Summary of Sportsngen: Sustained Generation Of Realistic Multi-player Sports Gameplay, by Lachlan Thorpe et al.
SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay
by Lachlan Thorpe, Lewis Bawden, Karanjot Vendal, John Bronskill, Richard E. Turner
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Applications (stat.AP)
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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 paper introduces SportsNGEN, a transformer decoder-based sports simulation engine trained on sports player and ball tracking sequences. This model can generate sustained gameplay and accurately mimic real players’ decision-making processes. By training on professional tennis data, the authors demonstrate that simulations produced by SportsNGEN can predict rally outcomes, determine best shot choices, and evaluate counterfactual scenarios for coaching decisions and broadcast coverage enhancement. The system combines generated simulations with a shot classifier and logic to simulate an entire tennis match. To evaluate SportsNGEN, the authors compare simulation statistics with real matches between the same players, showing that model output sampling parameters are crucial for simulation realism and that the model is probabilistically well-calibrated to real data. Additionally, the authors show that SportsNGEN can be customized to a specific player by fine-tuning on their match data subset, demonstrating the approach’s potential for football simulations as well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SportsNGEN is a new way to simulate sports games. It uses computer algorithms to make decisions like real players do. The system was trained on lots of tennis match data and can predict what will happen in a game. It can even suggest what shot a player should take next. This technology can help coaches make better decisions and make TV broadcasts more exciting. The scientists who created SportsNGEN tested it by comparing its predictions with what actually happened in real games. They found that the model was very good at predicting what would happen, but they had to tweak some settings to get it just right. Now, this technology can be used for other sports like football too. |
Keywords
* Artificial intelligence * Decoder * Fine tuning * Tracking * Transformer
