Squash is played by over 20 million players in 185 different countries around the world. According to the International Health, Racquet & Sportsclub Association (IHRSA), squash is 1 of the 3 fastest growing sports in the U.S., with participation increasing by 82% between 2007-2011. Despite this explosion in popularity, there has been scant exploration of squash from the sports analytics perspective. Only recently (in 2018) has the Professional Squash Association (PSA) partnered with Sports Data Labs, and the initial focus of this collaboration is to collect in-game biometric data of squash players to track player location. This poster analyzes the men’s final of the 2018 British Open between Miguel Rodriguez and Mohamed El Shorbagy. Akin to how shot charts are compiled for basketball, I track shot-by-shot placement to model each player’s tendency to hit straight drives, cross-court shots, and drop shots. By recording these sequentially, I acquire contextual data for shot selection, thus providing new insight into how players move each other “off the T” for strategic advantage. The resulting method, called S.Q.U.A.B.L. – or Sequence- and QUadrant-Based Learning – provides compelling data to explain how Rodriguez (a.k.a. The Colombian Cannonball) finally defeated the world #1.
There are several applications of this research. Player-specific models can identify tendencies to improve individual performance or inform winning strategies for opponents. With additional data, these models could be extended to quantitatively describe the different styles used by squash players across the world.