Around the Net, Issue 3

Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.

Articles

Multimedia

Data

  • Match Charting Project: The dataset has grown by more than 50 matches in the last week, from 5,143 to 5,194. Highlights include the 100th charted Elina Svitolina match, all of last week’s tour-level finals, and several classic Pete Sampras Wimbledon matches, which round out our complete set of his semi-finals and finals at the All-England Club.

Trivia

  • Nick Kyrgios beat Rafael Nadal in Acapulco, but he didn’t exactly play better than the Spaniard. Nadal’s dominance ratio (DR) in the match was 1.36, higher than in any loss of his career. (Usually a DR larger than 1.0 corresponds with a win.)
  • The Acapulco upset means that Kyrgios improved his record in completed matches against the Big Three to 6-6. Only three other players (plus Djokovic and Nadal) have won at least half of their matches against the famous trio, minimum five matches. Kyrgios joins Alex Corretja, Yevgeny Kafelnikov, and Dominik Hrbaty.
  • Nadal wasn’t the only unlucky loser this week. Henri Kontinen and John Peers lost their first round doubles match in Dubai to Raja/Nedunchezhiyan despite winning 59% of total points–20 more than their opponents.
  • Gael Monfils discovered that a well-timed exclamation can give his forehead a bit of extra juice.
  • Last week in Bergamo, 17-year-old Jannik Sinner won his first Challenger title, becoming the youngest ever champion from Italy, the first born in 2001, and youngest since Alexander Zverev won his first challenger at Braunschweig in 2014.
  • Felix Auger-Aliassime is a bit older, but by reaching the final in Rio de Janeiro, he became the first 2000-born player to crack the ATP top 100.

Beyond the Net

Thanks to Peter for help with this week’s issue.

The Best Draw That Money Can Buy

Italian translation at settesei.it

Last week featured two events on the WTA calendar. First, both chronologically and by every conceivable ranking except for “most Hungarian,” was the Dubai Open, a Premier 5 event offering over $500,000 and 900 ranking points for the winner. The other was the Hungarian Open in Budapest, a WTA International tournament with $43,000 and 280 ranking points going to the champion. No top player would seriously consider going to Budapest, even before considering potential appearance fees and WTA incentives.

Fifteen of the top twenty ranked women went to Dubai, and the top seed in Budapest, defending champ Alison Van Uytvanck, was ranked 50th. Every Budapest entrant ranked in the top 72 got a top-eight seed, including a couple of players who would have needed to play qualifying just to earn a place in the Dubai main draw.

The rewards offered by the Dubai event and supported by the structure of the WTA tour make this an easy scheduling decision for many players. But at some point, if the rest of the field is zigging toward the Gulf, might it be better to zag toward Central Europe? Van Uytvanck would have been an underdog to reach even the third round of the richer event, yet she defended her title in Budapest. Marketa Vondrousova, who would have been stuck in Dubai qualifying, reached the Hungarian Open final. Opting for the smaller stage almost definitely proved the wise choice for those two women. Did other, better-ranked players leave money or ranking points on the table?

Motivations

Scheduling decisions depend on a lot of factors. Some women might prefer to play the event with the highest-quality field, both to test themselves against the best and to give themselves an opportunity for the circuit’s richest prizes. Others might head for the marquee events because of their doubles prowess: Timea Babos was part of the top-seeded doubles team in Dubai, but was the lowest-ranked direct entry in singles. Still others might choose to play closer to home or at tournaments they’ve enjoyed in the past.

For all that, ranking points should come first, with prize money also among the top considerations. Ranking points determine one’s ability to enter future events and to remain on tour. Prize money is necessary to cover the vast expenses necessary to bankroll a traveling support staff.

Dubai-versus-Budapest offers a fairly “pure” experiment, because both are played on similar surfaces and neither event is in the middle of a mini-circuit of events in a single region. Yes, Dubai immediately follows Doha, but that trip requires a flight, and most players headed back to Europe or North America after the tournament. Opting for one event over the other doesn’t substantially complicate anyone’s travel plans, like it would for an ATPer to mix and match destinations from the South American golden swing and the simultaneous European indoor circuit.

