Jeff, Author at Heavy Topspin

Dominic Thiem and Reversible Blowouts

A few weeks ago in Rome, Dominic Thiem got destroyed by Novak Djokovic, 6-1 6-0. It was a letdown after Thiem’s previous-round upset of Rafael Nadal, and it seemed to provide a reminder of the old adage that tennis is about matchups. Even someone good enough to beat the King of Clay might struggle against a different sort of opponent.

Those struggles didn’t last. On Wednesday, Thiem faced Djokovic again, this time in the French Open quarterfinals, and won in straight sets. In less than three weeks, the Austrian bounced back from a brutal loss to defeat one of the greatest players of all time.

I’ve written before about the limited value of head-to-head records: When the head-to-head suggests that one player will win but the rankings disagree, the rankings prove to be the better forecaster. More sophisticated rating systems such as Elo would presumably do better still, though I haven’t done that exact test. There are certainly individual cases in which something specific about a matchup casts doubt on the predictiveness of the rankings, but if you have to pick one or the other, head-to-heads are the loser.

What about blowouts? Going into Wednesday’s quarterfinal, my surface-specific Elo ratings suggested that Thiem had a 26% chance of scoring the upset. The recent 6-1 6-0 loss was factored into those numbers, but only as a loss–there’s no consideration of severity. Should we have been even more skeptical of Thiem’s chances, given the most recent head-to-head result?

As it turns out, Thiem is far from the first player to turn things around after such a nasty scoreline. The most famous example is Robin Soderling, who lost 6-1 6-0 to Nadal in Rome in 2009, then bounced back to register one of the biggest upsets in tennis history, knocking out Rafa at Roland Garros. Few recoveries are so dramatic, but there are hundreds more.

Most players who lose lopsided scorelines–for today’s purposes, I’m considering any match in which the loser won two games or fewer–never get a chance to redeem themselves. I found roughly 2250 such matches in the ATP’s modern era, and the same two players met again less than half of those times. The fact that the head-to-head continues is a signal itself: Mediocre players–the ones you’d expect to lose badly–don’t get another chance. Even some top-20 players rarely meet each other on court, so the sort of player who earns the chance for redemption might have already proven that his lopsided loss was just an off day.

Of the 951 occasions that a player loses badly and faces the same opponent again, he gets revenge and wins the next match 277 times–about 29%. Crazy as it sounds, if the only thing we knew about Djokovic and Thiem entering Wednesday’s match was that Djokovic had won the last match 6-1 6-0, our base forecast would’ve been pretty close to the 26% that the much-more sophisticated Elo algorithm offered us.

29% is much higher than I expected, but it is lower than the typical rate for players in this situation. I found all head-to-heads of at least two meetings, and for every match after the first, counted whether it maintained or reversed the previous result. In addition to isolating lopsided scores, I also considered matches in which the loser won a set, on the assumption that those might be tighter matchups. Finally, for each of those categories, I tracked whether the follow-up matches were on the same surface as the previous one. Here are the results, with all win percentages shown from the perspective of the player who, like Thiem, lost the first encounter:

Score     Next Surface  Matches   Wins  Win %  
Any loss  All             68128  26586  39.0%  
Any loss  Same            31084  11855  38.1%  
Any loss  Diff            37044  14731  39.8%  
Bad loss  All               951    277  29.1%  
Bad loss  Same              457    128  28.0%  
Bad loss  Diff              494    149  30.2%  
Won set   All             26075  11286  43.3%  
Won set   Same            11766   4974  42.3%  
Won set   Diff            14309   6312  44.1%

The chances of recovering from a bad loss are better than I thought, but they are considerably worse than the odds that a player reverses the result after a less conspicuous scoreline–39%. The table also shows that the player seeking revenge is more likely to get it if the opportunity arises on a different surface, though not by a wide margin.

It’s clear that players are less likely to recover from a bad loss than from a more typical one, but how much of that is selection bias? After all, most of the players who lose 6-1 6-0 aren’t of the caliber of Thiem or Soderling, even if they are good enough to stick around in main draws and ultimately face the same opponent again.

To answer that question, I looked again at those 950 post-blowout matches, this time with pre-match Elo ratings. After eliminating everything before 1980 and a few other matchups with very little data, we were left with just under 600 data points. In this subset, Elo predicted that the players who lost badly had a 33.6% chance of winning the follow-up match. As we’ve seen, the actual success rate was 29%. Players who won lopsided matches outperformed their Elo forecast in the next meeting.

