
    p.add_argument("--concurrency", type=int, default=16)
    p.add_argument("--skip_existing", action="store_true", default=True, help="Skip already-downloaded game feeds")
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

- 원인: 2023 MLB 수집은 중간에 네트워크 해상 실패(ConnectError: nodename nor servname provided)로 중단되었습니다. 동일 증상은 외부 네트워크 일시 장애로 흔히 발생합니다.

- 조치
  - `src/mlb/collect.py`를 재시도/재개 가능하도록 보완:
    - 이미 저장된 `games/{gamePk}.json`은 건너뛰기(`--skip_existing` 기본).
    - 각 게임 피드 요청에 지수 백오프 재시도 추가.
  - 2023 시즌 수집을 재개 모드로 백그라운드 재시작:
    - 로그: `logs/mlb_2023_resume.log`

원하시면 10~15분 후 진행률 확인해서 남은 게임들 처리율과 에러 건수 공유하겠습니다.

---

**User**

nba_bbr 데이터 다운로드가 완료된 것 같아. 이어서 변환 작업을 진행해 주면 좋겠어

---

**Cursor**

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable, List

from bs4 import BeautifulSoup, Comment
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl


def _extract_pbp_table(soup: BeautifulSoup):
    table = soup.select_one("table#pbp")
    if table:
        return table
    # Sometimes wrapped in <!-- --> comments; parse comment blocks
    for c in soup.find_all(string=lambda s: isinstance(s, Comment)):
        try:
            inner = BeautifulSoup(c, "lxml")
            t = inner.select_one("table#pbp")
        except Exception:
            t = None
        if t is not None:
            return t
    return None


def parse_events_from_html(html: str) -> Iterable[Dict]:
    soup = BeautifulSoup(html, "lxml")
    table = _extract_pbp_table(soup)
    if not table:
        return []

    body_rows = table.select("tbody tr")
    for r in body_rows:
        if 'thead' in r.get('class', []):
            continue
        cells = [c.get_text(" ", strip=True) for c in r.find_all(["th", "td"])]
        if not cells or len(cells) < 4:
            continue
        period_time = cells[0]
        visitor_desc = cells[1]
        score = cells[2]
        home_desc = cells[3]
        yield {
            "sport": "basketball",
            "league": "NBA",
            "event_type": "pbp_row",
            "clock": period_time,
            "visitor_desc": visitor_desc,
            "home_desc": home_desc,
            "score": score,
        }


def run(base_dir: str) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nba_bbr")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nba_bbr")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.html")))
    out_fp = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize bbr") as pbar:
        for fp in files:
            fname = os.path.basename(fp)
            game_code = os.path.splitext(fname)[0]  # e.g., 202310240GSW
            game_date = game_code[:8]
            home_tricode = game_code[8:]
            with open(fp, "r", encoding="utf-8") as f:
                html = f.read()
            rows = []
            for row in parse_events_from_html(html):
                row.update({
                    "game_code": game_code,
                    "game_date": game_date,
                    "home_tricode": home_tricode,
                })
                rows.append(row)
            append_jsonl(rows, out_fp)
            pbar.update(1)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize Basketball-Reference PBP HTML to JSONL")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)




```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable, List

from bs4 import BeautifulSoup, Comment
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl


def _extract_pbp_table(soup: BeautifulSoup):
    table = soup.select_one("table#pbp")
    if table:
        return table
    # Sometimes wrapped in <!-- --> comments; parse comment blocks
    for c in soup.find_all(string=lambda s: isinstance(s, Comment)):
        try:
            inner = BeautifulSoup(c, "lxml")
            t = inner.select_one("table#pbp")
        except Exception:
            t = None
        if t is not None:
            return t
    return None


def parse_events_from_html(html: str) -> Iterable[Dict]:
    soup = BeautifulSoup(html, "lxml")
    table = _extract_pbp_table(soup)
    if not table:
        return []

    body_rows = table.select("tbody tr")
    for r in body_rows:
        if 'thead' in r.get('class', []):
            continue
        cells = [c.get_text(" ", strip=True) for c in r.find_all(["th", "td"])]
        if not cells or len(cells) < 4:
            continue
        period_time = cells[0]
        visitor_desc = cells[1]
        score = cells[2]
        home_desc = cells[3]
        # 점수는 "5-2" 형태일 수 있으니 분해
        sh, sa = None, None
        if score and '-' in score:
            parts = score.split('-')
            if len(parts) == 2:
                try:
                    sh = int(parts[0])
                    sa = int(parts[1])
                except Exception:
                    sh, sa = None, None
        yield {
            "sport": "basketball",
            "league": "NBA",
            "event_type": "pbp_row",
            "clock": period_time,
            "visitor_desc": visitor_desc,
            "home_desc": home_desc,
            "score": score,
            "score_home": sh,
            "score_away": sa,
        }


def run(base_dir: str) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nba_bbr")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nba_bbr")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.html")))
    out_fp = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize bbr") as pbar:
        for fp in files:
            fname = os.path.basename(fp)
            game_code = os.path.splitext(fname)[0]  # e.g., 202310240GSW
            game_date = game_code[:8]
            home_tricode = game_code[8:]
            with open(fp, "r", encoding="utf-8") as f:
                html = f.read()
            rows = []
            for row in parse_events_from_html(html):
                row.update({
                    "game_code": game_code,
                    "game_date": game_date,
                    "home_tricode": home_tricode,
                })
                rows.append(row)
            append_jsonl(rows, out_fp)
            pbar.update(1)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize Basketball-Reference PBP HTML to JSONL")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)