Revealed preferences

Let’s see which of the two main factors played a bigger role in scheduling decisions last week. To determine each player’s options, I tried to reconstruct as much as possible what information each woman had at her disposal six weeks earlier, on January 7th, when entry applications and stated preferences for Dubai and Budapest were due. I used the January 7th rankings to project how a player would be seeded at either event, and Elo ratings as of that date to forecast how far she would advance in each draw.

The major difficulty of this kind of simulation is the composition of the draws themselves. From our vantage point after the events, we know who opted for each draw as well as which players were unable to compete. In early January, none but the best-connected players would have known which of her peers would head in which direction, and no one at all could have known that Caroline Wozniacki would be a late withdrawal from Dubai, or that a viral illness would knock Kirsten Flipkens out of the Hungarian Open. Still, the resulting 2019 draws were very similar to what players could have predicted based on the player fields in 2018. So to simulate each player’s options, we’ll use the fields as they turned out to be.

Let’s start with Carla Suarez Navarro, the highest-ranked woman (at the January 7th entry deadline) who wasn’t seeded in Dubai. She ended up reaching the quarter-finals at the Premier event, in part because Kristina Mladenovic did her the favor of ousting Naomi Osaka from that section of the draw. For her efforts, Suarez Navarro grabbed 190 ranking points and almost $60,000. She would have needed to win the Budapest title to garner more points. And with a champion’s purse of “only” $43,000 in Hungary, she would have needed to rob a bank to improve on her Dubai prize money check.

However, that isn’t what Suarez Navarro should have anticipated taking home from Dubai. Sure, she should be optimstic about her own potential, but smart scheduling demands some degree of realism. I ran simulations of both the Dubai tournament (before the draw was made, so she doesn’t always end up in Osaka’s quarter) and the Budapest event with the Spaniard as the top seed and the rest of the field (minus last-in Arantxa Rus) unchanged. These forecasts suggest that Suarez Navarro only had a 12% chance of reaching the Dubai quarters, and that her expected ranking points in the Gulf were much lower:

Event     Points  Prize Money  
Dubai         76     $28.121   
Budapest     111     $15.384

(prize money in thousands of USD)

In all of these simulations, I’ve calculated points and prize money as weighted averages. Suarez Navarro had a 37% chance of a first-round loss, so that’s a 37% chance of one ranking point and first-round-loser prize money. And so on, for all of the possible outcomes at each event. For the Spaniard, her expected ranking points were nearly 50% higher as the top seed in Budapest. But because the Dubai prize pot is so much larger, her expected check was almost twice as big at the tournament she chose.

Consistent incentives

The total purse in Dubai was more than eleven times bigger than the prize money on offer in Hungary, while the points differed by only a factor of three. Thus, it’s no surprise that Suarez Navarro’s incentives are representative of those faced by many more women. I ran the same simulations for 26 more players: All of the competitors who gained direct entry into Dubai but were unseeded, plus Bernarda Pera, who would have been seeded in Budapest but instead played qualifying in the Gulf.

The following table shows each player’s expected points and prize money for Dubai (D-Pts and D-Prize), along with the corresponding figures for Budapest (B-Pts and B-Prize):

Player                    D-Pts   D-Prize   B-Pts   B-Prize   
Dominika Cibulkova           96   $36.794     130   $18.291   
Lesia Tsurenko               84   $31.528     119   $16.695   
Carla Suarez Navarro         76   $28.121     111   $15.384   
Aliaksandra Sasnovich        75   $27.920     111   $15.364   
Dayana Yastremska            72   $26.716     107   $14.803   
Anastasia Pavlyuchenkova     72   $26.590     106   $14.721   
Barbora Strycova             67   $24.809     102   $14.096   
Donna Vekic                  66   $24.143     100   $13.717   
Katerina Siniakova           63   $23.157      95   $13.062   
Ekaterina Makarova           58   $21.543      90   $12.265   
                                                              
