Temporal characteristics have been a main area of focus in the study of pre-performance routines in elite sports. Ball and player tracking data is currently available that precisely measures service preparation times in professional tennis. However, this data has yet to be utilized for studying service 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 s for male players and 20 s 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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