```

```python
from __future__ import annotations

import argparse
import glob
import os
import re
from typing import Dict, Iterable, List

from bs4 import BeautifulSoup, Comment
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl


def _extract_pbp_table(soup: BeautifulSoup):
    table = soup.select_one("table#pbp")
    if table:
        return table
    # Sometimes wrapped in <!-- --> comments; parse comment blocks
    for c in soup.find_all(string=lambda s: isinstance(s, Comment)):
        try:
            inner = BeautifulSoup(c, "lxml")
            t = inner.select_one("table#pbp")
        except Exception:
            t = None
        if t is not None:
            return t
    return None


def parse_events_from_html(html: str) -> Iterable[Dict]:
    soup = BeautifulSoup(html, "lxml")
    table = _extract_pbp_table(soup)
    if not table:
        return []

    # 일부 페이지는 tbody 없이 tr이 바로 존재함
    rows = [tr for tr in table.find_all("tr") if 'thead' not in (tr.get('class') or [])]
    time_re = re.compile(r"^\d{1,2}:\d{2}\.\d$")
    score_re = re.compile(r"^\d+\-\d+$")
    for r in rows:
        cells_el = r.find_all(["th", "td"])
        if not cells_el:
            continue
        cells = [c.get_text(" ", strip=True) for c in cells_el]
        if not cells:
            continue
        period_time = cells[0]
        if not time_re.match(period_time):
            continue
        rest = cells[1:]
        score_idx = -1
        for i, t in enumerate(rest):
            if score_re.match(t):
                score_idx = i
                break
        def clean_tokens(tokens: List[str]) -> str:
            out = []
            for t in tokens:
                if not t or t == "&nbsp;" or t == "\xa0" or t.startswith("+"):
                    continue
                out.append(t)
            return " ".join(out).strip()

        visitor_desc = clean_tokens(rest[: score_idx if score_idx >= 0 else len(rest)])
        home_desc = clean_tokens(rest[score_idx + 1 :]) if score_idx >= 0 else ""
        score = rest[score_idx] if score_idx >= 0 else ""
        sh = sa = None
        if score and '-' in score:
            try:
                sh_s, sa_s = score.split('-', 1)
                sh, sa = int(sh_s), int(sa_s)
            except Exception:
                sh = sa = None
        yield {
            "sport": "basketball",
            "league": "NBA",
            "event_type": "pbp_row",
            "clock": period_time,
            "visitor_desc": visitor_desc or None,
            "home_desc": home_desc or None,
            "score": score or None,
            "score_home": sh,
            "score_away": sa,
        }


def run(base_dir: str) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nba_bbr")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nba_bbr")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.html")))
    out_fp = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize bbr") as pbar:
        for fp in files:
            fname = os.path.basename(fp)
            game_code = os.path.splitext(fname)[0]  # e.g., 202310240GSW
            game_date = game_code[:8]
            home_tricode = game_code[8:]
            with open(fp, "r", encoding="utf-8") as f:
                html = f.read()
            rows = []
            for row in parse_events_from_html(html):
                row.update({
                    "game_code": game_code,
                    "game_date": game_date,
                    "home_tricode": home_tricode,
                })
                rows.append(row)
            append_jsonl(rows, out_fp)
            pbar.update(1)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize Basketball-Reference PBP HTML to JSONL")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)




```

진행 메모: nba_bbr 원시 HTML이 충분히 내려왔는지 확인한 뒤 정규화 파서를 보강했고, 전체 변환을 완료했습니다. 이제 NBA BBR 기준 3개년 수집/정규화 파이프라인이 동작합니다.

- 변환 결과
  - 출력 파일: `data/normalized/nba_bbr/events.jsonl`
  - 라인 수: 606,354
  - 샘플 필드: `clock`, `visitor_desc`, `home_desc`, `score`, `score_home`, `score_away`, `game_code`, `game_date`, `home_tricode`

- 파서 보강 사항
  - `table#pbp`가 주석/DOM 구조 변화로 감춰진 경우까지 탐지
  - `tbody` 유무와 무관하게 `tr` 처리
  - 스코어 형식 파싱, 공백/`&nbsp;`/`+n` 토큰 제거
  - `game_code`에서 `game_date`·`home_tricode` 추출

원하시면:
- 게임 메타(홈/원정 팀명, 경기 ID) 추가 추출
- 특정 시즌만 분할 저장(파일 분할)
- 컬럼 스키마를 MLB/WSL과 더 수렴하도록 조정

- 요약
  - nba_bbr 정규화 완료: 606,354 이벤트
  - 산출물 위치: `data/normalized/nba_bbr/events.jsonl`
  - 파서 안정성 개선으로 다양한 페이지 구조 대응

---

**User**

event_type 이 추출되지 않은 것은 문제라고 생각해. 'visitor_desc' 또는 'home_desc' 에서 추출해 와야 하지 않아? 농구경기에서의 event_type 은 닫힌계니까 desc 에서 충분히 뽑아낼 수 있을 것으로 보여.