Player                    D-Pts   D-Prize   B-Pts   B-Prize   
Petra Martic                 57   $21.019      88   $11.960   
Su Wei Hsieh                 54   $19.863      84   $11.396   
Belinda Bencic               53   $19.813      84   $11.372   
Ajla Tomljanovic             53   $19.530      82   $11.181   
Shuai Zhang                  49   $18.350      77   $10.416   
Sofia Kenin                  46   $17.109      72    $9.659   
Ons Jabeur                   45   $17.077      71    $9.624   
Viktoria Kuzmova             45   $17.009      70    $9.432   
Alize Cornet                 44   $16.823      69    $9.280   
Saisai Zheng                 40   $15.436      62    $8.307   
                                                              
Player                    D-Pts   D-Prize   B-Pts   B-Prize   
Vera Lapko                   37   $14.618      57    $7.695   
Mihaela Buzarnescu           36   $14.465      56    $7.548   
Alison Riske                 35   $14.309      55    $7.445   
Kristina Mladenovic          34   $13.910      51    $6.969   
Timea Babos                  32   $13.354      48    $6.572   
Yulia Putintseva             32   $13.407      48    $6.484   
Bernarda Pera*               25   $11.830      36    $5.061

Every single player could have expected more points in Budapest and more money in Dubai. The ratios are all similar to Suarez Navarro’s. The one possible expection is Pera (hence the asterisk). My simulation assumed she came through qualifying to make the main draw, and calculated only her expected points and prize money from main draw matches. Yet simply qualifying for the main draw is worth 30 ranking points, plus whatever points a player earns by winning main draw matches. Pera was no lock to qualify, but she was favored, and usually a couple of lucky loser spots make the main draw even more achieveable. It’s possible that if we ran all those scenarios, Pera is the one player for whom Dubai offered better hopes of prize money and points.

Loss aversion and game theory

It’s no accident that Van Uytvanck was one of the few players to choose the high-points, low-prize money route. She was defending 280 points from last year’s Hungarian Open, meaning that opting for a bigger check in Dubai would have a negative impact on her ranking. The thought of losing a couple hundred ranking points has a greater influence on behavior than the chance of gaining the same amount for a player who has few to defend.

For the majority of women who will face the same decision in 2020 without many points to defend, what should they do? Assuming, as I do, that they and their coaches will all carefully study this article, what happens if more top-70 players decide to chase ranking points and flock to the smaller event?

If the Budapest field gets stronger, each entrant’s expected points and prize money will decrease; if Dubai’s field weakens, each player there can anticipate a better chance of more points and even more money. As the entry system is currently structured, in which each player must state their preferences without knowledge of their peers’ choices, we can’t count on reaching an equilibrium. Even if every single player aimed solely to maximize ranking points, there wouldn’t be enough information available to reliably make the right choice. It’s conceivable, though unlikely, that a Budapest could attract a stronger field and end up offering lower expected prize money checks and ranking points.

But don’t fret, dear readers and schedule optimizers. There are external factors and there always will be. And in this case, virtually all of those factors pull players to the bigger money event. (Even Hungarian heroine Babos skipped her home tournament.) At least a half-dozen of the players listed above are doubles elites, making it likely they’ll choose the Premier event. Others–probably many others–will go where the money is, because they like money.

Even those who don’t play doubles and don’t like money will chase the biggest available pot of ranking points, not entirely unlike the way people play the lottery. The WTA offers a very limited set of opportunities to earn 900 points in a single week. You can get close to 900 points with three International championships, but there’s a finite number of weeks on the annual schedule–not to mention a limited number of matches in each player’s body! Lots of people stock up on lottery tickets despite unfavorable odds, and players will continue to enter higher-profile events even if their expected points are higher on smaller stages. The chance of a prestigious title, however slim, doesn’t show up in a purely actuarial calculation.

The success of Belinda Bencic–expected Dubai points, 53; expected Budapest points, 84; actual Dubai points, 900–will keep players chasing the big prizes. That’s good news for level-headed would-be optimizers. Those players willing to forego the skyscrapers, the shopping malls, and the prize money next year aren’t about to lose this opportunity. Budapest will almost certainly remain a better option for players who want to improve their ranking.

