Summary of Lasso Ridge Based Xgboost and Deep_lstm Help Tennis Players Perform Better, by Wankang Zhai et al.
Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better
by Wankang Zhai, Yuhan Wang
First submitted to arxiv on: 11 May 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 researchers develop a comprehensive analysis of the dynamics of momentum and game fluctuation in tennis matches using a dataset from the 2023 Wimbledon final. They introduce a novel approach, Lasso-Ridge-based XGBoost, to quantify momentum effects and leverage the predictive power of XGBoost while mitigating overfitting through regularization. The model achieves an accuracy of 94% in predicting match outcomes and identifies key factors influencing winning rates. Additionally, they propose a Derivative of the winning rate algorithm to quantify game fluctuation and employ an LSTM_Deep model to predict fluctuation scores. The findings provide valuable insights into momentum dynamics and game fluctuation, offering implications for sports analytics and player training strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers study how to understand tennis matches better by looking at how players do over time and how the match changes. They use a big dataset from Wimbledon and develop a special model that works well. This model can predict who will win with 94% accuracy, which is very good! It also helps them figure out what makes a player more likely to win. Additionally, they create another tool that looks at how the match goes back and forth, like a rollercoaster. They find that this approach captures the ups and downs of the game well. |
Keywords
» Artificial intelligence » Overfitting » Regularization » Xgboost
