Category: Surface speed

The Riddle of the Ruud-Rublev Reversal

Andrey Rublev and Casper Ruud

The story of the the 2024 ATP Tour Finals was the dominance of Jannik Sinner. I’ll refer you to what I wrote after the Australian Open: Yes, Jannik Sinner Really Is This Good. He just passed the 2,300 Elo threshold, becoming only the 12th man since Rod Laver to do so. When I update the Tennis 128 in a few weeks, he’ll be on it.

For all that, I’m preoccupied with something else. On the final day of round robin play, Casper Ruud beat Andrey Rublev to secure a place in the semi-finals. It was their eighth meeting and the Norwegian’s third victory:

Notice anything odd? Take away the unfinished Australian Open tilt, and the head-to-head breaks down precisely on surface lines. Rublev has won all four encounters on clay, while Ruud has run the table on hard courts. Not just any hard courts: indoors, at the Tour Finals.

We can look to external factors to explain some of the individual results. Casper had more at stake on Friday than the Russian did, with a chance to qualify for the final four. Rublev is older and broke through correspondingly earlier, so he was the natural favorite in their early meetings. Injuries and illness may have influenced another outcome or two, even aside from the retirement in Melbourne.

Still: 0-3 in completed matches for the ball-basher on hard courts, and 0-4 for the Roland Garros finalist on clay. Something’s going on here.

Not so fast

Rublev, to be fair, is hardly a fast-court specialist. His first tour-level title came on dirt in Umag, and he picked up a Masters crown last year in Monte Carlo, on one of the circuit’s slowest surfaces. The Russian’s forehand is a weapon in any conditions, and slow courts can disguise some of his weaknesses.

On the other hand, however much Rublev likes the dirt, Ruud likes it more. Earlier this year, I quantified the notion of “surface sensitivity,” the degree to which a player’s results are influenced by court speed. Rublev scored at -2.2, indicating that he does better on slower surfaces, a bit more so than the typical tour regular. Casper was considerably further down the list, at -5.0. The rating tells us that he’s more receptive to slow courts than Pablo Carreno Busta or Jaume Munar. He’s grades about the same as Diego Schwartzman.

Maybe the oddball head-to-head is a quirk of when the pair have met? Not all hard courts play fast, and not all clay behaves like the crushed brick at Roland Garros. Here are the venues for the seven completed meetings, along with my ace-based surface speed rating for each. Ratings above 1 are faster than average, below 1 are slower:

Year  Tournament   Winner  Speed  
2024  Tour Finals  Ruud     1.36  
2023  Bastad       Rublev   0.86  
2022  Tour Finals  Ruud     1.50  
2021  Tour Finals  Ruud     1.51  
2021  Monte Carlo  Rublev   0.54  
2020  Hamburg      Rublev   0.52  
2019  Hamburg      Rublev   0.74

Hypothesis denied! The 2021 and 2022 Tour Finals were the fastest conditions of their respective years, while Monte Carlo was the slowest of the entire 2021 season. Last year’s Bastad surface was fairly neutral for a clay court, but the rest of the Ruud-Rublev showdowns took place on fast hard courts or slow clay.

We could always mark down a string of seven surface-confounding results to luck, especially when both players are capable in all conditions. But it would be far more satisfying to find an explanation that tells us something about the players and their particular skills.

Stoppable

We don’t have to look far. Here are Casper’s win rates on first and second serve points against Rublev, separated by surface:

Surface  1st W%  2nd W%  
Clay      56.8%   48.4%  
Hard      72.7%   50.7%

Everybody wins fewer first serve points on clay than on hard courts, but not like this. The average gap for top-50 players in 2024 is four percentage points–not sixteen. At tour level against the entire field, Ruud has shown an even smaller difference, winning 73.1% of hard-court first-serve points against 71.2% of those on clay.

Rublev is not a brilliant returner. He’s a serviceable one with tactics to match. He often struggles to get first serves back in play. On second serve, he’s unafraid to unleash his weapons, accepting some errors in exchange for tilting other points in his favor. On clay, he’s able to turn a few more first serves into rally openers. In 2024, his gap between clay and hard-court first-serve return points won was bigger than average. But not nearly as wide as it is against Casper.

When it works…

Ruud, like many men who have developed into strong clay-courters, doesn’t have a monster serve. He can place it, he can disguise it, and he knows how to play behind it. On a fast hard court, those skills–combined with a bit more risk-taking–can result in numbers that look more like those of a big server. Against Rublev last week, he won just shy of 80% of his serve points, supported by 15 aces.

When the Norwegian is hitting corners and the ball is skimming off a court like the speedy one in Turin, Rublev is helpless. Over his career, according to the nearly 200 matches logged by the Match Charting Project, he puts 58% of first serves back in play. Against Casper on Friday, he didn’t manage 50%–a repeat of his performance on the same court two years earlier.

Rublev’s first-serve-return struggles on hard court contrast with how he feasts on Ruud’s serve on clay. The next table shows the rate at which the Russian puts Casper’s first serves in play, as well as his win percentage when he does so:

Match                Result  1st: RiP%  RiP W%  
2024 Tour Finals RR       L      46.9%   43.3%  
2023 Bastad F             W      64.6%   61.3%  
2022 Tour Finals SF       L      45.7%   50.0%  
2021 Tour Finals RR       L      67.5%   51.9%  
2021 Monte Carlo SF       W      84.4%   59.3%  
2020 Hamburg SF           W      91.4%   59.4%

The exception to the rule here is the 2021 Tour Finals match, which ended in a third-set tiebreak. It was the closest either man has come to securing one of the matches he “should” have won. Rublev won 52.9% of total points, but Ruud served his way out of just enough jams to come through.

On the very slow Monte Carlo and Hamburg courts, Rublev’s ability to handle the Norwegian’s first serve meant that Ruud was left with no edge whatsoever. Casper might be the superior baseliner, but he started too many points at a disadvantage. The picture might look different if they played on slow clay today, since Ruud is stronger and tactically savvier on serve. But I imagine it would still be a struggle, one in which few of Casper’s service games would sail by quickly.

Pundits like to say that tennis is a game of matchups. They often overstate their case: The better player (by ranking, or Elo rating, or whatever) usually wins. When they don’t, it isn’t always because of some quirk in the head-to-head. With Rublev and Ruud, though, such a quirk dictates the results. Few men are able to erase Rublev’s advantage on a hard court as much as the Norwegian can. Casper’s first serve is rarely so ineffectual as when the Russian is waiting for it on dirt. Ruud is set for another big European clay swing next year–so long as his buddy Andrey lands in the other half of the draw.

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Can Sebastian Baez Find Success on Hard Courts?

Sebastian Baez in Cordoba last month. Credit: jmmuguerza

Sebastian Baez is a marvel. In an era dominated by tall, all-court sluggers, the five-foot, seven-inch Argentinian has carved out a place on the circuit as a throwback clay-court specialist. Just a couple of months past his 23rd birthday, he has already won six tour-level titles and reached a new career-best ranking of 19th on the ATP computer.

