Mathematical models for off-ball scoring prediction in basketball

In professional basketball, the accurate prediction of scoring opportunities based on strategic decision-making is crucial for space and player evaluations. However, traditional models often face challenges in accounting for the complexities of off-ball movements, which are essential for accurate predictive performance. In this study, we propose two mathematical models to predict off-ball scoring opportunities in basketball, considering both pass-to-score and dribble-to-score movements: the Ball Movement for Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball Scoring (BIMOS) models. The BMOS adapts principles from the Off-Ball Scoring Opportunities (OBSO) model, originally designed for soccer, to basketball, whereas the BIMOS also incorporates the likelihood of interception during ball movements. We evaluated these models using player tracking data from 630 NBA games in the 2015-2016 regular season, demonstrating that the BIMOS outperforms the BMOS in terms of scoring prediction accuracy. Thus, our models provide valuable insights for tactical analysis and player evaluation in basketball.