Podcast Episode 50: Easy Draws, Tough Draws, and the Difficulty of Forecasting

Episode 50 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, grapples with familiar questions of forecasting, and the difficulty of incorporating various level of achievements into our predictions. Belinda Bencic won a major title after plowing through some of the WTA’s strongest competition, while four ATP finalists–Laslo Djere, Felix Auger-Aliassime, Radu Albot, and Daniel Evans–enjoyed breezier paths to trophy ceremonies and sharp rises on the ranking table.

We also talk a bit about the return of Federer and Nadal in Dubai and Acapulco, respectively, possible explanations for the (apparent) weakness in clay-court ATP 500s, our latest evaluation of Casper Ruud, and the lack of tennis analytics at this week’s Sloan conference.

Thanks for listening!

(Note: this week’s episode is about 64 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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

Dominic Thiem, Tennys Sandgren, and Playing Your Way In

Dominic Thiem is one of the best clay-court players on earth, with eight titles and a Roland Garros final to his credit. But his impressive track record wasn’t worth much last night, when he lost his opening-round match in Rio de Janeiro. The straight-set defeat to 90th-ranked Laslo Djere calls to mind other first-match failures, such as Thiem’s loss to Martin Klizan last summer in Hamburg, or his truly gobsmacking upset at the hands of 222nd-ranked Ramkumar Ramanathan on grass in Antalya two years ago.

It’s also not the first time this season that a top seed has proven unable to live up to their billing. Two weeks ago, the No. 1 seeds in three different ATP events all lost their first matches. I dug a bit deeper and discovered that top seeds underperform by a modest amount at these smaller tournaments. Rio is technically a higher-profile event, but the result is the same: An elite player at a non-mandatory event, heading home early.

You’ll hear all sorts of theories for this sort of thing. In ATP 250s, when top seeds get a bye, it’s possible that the elites are in danger because their opponents have played their way into form. At any optional events, it’s possible that the top seeds are not particularly motivated, making the trip for a quick appearance fee and nothing more. Finally, there’s the old saw that some competitors need to get used to their surroundings. In other words, they need to “play their way in” to the tournament. It’s this last theory that I’d like investigate.

Present and prepared

If a player needs time to get comfortable, we would expect him to underperform in the first round, and possibly continue playing below average to a lesser extent in the second round. The flip side of that is that the player would need to overperform in later rounds–if he didn’t, the earlier underperformance wouldn’t be below average, it would just be bad. These under- and over-performances are effects we can quantify.

Let’s start with Thiem. I went through his career results at the ATP level and broke his matches into several categories (some overlapping), like first match, second match, first match at a non-mandatory event, second-or-later match, finals, and so on. For each of those categories, I tallied up his results and compared them to expecatations (Expected Wins, or “ExpWins” in the table), based on what Elo forecasted at the time. Here are Thiem’s results:

Category     Matches  ExpWins  Wins  
1st              141     94.3    94  
1st (small)       84     52.9    54  
1st/2nd          238    151.3   151  
2nd               97     59.9    60  
2nd+             203    117.7   118  
3rd               58     34.9    35  
3rd+             106     60.7    61  
4th               32     18.5    19  
Finals            17     10.2    10

The Austrian has been almost comically predictable. In 84 non-mandatory tournaments through last week, Elo expected that he would win his first match 53 times. He won 54. In all tournaments, he has won his first match 94 times, exactly in line with the Elo estimation. In the nine categories shown here, his performances was never more than a 1.1 matches better or worse than expected. If he’s playing his way into tournaments, he’s doing it in a way that doesn’t show up in the results.

What about Tennys?

Thiem has suffered some rough early-round upsets, but over the course of his career, he’s usually ended up on the winning side. Maybe we’d do better to focus on a true feast-or-famine player, someone who more often loses his first-round encounters, but is dangerous when he advances further.

A great recent example of such a player is Tennys Sandgren. The American raced to the quarter-finals of last year’s Australian Open, reached a final in Houston, and won a title in Auckland to start the 2019 season. Other than that, he rarely turns up on the tennis fan’s radar. He acknowledged his inconsistency on a recent Thirty Love podcast, explaining from a player’s perspective why he thinks his results are so erratic. Like Thiem, he lost easily in an opening match last night, winning only four games against Reilly Opelka in Delray Beach.

