# Mathematical models for off-ball scoring prediction in basketball

arXiv: https://arxiv.org/abs/2406.08749 · HTML: https://mlsanity.com/paper/2406.08749/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [Generative Modeling of Multimodal Multi-Human Behavior](https://mlsanity.com/paper/1803.02015/) (2018)
- [Generating Long-term Trajectories Using Deep Hierarchical Networks](https://mlsanity.com/paper/1706.07138/) (2017)
- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Estimating counterfactual treatment outcomes over time in complex multiagent scenarios](https://mlsanity.com/paper/2206.01900/) (2022)
- [Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling](https://mlsanity.com/paper/2007.03155/) (2020)
- [Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball](https://mlsanity.com/paper/1401.0942/) (2014)
- [The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA](https://mlsanity.com/paper/1803.02940/) (2018)
- [A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory](https://mlsanity.com/paper/2307.14732/) (2023)
- [tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data](https://mlsanity.com/paper/1712.01199/) (2017)
- [Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball](https://mlsanity.com/paper/2311.16564/) (2023)

## Built on by (most cited first, 5 of 5)

- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Space evaluation at the starting point of soccer transitions](https://mlsanity.com/paper/2505.14711/) (2025)
- [Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals](https://mlsanity.com/paper/2508.17611/) (2025)
- [Analysis of Line Break prediction models for detecting defensive breakthrough in football](https://mlsanity.com/paper/2511.00121/) (2025)
- [Right Move, Right Time: Multi-Sport Space Evaluation Platform for Ultimate Frisbee, Basketball, and Soccer](https://mlsanity.com/paper/2603.06585/) (2026)

## Abstract (arXiv)

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.