2023/02/26 by Webster Guan, Guan, Webster, Nauman Javed +3
Economics, Econometrics and Finance · Medicine · Psychology · #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sport Psychology and Performance #Sports Analytics and Performance #Sports Performance and Training
paper · pdf · doi:10.48550/arxiv.2302.13386
openalex publication_date 2023/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding a player's performance in a basketball game requires an evaluation of the player in the context of their teammates and the opposing lineup. Here, we present NBA2Vec, a neural network model based on Word2Vec which extracts dense feature representations of each player by predicting play outcomes without the use of hand-crafted heuristics or aggregate statistical measures. Specifically, our model aimed to predict the outcome of a possession given both the offensive and defensive players on the court. By training on over 3.5 million plays involving 1551 distinct players, our model was able to achieve a 0.3 K-L divergence with respect to the empirical play-by-play distribution. The resulting embedding space is consistent with general classifications of player position and style, and the embedding dimensions correlated at a significant level with traditional box score metrics. Finally, we demonstrate that NBA2Vec accurately predicts the outcomes to various 2017 NBA Playoffs series, and shows potential in determining optimal lineup match-ups. Future applications of NBA2Vec embeddings to characterize players' style may revolutionize predictive models for player acquisition and coaching decisions that maximize team success.