---

**Cursor**

```python
*** Begin Patch
*** Update File: /Users/julian/Project/2025/Agents4Science/scoring-dynamics/src/nba_bbr/normalize.py
@@
-from typing import Dict, Iterable, List
+from typing import Dict, Iterable, List, Optional
@@
-from bs4 import BeautifulSoup, Comment
+from bs4 import BeautifulSoup, Comment
@@
 from common.io_utils import append_jsonl
@@
 def _extract_pbp_table(soup: BeautifulSoup):
@@
     return None
+
+
+def _infer_event_type(desc: str) -> str:
+    text = desc.lower()
+    if " jump ball" in text or text.startswith("jump ball"):
+        return "jump_ball"
+    if " enters the game for " in text:
+        return "substitution"
+    if " timeout" in text:
+        return "timeout"
+    if "makes free throw" in text:
+        return "free_throw_made"
+    if "misses free throw" in text:
+        return "free_throw_missed"
+    if text.startswith("offensive rebound"):
+        return "rebound_offensive"
+    if text.startswith("defensive rebound"):
+        return "rebound_defensive"
+    if text.startswith("team rebound"):
+        return "rebound_team"
+    if text.startswith("shooting foul"):
+        return "foul_shooting"
+    if text.startswith("personal foul"):
+        return "foul_personal"
+    if text.startswith("loose ball foul"):
+        return "foul_loose_ball"
+    if text.startswith("offensive foul"):
+        return "foul_offensive"
+    if text.startswith("technical foul"):
+        return "foul_technical"
+    if "flagrant" in text and "foul" in text:
+        return "foul_flagrant"
+    if text.startswith("violation"):
+        return "violation"
+    if text.startswith("turnover"):
+        return "turnover"
+    if text.startswith("goaltending"):
+        return "goaltending"
+    if text.startswith(" lane violation"):
+        return "violation"
+    if text.startswith("makes ") or " makes " in text:
+        return "shot_made"
+    if text.startswith("misses ") or " misses " in text:
+        return "shot_missed"
+    return "other"
+
+
+def _extract_additional_fields(desc: str) -> Dict:
+    fields: Dict[str, Optional[object]] = {
+        "assist_by": None,
+        "block_by": None,
+        "steal_by": None,
+        "foul_drawn_by": None,
+        "shot_points": None,
+        "shot_distance_ft": None,
+        "turnover_type": None,
+    }
+    m = re.search(r"assist by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
+    if m:
+        fields["assist_by"] = m.group(1).strip()
+    m = re.search(r"block by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
+    if m:
+        fields["block_by"] = m.group(1).strip()
+    m = re.search(r"steal by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
+    if m:
+        fields["steal_by"] = m.group(1).strip()
+    m = re.search(r"drawn by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
+    if m:
+        fields["foul_drawn_by"] = m.group(1).strip()
+    m = re.search(r"from\s+(\d+)\s*ft", desc, flags=re.IGNORECASE)
+    if m:
+        try:
+            fields["shot_distance_ft"] = int(m.group(1))
+        except Exception:
+            pass
+    m = re.search(r"\+(\d)\b", desc)
+    if m:
+        try:
+            fields["shot_points"] = int(m.group(1))
+        except Exception:
+            pass
+    m = re.search(r"turnover by .*?\(([^\)]+)\)", desc, flags=re.IGNORECASE)
+    if m:
+        fields["turnover_type"] = m.group(1).strip()
+    return fields
@@
-        visitor_desc = clean_tokens(rest[: score_idx if score_idx >= 0 else len(rest)]) or None
-        home_desc = clean_tokens(rest[score_idx + 1 :]) if score_idx >= 0 else ""
-        home_desc = home_desc or None
+        visitor_desc = clean_tokens(rest[: score_idx if score_idx >= 0 else len(rest)]) or None
+        home_desc = clean_tokens(rest[score_idx + 1 :]) if score_idx >= 0 else ""
+        home_desc = home_desc or None
         score = rest[score_idx] if score_idx >= 0 else None
         sh = sa = None
         if score and '-' in score:
             try:
                 sh_s, sa_s = score.split('-', 1)
                 sh, sa = int(sh_s), int(sa_s)
             except Exception:
                 sh = sa = None
 
-        yield {
+        desc = visitor_desc or home_desc or ""
+        team_side = "visitor" if visitor_desc else ("home" if home_desc else None)
+        event_type = _infer_event_type(desc) if desc else "other"
+        extra = _extract_additional_fields(desc)
+
+        yield {
             "sport": "basketball",
             "league": "NBA",
-            "event_type": "pbp_row",
+            "event_type": event_type,
             "clock": period_time,
+            "team_side": team_side,
             "visitor_desc": visitor_desc or None,
             "home_desc": home_desc or None,
             "score": score or None,
             "score_home": sh,
             "score_away": sa,
+            **extra,
         }
*** End Patch
```

```python
from __future__ import annotations

import argparse
import glob
import os
import re
from typing import Dict, Iterable, List, Optional

from bs4 import BeautifulSoup, Comment
from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl


def _extract_pbp_table(soup: BeautifulSoup):
    table = soup.select_one("table#pbp")
    if table:
        return table
    # Sometimes wrapped in <!-- --> comments; parse comment blocks
    for c in soup.find_all(string=lambda s: isinstance(s, Comment)):
        try:
            inner = BeautifulSoup(c, "lxml")
            t = inner.select_one("table#pbp")
        except Exception:
            t = None
        if t is not None:
            return t
    return None


def _infer_event_type(desc: str) -> str:
    text = desc.lower()
    if " jump ball" in text or text.startswith("jump ball"):
        return "jump_ball"
    if " enters the game for " in text:
        return "substitution"
    if " timeout" in text:
        return "timeout"
    if "makes free throw" in text:
        return "free_throw_made"
    if "misses free throw" in text:
        return "free_throw_missed"
    if text.startswith("offensive rebound"):
        return "rebound_offensive"
    if text.startswith("defensive rebound"):
        return "rebound_defensive"
    if text.startswith("team rebound"):
        return "rebound_team"
    if text.startswith("shooting foul"):
        return "foul_shooting"
    if text.startswith("personal foul"):
        return "foul_personal"
    if text.startswith("loose ball foul"):
        return "foul_loose_ball"
    if text.startswith("offensive foul"):
        return "foul_offensive"
    if text.startswith("technical foul"):
        return "foul_technical"
    if "flagrant" in text and "foul" in text:
        return "foul_flagrant"
    if text.startswith("violation"):
        return "violation"
    if text.startswith("turnover"):
        return "turnover"
    if text.startswith("goaltending"):
        return "goaltending"
    if text.startswith(" lane violation"):
        return "violation"
    if text.startswith("makes ") or " makes " in text:
        return "shot_made"
    if text.startswith("misses ") or " misses " in text:
        return "shot_missed"
    return "other"


def _extract_additional_fields(desc: str) -> Dict:
    fields: Dict[str, Optional[object]] = {
        "assist_by": None,
        "block_by": None,
        "steal_by": None,
        "foul_drawn_by": None,
        "shot_points": None,
        "shot_distance_ft": None,
        "turnover_type": None,
    }
    m = re.search(r"assist by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
    if m:
        fields["assist_by"] = m.group(1).strip()
    m = re.search(r"block by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
    if m:
        fields["block_by"] = m.group(1).strip()
    m = re.search(r"steal by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
    if m:
        fields["steal_by"] = m.group(1).strip()
    m = re.search(r"drawn by\s+([^\)]+)\)", desc, flags=re.IGNORECASE)
    if m:
        fields["foul_drawn_by"] = m.group(1).strip()
    m = re.search(r"from\s+(\d+)\s*ft", desc, flags=re.IGNORECASE)
    if m:
        try:
            fields["shot_distance_ft"] = int(m.group(1))
        except Exception:
            pass
    m = re.search(r"\+(\d)\b", desc)
    if m:
        try:
            fields["shot_points"] = int(m.group(1))
        except Exception:
            pass
    m = re.search(r"turnover by .*?\(([^\)]+)\)", desc, flags=re.IGNORECASE)
    if m:
        fields["turnover_type"] = m.group(1).strip()
    return fields


def parse_events_from_html(html: str) -> Iterable[Dict]:
    soup = BeautifulSoup(html, "lxml")
    table = _extract_pbp_table(soup)
    if not table:
        return []

    # 일부 페이지는 tbody 없이 tr이 바로 존재함
    rows = [tr for tr in table.find_all("tr") if 'thead' not in (tr.get('class') or [])]
    time_re = re.compile(r"^\d{1,2}:\d{2}\.\d$")
    score_re = re.compile(r"^\d+\-\d+$")
    for r in rows:
        cells_el = r.find_all(["th", "td"])
        if not cells_el:
            continue
        cells = [c.get_text(" ", strip=True) for c in cells_el]
        if not cells:
            continue
        period_time = cells[0]
        if not time_re.match(period_time):
            continue
        rest = cells[1:]
        score_idx = -1
        for i, t in enumerate(rest):
            if score_re.match(t):
                score_idx = i
                break
        def clean_tokens(tokens: List[str]) -> str:
            out = []
            for t in tokens:
                if not t or t == "&nbsp;" or t == "\xa0" or t.startswith("+"):
                    continue
                out.append(t)
            return " ".join(out).strip()

        visitor_desc = clean_tokens(rest[: score_idx if score_idx >= 0 else len(rest)])
        home_desc = clean_tokens(rest[score_idx + 1 :]) if score_idx >= 0 else ""
        score = rest[score_idx] if score_idx >= 0 else ""
        sh = sa = None
        if score and '-' in score:
            try:
                sh_s, sa_s = score.split('-', 1)
                sh, sa = int(sh_s), int(sa_s)
            except Exception:
                sh = sa = None
        desc = (visitor_desc or None) or (home_desc or None) or ""
        team_side = "visitor" if visitor_desc else ("home" if home_desc else None)
        event_type = _infer_event_type(desc) if desc else "other"
        extra = _extract_additional_fields(desc)
        yield {
            "sport": "basketball",
            "league": "NBA",
            "event_type": event_type,
            "clock": period_time,
            "team_side": team_side,
            "visitor_desc": visitor_desc or None,
            "home_desc": home_desc or None,
            "score": score or None,
            "score_home": sh,
            "score_away": sa,
            **extra,
        }


def run(base_dir: str) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nba_bbr")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nba_bbr")
    os.makedirs(out_dir, exist_ok=True)

    files = sorted(glob.glob(os.path.join(in_dir, "*.html")))
    out_fp = os.path.join(out_dir, "events.jsonl")
    with tqdm(total=len(files), desc="normalize bbr") as pbar:
        for fp in files:
            fname = os.path.basename(fp)
            game_code = os.path.splitext(fname)[0]  # e.g., 202310240GSW
            game_date = game_code[:8]
            home_tricode = game_code[8:]
            with open(fp, "r", encoding="utf-8") as f:
                html = f.read()
            rows = []
            for row in parse_events_from_html(html):
                row.update({
                    "game_code": game_code,
                    "game_date": game_date,
                    "home_tricode": home_tricode,
                })
                rows.append(row)
            append_jsonl(rows, out_fp)
            pbar.update(1)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize Basketball-Reference PBP HTML to JSONL")
    p.add_argument("--base", default="data")
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base)




```

네, 지적하신 대로 농구는 닫힌 이벤트 집합이라 `visitor_desc`/`home_desc`에서 `event_type`을 추론하도록 정규화기를 보강했습니다. 이제 다음이 포함됩니다.

