Statistical Analysis and Data Mining

The serve impact in tennis: First large‐scale study of big Hawk‐Eye data

Journal Article

  • Author(s): Sami Mecheri, François Rioult, Bruno Mantel, François Kauffmann, Nicolas Benguigui
  • Article first published online: 29 Jun 2016
  • DOI: 10.1002/sam.11316
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Analysis of dual sports performance typically involves observational techniques to gather data samples during actual competition. These techniques are limited by the amount of data that can be collected and the need to define the observable variables in advance. Today's advanced technologies have considerably overcome these limitations, enabling high‐volume data collection for post‐recording analysis. The present study was based on the three‐dimensional kinematic data recorded by the automated ball‐tracking Hawk‐Eye system between the 2003 and 2008 seasons in elite tennis tournaments, which provided a database of 262 596 points. The analysis consisted of an examination of the relationships between the various characteristics of the serve summed up by the resulting ball trajectory and winning‐point probabilities. The influence of factors such as serve speed, serve location, court surface, gender differences, and spin intensity on the winning‐point rate was assessed to gain insight into efficient serve tendencies in world‐class tennis. The implications for practitioners are highlighted and directions for future research in tennis performance analysis based on automatic ball tracking are proposed. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

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