Diego Schwartzman’s Return Game Is Even Better Than I Thought

Diego Schwartzman is one of the most unusual players on the ATP tour. Even shorter than David Ferrer, his serve will never be a weapon, so the only way he can compete is by neutralizing everyone else’s offerings and winning baseline battles. Up to No. 34 in this week’s official rankings and No. 35 on the Elo list, he’s proven he can do that against some very good players.

Using the ATP stats leaderboard at Tennis Abstract, we can get a quick sense of how his return game compares with the elites. At tour level in the last 52 weeks (through Monte Carlo), he ranks third with 42.3% return points won, behind only Andy Murray and Novak Djokovic. He is particularly effective against second serves, winning 56.6% of those, better than anyone else on tour. He has broken in 31.8% of his return games, another third-place showing, this time behind Murray and Rafael Nadal.

Yet the leaderboard warns us to tread carefully. In the last year, Murray’s opponents have been far superior to Schwartzman’s, with a median rank of 24 and a mean rank of 41.5. The Argentine’s opponents have rated at 45.5 and 54.8, respectively. Murray, Djokovic, and Nadal are far better all-around players than Schwartzman, so they regularly reach later rounds, where the quality of competition goes way up.

Competition quality is one of the knottiest aspects of tennis analytics, and it is far from being solved. If we want to compare Murray to Djokovic, competition quality isn’t such a big factor. One or the other might get lucky over a span of months, but in the long run, the two best players on tour will face roughly equivalent levels of competition. But when we expand our view to players like Schwartzman–or even a top-tenner such as Dominic Thiem–we can no longer assume that opponent quality will even out. To use a term from other sports, the ATP has a very unbalanced schedule, and the schedule is always more challenging for the best players.

Correcting for competition quality is also key to understanding how any particular player evolves over time. If a player’s results improve, he’ll usually start facing more challenging competition, as Schwartzman is doing this spring in his first shot at the full slate of clay-court Masters events. If his return numbers decline, is he actually playing worse, or is he simply competing at his past level against tougher opponents?

Adjusting for competition

To properly compare players, we need to identify similarities in their schedules. Any pair of tour regulars have played many of the same opponents, even if they’ve never played each other. For instance, since the beginning of last season, Murray and Djokovic have faced 18 of the same players–some more than once. Further down the ranking list, players tend to have fewer opponents in common, but as we’ll see, that’s an obstacle we can overcome.

Here’s how the adjustment works: For a pair of players, find all the opponents both men have faced on the same surface. For example, both Murray and Djokovic have played David Goffin on clay in the last 16 months. Murray won 53.7% of clay return points against the Belgian, while Djokovic won only 42.1%, meaning that Djokovic returned about 22% worse than Murray did. We repeat the process for every surface-player combination, weight the results so that longer matches (or larger numbers of matches) count more heavily, and find the average.

When we do that for the top two men, we find that Djokovic has returned 2.3% better. (That’s a percentage, not percentage points. A great returner wins about 40% of return points, and a 2.3% improvement on that is roughly 41%.) Our finding suggests that Murray has faced somewhat weaker-serving competition: Since the beginning of 2016, he has won 42.9% of return points, compared to Djokovic’s 43.3%–a smaller gap than the competition-adjusted one.

It takes more work to reliably compare someone like Schwartzman to the elites, since their schedules overlap so much less. So before adjusting Diego’s return numbers, we’ll take several intermediate steps. Let’s start with the world No. 3 Stanislas Wawrinka. We follow the above process twice: Once for Wawrinka and Murray, then again for Stan and Novak. Run the numbers, and we find that Wawrinka’s return game is 22.5% weaker than Murray’s and 24.3% weaker than Djokovic’s. Wawrinka’s rates relative to the other two players correspond very well with what we already found, suggesting that Djokovic is a little better than his rival. Weighting the two numbers by sample size–which, in this case, is almost identical–we slightly adjust those two comparisons and conclude that Wawrinka’s return game is 22.4% worse than Murray’s.