It’s not a huge difference, but enough to suggest that the matchup tells a little bit about how the next contest will go. One match can make a difference in the forecast–as long as it isn’t against Dominic Thiem.

—

Digging into the cases when a player lost badly and then recovered, I found a couple of entertaining examples:

Former No. 7 Harold Solomon beat Ivan Lendl in their first meeting, 6-1 6-1. Later that year, they met again at the US Open, and Lendl won, 6-1 6-0 6-0. Lendl also won their six matches after that.
Over the course of four years, Phil Dent and Mark Cox played three lopsided matches against each other. Cox won the first, Dent got revenge in the second, and Cox reversed things again in the third.

Simona Halep and Recoveries From Match Point Down

Italian translation at settesei.it

In yesterday’s French Open quarterfinals, Elina Svitolina held a commanding lead over Simona Halep, up a set and 5-1. Depending on what numbers you plug into the formula, Svitolina’s chance of winning the match at that stage was somewhere between 97% and 99%. Halep fought back to 5-5, and in the second-set tiebreak, Svitolina earned a match point at 6-5. Halep recovered again, won the breaker, and then cruised to a 6-0 victory in the third set.

It’s easy to fit a narrative to that sequence of events: After losing two leads, Svitolina was dispirited, and Halep was all but guaranteed a third-set victory. Maybe. It’s impossible to test that sort of thing on the evidence of a single match, but this is hardly the first time a player has failed to convert match point and needed to start fresh in a new set.

Even without a match point saved, the player who wins the second set has a small advantage going into the decider. In the last six-plus years of women’s Slam matches, the player who won the second set went on to win 51.3% of third sets. On the other hand, if the second set was a tiebreak, the winner of the second set won the decider only 43.7% of the time. Though it sounds contradictory at first, consider what we know about such sets. The second-set winner just barely claimed her set (in the tiebreak), while usually, her opponent took the first set more decisively. Momentum helps a little, but it can’t overcome much of a difference in skill level.

Let’s dig into the specific cases of second-set match points saved. Thanks to the data behind IBM’s Pointstream on Grand Slam websites, we have the point-by-point sequence for most Slam singles matches going back to 2011. (The missing matches are usually those on non-Hawkeye courts and a few small courts at Roland Garros.) That’s over 2,600 women’s singles matches. In just over 1,700 of them, one of the two players earned a match point in the second set. Over 97% of the time, that player converted–needing an average of 1.7 match points to do so–and avoiding playing a third set.

That leaves 45 matches in which one player held a match point in the second set, failed to finish the job, and was forced to play a third set. It’s a limited sample, and it doesn’t wholeheartedly support the third-set-collapse narrative suggested above. 60% of the time–27 of the 45 matches–the player who failed to convert match point in the second set, like Svitolina did, went on to lose the third set. The third set was often lopsided: 5 of the 27 were bagels (including yesterday’s match), and the average score was 6-2. None of the third sets went beyond 6-4.

The other 18 matches–the 40% of the time in which the player with the second-set match point bounced back to win the third set–featured rather one-way deciders, as well. In those, the third-set loser managed an average of only 2.3 games, also never doing better than 6-4.

This is a small sample, so it’s unwise to conclude that this 60/40 margin is anything close to an iron law of tennis. That said, it does provide some evidence that players don’t necessarily collapse after failing to convert a straight-sets win at match point. What happened to Svitolina yesterday is far from certain to happen next time.

Jelena Ostapenko and Teenage Slam Breakthroughs

Italian translation at settesei.it

Jelena Ostepenko is looking ahead to a big day on Thursday: She’ll celebrate her 20th birthday by playing her first Grand Slam semifinal.

A generation or two ago, a breakthrough accomplishment at age 20 would barely merit acknowledgement. In the late 1990s, women’s tennis was dominated by teens a recent teens: Serena Williams and Martina Hingis both won majors before their 20th birthday, and Venus Williams won her first Slam only a few days into her third decade. That youth brigade wasn’t just a couple of once-in-a-generation talents, either: 19-year-old Iva Majoli won a major, and Mirjana Lucic, Jelena Dokic, and Anna Kournikova all reached semifinals before their 18th birthdays.

Times have changed. The last teenage Slam champion was Maria Sharapova in 2006, and we haven’t had a teenager in a major final since Caroline Wozniacki in 2009. Since then, only four players–Ostapenko, Sloane Stephens, Eugenie Bouchard, and Madison Keys–have reached Grand Slam semifinals before their 20th birthdays. (To simplify matters, I’m defining tournament age as age at the beginning of the event, so Ostapenko is a 19-year-old for the purpose of this discussion.)

