Abstract

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.

Publication Date

5-30-2017

Document Type

Thesis

Student Type

Graduate

Degree Name

Applied and Computational Mathematics (MS)

Department, Program, or Center

School of Mathematical Sciences (COS)

Advisor

Matthew Hoffman

Advisor/Committee Member

Ernest Fokoue

Advisor/Committee Member

Nathan Cahill

Campus

RIT – Main Campus

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