Summary of Mathematical Models For Off-ball Scoring Prediction in Basketball, by Rikako Kono and Keisuke Fujii
Mathematical models for off-ball scoring prediction in basketball
by Rikako Kono, Keisuke Fujii
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study presents two mathematical models to predict off-ball scoring opportunities in professional basketball, considering pass-to-score and dribble-to-score sequences. The Ball Movement for Off-ball Scoring (BMOS) model adapts principles from the Off-Ball Scoring Opportunities (OBSO) model, originally designed for soccer, to basketball, while the Ball Intercept and Movement for Off-ball Scoring (BIMOS) model incorporates the likelihood of interception during ball movements. The study evaluates these models using player tracking data from 630 NBA games in the 2015-2016 regular season, finding that the BIMOS model outperforms the BMOS model in terms of team scoring prediction accuracy. This research provides valuable insights for tactical analysis and player evaluation in basketball. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about predicting where players are most likely to score in professional basketball games. The researchers created two models that take into account how players move around the court before shooting or passing the ball. They tested these models using data from over 600 NBA games, and found that one of the models was better at predicting when teams would score. This information can be useful for coaches to analyze their team’s performance and make strategic decisions. |
Keywords
» Artificial intelligence » Likelihood » Tracking
