In this paper, we use ball and player tracking data from “Hawk-Eye” to discover unique player styles and predict within-point events. We move beyond current analysis that only incorporates coarse match statistics (i.e. serves, winners, number of shots, volleys) and use spatial and temporal information which better characterizes the tactics and tendencies of each player. Using a probabilistic graphical model, we are able to model player behaviors which enables us to: 1) find the factors such as location and speed of the incoming shot which are most conducive to a player hitting a winner (i.e. “sweet-spot”) or cause an error, and 2) do “live in-point” prediction – based on the shots being played during a rally we estimate the probability of the outcome of the next shot (e.g. winner, continuation or error). As player behavior depends on the opponent, we use model adaptation to enhance our prediction. We show the utility of our approach by analyzing the play of Djokovic, Nadal, and Federer at the 2012 Australian Tennis Open.
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