The obvious comparison is Diego Schwartzman, another Argentinian on the small side who won titles and reached a French Open semi-final by grinding out victories and swinging above his weight. Schwartzman eventually cracked the top ten, but when he was Baez’s age, such a milestone looked extremely unlikely. When he turned 23, he stood outside the top 60, heading back to South America for another cycle through the continent’s late-season Challenger swing. He wouldn’t reach the top 20 for another two and half years.

If we assume Baez continues to improve throughout his mid-20s in the same way that Schwartzman did, a single-digit ranking seems achievable. He’s already 11th on tour in clay court Elo. Only a few players ahead of him on the official ranking table are younger.

The main stumbling block is the natural ceiling on dirtballers. There are far more ranking points available on hard courts than on clay, and one of the prime opportunities on the Argentinian’s favorite surface, in Madrid, plays fast because of its altitude. (Baez has competed there only once, losing in the second round last year to another sterling clay-courter, Stefanos Tsitsipas. Schwartzman never won more than two matches there, either.) For Schwartzman to gain a top-ten place, he needed more than a Roland Garros semi-final: He had recently reached indoor finals in Vienna and Cologne, and he was 14 months removed from defeating Taylor Fritz for a hard-court title in Los Cabos. Diego’s magic somehow worked on all surfaces. Even in a year when he posted outstanding results on clay, that was his only route to a single-digit ranking.

Baez owns one hard-court championship, from last year’s US Open warmup in Winston-Salem. But apart from that week, his story diverges from Schwartzman’s, with a career record on the surface of just 17-33. He has never won two completed matches at any other tour-level hard-court event. (His two third round appearances at majors were aided by retirements.) The Argentinian can be a star and a national hero without all-surface success, but surely he wants more. Can he achieve it?

Surface and speed

When I wrote about surface sensitivity a couple of weeks ago, Baez didn’t stand out as an extreme. Tsitsipas and Lorenzo Musetti were the men whose results were most dependent on slow courts. The numbers showed that Baez does better with a slower bounce, but the effect is less than half of what it is for Tsitsipas. The 23-year-old is more closely comparable in this department to Daniil Medvedev, who doesn’t like to play on clay–or eat it.

However, that analysis left out one major factor. I simply rated tournaments by the degree to which they helped servers end points quickly, regardless of surface. Indian Wells, on hard courts, came out as just barely speedier than Rome and slower than Madrid. Miami and the US Open were roughly equivalent to the Caja Magica as well.

Intuitively, there is more than one dimension to player preferences. Some men might just want more time to prepare, as could be the case with Tsitsipas and his one-handed backhand. But others–Baez among them–are much more comfortable on a certain type of surface, because of the type of bounce, the footing, or both. When we reduce surface type and speed to one variable, Baez and Medvedev come out equal. When we separate type from speed, they look very different.

This scatterplot shows 56 players on the two dimensions:

(The units are regression coefficients and essentially meaningless out of context. They do, however, show direction and magnitude of each player’s preferences.)

Among players with at least 100 tour-level matches since 2021, Baez ranks third in the degree of his preference for clay courts, behind Albert Ramos and roughly tied with Alexander Zverev. Once surface type is controlled for, he prefers faster courts. Santiago, where he won the title last week, is one of the quicker clay courts on the circuit, giving servers as many quick points as Wimbledon (really!). Rio de Janeiro, the site of his triumph the week before, is also faster than the average dirt, rating about the same as Indian Wells.

Medvedev is the exact opposite. Only Adrian Mannarino has a stronger demonstrated yen for hard courts. Once his choice of surface is secure, though, the Russian wants it as slow as possible. Only a few players (including Musetti and another slow-hard-specialist, Alex de Minaur) are so extreme.

Schwartzman–the model for a potential all-court Baez–prefers clay, and he likes all of his courts slow. His performance is even more speed-dependent than Medvedev’s, but his surface type preference isn’t nearly as strong as that of his younger countryman.

This is all rather abstract, and to some degree, it’s just a fancy way of saying that Baez struggles on hard courts. Let’s make things more concrete by looking at what happens when the Argentinian shifts to the tour’s more popular surface.

Translations

Hard-court tennis is more serve-dominated than the clay-cout variety. The typical tour regular wins, on average, 3% more service points on hard than on clay: 4% more first-serve points and 1% more second-serve points. They win 7% fewer return points. (That sounds like a paradox, since the serve and return numbers are different. The catch comes from specifying “tour regulars”–part-timers on hard courts have bigger serves than their equivalents at clay events.)

Here are the player-specific numbers for each man in the top 20 (except for Ben Shelton, who hasn’t played much on clay). The figures are ratios of each hard-court metric to the corresponding clay-court metric–serve points won, first-serve points won, and return points won–so the higher the number, the bigger the difference in favor of hard courts.

Player               SPW  1st SPW   RPW  
Ugo Humbert         1.10     1.08  0.95  
Novak Djokovic      1.09     1.11  0.91  
Alex de Minaur      1.08     1.08  0.99  
Daniil Medvedev     1.06     1.07  0.98  
Jannik Sinner       1.06     1.08  0.95  
Tommy Paul          1.06     1.08  1.08  
Grigor Dimitrov     1.06     1.04  0.95  
Holger Rune         1.05     1.06  0.92  
Taylor Fritz        1.05     1.09  0.93    
Alexander Zverev    1.05     1.03  0.89

Player               SPW  1st SPW   RPW  
Alexander Bublik    1.05     1.04  1.04  
Karen Khachanov     1.05     1.09  0.96  
Andrey Rublev       1.05     1.05  0.94  
Hubert Hurkacz      1.04     1.02  1.00  
Frances Tiafoe      1.03     1.05  0.95  
- ATP Regular -     1.03     1.04  0.93  
Carlos Alcaraz      1.02     1.02  0.92  
Stefanos Tsitsipas  1.02     1.03  0.89  
Casper Ruud         1.01     1.04  0.91  
Sebastian Baez      0.94     0.96  0.85

Baez is… different. Everyone in the top 20 wins more serve points on hard courts than on clay except for him. Only a few other men on tour have the same “backwards” split, and only Federico Coria is close to Baez in the degree of his weaker hard-court service performance. What costs the Argentinian even more is how his return numbers suffer away from clay. Almost everyone wins fewer return points on hard, but Baez takes the cake here too.

Perhaps needless to say, there’s no way that this can work. Baez wins about 62% of service points on clay, a respectable number and an impressive one for someone his size, but still below the average of a top-50 player on the surface. To win even fewer on hard suggests that he would struggle even at Challenger events. At Winston-Salem last August, Baez won 63.5% of his serve points and over 43% on return. That’s a combination that will win matches; he just hasn’t provided any evidence that he can pull it off once he crosses back out of North Carolina.