By just about any measure you can dream up, the sport is getting older. In 1990, the average age of the women in the French Open main draw was 21.8 years. In 2000, it was 23.5. This year, the average age at the start of the tournament was 25.6, just a tiny bit short of last year’s record–set at Roland Garros and Wimbledon–of 25.7. Veterans are sticking around longer, and it takes longer for young players to develop tour-ready games.

Accordingly, we need to revise our notion of what constitutes a big breakthrough. 20 years ago, the semifinal debut of a 19-year-old was a nice achievement for the player herself, but nothing earth-shaking. Today, it’s a once-in-two-years event, and immediately puts the debutante in elite company. While Stephens and Bouchard have stumbled since their own breakthroughs, they (along with Keys) are still among the most promising young players in the game.

To quantify Ostapenko’s achievement, let’s consider her age relative to the average of all main draw players–just the raw difference between those two numbers. Ostapenko is 5.68 years younger than the average woman at Roland Garros this year, making her the 7th youngest (relative to the field) semifinalist at a major since 2000:

Slam     Youngest SF         Age  Avg Age  Diff  
2004 W   Maria Sharapova   17.17    24.17  7.00  
2006 FO  Nicole Vaidisova  17.10    23.63  6.53  
2000 W   Jelena Dokic      17.21    23.69  6.48  
2005 W   Maria Sharapova   18.17    24.45  6.28  
2005 AO  Maria Sharapova   17.75    23.99  6.24  
2007 AO  Nicole Vaidisova  17.73    23.48  5.75  
2017 FO  Jelena Ostapenko  19.97    25.65  5.68  
2001 FO  Kim Clijsters     17.97    23.62  5.65  
2005 US  Maria Sharapova   18.36    23.78  5.42  
2015 AO  Madison Keys      19.92    25.33  5.41

Only three players–Sharapova, Dokic, and Nicole Vaidisova–have reached a Slam semifinal this century at such a young age compared to the rest of the draw.

Of course, names like Dokic and Vaidisova aren’t the most encouraging comparisons for an emerging star. Both players peaked in the top ten, but neither ever reached a major final. The WTA’s past is littered with teenage rising stars who ultimately fizzled.

Yet if we are to see one historically great player come from among today’s young players, she should start building her trophy collection now. It’s tough to put together a Hall of Fame-caliber career without winning some big titles by one’s early 20s. Madison Keys has put herself in that conversation, and this week, Ostapenko has done so as well.

Smaller Swings In Big Moments

Italian translation at settesei.it

Despite the name, unforced errors aren’t necessarily bad. Sometimes, the right tactic is to play more aggressively, and in order to hit more winners, most players will commit more errors as well. Against some opponents, increasing the unforced error count–as long as there is a parallel improvement in winners or other positive point-ending shots–might be the only way to win.

Last week, I showed that one of the causes of Angelique Kerber’s first-round loss was her disproportionate number of errors in big moments. But as my podcasting partner Carl Bialik pointed out, that isn’t the whole story. If Kerber played more aggressively on the most important points–one possible cause of more errors–it might be the case that her winner rate was higher, as well. Since the 6-2 6-2 scoreline was so heavily tilted against her, it was a safe bet that Kerber recorded more high-leverage errors than winners. Still, Carl makes a valid point, and one worth testing.

To do so, let’s revisit the data: 500 women’s singles matches from the last four majors and the first four rounds of this year’s French Open. By measuring the importance of each point, we can determine the average leverage (LEV) of every point in each match, along with the average leverage of points which ended with a player hitting an unforced error, or a winner. Last week, we found that Kerber’s UEs in her first-round loss had an average LEV of 5.5%, compared to a LEV of 3.8% on all other points. For today’s purposes, let’s use match averages as a reference point: Her average UE LEV of 5.5% also compares unfavorably to the overall match average LEV of 4.1%.

What about winners? Kerber’s 15 winners came on points with an average LEV of 3.9%, below the match average. Case closed: On more important points, Kerber was more likely to commit an error, and less likely to hit a winner.

Across the whole population, players hit more errors and fewer winners in crucial moments, but only slightly. Points ending in errors are about one percent more important than average (percent, not percentage point, so 4.14% instead of 4.1%), and points ending in winners are about two percent less important than average. In bigger moments, players increase their winner rate about 39% of the time, and they improve their W-UE ratio about 45% of the time. Point being, there are tour-wide effects on more important points, but they are quite small.