- event_type 추론 규칙
  - jump_ball, substitution, timeout
  - free_throw_made/free_throw_missed
  - rebound_offensive/rebound_defensive/rebound_team
  - foul_shooting/foul_personal/foul_loose_ball/foul_offensive/foul_technical/foul_flagrant
  - violation, turnover, goaltending
  - shot_made, shot_missed
- 추가 파생 필드
  - assist_by, block_by, steal_by, foul_drawn_by
  - shot_distance_ft, shot_points(+n 토큰 기반)
  - turnover_type(괄호 내 subtype)
  - team_side(visitor/home)

정규화 재실행 완료
- 파일: `data/normalized/nba_bbr/events.jsonl`
- 라인 수: 606,354
- 상위 레코드에서 `event_type` 정상 반영 확인

필요 시
- 규칙 확대(예: clear path, defensive 3-sec 등)나 팀/선수 추출 강화 요청 주세요.
- 시즌 단위로 파일 분할 출력도 가능합니다.

---

**User**

이제 다음 문제는 NHL 인 것 같아. NHL 데이터 내려받기에 실패한 요인을 다시 확인해줄래?

---

**Cursor**

진행 메모: NHL 수집 실패 원인을 정확히 진단하기 위해 DNS/네트워크 수준에서 `statsapi.web.nhl.com`의 해석과 통신 여부를 점검합니다. 이어서 간단한 `curl` HEAD로 API 응답 유무를 확인합니다.

- 원인 진단
  - `statsapi.web.nhl.com` DNS가 NXDOMAIN으로 해석 실패. 로컬/네트워크 DNS 레벨 이슈로 확인됨.
  - 이전 오류 `httpx.ConnectError: [Errno 8] nodename nor servname provided, or not known`와 일치.

- 해결 옵션
  - 옵션 A(현 엔드포인트 유지): OS/네트워크 DNS 수정 후 재시도
    - 시스템 DNS를 공용 DNS로 교체(예: 1.1.1.1, 8.8.8.8). 현재 환경에서는 공용 DNS 질의도 NXDOMAIN로 반환되어, 네트워크 정책/필터링 가능성 큼.
    - VPN/다른 네트워크에서 재시도.
  - 옵션 B(대체 엔드포인트 사용): `api-web.nhle.com` 기반 수집으로 전환
    - 스케줄/게임 나열: `https://api-web.nhle.com/v1/score/{YYYY-MM-DD}`(일자별 게임과 `gameId` 제공)
    - PBP: `https://api-web.nhle.com/v1/gamecenter/{gameId}/play-by-play`
    - 헤더에 일반 브라우저 UA 지정, 저동시성·백오프 적용

- 제안
  - 당장 수집을 진행하려면 옵션 B로 전환 구현을 시작하겠습니다. NHL 수집기/정규화기를 `api-web.nhle.com` 포맷에 맞춰 추가하고, 최근 시즌 하루치로 검증 후 3개년 범위 수집으로 확장할게요.
  - DNS 문제를 병행 해결할 수 있으면 기존 StatsAPI도 보조 루트로 남겨 이중화하겠습니다.

- 진행 요망
  - NHL 수집을 `api-web.nhle.com`로 전환해 바로 구현/실행할까요?

---

**User**

전환해서 실행하는게 좋겠어. 실제로 기존에 시도한 경로가 사라진 것 같아.
추출은 낮은 동시성과 충분한 시간 간격을 두고 1년 단위로 끊어서 진행해 보자

---

**Cursor**

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List, Set

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir
from common.io_utils import write_json


BASE = "https://api-web.nhle.com/v1"


async def fetch_json(client: HttpClient, url: str):
    return await client.get_json(url)


def iter_year_dates(year: int):
    start = dt.date(year, 1, 1)
    end = dt.date(year, 12, 31)
    cur = start
    one = dt.timedelta(days=1)
    while cur <= end:
        yield cur
        cur += one


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nhl_nhle", str(args.year))
    games_dir = os.path.join(raw_dir, "games")
    ensure_dir(games_dir)

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Referer": "https://www.nhl.com/",
        "Origin": "https://www.nhl.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    # 1) enumerate gameIds via daily scoreboard
    game_ids: List[str] = []
    for d in iter_year_dates(args.year):
        url = f"{BASE}/score/{d.isoformat()}"
        try:
            data = await fetch_json(client, url)
        except Exception:
            await asyncio.sleep(args.delay_sec)
            continue
        for g in (data.get("games") or []):
            gid = str(g.get("id") or g.get("gameId") or "")
            if gid:
                game_ids.append(gid)
        await asyncio.sleep(args.delay_sec)

    # de-dup, preserve order
    seen: Set[str] = set()
    uniq_ids: List[str] = []
    for gid in game_ids:
        if gid not in seen:
            uniq_ids.append(gid)
            seen.add(gid)

    # 2) fetch play-by-play per game
    async def fetch_and_save(gid: str):
        out_fp = os.path.join(games_dir, f"{gid}.json")
        if args.skip_existing and os.path.exists(out_fp):
            return True
        url = f"{BASE}/gamecenter/{gid}/play-by-play"
        # retries with backoff
        for attempt in range(5):
            try:
                data = await fetch_json(client, url)
                write_json(data, out_fp)
                await asyncio.sleep(args.delay_sec)
                return True
            except Exception:
                await asyncio.sleep(args.delay_sec * (2 ** attempt))
        return False

    ok = 0
    total = len(uniq_ids)
    for gid in tqdm(uniq_ids, desc=f"nhl {args.year} pbp", unit="game"):
        if await fetch_and_save(gid):
            ok += 1
        # limit per run if provided
        if args.limit and ok >= args.limit:
            break
    print(f"saved {ok}/{total} game pbp to {games_dir}")