Growth rate

The one reason for optimism is that Baez is young, inexperienced on surfaces other than clay. Like Schwartzman, he grew up playing on dirt, and he rose through the rankings by winning South American Challengers, then picking up victories on the continent’s Golden Swing. Maybe there’s a necessary transition period?

Here are the same ratios as above, now by season for our two Argentinian heroes:

Player  Year   SPW  1st SPW   RPW  
Diego   2015  1.01     0.99  1.01  
Diego   2016  1.04     1.07  0.94  
Diego   2017  1.08     1.09  0.91  
Diego   2018  1.03     1.03  0.89  
Diego   2019  1.08     1.09  0.94  
Diego   2020  1.03     1.03  0.85  
Diego   2021  1.02     1.03  0.95  
Diego   2022  1.01     1.04  0.89  
Diego   2023  1.09     1.08  0.88  
                                   
Player  Year   SPW  1st SPW   RPW  
Baez    2022  0.91     0.94  0.82  
Baez    2023  0.96     0.99  0.88

Baez did close the gap between his hard-court and clay-court performances in his second year on tour. But he still shows a more marked surface preference than Schwartzman ever did. As soon as Diego arrived on tour, he was able to win more service points on hard courts–roughly the same ratio as the typical tour regular. Baez isn’t even close. Schwartzman had to retool his game to succeed on hard courts, and Baez will need to do so even more.

The 23-year-old truly is a throwback, an undersized grinder who spins in his serves, plays defense, and constructs points. It’s a joy to watch, and the package makes him one of the best players on tour for the 14 weeks or so each year when there are top-level clay events on offer. It doesn’t, however, work so well when there’s no dirt to kick out of his cleats. Fortunately Baez is young, and he has many years left to figure it out. He’ll need to.

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Surface Sensitivity and Ugo Humbert’s Serve

Ugo Humbert in 2023. Credit: Hameltion

Let’s start off with a couple of puzzles. I realize they aren’t the sort of things that keep most of you up at night, but they were odd enough to drive me to a flurry of coding, data analysis, and now blog writing.

On Wednesday, Ugo Humbert lost his first-round match in Rotterdam to Emil Ruusuvuori. It marked an unceremonious end to a hot streak for Humbert: He not only won the title in Marseille last week–launching himself into the Elo top ten–but he strung together 31 consecutive holds. 1,000 kilometers north, on a different indoor hard court, he got broken twice by a man ranked outside the top 50.

That’s the first puzzle: Why did the Frenchman lose? Again, it’s not that odd, as my Elo ratings gave Ruusuvuori a one-in-three shot to pull the upset. But it’s a match that Humbert should have won.

Head-scratcher number two: Why does Humbert always lose to Ruusuvuori? Wednesday’s decision marked their fifth meeting, and the Finn is undefeated. While the outcome is always close–Rotterdam was their fourth deciding set, and the other match went to two tiebreaks–the results are starting to get boring. Ruusuvuori is a solid player, and he is consistently able to blunt the Frenchman’s serve. But five in a row?

The answer to both mysteries is the same, and it’s more satisfying than I expected. Rotterdam is unusually slow for a hard court, especially indoors. Like most (or perhaps all) of the previous Humbert-Ruusuvuori venues, it plays slower than tour average. Just as important, Humbert’s game is unusually sensitive to surface speed. While that isn’t always true of big servers, he stands out as a fast-court specialist. We couldn’t have confidently predicted a Finnish upset, but we could have guessed that the Marseille champion would find this week’s tournament tougher going.

Rotterdam, it’s slow

The last time I published surface speed numbers, in late 2019, Rotterdam rated as the slowest indoor hard court on tour. Adjusting for the mix of players at the event, there were 10% fewer aces at the tournament than expected. It was a sharp decline from 2017 and 2018, when the venue sported more typically speedy indoor conditions.

Since then, the results have remained similar. Last year, the rate was 5% lower than expected, roughly tied with Stockholm as the slowest indoor surface on tour. Marseille, by contrast, gave players 12% more aces than usual.

There are limitations to using aces as a proxy for surface speed; I use aces because it’s the most relevant data that is widely available. Still, while you can quibble about the methodology or about a specific tournament’s place on the list, the overall rank order seems about right. Aces–adjusted for each event’s field–tell you much of the story.

With a growing mass of Match Charting Project data, we can do a little better. We have shot-by-shot logs for over one thousand matches since 2021. To compare conditions, I used my Serve Impact metric, which estimates how many points a player wins, directly or indirectly, because of his serve. It counts aces, other unreturned serves, and a fraction of the service points that take longer to decide. Depending on your motivation in measuring court speed, this isn’t perfect either: It doesn’t directly tell you anything about bounce height, for instance. But if you want to know what sort of players a tournament favors, Serve Impact gets you close.

By this more sophisticated metric, Rotterdam is… still slow. The venue takes away 4% of the points a player typically earns from his serve. Marseille and Montpellier each swing 7% in the other direction, Stockholm and Vienna provide a modest 3% boost, and Basel adds 8% to the server’s punch. With the exception of the short-lived tour stop in Gijon, Rotterdam has been the slowest indoor hard court of the 2020s. Even the clay in Lyon plays faster.

Here are the Serve Impact adjustments for the tournaments best represented in the dataset. Higher numbers mean faster conditions with more points decided based on the serve:

Tournament            ServeImpact  
Stuttgart                    1.29  
NextGen Finals               1.20  
Tour Finals                  1.16  
Wimbledon                    1.11  
Shanghai Masters             1.11  
Halle                        1.10  
Queen's Club                 1.08  
Basel                        1.08  
Washington                   1.08  
Dubai                        1.07  
                                   
Tournament            ServeImpact  
Antwerp                      1.05  
Gstaad                       1.05  
Australian Open              1.04  
Davis Cup Finals             1.04  
Cincinnati Masters           1.04  
Paris Masters                1.03  
Vienna                       1.03  
Miami Masters                1.02  
Madrid Masters               1.01  
US Open                      1.01  
                                   
Tournament            ServeImpact  
Canada Masters               1.00  
Rotterdam                    0.96  
Indian Wells Masters         0.95  
Rome Masters                 0.92  
Acapulco                     0.87  
Barcelona                    0.87  
Roland Garros                0.83  
Monte Carlo Masters          0.83

Average Serve Impact is around 34%, so the 4% hit in Rotterdam knocks that down to about 32.6%. Humbert has an above-average serve, so the slow-court penalty is greater still. He isn’t going to win any awards for rallying prowess, especially against someone as sturdy as Ruusuvuori, so the points that he doesn’t secure with his serve will disproportionately go against him.

The first three meetings in the Humbert-Ruusuvuori head-to-head were on clay, at Roland Garros, Madrid, and Rome. The fourth came on grass, at ‘s-Hertogenbosch. It rates a bit faster from 2021-23 than Halle or Queen’s Club by the Serve Impact metric, though it rated as the slowest grass court on tour last year by my older ace-rate algorithm. Maybe it was less server-friendly in 2023, just in time for Humbert to be flummoxed once again.