Generating competition-adjusted numbers for each subsequent player follows the same pattern. For No. 4 Federer, we run the algorithm three times, one for each of the players ranked above him, then we aggregate the results. For No. 34 Schwartzman, we go through the process 33 times. Thanks to the magic of computers, it takes only a few seconds to adjust 16 months worth of return stats for the ATP top 50.

Below are the results for 2016-17. Players are ranked by “relative return points won” (REL RPW), where a rating of 1.0 is arbitrarily given to Murray, and a rating of 0.98 means that a player wins 2% fewer return points than Murray against equivalent opposition. The “EX RPW” column puts those numbers in a more familiar context: The top-ranked player’s rating is set equal to 43.0%–approximately the best RPW of any player in the last few seasons–and everyone else’s is adjusted accordingly.  The last two columns show each player’s actual rate of return points won and their rank among the ATP top 50:

1     Diego Schwartzman         1.04   43.0%   42.4%     4  
2     Novak Djokovic            1.02   42.1%   43.3%     1  
3     Andy Murray               1.00   41.2%   42.9%     2  
4     Rafael Nadal              0.98   40.3%   42.6%     3  
5     David Goffin              0.97   40.1%   41.3%     5  
6     Gilles Simon              0.96   39.6%   40.1%     9  
7     Kei Nishikori             0.95   39.3%   40.1%    10  
8     David Ferrer              0.95   39.1%   40.6%     7  
9     Roger Federer             0.94   38.7%   38.7%    15  
10    Gael Monfils              0.93   38.5%   39.8%    11  

11    Roberto Bautista Agut     0.93   38.3%   40.3%     8  
12    Ryan Harrison             0.92   37.9%   36.7%    33  
13    Richard Gasquet           0.92   37.9%   40.8%     6  
14    Daniel Evans              0.91   37.6%   36.9%    27  
15    Juan Martin Del Potro     0.91   37.5%   36.8%    32  
16    Benoit Paire              0.90   37.0%   38.1%    19  
17    Mischa Zverev             0.90   36.9%   36.9%    28  
18    Grigor Dimitrov           0.89   36.4%   38.2%    18  
19    Fabio Fognini             0.88   36.4%   39.7%    12  
20    Fernando Verdasco         0.88   36.4%   38.3%    16  

21    Joao Sousa                0.88   36.2%   38.3%    17  
22    Dominic Thiem             0.88   36.2%   38.1%    20  
23    Stani Wawrinka            0.88   36.1%   37.5%    22  
24    Alexander Zverev          0.88   36.0%   37.5%    23  
25    Albert Ramos              0.87   35.9%   38.9%    14  
26    Kyle Edmund               0.86   35.5%   36.1%    37  
27    Jack Sock                 0.86   35.5%   36.6%    34  
28    Viktor Troicki            0.86   35.4%   37.1%    26  
29    Marin Cilic               0.86   35.4%   37.3%    25  
30    Pablo Carreno Busta       0.86   35.3%   39.4%    13  

31    Milos Raonic              0.86   35.2%   36.1%    38  
32    Pablo Cuevas              0.85   35.1%   36.9%    29  
33    Tomas Berdych             0.85   35.1%   36.9%    30  
34    Borna Coric               0.85   34.9%   36.1%    39  
35    Nick Kyrgios              0.85   34.9%   35.7%    41  
36    Philipp Kohlschreiber     0.84   34.7%   37.9%    21  
37    Jo Wilfried Tsonga        0.84   34.6%   36.2%    36  
38    Sam Querrey               0.83   34.3%   34.6%    44  
39    Lucas Pouille             0.82   33.9%   36.9%    31  
40    Feliciano Lopez           0.81   33.2%   35.2%    43  