Sandgren’s round-by-round results are less predictable than Thiem’s, but for an apparently extreme example of the go-big-or-go-home-early phenomenon, there’s not much support for it in the numbers. Because Sandgren has played fewer tour events than Thiem, I included his Challenger results before separating his matches into the same categories:

Category     Matches  ExpWins  Wins  
1st              124     64.7    62  
1st (small)      113     60.2    60  
1st/2nd          186     96.4    98  
2nd               62     31.7    36  
2nd+             120     60.3    63  
3rd               35     17.3    15  
4th               15      7.3     9  
Finals             8      4.2     3

The American has underperformed a bit in his first matches and beaten expectations in his second rounders, but the effect disappears after two matches are in the books. In any case, none of the over- or under-performances are even close to statistically significant. His extra first-match losses have about a one-in-three probability of happening by chance, and his bonus second-match wins would occur about one time in six. There could be something interesting going on here, but the effects are small, and it’s very likely that we’re seeing nothing more than randomness.

Positive results, anyone?

So far, we’ve investigated two players who seemed likely to over- or under-perform in certain groups of matches. Yet we found nothing. The “playing your way in” theory will surely survive this blog post, but let’s make sure there aren’t players who embody it, even if Thiem and Sandgren don’t.

I went through the same steps for the other 98 men in this week’s top 100, grouping their matches into categories, tallying up Elo-based expected wins and actual wins, and calculating the probability that their results–above or below expectations–are due to chance. The result is 1,043 player-categories, from Novak Djokovic’s finals to Pedro Sousa’s first matches. (The number of player-categories isn’t a round number because not every player has matches in every category, like 6th matches or finals.)

Of those 1,000 player-categories, only 29 meet the usual standard of statistical significance, in that there is less than a 5% chance they can be explained by randomness. A familiar example is Gael Monfils’s record in finals. Even with last week’s title in Rotterdam, his eight wins are outweighed by 21 losses. But such cases are extremely rare. Since fewer than 3% of the player-categories meet the 5% threshold, it’s wrong to say that these categories represent real trends (like, perhaps, a psychological basis for Monfils’s inability to win tournaments). When we test over one thousand groups of matches, dozens of them should look like outliers.

In other words, there’s no statistical support for the claim that certain players are more or less effective in certain rounds. It’s always possible that a very small number of guys have certain characteristics along these lines, but among the 29 player-categories with particularly unlikely results, only Monfils’s finals record fits any kind of narrative I’ve heard before. Richard Gasquet has won 120 times–11 more than expected–in first matches at non-mandatory events. That overperformance is just as unlikely as Monfils’s letdown in finals, so maybe we should be talking about how assiduously he prepares for the start of each tournament, no matter the stakes?

It’s always possible that the top men do, in fact, play their way into tournaments. But based on this evidence, it’s only the case if everyone rounds their way into form at approximately the same rate. Maybe first rounders are lower in quality than semi-finals. But if we’re interested in predicting outcomes–even Thiem’s first-round results against journeymen–we’d do better to ignore the theories. Opening matches just aren’t that unique, even for the players who think they are.

Podcast Episode 49: The New York Open, Surprise Finalists, and Clay Court Tactics

Episode 49 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, focuses on Carl’s time at the second edition of the ATP New York Open. We discuss the breakout performances of Reilly Opelka and Brayden Schnur, whether professional men’s doubles is entertaining, and if a black tennis court is necessarily as fast as it looks.

We also talk clay courts: another title for late-blooming Marco Cecchinato, the difficulty of measuring the impact of surface speed, and the potential for clay court tactics like wide ad-court kickers and aggressive drop shots.

Thanks for listening!

(Note: this week’s episode is about 58 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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

Around the Net, Issue 1

Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.

Articles

Podcasts

Data

Trivia

Miscellaneous

Thanks to Peter, Jeff, and Carl for help with this week’s issue.

Do Rallies Get Longer as Matches Progress?