Surface sensitivity

We tend to take for granted that players are suited to conditions in predictable ways. Big servers like fast surfaces, right? Broadly speaking, yes, but it’s not a hard-and-fast rule. Bounce height makes a difference, footwork matters, and some players are just more comfortable on some surfaces than others.

Armed with surface speed ratings, this is something we can test. If a player is particularly sensitive to conditions, each tournament’s Serve Impact rating should have a predictable influence on his match outcomes. I tried that for all tour regulars, controlling for player strength by using overall Elo ratings at the time of each match.

The resulting numbers are an abstraction on top of an abstraction, so they’re a bit difficult to get your head around. I’ve tried to simplify matters by rendering them in terms of Elo points. A player who is very sensitive to surface and does better on hard courts is, effectively, a better player in faster conditions. The ‘Sensitivity’ numbers given here are the benefit–denominated in Elo points–of each single percentage point that a surface is faster than average. For players who like it slow, negative numbers express the same idea, the Elo-point advantage of a one-percentage-point slowdown.

Here is the list of all players with at least 100 tour-level matches since 2021, plus Rafael Nadal:

Player                       Sensitivity  
Tallon Griekspoor                   11.1  
Ugo Humbert                          9.5  
Richard Gasquet                      9.1  
Novak Djokovic                       8.7  
Adrian Mannarino                     7.9  
Sebastian Korda                      4.9  
Jordan Thompson                      4.3  
Matteo Berrettini                    4.0  
Aslan Karatsev                       3.6  
Tommy Paul                           3.4  
Marcos Giron                         2.9  
Marton Fucsovics                     2.9  
Marin Cilic                          2.6  
Felix Auger-Aliassime                2.1  
Hubert Hurkacz                       1.7  
                                          
Player                       Sensitivity  
Frances Tiafoe                       1.6  
Carlos Alcaraz                       1.4  
Emil Ruusuvuori                      1.3  
Brandon Nakashima                    1.3  
Cristian Garin                       0.6  
Alexander Zverev                     0.5  
Alexander Bublik                     0.5  
Ilya Ivashka                         0.0  
Arthur Rinderknech                  -0.1  
Taylor Fritz                        -0.3  
Jan Lennard Struff                  -0.3  
Lorenzo Sonego                      -0.4  
Mackenzie Mcdonald                  -0.5  
Andy Murray                         -0.9  
Grigor Dimitrov                     -1.1  
                                          
Player                       Sensitivity  
Roberto Bautista Agut               -1.2  
Alex de Minaur                      -1.2  
Karen Khachanov                     -1.4  
Jannik Sinner                       -1.4  
Yoshihito Nishioka                  -1.5  
Miomir Kecmanovic                   -1.9  
Andrey Rublev                       -2.2  
Daniel Evans                        -2.2  
Cameron Norrie                      -2.5  
Holger Rune                         -2.9  
Roberto Carballes Baena             -3.0  
Botic van de Zandschulp             -3.1  
Daniil Medvedev                     -3.4  
Denis Shapovalov                    -3.5  
Sebastian Baez                      -3.7  
                                          
Player                       Sensitivity  
Laslo Djere                         -4.1  
Dusan Lajovic                       -4.1  
Pablo Carreno Busta                 -4.4  
Jaume Munar                         -4.6  
Fabio Fognini                       -4.8  
Nikoloz Basilashvili                -4.9  
Casper Ruud                         -5.0  
Diego Schwartzman                   -5.4  
Francisco Cerundolo                 -5.9  
Alexei Popyrin                      -6.4  
Albert Ramos                        -6.8  
Rafael Nadal                        -9.9  
Alejandro Davidovich Fokina        -10.1  
Stefanos Tsitsipas                 -10.2  
Lorenzo Musetti                    -11.2

There’s Ugo! He’s not quite as surface sensitive as Tallon Griekspoor, but a couple of points is within the margin of error. A sensitivity rating of 9.5 means that Humbert is about 100 Elo points worse in Rotterdam than he is Marseille, as long as I’ve accurately estimated the server-friendliness of the respective playing conditions. Ruusuvuori may also like it faster, but only marginally so; he’s effectively neutral.

Keen-eyed readers may have noted that I earlier referred to “overall” Elo. I’m not using surface-specific Elo ratings here, because I don’t want to adjust for surface twice. Surface-specific ratings already capture some of this: Humbert’s hElo (for hard courts) is 120 points higher than his cElo (for clay courts), which tallies reasonably well with these more fine-grained distinctions. What hElo and cElo can’t tell us, though, is how much his (or anyone else’s) performance will vary on the same surface, depending on the conditions at each specific venue.

It’s easy to get lost in the weeds of Elo-based forecasting calculations, but it’s important to remember they are just tools to help measure a real-world phenomenon. Not every big server is equally at sea on clay; some dirtballers are less dependent on slow conditions than others. Small differences in surface speed are, for most matchups, a minor consideration. But for some players, conditions matter a lot. Ugo Humbert likes his surfaces fast, as much as almost anyone else on tour. In Rotterdam, the conditions did not cooperate.

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Surface Speed Convergence Revisited

Grass courts before the convergence

For more than a decade, players and pundits have complained that surface speeds are converging. To oversimplify their gripes: Everything is turning into clay. Hard courts have gotten slower, even many of the indoor ones. Grass courts, once a bastion of quick-fire attacking tennis, have slowed down as well.

I’ve attempted to confirm or refute the notion a couple of times. In 2013, I used break rate and ace rate to see whether hard and clay courts were getting closer to each other. The results said no. Many readers complained that I was using the wrong metrics: rally length is a better indicator. I agree, but rally length wasn’t widely available at the time.

In 2016, I looked at rally length for grand slam finals and found some evidence of surface speed convergence. The phenomenon was much clearer in men’s tennis than women’s, a hint that it wasn’t all about the surface, but that tactics had changed and that the mix of players in slam finals skewed the data.

Now, the Match Charting Project contains shot-by-shot logs of more than 12,000 matches. We can always dream of more and better data, but we’re well past the point where we can take a more detailed look at how rally length has changed over the years on different surfaces.

Forecasting rally length

Start with a simple model to forecast rally length for a single match. You don’t need much, just the average rally length for each player, plus the surface. Men who typically play short points have more influence on rally length than those who play long ones. (This is worthy of a blog post of its own–maybe another day.) Call the average rally length of the shorter-point guy X and the average rally length of the longer-point guy Y.

Using data from the last seven-plus seasons, you can predict the rally length of a hard court match as follows:

  • X + (0.7 * Y) – 2.6

The numbers change a bit depending on gender and time span, but the general idea is always the same. The short-point player usually has about half-again as much influence on rally length than his or her opponent.

For men since 2016, we can get the clay court rally length by adding 0.16 to the result above. For grass courts, subtract 0.45 instead.