Of course, Kerber’s first-round upset isn’t indicative of how she has played at Slams in general. In my article last week, I mentioned the four players who did the best job of reducing errors at big moments: Kerber, Agnieszka Radwanska, Timea Bacsinszky, and Kiki Bertens. Kerber and Radwanska both hit fewer winners on big points as well, but Bacsinszky and Bertens manage a perfect combination, hitting slightly more winners as the pressure cranks up. Among players with more than 10 Slam matches since last year’s French, Bacsinszky is the only one to hit winners on more important points than her unforced errors over 75% of the time.

Compared to her peers, Kerber’s big-moment tactics are remarkably passive. The following table shows the 21 women for whom I have data on at least 13 matches. “UE Rt.” (“UE Ratio”) is similar to the metric I used last week, comparing the average importance of points ending in errors to average points; “W Ratio” is the same, but for points ending in winners, and “W+UE Ratio” is–you guessed it–a (weighted) combination of the two. The combined measure serves as an rough approximation of aggression on big points, where ratios below 1 are more passive than the player’s typical tactics and ratios above 1 are more aggressive.

Player                     M  UE Rt.  W Rt.  W+UE Rt.  
Angelique Kerber          20    0.92   0.85      0.88  
Alize Cornet              13    0.92   0.87      0.94  
Agnieszka Radwanska       17    0.91   0.95      0.95  
Simona Halep              19    0.93   0.94      0.95  
Samantha Stosur           13    0.95   0.98      0.96  
Timea Bacsinszky          14    0.89   1.02      0.97  
Elina Svitolina           15    1.02   0.95      0.97  
Karolina Pliskova         18    0.97   0.98      0.97  
Caroline Wozniacki        14    0.93   1.00      0.97  
Johanna Konta             13    1.00   0.97      0.98  
Caroline Garcia           14    0.94   1.02      0.98  
Svetlana Kuznetsova       17    0.96   0.98      0.99  
Garbine Muguruza          20    1.02   0.94      0.99  
Venus Williams            25    1.00   0.97      0.99  
Elena Vesnina             13    0.96   1.03      0.99  
Anastasia Pavlyuchenkova  15    1.03   0.99      0.99  
Coco Vandeweghe           13    1.08   0.95      1.01  
Madison Keys              13    1.01   1.02      1.01  
Serena Williams           27    0.99   1.05      1.02  
Carla Suarez Navarro      14    1.00   1.14      1.05  
Dominika Cibulkova        14    1.11   1.03      1.07

Kerber’s combined measure stands out from the pack. Her point-ending shots–both winners and errors, but especially winners–occur disproportionately on less important points, and the overall effect is double that of the next most passive big-moment player, Alize Cornet. Every other player is close enough to neutral that I would hesitate before making any conclusions about their pressure-point tactics.

Even when Kerber wins, she does so with effective defense at key points. In only two of her last 20 matches at majors did her winners occur on particularly important points. (Incidentally, one of those two was last year’s US Open final.) In general, her brand of passivity works–she won 16 of those matches. But defensive play doesn’t leave very much room for error–figuratively or literally. The tactics were familiar and proven, but against Makarova, they were poorly executed.

Podcast Episode 10: On and Off Script at Roland Garros

In the Episode 10 of the Tennis Abstract Podcast, Carl Bialik and I check in on the French Open at the halfway point. Some of the topics we touched on:

what Pablo Carreno Busta will need to do to knock off Rafael Nadal
whether the rest of the top four seeds can round into form
Carlos Ramos’s audacity in calling time violations on Djokovic and Nadal
Simona Halep’s status as favorite
surprise quarterfinalist Caroline Wozniacki
the upset-ridden men’s doubles draw, and some clay-specialist teams to watch

Enjoy!

Click to listen, subscribe on iTunes, find us on Stitcher, or use our feed to get updates on your favorite podcast software.

The Negative Impact of Time of Court

Italian translation at settesei.it

With 96 men’s matches in the books so far at Roland Garros this year, we’ve seen only one go to the absolute limit, past 6-6 in the fifth set. Still, we’ve had our share of lengthy, brutal five-set fights, including three matches in the first round that exceeded the four-hour mark. The three winners of those battles–Victor Estrella, David Ferrer, and Rogerio Dutra Silva–all fell to their second-round opponent.

