Iteration final - PROBLEM_DESCRIPTION
Sequence: 9
Timestamp: 2025-07-25 22:37:12

Prompt:
You are a business analyst creating structured optimization problem documentation.

DATA SOURCES EXPLANATION:
- FINAL OR ANALYSIS: Final converged optimization problem from alternating process (iteration 3), contains business context and schema mapping evaluation
- DATABASE SCHEMA: Current database structure after iterative adjustments  
- DATA DICTIONARY: Business meanings and optimization roles of tables and columns
- CURRENT STORED VALUES: Realistic business data generated by triple expert (business + data + optimization)
- BUSINESS CONFIGURATION: Scalar parameters and business logic formulas separated from table data

CRITICAL REQUIREMENTS: 
- Ensure problem description naturally leads to LINEAR or MIXED-INTEGER optimization formulation
- Make business context consistent with the intended decision variables and objectives
- Align constraint descriptions with expected mathematical constraints
- Ensure data descriptions map clearly to expected coefficient sources
- Maintain business authenticity while fixing mathematical consistency issues
- Avoid business scenarios that would naturally require nonlinear relationships (variable products, divisions, etc.)

AUTO-EXTRACTED CONTEXT REQUIREMENTS:
- Business decisions match expected decision variables: Team_Selection[Team_ID] (binary)
- Operational parameters align with expected linear objective: maximize ∑(Win_Percent × Team_Selection)
- Business configuration includes: Total number of teams to be selected for the tournament (used for Constraint bound for total teams selected), Minimum number of teams to be selected from the East conference (used for Constraint bound for East conference diversity), Minimum number of teams to be selected from the West conference (used for Constraint bound for West conference diversity), Minimum number of teams to be selected from the South conference (used for Constraint bound for South conference diversity), Maximum number of teams to be selected from New York (used for Constraint bound for geographical distribution), Maximum number of teams to be selected from Los Angeles (used for Constraint bound for geographical distribution), Maximum number of teams to be selected from Chicago (used for Constraint bound for geographical distribution)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate

FINAL OR ANALYSIS:
{
  "database_id": "university_basketball",
  "iteration": 3,
  "business_context": "Optimize the selection of basketball teams for a tournament to maximize the overall win percentage while respecting constraints on team diversity and geographical distribution.",
  "optimization_problem_description": "Maximize the total win percentage of selected teams for a tournament. Constraints include selecting a specific number of teams, ensuring a minimum number of teams from different conferences, and limiting the number of teams from the same location.",
  "optimization_formulation": {
    "objective": "maximize \u2211(Win_Percent \u00d7 Team_Selection)",
    "decision_variables": "Team_Selection[Team_ID] (binary)",
    "constraints": [
      "\u2211(Team_Selection) = Total_Teams_Selected",
      "\u2211(Team_Selection \u00d7 Conference_Indicator = 'East') \u2265 Min_East_Teams",
      "\u2211(Team_Selection \u00d7 Conference_Indicator = 'West') \u2265 Min_West_Teams",
      "\u2211(Team_Selection \u00d7 Conference_Indicator = 'South') \u2265 Min_South_Teams",
      "\u2211(Team_Selection \u00d7 Location_Indicator = 'New York') \u2264 Max_New_York_Teams",
      "\u2211(Team_Selection \u00d7 Location_Indicator = 'Los Angeles') \u2264 Max_Los_Angeles_Teams",
      "\u2211(Team_Selection \u00d7 Location_Indicator = 'Chicago') \u2264 Max_Chicago_Teams"
    ]
  },
  "current_optimization_to_schema_mapping": {
    "objective_coefficients": {
      "Win_Percent[Team_ID]": {
        "currently_mapped_to": "win_percentage.Win_Percent",
        "mapping_adequacy": "good",
        "description": "Win percentage of each team used as the coefficient in the objective function"
      }
    },
    "constraint_bounds": {
      "Total_Teams_Selected": {
        "currently_mapped_to": "business_configuration_logic.Total_Teams_Selected",
        "mapping_adequacy": "good",
        "description": "Total number of teams to be selected for the tournament"
      },
      "Min_East_Teams": {
        "currently_mapped_to": "business_configuration_logic.Min_East_Teams",
        "mapping_adequacy": "good",
        "description": "Minimum number of teams to be selected from the East conference"
      },
      "Min_West_Teams": {
        "currently_mapped_to": "business_configuration_logic.Min_West_Teams",
        "mapping_adequacy": "good",
        "description": "Minimum number of teams to be selected from the West conference"
      },
      "Min_South_Teams": {
        "currently_mapped_to": "business_configuration_logic.Min_South_Teams",
        "mapping_adequacy": "good",
        "description": "Minimum number of teams to be selected from the South conference"
      },
      "Max_New_York_Teams": {
        "currently_mapped_to": "business_configuration_logic.Max_New_York_Teams",
        "mapping_adequacy": "good",
        "description": "Maximum number of teams to be selected from New York"
      },
      "Max_Los_Angeles_Teams": {
        "currently_mapped_to": "business_configuration_logic.Max_Los_Angeles_Teams",
        "mapping_adequacy": "good",
        "description": "Maximum number of teams to be selected from Los Angeles"
      },
      "Max_Chicago_Teams": {
        "currently_mapped_to": "business_configuration_logic.Max_Chicago_Teams",
        "mapping_adequacy": "good",
        "description": "Maximum number of teams to be selected from Chicago"
      }
    },
    "decision_variables": {
      "Team_Selection[Team_ID]": {
        "currently_mapped_to": "team_selection.Team_Selection",
        "mapping_adequacy": "good",
        "description": "Binary decision variable indicating whether a team is selected for the tournament",
        "variable_type": "binary"
      }
    }
  },
  "missing_optimization_requirements": [],
  "iteration_status": {
    "complete": true,
    "confidence": "high",
    "next_focus": "Ready for convergence"
  }
}

