Summary of Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data, by Chang Liu et al.
Momentum Capture and Prediction System Based on Wimbledon Open2023 Tournament Data
by Chang Liu, Tongyuan Yang, Yan Zhao
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This study proposes a novel evaluation model that combines Entropy Weight Method (EWM) and Gray Relation Analysis (GRA) to quantify the impact of momentum on match outcomes in tennis. The proposed model is based on XGBoost and SHAP frameworks, which enable precise predictions of match swings with high accuracy. The authors conducted empirical validation through Mann-Whitney U and Kolmogorov-Smirnov tests, demonstrating a non-random association between momentum shifts and match outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at the mysterious force in tennis called Momentum, which can’t be seen or touched but controls the flow of the game. Researchers created a new way to measure Momentum’s impact on match outcomes by combining two methods: EWM and GRA. They tested their idea with data from four big tennis tournaments and found that it works well even when small things change. |
Keywords
» Artificial intelligence » Xgboost