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NHL PBP via api-web.nhle.com (year-based)")
    p.add_argument("--year", type=int, required=True)
    p.add_argument("--out", default="data")
    p.add_argument("--delay_sec", type=float, default=1.5, help="Delay between requests")
    p.add_argument("--skip_existing", action="store_true", default=True)
    p.add_argument("--limit", type=int, default=None)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    game_id = feed.get("id") or feed.get("gameId")
    plays = (feed.get("plays") or {}).get("all") or feed.get("plays") or []
    for idx, p in enumerate(plays):
        about = p.get("about") or {}
        details = p.get("details") or {}
        coordinates = p.get("coordinates") or {}
        team = (p.get("team") or {}).get("name")
        yield {
            "sport": "ice_hockey",
            "league": "NHL",
            "game_id": game_id,
            "event_index": idx,
            "event_type": p.get("type") or details.get("type") or "",
            "description": details.get("eventDescription") or p.get("result") or "",
            "period": about.get("period") or p.get("period") or None,
            "period_time": about.get("time") or about.get("periodTime") or None,
            "goals_home": about.get("goals", {}).get("home") if isinstance(about.get("goals"), dict) else None,
            "goals_away": about.get("goals", {}).get("away") if isinstance(about.get("goals"), dict) else None,
            "x": coordinates.get("x"),
            "y": coordinates.get("y"),
            "team": team,
            "raw": p,
        }


def run(base_dir: str, year: int) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nhl_nhle", str(year), "games")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nhl_nhle")
    os.makedirs(out_dir, exist_ok=True)
    out_fp = os.path.join(out_dir, f"{year}_events.jsonl")

    files = sorted(glob.glob(os.path.join(in_dir, "*.json")))
    with tqdm(total=len(files), desc=f"normalize nhl {year}"):
        for fp in files:
            feed = read_json(fp)
            rows = list(iter_events(feed))
            append_jsonl(rows, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NHL api-web.nhle.com play-by-play to JSONL")
    p.add_argument("--base", default="data")
    p.add_argument("--year", type=int, required=True)
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.year)


```

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def iter_events(feed: Dict) -> Iterable[Dict]:
    game_id = feed.get("id") or feed.get("gameId")
    plays_section = feed.get("plays") or {}
    if isinstance(plays_section, dict):
        plays = plays_section.get("all") or plays_section.get("plays") or []
    else:
        plays = plays_section if isinstance(plays_section, list) else []
    for idx, p in enumerate(plays):
        about = p.get("about") or {}
        details = p.get("details") or {}
        coordinates = p.get("coordinates") or {}
        team = (p.get("team") or {}).get("name")
        yield {
            "sport": "ice_hockey",
            "league": "NHL",
            "game_id": game_id,
            "event_index": idx,
            "event_type": p.get("type") or details.get("type") or "",
            "description": details.get("eventDescription") or p.get("result") or "",
            "period": about.get("period") or p.get("period") or None,
            "period_time": about.get("time") or about.get("periodTime") or None,
            "goals_home": about.get("goals", {}).get("home") if isinstance(about.get("goals"), dict) else None,
            "goals_away": about.get("goals", {}).get("away") if isinstance(about.get("goals"), dict) else None,
            "x": coordinates.get("x"),
            "y": coordinates.get("y"),
            "team": team,
            "raw": p,
        }


def run(base_dir: str, year: int) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nhl_nhle", str(year), "games")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nhl_nhle")
    os.makedirs(out_dir, exist_ok=True)
    out_fp = os.path.join(out_dir, f"{year}_events.jsonl")

    files = sorted(glob.glob(os.path.join(in_dir, "*.json")))
    with tqdm(total=len(files), desc=f"normalize nhl {year}"):
        for fp in files:
            feed = read_json(fp)
            rows = list(iter_events(feed))
            append_jsonl(rows, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NHL api-web.nhle.com play-by-play to JSONL")
    p.add_argument("--base", default="data")
    p.add_argument("--year", type=int, required=True)
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.year)



```

진행 메모: NHL 수집 경로를 `api-web.nhle.com`으로 전환해 연도별 저속 수집/정규화 스크립트를 추가했습니다. DNS 이슈 없이 통신 가능함을 단건 호출로 확인했고, 이제 1년 단위 저동시성으로 실행하면 됩니다.

- 추가 파일
  - `src/nhl_nhle/collect.py`: 연도별 수집. 일 단위 `score/{YYYY-MM-DD}`에서 `gameId` 수집 → `gamecenter/{gameId}/play-by-play` 저장. 지연/백오프/스킵 지원
  - `src/nhl_nhle/normalize.py`: 저장된 PBP를 JSONL 이벤트로 변환

- 실행 예시
  - 수집(저속, 1년 단위)
    ```bash
    source .venv/bin/activate
    # 2024
    nohup python src/nhl_nhle/collect.py --year 2024 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2024.log 2>&1 &
    # 2023
    nohup python src/nhl_nhle/collect.py --year 2023 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2023.log 2>&1 &
    # 2022
    nohup python src/nhl_nhle/collect.py --year 2022 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2022.log 2>&1 &
    ```
  - 정규화(연도별)
    ```bash
    python src/nhl_nhle/normalize.py --base data --year 2024
    python src/nhl_nhle/normalize.py --base data --year 2023
    python src/nhl_nhle/normalize.py --base data --year 2022
    ```

- 현재 상태
  - API 단건 확인: scoreboard/ pbp 응답 OK
  - 포그라운드 샘플 수집 도중 인터럽트로 중단됨. 백그라운드로 2024부터 순차 실행 권장

바로 2024 → 2023 → 2022 순서로 백그라운드 수집 시작할까요? 딜레이(기본 2.0초)나 동시성(현재 1 게임씩 순차) 조정 원하시면 말씀해 주세요.

```shellscript
    source .venv/bin/activate
    # 2024
    nohup python src/nhl_nhle/collect.py --year 2024 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2024.log 2>&1 &
    # 2023
    nohup python src/nhl_nhle/collect.py --year 2023 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2023.log 2>&1 &
    # 2022
    nohup python src/nhl_nhle/collect.py --year 2022 --out data --delay_sec 2.0 --skip_existing > logs/nhl_2022.log 2>&1 &
```

```shellscript
    python src/nhl_nhle/normalize.py --base data --year 2024
    python src/nhl_nhle/normalize.py --base data --year 2023
    python src/nhl_nhle/normalize.py --base data --year 2022
```

---

**User**

포어그라운드 실행이 너무 오래 걸리고 있어서 중단했던 거야. 우선 백그라운드 수집 진행해 봐줘.

---

**Cursor**

진행 메모: NHL 수집을 연도별(2024→2023→2022) 저속 모드로 백그라운드 실행합니다. 각 작업은 동시성 1(기본), 요청 간 지연 2.5초, 기존 파일 스킵으로 설정하고 로그를 남깁니다. 실행 후 로그/파일 생성 여부를 간단 확인하겠습니다.

- NHL 백그라운드 수집을 시작했습니다.
  - 2024: `logs/nhl_2024.log`
  - 2023: `logs/nhl_2023.log`
  - 2022: `logs/nhl_2022.log`
  - 설정: 동시성 1, 요청 간 지연 2.5초, 기존 파일 스킵

원하시면 5–10분 뒤 진행률(로그 tail, 파일 수/용량) 점검 후 보고드리겠습니다.

---

**User**

로그가 하나도 안 잡히고 있는데 정상 실행중인게 맞니?

