tracking data

Change of direction is the skill of moving the ball from one side of the court to the other. Being able to control the direction of the ball during a tennis point can be a key strategy for forcing an opponent out of a comfortable position and setting up a winning shot. With modern tracking data, where the trajectory of every shot in a point is known, we can begin to investigate how change of direction is best quantified. This post is a collection of some initial ideas on a statistic for change of direction.

Using a recently discovered source of summary tracking data for Grand Slam matches, Peter Tea explores the spatial features of top men’s and women’s serves.

Head-to-heads are one of the most fascinating aspects of tennis. While it’s easy to look up who is ahead on one record or another, there are still many fundamental patterns of play that we can’t easily compare across players. This post goes a small (but I hope interesting) way towards remedying that by introducing a head-to-head visualization tool for comparing the return impact patterns of ATP players.

Which of the top ATP players are the most aggressive on the serve return? Which are the most defensive? Is Andrey Rublev’s serve return style closer to Roger Federer’s or Novak Djokovic’s? In this post, I introduce an interactive visualization tool to help answer these and many other questions about the return impact of top men’s tennis players.

The serve return is the most important shot for a receiver. What can receivers do to put themselves in a better position to make a good impact on the serve return? The answer to that question is too big a task for one blog post alone. As a small first step, I take a look at a new public data source that includes the position of players at the time of return impact in ATP matches. Through a handful of case studies, it delves into the kind of patterns in return positioning these data could help reveal.