With sport at a standstill, there is ample time for sports nerds to get really wonky. In this post, I look at a question that should go to the heart of any tennis analyst: are players non-iid? I look at how we can measure non-iid effects, why we should care, and who has been the most non-iid among the Top 50 ATP players.
The announcement of a 6-week suspension of the ATP Tour was just the latest in a series of disruptions of the tennis calendar owing to the COVID-19 pandemic. With no official decisions on whether players will retain their current ranking, I consider what the possible impact would be if the tours were to decide to carry current rankings forward for a prolonged period.
Since introducing the Research Paper Competition in 2010, the MIT Sloan Sports Analytics Conference has become one of the biggest stages for statistical research in sport. In this post, I review the methods used by the finalists and reflect on what this could suggest about current trends in statistical analysis in the sports industry.
In a recent article on Liverpool’s dominance of the English Premier League, John Burn-Murdoch created a visualization of the height and weight of EPL, NBA and NFL players. This got me interested in how current ATP players compare to the build of those other professional athletes and whether the build of top players has changed since the beginning of this century.
In her debut match after nearly 8 years out of professional play, Kim Clijsters impressed many by taking Garbine Muguruza to a second set tiebreak. Neither player was winning more than 60% of points on serve, which made me wonder how likely a tiebreak was in this case. In this post, I use a simple Monte Carlo model to examine the most likely set scores for a variety of server matchups.