Italian translation at settesei.it

Yesterday at the New York Open, Paolo Lorenzi battled through three sets to defeat Ryan Harrison. It was a notable result for a number of reasons, starting with the fact that Lorenzi is rarely seen on a hard court when there’s any other option. The 37-year-old Italian is one of the many men defying the aging curve these days, and with the victory, he’ll play at least one tour-level quarter-final for the eighth year in a row, despite not reaching his first until he was 30.

The way in which Lorenzi won the match was almost as unique as his career trajectory. Take a look at the average rally length per set:

Set  Avg Rally  
1          3.2  
2          4.0  
3          4.9

You probably don’t need me to tell you which set Harrison won. The opening frame was serve-dominated, typical of American indoor hard court events. As the match progressed, the points increasingly resembled the clay-court sparring that Lorenzi surely would have preferred.

Theorizing

The Lorenzi-Harrison match was extreme, but it tracks with what I believe to be the conventional wisdom. Throughout a match, players get better at reading their opponents’ games, cutting down on unreturned serves and making it more likely that each point will turn into a more protracted exchange. That’s the theory, anyway. There are some countervailing forces, such as fatigue, which work in the other direction, but in general we expect points to get longer.

Yesterday’s contest didn’t exactly follow that script, though. The rallies might have gotten longer because the two men better predicted each other’s shots, but it doesn’t show up so neatly in aces–Harrison hit aces on between 18% of 21% of his points in each set–or the more inclusive category of unreturned serves:

Set  Points  Unret%  
1        47   42.6%  
2        65   32.3%  
3        73   37.0%

While serve recognition may explain the rally length jump from set 1 to set 2, it goes in the opposite direction from set 2 to set 3. Yes, these are small samples, and yes, unreturned serves don’t tell the whole story. But there are signs that our initial theory is missing something.

More matches

As interesting as Lorenzi is, we’re going to need more players, and more data, to better understand what happens to serve returns and rally length over the course of a match. Let’s start with the main draw singles matches from the 2019 Australian Open. Not only are there are a lot of them, but since they are best of five, we have an opportunity to see how these trends unfold over several sets per match.

For each match, I measured the average rally length and rate of unreturned serves for each set, and then made set-by-set comparisons for the length of the match. For instance, in Lorenzi-Harrison, rally length increased by 25% from set 1 to set 2. Then, for each set, I aggregated all the matches of sufficient length to figure out how much the tour as a whole was changing from one set to the next.

The results are considerably less eye-catching than those of the Lorenzi match. In the following table, the “Avg Rally” and “Unret%” columns show the change in ratio form: If the baseline rate in the first set is 1.0, the rally length in set 2 increases by 0.8% and the number of unreturned serves goes up by 2.4%. I’ve also included example columns, showing realistic rally lengths and unreturned-serve rates for each set based on tournament averages of 3.2 shots by point and 34% of serves unreturned:

Set  Avg Rally  Ex Rally  Unret%  Ex Unret  
1            1      3.20       1     34.0%  
2        1.008      3.23   1.024     34.8%  
3        1.019      3.26   1.033     35.1%  
4        0.987      3.16   1.155     39.3%  
5        1.021      3.27   1.144     38.9%

The set-to-set differences in rally length are barely enough to qualify for the name. The shift in the rate of unreturned serves, however, is much more striking, all the more so because it moves in the opposite direction that we expected.* Perhaps fatigue–or strategic energy conservation–plays a bigger role than I thought, or servers gain more from familiarity with their opponent than returners do.

* You might wonder if the effect is an artifact of the data, that players who reach 4th and 5th sets are bigger servers. That may be true, but it’s not what we’re seeing here. I’m comparing the stats in each set to the previous set in the match itself, and then averaging the set-to-set changes, weighted by the number of points in the sets. A John Isner 5th set, then, is compared only to an Isner 4th set.

WTA to the rescue

The results are completely different for women. Here is the same data for the 127 main draw women’s singles matches at the Australian Open:

Set  Avg Rally  Ex Rally  Unret%  Ex Unret  
1            1      3.40       1     27.0%  
2        1.035      3.52   0.974     26.3%  
3        1.103      3.75   0.915     24.7%

Still not as dramatic as Harrison-Lorenzi, but the trends are more marked than for the men. The number of unreturned serves drops quite a bit, and rally length increases by an amoun that an attentive spectator might notice. Those two are related–if there are fewer unreturned serves, there are more shots per point, even if we only consider the second shot. Beyond that, there are more opportunities for longer exchanges. In any case, the set-by-set trends for women fit closer to the intial theory than the men’s results did.