For example, take a hypothetical matchup between Carlos Alcaraz and Alexander Bublik. In charted matches, Alcaraz’s average rally length is 4.0 and Bublik’s is 3.2. The formula above predicts the following number of shots per point:

  • Hard: 3.39
  • Clay: 3.55
  • Grass: 2.94

The error bars on the surface adjustments are fairly wide, for all sorts of reasons. Courts are not identical just because their surfaces are given the same names. Other factors, like balls, influence how a match goes on a given day. Players adapt differently to changing surfaces. The usual dose of randomness adds even more variance to rally-length numbers.

Changing coefficients

These surface adjustments aren’t very big. A difference of 0.16 shots per point is barely noticeable, unless you’re keeping score. Given the variation within each surface, it means that rallies would be longer on some hard courts than some clay courts, even for the same pair of players.

That brings us back to the issue of surface speed convergence. 0.16 shots per point is my best attempt at quantifying the difference between hard courts and clay courts now–or, more precisely, for men between 2016 and the present. If surfaces have indeed converged, we would find a more substantial gap in older data.

That’s exactly what we see. I ran the same analysis for three other time periods: 1959-95, 1996-2005, and 2006-2015. The following graph shows the rally-length gap between surfaces for each of the four spans:

For example, in the years up to 1995, a pair of players who averaged 4 shots per point on a hard court would be expected to last 5 shots per point (4 + 1) on clay. They’d tally just 3.25 shots per point (4 – 0.75) on grass.

By the years around the turn of the century, the gap between hard courts and grass courts had narrowed to its present level. But the difference between hard and clay continued to shrink. The current level of 0.16 additional shots per point is only about one-sixth as much as the equivalent in the 1980s and early 1990s.

The graph implies that hard courts are constant over time. That’s just an artifact of how I set up this analysis, and it may not be true. It could be that clay courts have been more consistent, something that my earlier analysis suggested and that many insiders seem to believe. In that case, rather than a downward-sloping clay line and an upward-sloping grass line, the graph would show two upward-sloping lines reflecting longer rallies on non-clay surfaces.

Women, too

The women’s game has evolved somewhat differently than the men’s has, but the trends are broadly similar. Here is the same graph for women’s rally lengths across surfaces:

For the last two decades, there has been essentially no difference in point length between hard courts and clay courts. A gap remains between hard and grass, though like in the men’s game, it is trending slightly downwards.

Why the convergence?

The obvious culprit here is the literal one: the surface. Depending on who you ask, tournament directors have chosen to slow down hard and grass surfaces because fans prefer longer rallies, because the monster servers of the turn of the century were boring, because slow surfaces favored the Big Four, or because they like seeing players puke on court after five hours of grueling tennis.

That’s probably part of it.

I would offer a complementary story. Racket technology and the related development of return skill essentially killed serve-and-volley tennis. Slower surfaces would have aided that process, but they weren’t necessary. In the 1980s, a top player like Ivan Lendl or Mats Wilander would use entirely different tactics depending on the surface, grinding on clay while serve-and-volleying indoors and on grass. Now, a Djokovic-Alcaraz match is roughly the same beast no matter the venue. If Alcaraz serve-and-volleyed on every point, Novak would have a far easier time competing on return points than the opponents of Lendl and Wilander ever did.

My best guess is that rally lengths have converged because of some combination of the two. I believe that conditions (surfaces, balls, etc) are the lesser of the two factors. But I don’t know how we could use the data we have to prove it either way.

In the end, it doesn’t particularly matter why. Much more than in my previous studies, we have enough rally-length data to see how players cope with different surfaces. The evidence is strong that, for whatever reason, hard-court tennis, clay-court tennis, and grass-court tennis are increasingly similar, a trend that began at least 25 to 30 years ago and shows no sign of reversing. Whether or not surfaces have converged, tactics have definitely done so.

* * *

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Are Conditions Slower? Faster? Weirder?

Many players didn’t like the conditions at Roland Garros this year. The clay, apparently, was slower and heavily watered, at least on some courts. The balls were heavier than usual, especially when they had been in play for a little while and the clay began to stick to them.

Maybe the courts really did play differently. We could compare ace rate, rally length, or a few other metrics to see whether the French played slower this year.

I’m interested in a broader question. Were the conditions weirder? To put it another way, were they outside the normal range of variation on tour? We could be talking about anything that impacts play, including surface, balls, weather, you name it.

This is surprisingly easy to test. The weirder the conditions, the more unpredictable the results should be. If you don’t get the connection, think about really strange conditions, like playing in mud, or in the dark, or with rackets that have broken strings. In those situations, the factors that determine the winner of a match are so different than usual that they will probably seem random. At the very least, there will be more upsets. Holding a top ranking in “normal” tennis doesn’t mean as much in “dark” tennis or “broken string” tennis. While unusually heavy balls don’t rank up there with my hypotheticals, the idea is the same: The more you deviate from typical conditions, the less predictable the results.

We measure predictability by taking the Brier score of my Elo-based pre-match forecasts. Elo isn’t perfect, but it’s pretty good, and the algorithm allows us to compare seasons and tournaments against each other. Brier score tells us the calibration of a group of predictions: Were they correct? Did they have the right level of confidence? The lower the score, the better the forecast. Or put another way, for our purposes today: The lower the score, the more predictable the outcomes.

Conclusion: This year’s French wasn’t that weird. Here are the Brier scores for men’s and women’s completed main draw matches, along with several other measures for context:

Tourney(s)     Men  Women  
2023 RG      0.177  0.193  
2022 RG      0.174  0.189  
2021 RG      0.177  0.194  
2020 RG      0.200  0.230  
2000-23 RG   0.169  0.184  
00-23 Slams  0.171  0.182  
Min RG       0.133  0.152  
Max RG       0.214  0.230

(“Min RG” and “Max RG” show the lowest and highest tournament Brier scores for each gender at the French since 2000.)

Again, lower = more predictable. For both men and women, the 2023 French was no more upset-ridden than the 2021 edition, and it ran considerably closer to script than the zany Covid tournament in autumn 2020. The results this year were a bit more unpredictable than the typical major since 2000. But the metrics tell us that the outcomes were closer to the average than to the extremes.

However unusual the conditions at Roland Garros felt to the players, the weirdness didn’t cause the results to be any more random than usual. While adjustments were surely necessary, most players were able to make them, and to similar degrees. The best players–based on their demonstrated clay-court prowess–tended to win, about as often as they always do at the French.

The Speed of Every Surface, 2019 Edition

Fans are constantly talking about surface speed … I have written a lot about surface speed … yet somehow, I haven’t published complete surface speed numbers for three years. Time to remedy that.

If you’re interested in the long-form explanation of how these numbers work, what their limitations are, and so on, check out my post from three years ago. I’ll give a brief overview here, as well:

I rate the playing speed of every ATP surface using ace rate as a proxy for surface characteristics. Ace rate doesn’t tell the whole story, of course, but as you’ll see, it’s a pretty good first- or second-order approximation. For each tournament, I look at the ace rates in every match, and control for the servers and returners in those matches. (The ace rate for every John Isner match will be high, but that doesn’t necessarily mean the surface is fast.) I say “playing speed” because ace rate depends on a wide range of variables (heat, humidity, balls, etc), so it reflects how the court “plays”–not anything inherent about the physical makeup of the surface itself.

