Large-Scale Adversarial Sports Play Retrieval with Learning to Rank

Mingyang Di, Diego Klabjan, Long Sha, Patrick Lucey

Abstract: As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users’ clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.