A few years ago, I identified a “hangover effect” after Grand Slam marathons, defined as those matches that reach 6-6 in the fifth. Players who emerge victorious from such lengthy struggles would often already be considered underdogs in their next matches–after all, elite players rarely need to work so hard to advance–but marathon winners underperform even when we take their underdog status into account. (Earlier this week, I showed that women suffer little or no hangover effect after marathon third sets.)

A number of readers suggested I take a broader look at the effect of match length. After all, there are plenty of slugfests that fall just short of the marathon threshold, and some of those, like Ferrer’s loss yesterday to Feliciano Lopez, 6-4 in the final set, are more physically testing than some of those that reach 6-6. Match time still isn’t a perfect metric for potential fatigue–a four-hour match against Ferrer is qualitatively different from four hours on court with Ivo Karlovic–but it’s the best proxy we have for a very large sample of matches.

What happens next?

I took over 7,200 completed men’s singles matches from Grand Slams back to 2001 and separated them into groups by match time: one hour to 1:29, 1:30 to 2:00, and so on, up to a final category of 4:30 and above. Then I looked at how the winners of all those matches fared against their next opponents:

Prev Length   Matches  Wins  Win %  
1:00 to 1:29      448   275  61.4%  
1:30 to 1:59     1918  1107  57.7%  
2:00 to 2:29     1734   875  50.5%  
2:30 to 2:59     1384   632  45.7%  
3:00 to 3:29      976   430  44.1%  
3:30 to 3:59      539   232  43.0%  
4:00 to 4:29      188    64  34.0%  
4:30 and up        72    23  31.9%

The trend couldn’t be any clearer. If the only thing you know about a Slam matchup is how long the players spent on court in their previous match, you’d bet on the guy who recorded his last win in the shortest amount of time.

Of course, we know a lot more about the players than that. Andy Murray spent 3:34 on court yesterday, but even with his clay-court struggles this year, we would favor him in the third round against most of the men in the draw. As I’ve done in previous studies, let’s account for overall player skill by estimating the probability of each player winning each of these 7,200+ matches. Here are the same match-length categories, with “expected wins” (based on surface-specific Elo, or sElo) shown as well:

Prev Length   Wins  Exp Wins  Exp Win %  Ratio  
1:00 to 1:29   275       258      57.5%   1.07  
1:30 to 1:59  1107      1058      55.2%   1.05  
2:00 to 2:29   875       881      50.8%   0.99  
2:30 to 2:59   632       657      47.5%   0.96  
3:00 to 3:29   430       445      45.6%   0.97  
3:30 to 3:59   232       244      45.3%   0.95  
4:00 to 4:29    64        77      41.2%   0.83  
4:30 and up     23        30      42.1%   0.76

Again, there’s not much ambiguity in the trend here. Better players spend less time on court, so if you know someone beat their previous opponent in 1:14, you can infer that he’s a very good player. Often that assumption is wrong, but in the aggregate, it holds up.

The “Ratio” column shows the relationship between actual winning percentage (from the first table) and expected winning percentage. If previous match time had no effect, we’d expect to see ratios randomly hovering around 1. Instead, we see a steady decline from 1.07 at the top–meaning that players coming off of short matches win 7% more often than their skill level would otherwise lead us to forecast–to 0.76 at the bottom, indicating that competitors tend to underperform following a battle of 4:30 or longer.

It’s difficult to know whether we’re seeing a direct effect of time of court or a proxy for form. As good as surface-specific Elo ratings are, they don’t capture everything that could possibly predict the outcome of a match, especially micro-level considerations like a player’s comfort on a specific type of surface or at a certain tournament. sElo also needs a little time to catch up with players making fast improvements, particularly when they are very young. All this is to say that our correction for overall skill level will never be perfect.

Thus, a 75-minute win may improve a player’s chances by keeping him fresh for the next round … or it might tell us that–for whatever reason–he’s a stronger competitor right now than our model gives him credit for. One point in favor of the latter is that, at the most extreme, less time on court doesn’t help: Players don’t appear to benefit from advancing via walkover. That isn’t a slam-dunk argument–some commentators believe that walkovers could be detrimental due to the long resulting layoff at a Slam–but it does show us that less time on court isn’t always a positive.

Whatever the underlying cause, we can tweak our projections accordingly. Murray could be a little weaker than usual tomorrow after his length battle yesterday with Martin Klizan. Albert Ramos, the only man to complete a second-rounder in less than 90 minutes, might be playing a bit better than his rating suggest. It’s certainly evident that match time has something to tell us even when players aren’t stretched to the breaking point of a marathon fifth set.