---

**Cursor**

```python
from __future__ import annotations

import argparse
import asyncio
import datetime as dt
import os
from typing import List, Set

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.http_utils import HttpClient, ensure_dir
from common.io_utils import write_json


BASE = "https://api-web.nhle.com/v1"


async def fetch_json(client: HttpClient, url: str):
    return await client.get_json(url)


def iter_year_dates(year: int):
    start = dt.date(year, 1, 1)
    end = dt.date(year, 12, 31)
    cur = start
    one = dt.timedelta(days=1)
    while cur <= end:
        yield cur
        cur += one


async def main(args: argparse.Namespace) -> None:
    out_dir = os.path.abspath(args.out)
    raw_dir = os.path.join(out_dir, "raw", "nhl_nhle", str(args.year))
    games_dir = os.path.join(raw_dir, "games")
    ensure_dir(games_dir)

    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/126.0.0.0 Safari/537.36"
        ),
        "Accept": "application/json, text/plain, */*",
        "Referer": "https://www.nhl.com/",
        "Origin": "https://www.nhl.com",
        "Connection": "keep-alive",
    }
    client = HttpClient(headers=headers)

    # 1) enumerate gameIds via daily scoreboard
    game_ids: List[str] = []
    day_idx = 0
    for d in iter_year_dates(args.year):
        url = f"{BASE}/score/{d.isoformat()}"
        try:
            data = await fetch_json(client, url)
        except Exception:
            await asyncio.sleep(args.delay_sec)
            continue
        games = (data.get("games") or [])
        if day_idx % 10 == 0:
            print(f"[{args.year}] day {d.isoformat()} games={len(games)}", flush=True)
        for g in games:
            gid = str(g.get("id") or g.get("gameId") or "")
            if gid:
                game_ids.append(gid)
        day_idx += 1
        await asyncio.sleep(args.delay_sec)

    # de-dup, preserve order
    seen: Set[str] = set()
    uniq_ids: List[str] = []
    for gid in game_ids:
        if gid not in seen:
            uniq_ids.append(gid)
            seen.add(gid)
    print(f"[{args.year}] total gameIds found: {len(uniq_ids)} (before limit)", flush=True)

    # 2) fetch play-by-play per game
    async def fetch_and_save(gid: str):
        out_fp = os.path.join(games_dir, f"{gid}.json")
        if args.skip_existing and os.path.exists(out_fp):
            return True
        url = f"{BASE}/gamecenter/{gid}/play-by-play"
        # retries with backoff
        for attempt in range(5):
            try:
                data = await fetch_json(client, url)
                write_json(data, out_fp)
                await asyncio.sleep(args.delay_sec)
                return True
            except Exception:
                await asyncio.sleep(args.delay_sec * (2 ** attempt))
        return False

    ok = 0
    total = len(uniq_ids)
    for gid in tqdm(uniq_ids, desc=f"nhl {args.year} pbp", unit="game"):
        if await fetch_and_save(gid):
            ok += 1
            if ok % 10 == 0:
                print(f"[{args.year}] saved {ok} / {total}", flush=True)
        # limit per run if provided
        if args.limit and ok >= args.limit:
            break
    print(f"saved {ok}/{total} game pbp to {games_dir}", flush=True)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Collect NHL PBP via api-web.nhle.com (year-based)")
    p.add_argument("--year", type=int, required=True)
    p.add_argument("--out", default="data")
    p.add_argument("--delay_sec", type=float, default=1.5, help="Delay between requests")
    p.add_argument("--skip_existing", action="store_true", default=True)
    p.add_argument("--limit", type=int, default=None)
    return p.parse_args()


if __name__ == "__main__":
    asyncio.run(main(parse_args()))



```

- 상태
  - 2024/2023/2022 NHL 수집 프로세스는 정상 실행 중입니다.
  - 로그 출력이 보이도록 스크립트를 수정·재기동했습니다.
    - 2024: `logs/nhl_2024.log` 예) `[2024] day 2024-01-01 games=1` / “total 0”(초기 집계 출력)
    - 2023: `logs/nhl_2023.log` 예) `[2023] day 2023-01-01 games=5`
    - 2022: `logs/nhl_2022.log` 예) `[2022] day 2022-01-01 games=9`
  - 파일 생성은 day loop 후 순차적으로 `data/raw/nhl_nhle/{year}/games/{gameId}.json`에 쌓입니다.

- 참고
  - 저속 설정: 요청 간 지연 2.5초, 기존 파일 스킵.
  - 진행률은 로그에 10일 간격으로 day 스캔 요약과, 10게임 단위로 저장 카운터가 찍히도록 되어 있습니다.

원하시면 일정 시간 후 파일 건수/용량과 저장 진행률을 다시 점검해서 공유하겠습니다.

---

**User**

NHL 추출 결과가 잘 나왔는지 확인해 줄래?

---

**Cursor**

짧은 진행 메모: NHL 원시 파일 존재/건수를 연도별로 확인하고, 2024년 데이터를 우선 정규화해 이벤트 라인 수와 샘플 레코드를 점검하겠습니다. 바로 실행합니다.

- 확인 결과
  - 원시 수집 완료
    - 2024: 1,538 게임 파일 존재 (`data/raw/nhl_nhle/2024/games/*.json`)
    - 2023: 1,502 게임
    - 2022: 1,603 게임
  - 정규화(2024) 실행 결과
    - 파일: `data/normalized/nhl_nhle/2024_events.jsonl`
    - 라인 수: 488,736

- 추가 메모
  - 현재 정규화 첫 레코드들의 `event_type/description` 등이 비어 있어, NHLe PBP의 키(`plays[*].typeDescKey`, `details.eventDescription`, `about.timeInPeriod`, `periodDescriptor.number`, 좌표 등`) 매핑을 보강하겠습니다. 원하시면 바로 수정·재생성할게요.

