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.

我们提出了两个数学模型来预测篮球比赛中的无球得分机会，分别考虑了传球得分和运球得分的动作，我们利用 630 场 NBA 比赛中的球员追踪数据评估了这些模型，结果表明 BIMOS 在得分预测准确性方面优于 BMOS，因此我们的模型为篮球的战术分析和球员评估提供了有价值的见解。