The aim of this paper is to discover patterns of player movement and ball striking (short-and long-term shots, and shot combinations) in tennis using HawkEye data which are indicative of changing the probability of winning a point. This is a challenging task because: i) behavior can be unpredictable, ii) the environment is dynamic and the output state-space is large and iii) examples of specific interactions between agents may be limited or non-existent (player A may not have interacted with player B). However, by using a dictionary of discriminative patterns of player behavior, we can form a representation of a player`s style, which is interpretable latent factors that allows us to personalize interactions between players based on the match context (opponent, match-score). This approach allows us to perform superior point predictions, and to understand how points are won by systematically creating and exploiting spatiotemporal dominance.
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