The main advantages of this approach are that it is simple to understand (more aces = higher rating!), and that we can calculate it with limited information–data that is available for ATP matches back to the early 1990s. Court Pace Index and other Hawkeye-based metrics surely have a lot more to add, but they require much more sophisticated tools–tools that federations and tours aren’t about to share with lowly fans like us.

A tour-average surface rates 1.0. The usual range for tour events is between 0.50 (slow clay) and 1.50 (fast hard or grass). The following table shows the 2017-19 speed ratings for all tour events on the 2019 calendar, including the Davis Cup Finals:

Tournament        Surface  2019 Ace%  2019  2018  2017  
Chengdu              Hard      14.8%  1.57  1.05  1.16  
Antalya             Grass      14.6%  1.47  1.25  1.74  
Tour Finals          Hard      11.7%  1.31  1.12  0.75  
Marseille            Hard      11.7%  1.29  1.21  1.34  
Newport             Grass      12.7%  1.27  0.87  0.76  
Australian Open      Hard      12.9%  1.27  1.16  1.14  
Brisbane             Hard      13.3%  1.26  1.35  0.99  
Atlanta              Hard      14.3%  1.25  1.01  0.86  
Shanghai             Hard      13.0%  1.24  1.17  1.53  
Sao Paulo            Clay       9.8%  1.24  0.89  0.92  
Halle               Grass      12.8%  1.23  1.16  1.18  
Stuttgart           Grass      14.5%  1.23  1.42  1.27  
Sofia                Hard      11.1%  1.21  1.14  1.33  
Antwerp              Hard      11.2%  1.21  1.25  1.06  
Davis Cup Finals     Hard      11.9%  1.20              
Metz                 Hard      13.5%  1.20  1.51  1.34  
Paris Bercy          Hard      11.9%  1.19  1.06  1.03  
Montpellier          Hard      13.4%  1.17  1.13  1.11  
Vienna               Hard      11.4%  1.16  1.16  0.98  
New York             Hard      17.0%  1.16  1.05        
                                                        
Tournament        Surface  2019 Ace%  2019  2018  2017  
Winston Salem        Hard      12.1%  1.15  1.01  1.07  
Basel                Hard      14.2%  1.14  1.03  0.77  
Beijing              Hard      11.6%  1.12  1.03  0.91  
Washington           Hard      15.5%  1.11  0.99  1.11  
Moscow               Hard      13.5%  1.11  1.21  1.45  
Delray Beach         Hard      13.9%  1.10  0.98  0.97  
Doha                 Hard      10.0%  1.10  0.88  1.02  
St. Petersburg       Hard       8.4%  1.09  1.13  0.80  
Stockholm            Hard      11.2%  1.08  1.03  1.05  
Tokyo                Hard      11.6%  1.08  1.34  1.18  
London              Grass      12.8%  1.07  1.25  1.20  
Auckland             Hard      10.7%  1.06  1.17  1.11  
Pune                 Hard      14.8%  1.05  0.99        
Cincinnati           Hard      11.6%  1.04  0.98  1.22  
Canada               Hard      10.8%  1.03  1.17  0.97  
Dubai                Hard       8.4%  1.02  1.04  0.91  
Eastbourne          Grass      13.2%  0.99  0.94  1.00  
Wimbledon           Grass      10.5%  0.99  1.14  1.03  
Sydney               Hard       9.3%  0.98  1.25  1.10  
Zhuhai               Hard       6.9%  0.97              
                                                        
Tournament        Surface  2019 Ace%  2019  2018  2017  
Marrakech            Clay       8.4%  0.97  0.62  0.77  
US Open              Hard      10.2%  0.97  0.98  0.96  
s'Hertogenbosch     Grass      10.2%  0.95  0.99  0.89  
Cordoba              Clay       6.9%  0.94              
Rotterdam            Hard       8.0%  0.90  1.13  1.09  
Lyon                 Clay       9.9%  0.90  0.89  0.85  
Gstaad               Clay       5.6%  0.88  1.16  0.92  
Acapulco             Hard      11.1%  0.86  1.03  0.92  
Miami Masters        Hard       9.5%  0.86  0.78  0.84  
Los Cabos            Hard       6.5%  0.85  0.80  1.28  
Geneva               Clay       6.6%  0.81  1.04  0.85  
Bastad               Clay       7.1%  0.80  0.72  0.88  
Kitzbuhel            Clay       6.3%  0.77  0.84  1.02  
Hamburg              Clay       7.7%  0.76  0.69  1.02  
Indian Wells         Hard       7.6%  0.76  0.84  1.03  
Houston              Clay       9.2%  0.75  0.81  0.94  
Madrid               Clay       7.0%  0.71  0.84  0.89  
Roland Garros        Clay       7.0%  0.71  0.72  0.76  
Rome                 Clay       7.0%  0.69  0.69  0.85  
Munich               Clay       7.0%  0.67  0.74  0.99  
                                                        
Tournament        Surface  2019 Ace%  2019  2018  2017  
Umag                 Clay       5.6%  0.65  0.78  0.61  
Rio de Janeiro       Clay       5.9%  0.63  0.71  0.68  
Budapest             Clay       7.0%  0.62  0.62  0.59  
Barcelona            Clay       5.6%  0.59  0.57  0.55  
Estoril              Clay       4.7%  0.54  0.58  0.53  
Buenos Aires         Clay       3.9%  0.52  0.65  0.88  
Monte Carlo          Clay       4.7%  0.50  0.56  0.50

The Tour Finals played as fast as it has in years–the 2014-16 ratings were 0.89 and 1.06–suggesting either that the organizers finally laid down a proper hard court, or that 15 matches is an insufficient sample. (It certainly isn’t ideal, and the same can be said for 28- and 32-draw tourneys.)

The Davis Cup Finals played more like a typical indoor hard court. At the other extreme, Indian Wells was particularly slow this year, even by its own clay-like standards. At least at a few events, surface speed convergence may have slowed down.

Slow Conditions Might Just Flip the Outcome of Federer-Nadal XL

Italian translation at settesei.it

Roger Federer likes his courts fast. Rafael Nadal likes them slow. With eight Wimbledon titles to his name, Federer is the superior grass court player, but the conditions at the All England Club have been unusually slow this year, closer to those of a medium-speed hard court.

On Friday, Federer and Nadal will face off for the 40th time, their first encounter at Wimbledon since the Spaniard triumped in their historical 2008 title-match battle. Rafa leads the head-to-head 24-15, including a straight-set victory at his favorite slam, Roland Garros, several weeks ago. But before that, Roger had won five in a row–all on hard courts–the last three without dropping a set.

