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A Data-Driven Approach To Tennis Player Performanc by Enertskiao Zhuetao is a document available to read on EtoBox.

This study introduces a data-driven framework for predicting and evaluating tennis player performance using historical match data, employing principal component analysis and machine learning algorithms. The gradient-boosted decision tree (GBDT) model achieved an accuracy of 89.04% in predicting match outcomes, while a comprehensive evaluation system was developed using the entropy weight method and TOPSIS model. Additionally, a visualization method was created to enhance understanding of player performance

Author

Enertskiao Zhuetao

Language

EN