Mathematical models for off-ball scoring prediction in basketball

TL;DR

This paper introduces two mathematical models, BMOS and BIMOS, to predict off-ball scoring opportunities in basketball, with BIMOS showing superior accuracy using NBA player tracking data.

Contribution

The study develops and evaluates two novel models for off-ball scoring prediction, adapting soccer-based principles and incorporating interception likelihood.

Findings

BIMOS outperforms BMOS in prediction accuracy.

Models evaluated on 630 NBA games.

BIMOS offers insights for tactical analysis.

Abstract

In professional basketball, the accurate prediction of scoring opportunities based on strategic decision-making is crucial for spatial and player evaluations. However, traditional models often face challenges in accounting for the complexities of off-ball movements, which are essential for comprehensive performance evaluations. In this study, we propose two mathematical models to predict off-ball scoring opportunities in basketball, considering pass-to-score and dribble-to-score sequences: the Ball Movement for Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball Scoring (BIMOS) models. The BMOS model adapts principles from the Off-Ball Scoring Opportunities (OBSO) model, originally designed for soccer, to basketball, whereas the BIMOS model 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 model outperforms the BMOS model in terms of team scoring prediction accuracy, while also highlighting its potential for further development. Overall, the BIMOS model provides valuable insights for tactical analysis and player evaluation in basketball.