Because of the contrast in styles and surface preferences, the speed of the conditions–a catch-all term for surface, balls, weather, and so on–is particularly important. Nadal is 14-2 against his rival on clay, with Federer holding a 13-10 edge on hard and grass. Another way of splitting up the results is by my surface speed metric, Simple Speed Rating (SSR). 22 of the matches have been been on a court that is slower than tour average, with the other 17 at or above tour average speed:

Matches     Avg SSR  RN - RF  Unret%  <= 3 shots  Avg Rally  
SSR < 0.92     0.74     17-5   21.2%       49.5%        4.7  
SSR >= 1.0     1.14     7-10   27.0%       56.9%        4.3

At faster events–all of which are on hard or grass–fewer serves come back, more points end by the third shot, and the overall rally length is shorter. Fed has the edge, with 10 wins in 17 tries, while on slower surfaces–all of the clay matches, plus a handful of more stately hard courts–Rafa cleans up.

Rafa broke Elo

According to my surface-weighted Elo ratings, Federer is the big semi-final favorite. He leads Nadal by 300 points in the grass-only Elo ratings, which gives him a 75% chance of advancing to the final. The betting market strongly disagrees, believing that Rafa is the favorite, with a 57% chance of winning.

The collective wisdom of the punters is onto something. Elo has systematically underwhelmed when it comes to forecasting the 39 previous Fedal matches. Federer has more often been the higher-rated player, and if Roger and Rafa behaved like the algorithm expected them to, the Swiss would be narrowly leading the head-to-head, 21-18. We might reasonably conclude that, going into Friday’s semi-final, Elo is once again underestimating the King of Clay.

How big of Fedal-specific adjustment is necessary? I fit a logit model to the previous 39 matches, using only the surface-weighted Elo forecast. The model makes a rough adjustment to account for Elo’s limitations, and reduces Roger’s chances of winning the semi-final from 74.8% all the way down to 48.5%.

Now, about those conditions

The updated 48.5% forecast takes the surface into account–that’s part of my Elo algorithm. But it doesn’t distinguish between slow grass and fast grass.

To fix that, I added SSR, my surface speed metric, to the logit model. The model’s prediction accuracy improved from 64% to 72%, its Brier score dropped slightly (a lower Brier score indicates better forecasts), and the revised model gives us a way of making surface-speed-specific forecasts for this matchup. Here are the forecasts for Federer at several surface speed ratings, from tour average (1.0) to the fastest ratings seen on the circuit:

SSR  p(Fed Wins)  
1.0        49.3%  
1.1        51.4%  
1.2        53.4%  
1.3        55.5%  
1.4        57.5%  
1.5        59.5%

In the fifteen years since Rafa and Roger began their rivalry, the Wimbledon surface has averaged around 1.20, 20% quicker than tour average. In 2006, when they first met at SW19, it was 1.24, and in 2008, it was 1.15. Three times in the last decade it has topped 1.30, 30% faster than the average ATP surface. This year, it has dropped almost all the way to average, at 1.00, when both men’s and women’s results are taken into account.

As the table shows, such a dramatic difference in conditions has the potential to influence the outcome. On a faster surface, which we’ve seen as recently as 2014, Federer has the edge. At this year’s apparent level, the model narrowly favors Nadal. Rafa has said that the surface itself is unchanged, but that the balls have been heavier due to humidity. He should hope for another muggy day on Friday–the end result could depend on it.

The Grass Dies, But the Speed Lives On

Italian translation at settesei.it

Earlier this week, I trotted out some stats showing that the Wimbledon grass is playing slower this year, the latest tick in a years-long trend. Many fans suspect that by the second week, the conditions are even slower still, with huge brown spots around each baseline where the players have worn away the grass. Assuming that the dying-grass effect is similar each year, this is something we can test.

I ran my surface speed algorithm for several subsets of Wimbledon men’s singles matches: week 1, week 2, each round from 1 to 4, and the final 8. For a single year, the “week 2,” “round 4,” and “final 8” samples are too small to give us any reliable indicators. But over the course of two decades, the differences between weeks and rounds–the effect we’re interested in today–should become clear.

(Quick refresher on my surface speed method: It uses ace rate as a proxy for speed–not perfect, but functional, using a stat that is universally available–and takes into account the server and returner in each match. An average court speed is 1.0, and ratings typically range from about 0.5 for a venue like Monte Carlo to 1.5 for the fastest grass and indoor hard courts.)

For example, here are the week-by-week and round-by-round speed ratings for the 2018 Wimbledon men’s draw:

  • Week 1: 1.16
  • Week 2: 1.16
  • Round 1: 1.02
  • Round 2: 1.29
  • Round 3: 1.33
  • Round 4: 1.25
  • Last 8: 1.08

I promised noise, and there it is. Each week is equally speedy, but the first round and last few rounds are oddly slower than the rest. I don’t have a good explanation for the first round (and there might not be one–it could be random), but the last 8 often features fewer aces, even when adjusting for the players involved. We’ll come back to that in a bit.

Wimbledon, 2000-18

Here are the same numbers, averaged over the last 19 Wimbledons:

  • Week 1: 1.20
  • Week 2: 1.21
  • Round 1: 1.19
  • Round 2: 1.20
  • Round 3: 1.21
  • Round 4: 1.25
  • Last 8: 1.16

The sample of the last 8 still deviates from the rest, but with more data, the difference is much smaller. The gap between 1.20 and 1.16 is just an ace or two per match. That’s not enough to reverse the outcome of any but the very closest matches.

As usual, I must acknowledge that an ace-based metric isn’t definitive. There’s more to court speed than what aces can tell us. It’s possible that the surface behaves differently as the grass is worn away, even if it doesn’t show up in serve stats. Since net approaches are increasingly rare, the service-box grass lasts longer than the baseline grass, meaning that the speed at which serves move through the court would be relatively unchanged. On the other hand, the biggest brown spots on court are behind the baseline, so most groundstrokes also bounce on green grass, not on brown dirt.

The best versus the best

Even the small difference between the last 8 and the rest of the tournament may not have anything to do with the decaying of the surface. Since 2000, the US Open has exhibited the same trend: 1.07 for week 1, 1.06 for round 4, and 0.97 for the final 8. (The Australian Open numbers are much noisier than the other slams, perhaps due to frequent use of the roof, so I’m hesitant to use them.)

It seems safe to assume that the hard courts in Flushing don’t suddenly get slower starting on Tuesday or Wednesday of the second week. Instead, I think the answer is in the mix of players–or more precisely, how those players interact with each other. By this ace-based metric, the Tour Finals have often been rated as one of the slowest indoor hard court events–even though the official Court Pace Index (CPI) ratings disagree.

In other words, aces tend to go down when the best play the best. Maybe the elites serve more tactically when facing tough opponents? Perhaps they focus more consistently on return, rarely allowing cheap aces? Maybe the best players know each other’s games so well that they anticipate even better than usual? This seems like an interesting line of research, even if it’s not something I’m going to resolve today.

