Finding the Best Tennis Serves with K-Means and GMM Clusters of Ball Tracking data to Interpret Serve Strategies

• Kalil Saldanha Kaliffe UFPR
• Reginaldo Santos UFPR

DOI: https://doi.org/10.5753/kdmile.2024.244569

Resumo

The serve is a crucial shot in tennis, that dictates a player’s advantage. However, there has been a noticeable gap in recent data analysis focused on player behavior during serves, when compared to data analysis adoption in other sports. With high speeds, precision, and small margins, ball-tracking systems like Hawkeye are essential for capturing serve steps with fidelity. This data is crucial for decision-making improvements, performance enhancement, and knowledge discovery. However, the Full Hawkeye data is not publicly available. In this manner, this article uses scraping techniques to harness Hawk-Eye serve tracking data from the Australian Open (2020-2024) and Roland Garros (2019-2024), consisting of 152.761 serves from 951 matches. K-Means and Gaussian Mixture Model (GMM) clustering models were employed to discover clusters that summarize thousands of servers into interpretable serve strategies. The best serve strategies optimize success percentages, risk of missing the serve (fault), and may vary from first to second serves, or be affected by pressure in breakpoints, thus the best serve is a serve that best fits a situation and matches a desired outcome. The relation between the serve success and best players was checked, by correlating the server ranking with cluster success using serves from these clusters in different context scenarios. We discovered that the success rate in the clusters increases with player ranking points in high-pressure situations, such as breakpoints and tiebreaks, also that, the hard courts at the Australian Open have greater success rates, while the slower clay courts at Roland Garros have lower first and second serve success rates, despite using similar serve strategies, and that rankings had little bearing on serve performance on these slower courts, indicating that in this surface, other factors may matter more for player advantage in the end than just winning points with the serve right away.

Palavras-chave: Ball-Tracking, Clustering, Data Mining, GMM, K-Means, Serve Strategies, Successful Serves, Tennis Serves

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