Angelique Kerber’s Unclutch Unforced Errors

Italian translation at settesei.it

It’s been a rough year for Angelique Kerber. Despite her No. 1 WTA ranking and place at the top of the French Open draw, she lost her opening match on Sunday against the unseeded Ekaterina Makarova. Adding insult to injury, the loss goes down in the record books as a lopsided-looking 6-2 6-2.

Andrea Petkovic chimed in with her diagnosis of Kerber’s woes:

She’s simply playing without confidence right now. It was tight, even though the scoreline was 2 and 2 but everyone who knows a thing about tennis knew that Angie made errors whenever it mattered because she’s playing without any confidence right now – errors she didn’t make last year.

This is one version of a common analysis: A player lost because she crumbled on the big points. While that probably doesn’t cover all of Kerber’s issues on Sunday–Makarova won 72 points to her 55–it is true that big points have a disproportionate effect on the end result. For every player who squanders a dozen break points yet still wins the match, there are others who falter at crucial moments and ultimately lose.

This family of theories–that a player over- or under-performed at big moments–is testable. For instance, I showed last summer that Roger Federer’s Wimbledon loss to Milos Raonic was due in part to his weaker performance on more important points. We can do the same with Kerber’s early exit.

Here’s how it works. Once we calculate each player’s probability of winning the match before each point, we can assign each point a measure of importance–I prefer to call it leverage, or LEV–that quantifies how much the single point could effect the outcome of the match. At 3-0, 40-0, it’s almost zero. At 3-3, 40-AD in the deciding set, it might be over 10%. Across an entire tournament’s worth of matches, the average LEV is around 5% to 6%.

If Petko is right, we’ll find that the average LEV of Kerber’s unforced errors was higher than on other points. (I’ve excluded points that ended with the serve, since neither player had a chance to commit an unforced error.) Sure enough, Kerber’s 13 groundstroke UEs (that is, excluding double faults) had an average LEV of 5.5%, compared to 3.8% on points that ended some other way. Her UE points were 45% more important than non-UE points.

Let’s put that number in perspective. Among the 86 women for whom I have point-by-point UE data for their first-round matches this week*, ten timed their errors even worse than Kerber did. Magdalena Rybarikova was the most extreme: Her eight UEs against Coco Vandeweghe were more than twice as important, on average, as the rest of the points in that match. Seven of the ten women with bad timing lost their matches, and two others–Agnieszka Radwanska and Marketa Vondrousova–committed so few errors (3 and 4, respectively), that it didn’t really matter. Only Dominika Cibulkova, whose 15 errors were about as badly timed as Kerber’s, suffered from unclutch UEs yet managed to advance.

* This data comes from the Roland Garros website. I aggregate it after each major and make it available here.

Another important reference point: Unforced errors are evenly distributed across all leverage levels. Our instincts might tell us otherwise–we might disproportionately recall UEs that came under pressure—-but the numbers don’t bear it out. Thus, Kerber’s badly timed errors are just as badly timed when we compare her to tour average.

They are also poorly timed when compared to her other recent performances at majors. Petkovic implied as much when she said her compatriot was making “errors she didn’t make last year.” Across her 19 matches at the previous four Slams, her UEs occurred on points that were 11% less important than non-UE points. Her errors caused her to lose relatively more important points in only 5 of the 19 matches, and even in those matches, the ratio of UE leverage to non-UE leverage never exceeded 31%, her ratio in Melbourne this year against Tsurenko. That’s still better than her performance on Sunday.

Across so many matches, a difference of 11% is substantial. Of the 30 players with point-by-point UE data for at least eight matches at the previous four majors, only three did a better job timing their unforced errors. Radwanska heads the list, at 16%, followed by Timea Bacsinszky at 14% and Kiki Bertens at 12%. The other 26 players committed their unforced errors at more important moments than Kerber did.

As is so often the case in tennis, it’s difficult to establish if a stat like this is indicative of a longer-trend trend, or if it is mostly noise. We don’t have point-by-point data for most of Kerber’s matches, so we can’t take the obvious next step of checking the rest of her 2017 matches for similarly unclutch performances. Instead, we’ll have to keep tabs on how well she limits UEs at big moments on those occasions where we have the data necessary to do so.

Men’s Doubles On the Dirt

Angelique Kerber wasn’t the only top seed to crash out early at this year’s French Open. In the men’s doubles draw, the top section opened up when Henri Kontinen and John Peers, the world’s top-ranked team, lost to the Spanish pair of David Marrero and Tommy Robredo. It’s plausible to attribute the upset to the clay, as Kontinen-Peers have tallied a pedestrian five wins against four losses on the dirt this season and one could guess that the Spaniards are at their strongest on clay.