---

**User**

응 매핑 보완을 진행해 줘

---

**Cursor**

```python
from __future__ import annotations

import argparse
import glob
import os
from typing import Dict, Iterable, Optional

from tqdm import tqdm

import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).resolve().parents[1]))

from common.io_utils import append_jsonl, read_json


def _map_event_type(type_key: Optional[str], details: Dict) -> str:
    key = (type_key or "").lower()
    mapping = {
        "goal": "goal",
        "shot-on-goal": "shot_on_goal",
        "missed-shot": "shot_missed",
        "blocked-shot": "shot_blocked",
        "shot": "shot",
        "faceoff": "faceoff",
        "giveaway": "giveaway",
        "takeaway": "takeaway",
        "hit": "hit",
        "penalty": "penalty",
        "delayed-penalty": "delayed_penalty",
        "stoppage": "stoppage",
        "period-start": "period_start",
        "period-end": "period_end",
        "game-start": "game_start",
        "game-end": "game_end",
        "challenge": "coach_challenge",
        "shootout-complete": "shootout_complete",
        "icing": "icing",
        "offside": "offside",
    }
    return mapping.get(key, key or (details.get("eventTypeId") or ""))


def iter_events(feed: Dict) -> Iterable[Dict]:
    game_id = feed.get("id") or feed.get("gameId")
    plays_section = feed.get("plays") or {}
    if isinstance(plays_section, dict):
        plays = plays_section.get("all") or plays_section.get("plays") or []
    else:
        plays = plays_section if isinstance(plays_section, list) else []
    for idx, p in enumerate(plays):
        about = p.get("about") or {}
        details = p.get("details") or {}
        coordinates = p.get("coordinates") or {}
        team_obj = p.get("team") or {}
        team_name = team_obj.get("name")
        team_abbrev = team_obj.get("abbrev") or team_obj.get("triCode")

        # event type and description
        type_key = p.get("typeDescKey") or p.get("type") or details.get("typeDescKey")
        event_type = _map_event_type(type_key, details)
        description = (
            details.get("eventDescription")
            or details.get("secondaryEventDescription")
            or details.get("description")
            or ""
        )

        # period and clock
        period = (
            (p.get("periodDescriptor") or {}).get("number")
            or about.get("period")
            or p.get("period")
        )
        period_time = (
            p.get("timeInPeriod")
            or about.get("time")
            or about.get("periodTime")
        )

        # score
        goals_obj = about.get("goals") if isinstance(about.get("goals"), dict) else None
        goals_home = (
            details.get("homeScore")
            if isinstance(details, dict)
            else None
        )
        goals_away = (
            details.get("awayScore")
            if isinstance(details, dict)
            else None
        )
        if goals_obj is not None:
            goals_home = goals_home if goals_home is not None else goals_obj.get("home")
            goals_away = goals_away if goals_away is not None else goals_obj.get("away")

        yield {
            "sport": "ice_hockey",
            "league": "NHL",
            "game_id": game_id,
            "event_index": idx,
            "event_type": event_type,
            "description": description,
            "period": period,
            "period_time": period_time,
            "goals_home": goals_home,
            "goals_away": goals_away,
            "x": coordinates.get("x"),
            "y": coordinates.get("y"),
            "team_name": team_name,
            "team_abbrev": team_abbrev,
            "raw": p,
        }


def run(base_dir: str, year: int) -> None:
    in_dir = os.path.join(os.path.abspath(base_dir), "raw", "nhl_nhle", str(year), "games")
    out_dir = os.path.join(os.path.abspath(base_dir), "normalized", "nhl_nhle")
    os.makedirs(out_dir, exist_ok=True)
    out_fp = os.path.join(out_dir, f"{year}_events.jsonl")

    files = sorted(glob.glob(os.path.join(in_dir, "*.json")))
    with tqdm(total=len(files), desc=f"normalize nhl {year}"):
        for fp in files:
            feed = read_json(fp)
            rows = list(iter_events(feed))
            append_jsonl(rows, out_fp)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Normalize NHL api-web.nhle.com play-by-play to JSONL")
    p.add_argument("--base", default="data")
    p.add_argument("--year", type=int, required=True)
    return p.parse_args()


if __name__ == "__main__":
    args = parse_args()
    run(args.base, args.year)



```

- NHL 수집/정규화 현황
  - 원시 수집: 완료
    - 2024: 1,538 경기 파일
    - 2023: 1,502
    - 2022: 1,603
  - 2024 정규화 재생성 완료
    - 파일: `data/normalized/nhl_nhle/2024_events.jsonl`
    - 라인 수: 488,736
    - `event_type`/`period`/`period_time`/`team_name`/`team_abbrev` 등 매핑 보강 반영됨

- 변경점
  - `event_type` 매핑 추가: goal, shot_on_goal, shot_missed, shot_blocked, faceoff, giveaway, takeaway, hit, penalty, delayed_penalty, stoppage, period_start/end, game_start/end, coach_challenge, shootout_complete, icing, offside 등
  - 설명·시간·스코어 필드 보강: `description`, `timeInPeriod`, `periodDescriptor.number`, `details.homeScore/awayScore` 병합
  - 팀 정보: `team_name`, `team_abbrev` 추출

원하시면 2023/2022도 같은 방식으로 정규화 돌려 산출물을 맞춰 드리겠습니다.

---

**User**

나머지 연도들에 대해서도 모두 처리해 줘

---

**Cursor**

진행 메모: NHL 2023, 2022 시즌 정규화를 순차 실행하고, 생성된 JSONL 라인 수와 샘플 레코드를 확인하겠습니다. 완료 후 요약을 드립니다.

- NHL 정규화 완료
  - 2024: `data/normalized/nhl_nhle/2024_events.jsonl` 488,736 라인
  - 2023: `data/normalized/nhl_nhle/2023_events.jsonl` 465,392 라인
  - 2022: `data/normalized/nhl_nhle/2022_events.jsonl` 497,258 라인
  - `event_type`/`period`/`period_time`/팀 정보 매핑 반영됨

필요 시 추가 매핑(설명 텍스트 세부 파싱, 페널티 상세, 슛 타입 분류 등)도 확장하겠습니다.

---

**User**

이제 전체적으로 추출된 전체 종목들의 데이터에 대해서 이상이 없는지 점검하는 스크립트를 작성해 줄래?
각 종목별로 검증 로직을 작성하고 전체 추출 데이터에서 빈 파일이나 파싱되지 않은 데이터가 있는지 확인해 줘.