FINAL DATABASE SCHEMA:
```sql
-- Iteration 3 Database Schema
-- Objective: Added missing constraint bounds to business configuration logic and updated data dictionary to reflect these changes. No new tables were created as the missing requirements were better suited for configuration logic.

CREATE TABLE win_percentage (
  Team_ID INTEGER,
  Win_Percent FLOAT
);

CREATE TABLE team_selection (
  Team_ID INTEGER,
  Team_Selection BOOLEAN
);

CREATE TABLE conference_indicator (
  Team_ID INTEGER,
  Conference_Indicator STRING
);

CREATE TABLE location_indicator (
  Team_ID INTEGER,
  Location_Indicator STRING
);


```

CURRENT STORED VALUES:
```sql
-- Iteration 3 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic basketball tournament scenarios, considering team performance, conference distribution, and geographical constraints. Data was generated to ensure diversity and competitiveness while respecting business configuration parameters.

-- Realistic data for win_percentage
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (1, 85.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (2, 78.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (3, 72.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (4, 80.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (5, 75.0);

-- Realistic data for team_selection
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (1, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (2, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (3, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (4, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (5, False);

-- Realistic data for conference_indicator
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (1, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (2, 'West');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (3, 'South');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (4, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (5, 'West');

-- Realistic data for location_indicator
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (1, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (2, 'Los Angeles');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (3, 'Chicago');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (4, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (5, 'Los Angeles');


```

DATA DICTIONARY:
{
  "tables": {
    "win_percentage": {
      "business_purpose": "Win percentage of each team for the tournament selection optimization",
      "optimization_role": "objective_coefficients",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for win percentage",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Win_Percent": {
          "data_type": "FLOAT",
          "business_meaning": "Win percentage of the team",
          "optimization_purpose": "Objective coefficient in optimization model",
          "sample_values": [
            75.5,
            80.0,
            65.3
          ]
        }
      }
    },
    "team_selection": {
      "business_purpose": "Binary decision variable indicating whether a team is selected for the tournament",
      "optimization_role": "decision_variables",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for decision variable",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Team_Selection": {
          "data_type": "BOOLEAN",
          "business_meaning": "Binary indicator of team selection",
          "optimization_purpose": "Decision variable in optimization model",
          "sample_values": [
            true,
            false,
            true
          ]
        }
      }
    },
    "conference_indicator": {
      "business_purpose": "Indicator of which conference each team belongs to",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for conference indicator",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Conference_Indicator": {
          "data_type": "STRING",
          "business_meaning": "Conference affiliation of the team",
          "optimization_purpose": "Constraint bound for conference diversity",
          "sample_values": [
            "East",
            "West",
            "South"
          ]
        }
      }
    },
    "location_indicator": {
      "business_purpose": "Indicator of the location of each team",
      "optimization_role": "constraint_bounds",
      "columns": {
        "Team_ID": {
          "data_type": "INTEGER",
          "business_meaning": "Unique identifier for each team",
          "optimization_purpose": "Index for location indicator",
          "sample_values": [
            1,
            2,
            3
          ]
        },
        "Location_Indicator": {
          "data_type": "STRING",
          "business_meaning": "Location of the team",
          "optimization_purpose": "Constraint bound for geographical distribution",
          "sample_values": [
            "New York",
            "Los Angeles",
            "Chicago"
          ]
        }
      }
    }
  }
}


