Small sample size is typical of head-to-heads in pro tennis. Both seeding and knockout tournament designs mean that many pro players have played each other no more than a handful of times or sometimes never at all. Still, I find myself frequently surprised when I come across sparse head-to-heads between some seasoned players. It got me thinking if that reaction is even reasonable and how you might quantify how much some matchups are overdue?
The pandemic has caused the most sustained disruption to the tennis calendar the sport has ever faced. And, while tennis has returned in some form over the past six months, it has not been a return to normal. With all of the challenges players and events have undergone, many of us are likely wondering whether pro competition has changed in some fundamental way? In this post, I try to shed light on this question by looking at trends in event predictability before and during the pandemic.
Once we can measure player impact patterns on the return, it is natural to use these patterns to compare players. This post delves into these comparisons and presents a set of distinct impact types that describe the impact styles of top ATP players when returning the first serve on hardcourts.
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.
