A new section of the website is debuted: ‘Ratings’. Here (the hope) is to provide the top 100 singles and doubles men and women on tour each week using MOV Elo ratings.
With the US Open hard court swing just around the corner, Ash Barty heads into the last leg of the season with the highest rating in women’s singles and doubles. Such simultaneous dominance in singles and doubles has, in the past decade, only been matched by Serena Williams.
After the first weeks of the clay season, who is making fwaves and who has had a wipe out? Here are men’s and women’s twenty biggest movers so far on clay.
Any measure of playing style is only as good as what it can explain beyond player overall ability. If we apply this standard to playing style categories derived from basic match stats, how do they hold up?
Last week, I looked at whether basic match stats, like aces and minutes played per point, could help describe a player’s playing style. In this post, I expand on the set of style features and delve into the clusters of playing styles they reveal.
Recently, I’ve had a few posts on head-to-head effects. The biggest takeaway was one we probably all knew going in: Head-to-head effects may exist, but good luck finding them. With so many small sample sizes for most head-to-heads, we need a way to group ‘similar’ players. In this post, I look at whether categorizing players by playing style might be possible using basic match stats.
Ahead of the final matches in Miami this weekend, we look back on the players who triumphed in the face of the most pressure-filled matches so far this month.
If you thought Serena vs Maria was a lopsided head-to-head, you are right. But it isn’t the biggest head-to-head effect in recent WTA history.
Matchup effects are a common idea in tennis commentary. It is the thing at the heart of comments like ‘her game matches up well’ against her opponent. One way to think of a matchup effect is as a surprising head-to-head, when results go against what the overall ability of both players would have us expect. Do such effects exist? And are they substantial enough that they matter when it comes to making better predictions about tennis results?
Novak Djokovic looked unstoppable at the Australian Open and he heads into Indian Wells as the hands down favourite for the title. With GIG predictions giving him a 1 and 2 chance to win the whole thing, the question of the event will be whether anyone can stop Djokovic?