The bottom line is that partly-brown Wimbledon courts play just about as fast as totally-green Wimbledon courts do. There might be a very minor slowdown toward the end of the fortnight, but even there, we should remain skeptical. The conditions are slow this year, but at least they won’t get much slower.

Yep, Wimbledon is Playing Slower This Year

Italian translation at settesei.it

The players are right. Wimbledon’s surface–or balls, or atmosphere, or aura–has slowed down in comparison with recent years. We’ve heard comments to that effect from Roger Federer, Milos Raonic, Boris Becker, Rafael Nadal, and many others. Raonic attributes the change to the grass, and Nadal to the balls. Regardless of the reason, the numbers back up their perceptions.

Here is an overview of several surface-speed indicators for the first three rounds of singles matches at Wimbledon, 2017-19:

                     2017   2018   2019  
Aces (Men)           8.9%  10.0%   8.5%  
Aces (Women)         4.1%   4.2%   4.1%  
                                         
Unret (Men)         36.0%  36.6%  33.3%  
Unret (Women)       25.9%  27.6%  25.2%  
                                         
<= 3 Shots (Men)    65.2%  65.6%  61.9%  
<= 3 Shots (Women)  55.3%  57.9%  55.0%  
                                         
Avg Rally (Men)       3.4    3.5    3.7  
Avg Rally (Women)     4.0    3.8    4.1

The second set of rows, "Unret," is the percent of unreturned serves. The next set, "<=3 Shots," is the percent of points that ended in three shots or less. For all four of the stats shown, including aces and average rally length, men's numbers point to slower conditions. The women's numbers are less clear, but to the extent that they point in either direction, they concur.

Not just 2019

Aggregate numbers such as these usually give us an idea of what's going on. But we can do better. The numbers above do not control for the mix of players or the length of their matches. For instance, 2019's rates would be different if John Isner, instead of Mikhail Kukushkin, had played a third-round match. The surface speed might have affected that result, but if we're going to compare ace rate from one year to the next, we shouldn't compare Isner's ace rate with Kukushkin's ace rate.

That's where my surface speed metric comes in. For each tournament, I control for the mix of servers and returners (yes, returners affect ace rate, too) to boil down each event to one number, representing how the tournament's ace rate compares to tour average. While there's more to surface speed than ace rate, aces are a good proxy for many of those other indicators, and more importantly, aces are one of the few stats that are available for every match.

The resulting score usually ranges between 0.5--50% fewer aces than average, usually on a slow clay court like Monte Carlo--and 1.5--50% more aces than average, on a fast grass or indoor hard court, like Antalya or Metz. Over the last decade, Wimbledon's conditions have drifted from the high end of that range to the middle:

Year      Men    Women  Average  
2011     1.26     1.37     1.31  
2012     1.27     1.06     1.17  
2013     1.29     1.04     1.17  
2014     1.35     1.19     1.27  
2015     1.20     1.16     1.18  
2016     1.06     1.03     1.04  
2017     1.03     1.07     1.05  
2018     1.14     0.98     1.06  
2019     1.04     0.96     1.00

The men's numbers are usually more reliable measurements, because they are based on many more aces, which means that the ace rate for any given match is less fluky. Ideally, we'd see the men's and women's speed ratings move in lockstep, but there is some noise in the calculation, and the ratings are also relative to that year's tour average, which depends in turn on the changing speeds of dozens of other surfaces.

Caveats aside, the direction of the trend is clear. There isn't a substantial difference between 2019 and the last few years, but the gap between the first and second half of the decade is dramatic.

What is less clear--and will require considerable further research--is how much it matters. In 2014, Nick Kyrgios upset Nadal in four sets, while last week, the result was reversed. How much of that can we attribute to the surface? Would faster conditions have allowed Isner to outlast Kukushkin? Kevin Anderson to hold off Guido Pella? Jelena Ostapenko to withstand Su Wei Hsieh?

For now, those questions remain in the speculation-only file. Now that we can conclude that the grass really has gotten slower, we can focus that speculation on the fates of several grass court savants, including Federer, Raonic, and Karolina Pliskova. By the end of the fortnight, they--like Kyrgios--might be wishing it was 2014 again.

The Happy Slam is the Speedy Slam

Italian translation at settesei.it

Two years ago, during the 2017 Australian Open, I offered a partial explanation of the many upsets at that year’s first major. Novak Djokovic, Andy Murray, Angelique Kerber, Simona Halep and many others had been ousted before the quarter-finals, all to players with a more aggressive, attacking style. It turned out that the courts that year were playing particularly fast–quicker than any of the other slams, including Wimbledon, as well as most hard-court tour stops.

In Melbourne this year, the courts are playing even faster.

Through three rounds of play, almost 90% of the tournament’s singles matches are in the books. Based on my surface-speed metric, which measures how many aces are struck at each tournament while controlling for the mix of servers and returners, the 2019 Australian Open can boast the quickest surface at the event since at least 2011*, and the second-fastest conditions of any major in that time span.

* Match stats, even simple ones such as service points and aces, are increasingly tough to come by for the women’s game before 2011.

The average of my surface-speed ratings for the men’s and women’s events at 2019’s first major is 1.28, meaning that there have been 28% more aces than expected, given the mix of servers and returners across the matches played so far. The notably fast 2017 event was 1.23, the fastest US Open of the last eight years was 1.14 (in 2015), and last year’s Wimbledon, played on the surface that is supposed to be fastest of all, was a mere 1.06.

Here are the top ten fastest slam surfaces from 2011 to the present:

Speed Rating Tournament      
1.31     2011 Wimbledon    
1.28     2019 Australian Open* 
1.27     2014 Wimbledon    
1.27     2016 Australian Open 
1.23     2017 Australian Open 
1.20     2015 Australian Open 
1.18     2015 Wimbledon    
1.17     2013 Wimbledon    
1.17     2012 Wimbledon    
1.15     2014 Australian Open

* through first three rounds

Last year’s Aussie Open was a bit of an outlier, but even still, it barely missed this list, coming in 12th at 1.12.

At least most players arrived prepared. The warm-up events in Brisbane and Auckland ranked among the fastest conditions since the beginning of last season: Brisbane rates at 1.29 while Auckland came in at a blink-and-you’ll-miss-it 1.35. Last year, only four events per tour were faster.

In theory, such a speedy surface should work to the advantage of big servers with aggressive games. At least so far, it hasn’t worked out that way. Unlike in 2017, Djokovic, Halep, and Kerber are still in the running, while Kevin Anderson was an early casualty. On the other hand, the court speed does jibe with some results, like Maria Sharapova’s third-round upset of defending champion Caroline Wozniacki.

If the conditions are to impact the result of the tournament, it will have to happen in matches yet to come. A slick surface tends to favor Roger Federer, even if Djokovic remains the popular pick to hoist the trophy next Sunday. More immediately, a fast surface doesn’t bode well for Halep’s chances in her fourth-round match against Serena Williams. Facing Serena is difficult enough without the conditions working against you, too.