BUSINESS CONFIGURATION:

BUSINESS CONFIGURATION:
{
  "Total_Teams_Selected": {
    "data_type": "INTEGER",
    "business_meaning": "Total number of teams to be selected for the tournament",
    "optimization_role": "Constraint bound for total teams selected",
    "configuration_type": "scalar_parameter",
    "value": 5,
    "business_justification": "A manageable number of teams for a competitive tournament"
  },
  "Min_East_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Minimum number of teams to be selected from the East conference",
    "optimization_role": "Constraint bound for East conference diversity",
    "configuration_type": "scalar_parameter",
    "value": 2,
    "business_justification": "Ensures representation from the East conference"
  },
  "Min_West_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Minimum number of teams to be selected from the West conference",
    "optimization_role": "Constraint bound for West conference diversity",
    "configuration_type": "scalar_parameter",
    "value": 2,
    "business_justification": "Ensures representation from the West conference"
  },
  "Min_South_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Minimum number of teams to be selected from the South conference",
    "optimization_role": "Constraint bound for South conference diversity",
    "configuration_type": "scalar_parameter",
    "value": 1,
    "business_justification": "Ensures representation from the South conference"
  },
  "Max_New_York_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of teams to be selected from New York",
    "optimization_role": "Constraint bound for geographical distribution",
    "configuration_type": "scalar_parameter",
    "value": 2,
    "business_justification": "Limits the number of teams from New York to ensure geographical diversity"
  },
  "Max_Los_Angeles_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of teams to be selected from Los Angeles",
    "optimization_role": "Constraint bound for geographical distribution",
    "configuration_type": "scalar_parameter",
    "value": 2,
    "business_justification": "Limits the number of teams from Los Angeles to ensure geographical diversity"
  },
  "Max_Chicago_Teams": {
    "data_type": "INTEGER",
    "business_meaning": "Maximum number of teams to be selected from Chicago",
    "optimization_role": "Constraint bound for geographical distribution",
    "configuration_type": "scalar_parameter",
    "value": 1,
    "business_justification": "Limits the number of teams from Chicago to ensure geographical diversity"
  }
}

Business Configuration Design: 
Our system separates business logic design from value determination:
- Configuration Logic (business_configuration_logic.json): Templates designed by data engineers with sample_value for scalars and actual formulas for business logic
- Configuration Values (business_configuration.json): Realistic values determined by domain experts for scalar parameters only
- Design Rationale: Ensures business logic consistency while allowing flexible parameter tuning


TASK: Create structured markdown documentation for SECTIONS 1-3 ONLY (Problem Description).

EXACT MARKDOWN STRUCTURE TO FOLLOW:

# Complete Optimization Problem and Solution: university_basketball

## 1. Problem Context and Goals

### Context  
[Regenerate business context that naturally aligns with LINEAR optimization formulation. Ensure:]
- Business decisions match expected decision variables: Team_Selection[Team_ID] (binary)
- Operational parameters align with expected linear objective: maximize ∑(Win_Percent × Team_Selection)
- Business configuration includes: Total number of teams to be selected for the tournament (used for Constraint bound for total teams selected), Minimum number of teams to be selected from the East conference (used for Constraint bound for East conference diversity), Minimum number of teams to be selected from the West conference (used for Constraint bound for West conference diversity), Minimum number of teams to be selected from the South conference (used for Constraint bound for South conference diversity), Maximum number of teams to be selected from New York (used for Constraint bound for geographical distribution), Maximum number of teams to be selected from Los Angeles (used for Constraint bound for geographical distribution), Maximum number of teams to be selected from Chicago (used for Constraint bound for geographical distribution)
- Use natural language to precisely describe linear mathematical relationships
- NO mathematical formulas, equations, or symbolic notation
- Present data as current operational information
- Focus on precise operational decision-making that leads to linear formulations
- Resource limitations match expected linear constraints
- Avoid scenarios requiring variable products, divisions, or other nonlinear relationships
- Include specific operational parameters that map to expected coefficient sources
- Reference business configuration parameters where appropriate
- CRITICAL: Include ALL business configuration information (scalar parameters AND business logic formulas) in natural business language

