Summary of Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory, by Jingdi Lei et al.
Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory
by Jingdi Lei, Tianqi Kang, Yuluan Cao, Shiwei Ren
First submitted to arxiv on: 20 Apr 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 paper analyzes the momentum in tennis matches, demonstrating its potential application in predicting sports game outcomes and analyzing player performances. By employing hidden Markov models to predict momentum, which is defined as player performance, the authors utilize Xgboost to establish the significance of momentum. Furthermore, LightGBM is used to evaluate the model’s performance, with SHAP feature importance ranking and weight analysis revealing key factors affecting player performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well tennis players do in a match, which can help predict the outcome of other sports games. The authors use special math models (hidden Markov models) to figure out what makes a good tennis player. They then use another tool (Xgboost) to show that this “momentum” is important. Finally, they use one more tool (LightGBM) to check how well their model works and identify the key factors that make a tennis player successful. |
Keywords
» Artificial intelligence » Xgboost
