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Topological Data Analysis Enhances Tennis Match Prediction Accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed novel methods for predicting tennis match outcomes by applying topological data analysis and graph theory to ATP singles match data from 2000-2025. One approach uses lower-star filtration on player competitive networks, extracting topological features through persistent homology. This method, combined with graph-theoretic and ranking features, achieved 66.2% accuracy with a Random Forest model, indicating that topological features provide a complementary signal to traditional metrics. Notably, a topology-only model maintained 63.56% accuracy, demonstrating the power of network-derived features alone. AI

RANK_REASON The cluster contains a research paper detailing novel methods for sports analytics. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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Topological Data Analysis Enhances Tennis Match Prediction Accuracy

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The cluster contains a research paper detailing novel methods for sports analytics. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jake Schwaderer, Alexander Bastien, Omid Khormali, Alejandro Navarrete, Mia Pesavento, Angelika Elderbrook ·

    Topological Data Analysis and Graph-Theoretic Approaches for Tennis Match Prediction

    arXiv:2607.23509v1 Announce Type: cross Abstract: We present two approaches for predicting tennis match outcomes using topological data analysis and graph theory on ATP singles matches from 2000-2025. The first method applies lower-star filtration to player competitive networks, …

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