### Goals  
[Regenerate goals that clearly lead to LINEAR mathematical objective:]
- Optimization goal: maximize
- Metric to optimize: maximize ∑(Win_Percent × Team_Selection)
- Success measurement aligned with expected coefficient sources
- Use natural language to precisely describe linear optimization goal
- NO mathematical formulas, equations, or symbolic notation

## 2. Constraints    

[Regenerate constraints that directly match expected LINEAR mathematical constraints:]
- Expected constraint: ['∑(Team_Selection) = Total_Teams_Selected', "∑(Team_Selection × Conference_Indicator = 'East') ≥ Min_East_Teams", "∑(Team_Selection × Conference_Indicator = 'West') ≥ Min_West_Teams", "∑(Team_Selection × Conference_Indicator = 'South') ≥ Min_South_Teams", "∑(Team_Selection × Location_Indicator = 'New York') ≤ Max_New_York_Teams", "∑(Team_Selection × Location_Indicator = 'Los Angeles') ≤ Max_Los_Angeles_Teams", "∑(Team_Selection × Location_Indicator = 'Chicago') ≤ Max_Chicago_Teams"] (Form: Standard constraint form based on business requirements)

[Each constraint should be described in business terms that naturally lead to LINEAR mathematical forms (no variable products or divisions)]

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 3 Database Schema
-- Objective: Added missing constraint bounds to business configuration logic and updated data dictionary to reflect these changes. No new tables were created as the missing requirements were better suited for configuration logic.

CREATE TABLE win_percentage (
  Team_ID INTEGER,
  Win_Percent FLOAT
);

CREATE TABLE team_selection (
  Team_ID INTEGER,
  Team_Selection BOOLEAN
);

CREATE TABLE conference_indicator (
  Team_ID INTEGER,
  Conference_Indicator STRING
);

CREATE TABLE location_indicator (
  Team_ID INTEGER,
  Location_Indicator STRING
);


```

### Data Dictionary  
[Create comprehensive business-oriented data dictionary mapping tables and columns to their business purposes and optimization roles - NOT technical database terms. Base this on the data_dictionary provided but rewrite in business language that clearly connects to the expected linear mathematical formulation]

### Current Stored Values  
```sql
-- Iteration 3 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic basketball tournament scenarios, considering team performance, conference distribution, and geographical constraints. Data was generated to ensure diversity and competitiveness while respecting business configuration parameters.

-- Realistic data for win_percentage
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (1, 85.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (2, 78.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (3, 72.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (4, 80.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (5, 75.0);

-- Realistic data for team_selection
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (1, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (2, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (3, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (4, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (5, False);

-- Realistic data for conference_indicator
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (1, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (2, 'West');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (3, 'South');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (4, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (5, 'West');