Fortunately we don’t have to guess. Using a doubles variant of sElo–surface-specific Elo, which I began writing about a few days ago in the context of women’s singles–we can make rough estimates of how Kontinen/Peers would fare against Marrero/Robredo on each surface. The top seeds are solid on all surfaces–less than a year ago, they won a clay title in Hamburg–but stronger on hard courts. sElo ranks them 4th and 8th on hard, but 10th and 13th on clay among tour regulars. Marrero is the surface-specialist of the bunch, ranking 37th on clay and 78th on hard. Robredo throws a wrench into the exercise, as he has played very little doubles recently, only eight events since the beginning of 2016.

Using these numbers–including those derived from Robredo’s limited sample–we find that sElo would have given Kontinen/Peers a 73.6% chance of winning yesterday, compared to a 78.3% advantage on a hard court. Even if we adjust Robredo’s clay-court sElo to something closer to his all-surface rating, the top seeds still look like 69% favorites.

A more striking example comes from yesterday’s other big upset, in which Julio Peralta and Horacio Zeballos took out Feliciano Lopez and Marc Lopez. On any surface, the Lopezes are the superior team, but Peralta and Zeballos have a much larger surface differential:

Player    Hard sElo  Clay sElo  
M Lopez        1720       1804  
F Lopez        1713       1772  
Zeballos       1651       1756  
Peralta        1517       1770

On a hard court, sElo gives the Lopezes a 68.1% chance of winning this matchup. But on clay, the gap narrows all the way to 53.6%. It’s still a bit of an upset for the South Americans, but not one that should come as much of a surprise.

Mismatches

I’ve speculated in the past that surface preferences aren’t as pronounced in doubles as they are in singles. Regardless of surface, points are shorter, and many teams position one player at the net even on the dirt. While some hard-courters are probably uncomfortable on clay (and vice versa), I wouldn’t expect the effects to be as substantial as they are in singles.

The numbers tell a different story. Here are the top ten, ranked by hard court sElo:

Rank  Player          Hard sElo  
1     Jack Sock            1947  
2     Nicolas Mahut        1893  
3     Marcelo Melo         1883  
4     Henri Kontinen       1879  
5     P-H Herbert          1862  
6     Bob Bryan            1851  
7     Mike Bryan           1846  
8     John Peers           1842  
9     Bruno Soares         1829  
10    Jamie Murray         1828

By clay court sElo:

Rank  Player                Clay sElo  
1     Mike Bryan                 1950  
2     Bob Bryan                  1950  
3     P-H Herbert                1894  
4     Nicolas Mahut              1889  
5     Jack Sock                  1887  
6     Robert Farah               1850  
7     Juan Sebastian Cabal       1849  
8     Pablo Cuevas               1824  
9     Rohan Bopanna              1812  
10    John Peers                 1810

Jamie Murray and Bruno Soares, who appear in the hard court top ten, sit outside the top 25 in clay court sElo. Robert Farah and Juan Sebastian Cabal are 41st and 42nd in hard court sElo, despite ranking in the clay court top seven. Pablo Cuevas, another clay court top-tenner, is 87th on the hard court list.

To go beyond these anecdotes–noteworthy as they are–we need to compare the level of surface preference in men’s doubles to other tours. To do that, I calculated the correlation coefficent between hard court and clay court sElo for the top 50 players (ranked by overall Elo) in men’s doubles, men’s singles, and women’s singles. (I don’t yet have an adequate database to generate ratings for women’s doubles.)

In other words, we’re testing how much a player’s results on one surface predict his or her results on the other major surface. The higher the correlation coefficient, the more likely it is that a player will have similar results on hard and clay. Here’s how the tours compare:

Tour             Correl  
Men's Singles     0.708  
Women's Singles   0.417  
Men's Doubles     0.323

In contrast to my hypothesis above, surface preferences in men’s doubles appear to be much stronger than in either men’s or women’s singles. (And there’s a huge difference between men’s and women’s singles, but that’s a subject for another day.)

Randomness

I suspect that the low correlation of surface-specific Elos in men’s doubles is partly due to the more random nature of doubles results. Because the event is more serve-dominated, there are more close sets ending in tiebreaks, and because of the no-ad, super-tiebreak format used outside of Slams, tight matches are decided by a smaller number of points. Thus, every doubles player’s results–and their various Elo ratings–reflect the influence of chance more than the singles results are.