---

**Cursor**

```python
from __future__ import annotations

import argparse
import json
import os
import re
from pathlib import Path
from typing import Dict, Iterable, List, Tuple


def iter_files(glob_pattern: str) -> Iterable[Path]:
    for p in Path('.').glob(glob_pattern):
        if p.is_file():
            yield p


def safe_read_text(p: Path) -> Tuple[bool, str]:
    try:
        txt = p.read_text(encoding='utf-8', errors='ignore')
        return True, txt
    except Exception:
        return False, ''


def safe_read_json(p: Path):
    try:
        return True, json.loads(p.read_text(encoding='utf-8'))
    except Exception:
        return False, None


def validate_raw_soccer_wsl(base: Path, report: Dict):
    root = base / 'raw' / 'soccer_wsl'
    files = list(root.glob('season_*/events/*.json'))
    empty, malformed = 0, 0
    total = len(files)
    for f in files:
        ok, obj = safe_read_json(f)
        if not ok:
            malformed += 1
            continue
        if not isinstance(obj, list) or len(obj) == 0:
            empty += 1
    report['raw_soccer_wsl'] = {
        'total_files': total,
        'empty_or_no_events': empty,
        'malformed_json': malformed,
    }


def validate_raw_mlb(base: Path, report: Dict):
    files = list((base / 'raw' / 'mlb').glob('*_*/games/*.json'))
    total = len(files)
    empty, malformed, missing_keys = 0, 0, 0
    for f in files:
        ok, obj = safe_read_json(f)
        if not ok:
            malformed += 1
            continue
        if not obj:
            empty += 1
            continue
        if 'liveData' not in obj:
            missing_keys += 1
    report['raw_mlb'] = {
        'total_files': total,
        'empty': empty,
        'malformed_json': malformed,
        'missing_liveData': missing_keys,
    }


def validate_raw_nba_bbr(base: Path, report: Dict):
    files = list((base / 'raw' / 'nba_bbr').glob('*.html'))
    total = len(files)
    empty, missing_pbp = 0, 0
    for f in files:
        ok, txt = safe_read_text(f)
        if not ok or len(txt.strip()) == 0:
            empty += 1
            continue
        if 'table id="pbp"' not in txt and 'table id="pbp"' not in txt.replace("'", '"'):
            # try to detect wrapper block
            if 'div id="all_pbp"' not in txt and 'div id="all_pbp"' not in txt.replace("'", '"'):
                missing_pbp += 1
    report['raw_nba_bbr'] = {
        'total_files': total,
        'empty': empty,
        'missing_pbp_table': missing_pbp,
    }


def validate_raw_nba_cdn(base: Path, report: Dict):
    files = list((base / 'raw' / 'nba' / 'pbp').glob('*.json'))
    total = len(files)
    empty, malformed, missing_actions = 0, 0, 0
    for f in files:
        ok, obj = safe_read_json(f)
        if not ok:
            malformed += 1
            continue
        if not obj:
            empty += 1
            continue
        game = obj.get('game') or {}
        if not isinstance(game.get('actions'), list) or len(game.get('actions')) == 0:
            missing_actions += 1
    report['raw_nba_cdn'] = {
        'total_files': total,
        'empty': empty,
        'malformed_json': malformed,
        'missing_actions': missing_actions,
    }


def validate_raw_nhl_nhle(base: Path, report: Dict):
    files = list((base / 'raw' / 'nhl_nhle').glob('*/*/*.json'))
    total = len(files)
    empty, malformed, missing_plays = 0, 0, 0
    for f in files:
        ok, obj = safe_read_json(f)
        if not ok:
            malformed += 1
            continue
        if not obj:
            empty += 1
            continue
        plays = obj.get('plays')
        if plays is None:
            missing_plays += 1
    report['raw_nhl_nhle'] = {
        'total_files': total,
        'empty': empty,
        'malformed_json': malformed,
        'missing_plays': missing_plays,
    }


def iter_jsonl(path: Path) -> Iterable[Dict]:
    with path.open('r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                yield json.loads(line)
            except Exception:
                yield {'__parse_error__': True, '__raw__': line}


def summarize_jsonl(file: Path, required_fields: List[str]) -> Dict:
    total = 0
    parse_errors = 0
    missing_required = 0
    for obj in iter_jsonl(file):
        total += 1
        if obj.get('__parse_error__'):
            parse_errors += 1
            continue
        if any((obj.get(f) is None) or (obj.get(f) == '') for f in required_fields):
            missing_required += 1
    return {
        'lines': total,
        'parse_errors': parse_errors,
        'missing_required': missing_required,
    }


def validate_normalized(base: Path, report: Dict):
    # Soccer WSL
    wsl_files = sorted((base / 'normalized' / 'soccer_wsl').glob('season_*_events.jsonl'))
    report['norm_soccer_wsl'] = {f.name: summarize_jsonl(f, ['event_type']) for f in wsl_files}

    # MLB
    mlb_files = sorted((base / 'normalized' / 'mlb').glob('*_events.jsonl'))
    report['norm_mlb'] = {f.name: summarize_jsonl(f, ['event_type']) for f in mlb_files}

    # NBA BBR
    bbr_file = base / 'normalized' / 'nba_bbr' / 'events.jsonl'
    if bbr_file.exists():
        report['norm_nba_bbr'] = {bbr_file.name: summarize_jsonl(bbr_file, ['event_type', 'clock'])}
    else:
        report['norm_nba_bbr'] = {'events.jsonl': 'missing'}

    # NBA CDN (optional)
    nba_cdn = base / 'normalized' / 'nba' / 'events.jsonl'
    if nba_cdn.exists():
        report['norm_nba_cdn'] = {nba_cdn.name: summarize_jsonl(nba_cdn, ['event_type', 'clock'])}
    else:
        report['norm_nba_cdn'] = {'events.jsonl': 'missing'}

    # NHL NHLe
    nhl_dir = base / 'normalized' / 'nhl_nhle'
    nhl_files = sorted(nhl_dir.glob('*_events.jsonl'))
    report['norm_nhl_nhle'] = {f.name: summarize_jsonl(f, ['event_type', 'period_time']) for f in nhl_files}


def main():
    p = argparse.ArgumentParser(description='Validate extracted and normalized datasets across sports')
    p.add_argument('--base', default='data', help='Base data directory (default: data)')
    p.add_argument('--out_json', default=None, help='Optional path to save JSON report')
    args = p.parse_args()

    base = Path(args.base).resolve()
    report: Dict = {}

    # RAW
    validate_raw_soccer_wsl(base, report)
    validate_raw_mlb(base, report)
    validate_raw_nba_bbr(base, report)
    validate_raw_nba_cdn(base, report)
    validate_raw_nhl_nhle(base, report)

    # NORMALIZED
    validate_normalized(base, report)

    # Print human-readable summary
    print(json.dumps(report, indent=2, ensure_ascii=False))

    if args.out_json:
        Path(args.out_json).write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding='utf-8')


if __name__ == '__main__':
    main()


```

- 검증 스크립트 생성/실행 완료
  - 위치: `src/validate/validate_datasets.py`
  - 기능:
    - 원시 데이터 점검
      - WSL: 빈/오류 파일
      - MLB: 빈/오류/`liveData` 누락
      - NBA BBR: PBP 테이블 존재 여부
      - NBA CDN: `game.actions` 유무
      - NHL NHLe: `plays` 존재 여부
    - 정규화 데이터 점검
      - 필수 필드 누락(`event_type` 등), 파싱 오류 라인 카운트

- 실행 결과 요약
  - 원시
    - WSL: 326 파일, 빈/오류 0
    - MLB: 8,702 파일, 빈/오류/`liveData` 누락 0
    - NBA BBR: 1,319 HTML, 빈/테이블 누락 0
    - NBA CDN: 2 JSON, 빈/오류/`actions` 누락 0
    - NHL NHLe: 4,643 JSON, 빈/오류/`plays` 누락 0
  - 정규화
    - WSL: `season_4/42/90_events.jsonl` 모두 missing_required 0
    - MLB: `2024-04-01_2024-04-02_events.jsonl` missing_required 0
    - NBA BBR: `events.jsonl` missing_required 0
    - NBA CDN: `events.jsonl` missing_required 0
    - NHL NHLe: `2022/2023/2024_events.jsonl` missing_required 0
  - 저장된 리포트: `validation_report.json`

원하시면 이 스크립트를 CI에 연결해 수집 후 자동 검증하도록 추가하겠습니다.

---

**User**

nba 데이터는 nba_bbr 데이터로 사용하기로 했고, nhl 데이터는 nhl_nhle 데이터를 사용하기로 했으니 미사용 데이터는 지우고 추출 코드는 남겨둬.

---

**Cursor**

- 미사용 데이터 정리 완료
  - 삭제: `data/raw/nba`, `data/normalized/nba` (CDN 기반), `data/raw/nhl`, `data/normalized/nhl` (StatsAPI 기반)
  - 유지: `data/raw/nba_bbr`, `data/normalized/nba_bbr`, `data/raw/nhl_nhle`, `data/normalized/nhl_nhle`
  - 추출 코드는 모두 유지됨

원하시면 README에 “사용 데이터 소스” 섹션을 업데이트해 NBA=BBR, NHL=NHLe로 명시하겠습니다.

