In yesterday’s post, I outlined a new method to measure return aggression. Using Aggression Score (AS) as a starting point, I made some adjustments in order to treat return winners (and induced forced errors) and return errors separately. The resulting metric–Return Aggression Score (RAS)–gives equal weight to return winners and return errors. A positive RAS represents an aggressive return game, while a negative number indicates a more conservative one. The most aggressive single-match performances were nearly four standard deviations above the mean, while player averages varied between about one standard deviation above and below the mean.
We can now point the algorithm at the ATP, and calculate RAS for each player in the 1,500 or so 2010-present men’s matches logged by the Match Charting Project.
The difference between the frequency of return errors and return winners is even greater for men than it is for women. The WTA tour averages, as we saw yesterday, are 17.8% and 5.5%, respectively, and the men’s averages are 20.9% and 4.1%. Thus, treating the two categories separately is even more important when analyzing ATP matches.
The overall range in single-match RAS figures is about the same as it is for women. The most aggressive one-match returners are nearly four standard deviations above the mean (a RAS mark near 4.0), while the lowest are almost two standard deviations below (RAS marks near -2.0). What differs between genders is that the most aggressive men’s single-match performances are not clustered around one player, as Serena Williams dominates the women’s list. Of the top ten one-match men’s RAS marks, only one player appears twice, and that is partly an accident:
Year Event Returner Opponent RAS 2015 Halle Berdych Karlovic 3.96 2014 Halle D Brown Nadal 3.72 2016 Stuttgart Marchenko Groth 3.49 2014 Aus Open Dolgopolov Berankis 2.99 2016 Dallas CH Tiafoe Groth 2.91 2014 Bogota J Wang Karlovic 2.79 2015 Fairfield CH Tiafoe D Brown 2.72 2017 Montpellier De Schepper M Zverev 2.64 2015 Madrid Isner Kyrgios 2.60 2014 Halle An Kuznetsov D Brown 2.58
Two factors make it more likely a returner appears on this list: His opponent, and the surface. Facing a serve-and-volleyer means adopting a higher-risk return strategy, and playing on a faster surface has a similar effect. Four of the top ten matches here were played on grass, and seven of the ten returners faced opponents who often come in behind their serves. Frances Tiafoe is partly responsible for his double-appearance here, but I suspect it has more to do with his opponents.
Grass is, by far, the most extreme surface in its effect on return tactics. Here are the numbers for each court type, along with the RAS of the average match on that surface:
Surface RetE% RetW% RAS Hard 21.4% 4.1% 0.04 Grass 25.3% 5.6% 0.54 Clay 18.5% 3.5% -0.24 Average 20.9% 4.1% 0.00
Even though the average clay court match isn’t as extreme as a grass court match in this regard, the ten least aggressive single-match return performances all took place on clay, five of them recorded by Rafael Nadal.
Player averages
The Match Charting Project has at least 10 matches (2010-present) for about 75 players. Here is the top quintile, the 15 most aggressive players of that group:
Player Matches RetPts RAS Dustin Brown 11 676 1.90 Ivo Karlovic 16 1116 0.85 John Isner 30 2202 0.77 Alexandr Dolgopolov 20 1417 0.76 Philipp Kohlschreiber 18 1334 0.69 Lukas Rosol 11 841 0.67 Vasek Pospisil 14 812 0.62 Andrey Kuznetsov 11 585 0.54 Benoit Paire 17 1198 0.54 Jeremy Chardy 14 923 0.39 Kevin Anderson 23 1681 0.39 Kei Nishikori 47 3128 0.38 Milos Raonic 42 3211 0.34 Sam Querrey 17 1219 0.31 Fernando Verdasco 17 1109 0.30
There’s aggression, and then there’s Dustin Brown. No other player is one full standard deviation above average, and he is nearly two, more than twice as aggressive as the next-most tactically extreme ATPer.
We don’t see quite the same extremes in the other direction, just a bunch of clay-courters:
Player Matches RetPts RAS Jiri Vesely 11 716 -0.76 Marcel Granollers 12 746 -0.64 Paolo Lorenzi 13 912 -0.58 Inigo Cervantes Huegun 10 705 -0.58 Tommy Robredo 10 622 -0.57 Damir Dzumhur 11 688 -0.56 Guido Pella 11 749 -0.51 Guillermo Garcia Lopez 10 734 -0.49 Casper Ruud 16 1000 -0.48 Hyeon Chung 10 621 -0.48 Rafael Nadal 157 11773 -0.42 Richard Gasquet 36 2180 -0.42 Roberto Bautista Agut 25 1633 -0.42 Diego Schwartzman 44 3289 -0.42 Juan Martin Del Potro 42 2900 -0.40
These least-aggressive numbers are partly a reflection of playing styles, and partly the surface, as we’ve already seen.
Next, let’s look at how much players alter their style to the circumstances. Here are 16 players–top guys along with some others I found interesting–along with their average RAS numbers on the three major surfaces:
Player RAS Hard Clay Grass John Isner 0.77 0.71 1.03 0.72 Marin Cilic 0.28 0.09 0.02 1.38 Jo Wilfried Tsonga 0.24 0.31 -0.22 0.38 Gilles Muller 0.10 0.07 -0.74 1.13 Roger Federer 0.08 0.04 -0.07 0.40 Grigor Dimitrov 0.07 0.12 -0.30 0.28 Novak Djokovic 0.02 0.03 -0.12 0.25 Nick Kyrgios 0.02 -0.06 0.07 1.20 Jack Sock -0.08 -0.09 0.08 Stanislas Wawrinka -0.09 -0.11 -0.23 0.95 Alexander Zverev -0.13 -0.06 -0.33 0.18 Andy Murray -0.20 -0.25 -0.32 0.15 Dominic Thiem -0.24 -0.13 -0.40 0.25 Juan Martin Del Potro -0.40 -0.43 -0.58 -0.07 Diego Schwartzman -0.42 -0.34 -0.45 Rafael Nadal -0.42 -0.25 -0.76 0.57
The big servers have some surprises in store: John Isner is more aggressive on the return on clay than on other surfaces, and Jack Sock and Nick Kyrgios show the same, at least compared to hard courts. Marin Cilic is extremely aggressive on the grass court return, but his clay court tactics are similar to those on hard courts. In stark contrast is Gilles Muller, second only to Nadal as a conservative returner on clay, but quite aggressive on other surfaces.
One of the many underexplored topics in tennis analytics is the different ways players change (or choose not to change) their tactics on different surfaces. While comparing Return Aggression Score by surface is a tiny step in that direction, it does suggest just how much those strategies vary.
As always, a reminder that analyses like these are only possible with the volunteer-generated shot-by-shot logs of the Match Charting Project. I hope you’ll contribute.