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Abstract:
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As match durations in elite tennis have increased over time, there is a growing focus on time violation policies. However, the temporal characteristics and patterns of player pre-performance routines have received little attention. Ball and player tracking data is currently available to precisely measures service preparation times, but it has yet to be utilized for studying pre-performance routines. In this paper, we present a Bayesian multilevel model of the time-to-serve in a professional tennis match, which includes heterogeneous means, variances and covariate effects. Applying the model to a sample of serves played at the 2016 Australian Open reveals that the typical time-to-serve was 19 seconds for male players and 20 seconds for female players. Point importance and the length of the previous rally account for approximately 15% of the within-match variance. However, even with this adjustment, within-match variation is notably larger than between-player variation, 60% greater for men and 30% greater for women. The proposed Bayesian modeling approach is demonstrated to be a useful tool for analyzing in-competition temporal data on preparation time for tennis and other sports.
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