Basketball Lineup Performance Prediction Using Network Analysis

摘要

Winning a game in professional sports is the most significant matter for a team. All teams strive to bring their best performance to a game, and this requires considering all the possible lineups which coaches have available. Therefore, determining the lineup is more and more significant for a team in their winning endeavour. The ongoing result during a game defines the next decision coaches have to make to maintain or improve the outcome. Adaptive changes in a lineup of a team requires a complex decision making system. This system must consider the advantages, drawbacks, and previous experience about both teams' performance under similar situations. In order to analyze and predict lineups' performance, the authors create a directed, weighted, and signed network of all lineups that teams use against each other from 2007–2016 seasons in National Basketball Association (NBA) games. The proposed model uses machine learning and network analysis techniques to predict the performance of a lineup under a given situation by utilizing graph theory and Inverse Squared Metric. In order to evaluate the performance of the proposed method, several baseline models are established and results are compared. The final results over the span of ten years show that the proposed method in this paper improves the baseline results by 10% accuracy. The average of the best baseline results has an accuracy of 58% in lineup outcome prediction; however, the new method yields accuracy of 68%.