Individual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this thesis, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how much a player can expect each shot to contribute to a won point, given who struck the shot and where both players are located. This measurement, named ``Expected Shot Win Rate" (ESWR), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESWR, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player's in-match strategy.
Applied and Computational Mathematics (MS)
Department, Program, or Center
School of Mathematical Sciences (COS)
Floyd, Calvin Michael, "Applying Multi-Resolution Stochastic Modeling to Individual Tennis Points" (2017). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus