In professional sport, an enormous amount of fine-grain performance data can be generated at near millisecond intervals in the form of vision-based tracking data. One of the first sports to embrace this technology has been tennis, where Hawk-Eye technology has been used to both aid umpiring decisions, and to visualize shot trajectories for broadcast purposes. These data have tremendous untapped applications in terms of "opponent planning'', where a large amount of recent data is used to learn contextual behavior patterns of individual players, and ultimately predict the likelihood of a particular type of serve. Since the type of serve selected by a player may be contingent on the match context (i.e., is the player down break-point, or is serving for the match etc.), the characteristics of the player (i.e., the player may have a very fast serve, hit heavy with topspin or kick, or slice serves into the body) as well as the characteristics of the opponent (e.g., the opponent may prefer to play from the baseline or "chip-and-charge'' into the net). In this paper we present a method which recommends the most likely serves of a player in a given context. We show by utilizing a "style prior", we can improve the prediction/recommendation. Such an approach also allows us to quantify the similarity between players, which is useful in enriching the dataset for future prediction. We conduct our analysis on Hawk-Eye data collected from three recent Australian Open Grand-Slam Tournaments and show how our approach can be used in practice.