41    Robin Haase               0.80   33.0%   36.1%    40  
42    Paolo Lorenzi             0.80   32.9%   37.5%    24  
43    Donald Young              0.78   32.2%   36.3%    35  
44    Bernard Tomic             0.78   32.1%   34.1%    45  
45    Nicolas Mahut             0.76   31.4%   35.4%    42  
46    Steve Johnson             0.75   31.0%   33.8%    46  
47    Florian Mayer             0.74   30.3%   33.5%    47  
48    John Isner                0.73   30.0%   29.8%    49  
49    Gilles Muller             0.72   29.8%   32.4%    48  
50    Ivo Karlovic              0.63   25.9%   26.4%    50

The big surprise: Schwartzman is number one! While the average ranking of his opponents was considerably lower than that of the elites, it appears that he has faced bigger-serving opponents than have Murray or Djokovic. The top five on this list–Schwartzman, Murray, Djokovic, Nadal, and Goffin–do not force any major re-evaluation of who we consider to be the game’s best returners, but the competition-adjusted metric does offer more evidence that Schwartzman really belongs there.

There is a similar predictability at the bottom of the list. The five players rated the worst by the competition-adjusted metric–Steve Johnson, Florian Mayer, John Isner, Gilles Muller, and Ivo Karlovic–are the same five who sit at the bottom of the actual RPW ranking, with only Isner and Muller swapping places. This degree of consistency at the top and bottom of the list is reassuring: The metric is correcting for something important, but it isn’t spitting out any truly crazy results.

There are, however, some surprises. Three players do very well when their return games are adjusted for competition: Ryan Harrison, Daniel Evans, and Juan Martin del Potro, all of whom jump from the bottom half to the top 15. In a sense, this is a surface adjustment for Harrison and Evans, both of whom have played almost exclusively on hard courts. Players win fewer return points on faster surfaces (and faster surfaces attract bigger-serving competitors, magnifying the effect), so when adjusted for competition, someone who plays only on hard courts will see his numbers improve. Del Potro, on the other hand, has been absolutely hammered by tough competition, so in his case the correction is giving him credit for the difficult opponents he has had to face.

Several clay court specialists find their return stats adjusted in the wrong direction. Last week’s finalist, Albert Ramos, falls from 14th to 25th, Pablo Carreno Busta drops from 13th to 30th, and Roberto Bautista Agut and Paolo Lorenzi see their numbers take a hit as well. This is the reverse of the effect that pushed Harrison and Evans up the list: Clay-court specialists spend more time on the dirt and they play against weaker-serving opponents, so their season averages make them look like better returners than they really are. It appears that these players are all particularly bad on hard courts: When I ran the algorithm with only clay-court results, Bautista Agut, Ramos, and Carreno Busta all appeared among the top 12 in competition-adjusted return points won. It’s their abysmal hard-court performances that pull down their longer-term numbers.

Beyond RPW

This algorithm–or something like it–has a great deal of potential beyond simply correcting return points won for tour-level competition quality. It could be used for any stat, and if competition-adjusted return rates were combined with corrected rates of service points won, it would generate a plausible overall player rating system.

Such a rating system would be more valuable if the algorithm were extended to players beyond the top 50, as well. Just as Schwartzman doesn’t yet have that many common opponents with the elites, Challenger-level stalwarts don’t have share many opponents with tour regulars. But there is enough overlap that, when combining the shared opponents of dozens of players, we might be able to get a better grip on how Challenger-level competition compares to that of the highest levels. Essentially, we can compare adjacent levels–the elites to the middle of the pack (say, ATP ranks 21 to 50), the middle of the pack to the next 50, and so on–to get a more comprehensive idea of how much players must improve to achieve certain goals.

Finally, adjusting serve and return stats so that we have a set of competition-neutral numbers for every player, for each season of his career, we will gain a clearer picture of which players are improving and by how much. Official rankings and Elo ratings tell us a lot, but they are sometimes fooled by lucky breaks, close wins, or inconsistent opposition. And they cannot isolate individual stats, which may be particularly useful for developmental purposes.

Adjusting for opposition quality is standard practice for analysts of many other sports, and it will help tennis analytics move forward as well. If nothing else, it has shown us that one extreme performance–Schwartzman’s return game–is much more than a fluke, and that service return greatness isn’t limited to the big four.