-- Realistic data for location_indicator
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (1, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (2, 'Los Angeles');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (3, 'Chicago');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (4, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (5, 'Los Angeles');


```

STOP HERE - Do not include section 4 (Mathematical Optimization Formulation).

Response:
# Complete Optimization Problem and Solution: university_basketball

## 1. Problem Context and Goals

### Context  
The business problem revolves around selecting basketball teams for a tournament to maximize the overall win percentage of the selected teams. The decision-making process involves choosing which teams to include in the tournament based on their performance, conference affiliation, and geographical location. Each team is represented by a unique identifier, and the decision to select a team is binary—either the team is selected or not.  

The operational parameters guiding this decision include:  
- **Win Percentage**: The win percentage of each team, which serves as the primary metric for evaluating team performance.  
- **Total Teams Selected**: The total number of teams to be selected for the tournament, ensuring a manageable and competitive tournament size.  
- **Conference Diversity**: Minimum requirements for the number of teams selected from the East, West, and South conferences to ensure fair representation across conferences.  
- **Geographical Distribution**: Maximum limits on the number of teams selected from specific locations (New York, Los Angeles, and Chicago) to promote geographical diversity.  

These parameters are derived from the business configuration, which includes scalar values such as the total number of teams to be selected, minimum team requirements per conference, and maximum team limits per location. The decision-making process is designed to be linear, avoiding complex relationships such as variable products or divisions, ensuring a straightforward and efficient optimization formulation.  

### Goals  
The primary goal of this optimization problem is to maximize the total win percentage of the selected teams for the tournament. Success is measured by the cumulative win percentage of the chosen teams, ensuring that the tournament features the highest-performing teams while adhering to the constraints on conference diversity and geographical distribution.  

The optimization process focuses on selecting the best combination of teams based on their win percentages, ensuring that the final selection aligns with the business requirements for team diversity and location limits. This goal is achieved through a linear formulation that directly maps the win percentages of teams to the decision variables, ensuring clarity and simplicity in the optimization process.  

## 2. Constraints  

The optimization problem is subject to the following constraints, which ensure that the selected teams meet the business requirements for diversity and distribution:  

1. **Total Teams Selected**: The total number of teams selected for the tournament must equal the predefined value specified in the business configuration.  
2. **East Conference Diversity**: The number of teams selected from the East conference must meet or exceed the minimum requirement set in the business configuration.  
3. **West Conference Diversity**: The number of teams selected from the West conference must meet or exceed the minimum requirement set in the business configuration.  
4. **South Conference Diversity**: The number of teams selected from the South conference must meet or exceed the minimum requirement set in the business configuration.  
5. **New York Geographical Limit**: The number of teams selected from New York must not exceed the maximum limit specified in the business configuration.  
6. **Los Angeles Geographical Limit**: The number of teams selected from Los Angeles must not exceed the maximum limit specified in the business configuration.  
7. **Chicago Geographical Limit**: The number of teams selected from Chicago must not exceed the maximum limit specified in the business configuration.  

These constraints are designed to ensure that the final selection of teams is balanced and representative, aligning with the business goals for diversity and geographical distribution.  

## 3. Available Data  

### Database Schema  
```sql
-- Iteration 3 Database Schema
-- Objective: Added missing constraint bounds to business configuration logic and updated data dictionary to reflect these changes. No new tables were created as the missing requirements were better suited for configuration logic.

CREATE TABLE win_percentage (
  Team_ID INTEGER,
  Win_Percent FLOAT
);

CREATE TABLE team_selection (
  Team_ID INTEGER,
  Team_Selection BOOLEAN
);

CREATE TABLE conference_indicator (
  Team_ID INTEGER,
  Conference_Indicator STRING
);

CREATE TABLE location_indicator (
  Team_ID INTEGER,
  Location_Indicator STRING
);
```

### Data Dictionary  
The following tables and columns are used in the optimization problem, with their business purposes and optimization roles clearly defined:  

- **win_percentage**:  
  - **Team_ID**: Unique identifier for each team.  
  - **Win_Percent**: The win percentage of the team, used as the coefficient in the objective function to maximize the total win percentage of selected teams.  

- **team_selection**:  
  - **Team_ID**: Unique identifier for each team.  
  - **Team_Selection**: Binary indicator of whether a team is selected for the tournament, serving as the decision variable in the optimization model.  

- **conference_indicator**:  
  - **Team_ID**: Unique identifier for each team.  
  - **Conference_Indicator**: The conference affiliation of the team, used to enforce constraints on conference diversity.  

- **location_indicator**:  
  - **Team_ID**: Unique identifier for each team.  
  - **Location_Indicator**: The location of the team, used to enforce constraints on geographical distribution.  

### Current Stored Values  
```sql
-- Iteration 3 Realistic Data
-- Generated by triple expert (business + data + optimization)
-- Values were determined based on realistic basketball tournament scenarios, considering team performance, conference distribution, and geographical constraints. Data was generated to ensure diversity and competitiveness while respecting business configuration parameters.

-- Realistic data for win_percentage
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (1, 85.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (2, 78.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (3, 72.0);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (4, 80.5);
INSERT INTO win_percentage (Team_ID, Win_Percent) VALUES (5, 75.0);

-- Realistic data for team_selection
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (1, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (2, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (3, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (4, False);
INSERT INTO team_selection (Team_ID, Team_Selection) VALUES (5, False);

-- Realistic data for conference_indicator
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (1, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (2, 'West');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (3, 'South');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (4, 'East');
INSERT INTO conference_indicator (Team_ID, Conference_Indicator) VALUES (5, 'West');

-- Realistic data for location_indicator
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (1, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (2, 'Los Angeles');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (3, 'Chicago');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (4, 'New York');
INSERT INTO location_indicator (Team_ID, Location_Indicator) VALUES (5, 'Los Angeles');
```