Another consideration–one that I haven’t yet made sense of–is that surface-specific ratings don’t improve doubles forecasts they way that they do men’s and women’s singles predictions. As I wrote on Sunday, sElo represents a big improvement over surface-neutral Elo for women’s forecasts, and in an upcoming post, I’ll be able to make some similar observations for the men’s game. Using Brier score, a measure of the calibration of predictions, we can see the effect of using surface-specific Elo ratings in 2016 tour-level matches:

Tour             Elo Brier  sElo Brier  
Men's Singles        0.202       0.169  
Women's Singles      0.220       0.179  
Men's Doubles        0.171       0.181

The lower the Brier score, the more accurate the forecasts. This isn’t a fluke of 2016: The differences in men’s doubles Brier scores are around 0.01 for each of the last 15 seasons. By this measure, Elo does a very good job predicting the outcome of men’s doubles matches, but the surface-specific sElo represents a small step back. It could be that the smaller sample–using only one surface’s worth of results–is more damaging to forecasts in doubles than it is in singles.

Doubles analytics is particularly uncharted territory, and there’s plenty of work remaining for researchers even in this narrow subtopic. There’s lots of work to do for the world’s top doubles players as well, now that we can point to a noticeably weaker surface for so many of them.

Bouncing Back From a Marathon Third Set

Italian translation at settesei.it

In this year’s edition of the French Open, we’ve already seen two women’s matches charge past the 6-6 mark in the third set. On Sunday, Madison Brengle outlasted Julia Goerges 13-11 in the decider, and yesterday, Kristina Mladenovic overcame Jennifer Brady 9-7 in the final set. Marathon three-setters aren’t as gut-busting as the five-set equivalent on the men’s tour, yet they still require players to go beyond the usual limit of a tour match.

Do marathon three-setters affect the fortunes of those players that move on to the next round? Back in 2012, I published a study showing that men who win marathon five-setters (that is, matches that go to 8-6 or longer) win fewer than 30% of their following matches, a rate far worse than what we would expect, given the quality of their next opponents. It seems likely that long three-setters wouldn’t have the same effect, especially since many top women are willing to play five-setters themselves.

The numbers bear out the intuition. From 2001 to the 2017 Australian Open, there have been 185 marathon three-setters in Grand Slam main draws, and the winners of those matches have gone on to win 42.2% of their next contests. That’s more than the equivalent number for men, and it’s even better than it sounds.

Players who need to go deep into a third set to vanquish an early-round opponent are, on average, weaker than those who win in straight sets, so many of the marathon women would already be considered underdogs in their next matches. Using sElo–surface-specific Elo, which I recently introduced–we see that these 185 marathon women would have been expected to win only 44.0% of their following matches. There may be a real effect here, but it is a minor one, especially compared to the fortunes of players who struggle through marathon five-setters.

I ran the same algorithm for women’s Slam matches that ended at 7-6, 7-5, and 6-4 or 6-3 in the final set. Since only the US Open uses the third-set tiebreak format, the available sample for that score is limited, which may explain a slightly wacky result. For the other scores, we see numbers that are roughly similar to the marathon findings. Winners tend to be underdogs against their next opponents, but there is little, if any, hangover effect:

3rd Set Score  Sample  Next W%  Next ExpW%  
Marathons         185    42.2%       44.0%  
7-6                56    48.2%       42.2%  
7-5               232    43.1%       42.7%  
6-4 / 6-3         421    41.6%       43.2%

In short: A long match often tells us something about the winner’s chances against her next foe, but it’s something that we already knew. The tight three-setter itself–marathon or otherwise–has little effect on her chances later on. That’s good news for Mladenovic, who will be back on court tomorrow against Sara Errani, an opponent likely to give her another grueling workout.

Podcast Episode 9: Roland Garros Preview and the Value of Surface-Specific Forecasts

In the Episode 9 of the Tennis Abstract Podcast, Carl Bialik and I preview the upcoming fortnight at Roland Garros. We discuss possible threats to Rafael Nadal, likely beneficiaries of some wide-open sections of the draw, and a number of lesser-known names worth watching in Paris.

We apologize for the sound quality this week–due to personal commitments, we had to improvise a bit, and it was either a podcast with subpar sound quality or no podcast at all. I think it’s still very listenable, but if you’re sensitive to that sort of thing, you may disagree. In any case, thanks for listening!

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