As with every aggregate stat, I’m guessing that there is a huge amount of variation among players. Perhaps players who are particularly good in third sets really do return more serves or, as Lorenzi did, shift their tactics in the direction of a more favorable style of play. Looking at these types of numbers for individual competitors is a reasonable next step, but it’s one that will need to wait for another day.

Juan Ignacio Londero’s First Five ATP Match Wins

Italian translation at settesei.it

Last week’s ATP 250s had their share of surprises, with all three top seeds falling in their first matches. But the biggest shock of all was reserved for Sunday, when 25-year-old Argentine Juan Ignacio Londero capped an unexpected breakthrough week in Cordoba with a title. The hometown wild card pummelled Federico Delbonis and then came from behind to defeat Guido Pella a three-set final. Londero was playing just his fourth tour-level event, and his first tour-level win came on Tuesday, a first-round upset of fifth-seed Nicolas Jarry.

There aren’t many players who’ve managed to win a title the same week as their first match win on tour. Going back to 1990, I found only five others who matched Londero’s feat:

Player                Age   Year  Event        
Nicolas Lapentti      19.1  1995  Bogota       
Lleyton Hewitt        16.9  1998  Adelaide     
Juan Ignacio Chela    20.5  2000  Mexico City  
Santiago Ventura      24.4  2004  Casablanca   
Steve Darcis          23.3  2007  Amersfoort   
Juan Ignacio Londero  25.5  2019  Cordoba

It’s a diverse group. Lleyton Hewitt announced his presence with a title as he embarked on a Hall of Fame career, Nicolas Lapentti had great things ahead of him as well, and Juan Ignacio Chela would go on to win six more titles. (Next time a player named Juan Ignacio wins his first ATP match, watch out!) The other two players broke through at older ages and provide better clues as to what we should expect from Londero. Steve Darcis won one more title within a year of his first, and stuck around long enough to crack the top 40 at age 33. Santiago Ventura never played another final, and his career peak ranking was 65, just four spots above Londero’s new level.

A perfect 25

Still, pointing out that Londero is unlikely to develop into a top ten player doesn’t mean we shouldn’t celebrate his accomplishment. I found over 1,000 players who won their first tour-level match since 1990, and only 24% of them managed to win their second match at the same tournament, let alone the title. The average first-time winner claimed a mere 1.3 matches, including their debut win. In addition to the six titlists, only nine reached the final and 43 made it to the semis after recording their first win.

The results for debut winners are even more bleak when we narrow our focus to players in Londero’s age group. Despite the increasing age of the men’s tennis population, if a player hasn’t made an impact on tour before age 25, he is unlikely to do so. 17% of our first-time winners were 25 or older, and Londero is the only one of them to reach the final in his breakthrough event. These 185 players combined for only 53 wins after their first-round milestones, and four of those wins were recorded by Londero last week.

Pessimistic as this sounds, there are a few encouraging precedents for the Argentine to follow. Paolo Lorenzi won his first tour-level match about one month younger than Londero’s current age. It took Lorenzi nearly another decade to hoist his first ATP trophy, and he’s still hovering just ouside the top 100 at age 37. Tennys Sandgren (who, coincidentally, lost to Lorenzi in New York last night), didn’t win a tour-level match until he was 26. Six months later he was in the quarter-finals of the Australian Open. The most extreme late bloomer is Victor Estrella, who was almost 33 years old at the time of his first ATP match win, which he followed with a tour-level title 18 months later.

Of course, Lorenzi is one of a kind, and the unexpected feats achieved by Sandgren and Estrella have minimal predictive value. Beyond the thrill of winning his hometown tournament, the most important implication of the title for Londero is that it launches his ranking into the top 70. He gets a place in the Roland Garros main draw, and in the next twelve months, he’ll have a number of other opportunities to play tour-level events. He deserves it: My Elo rankings suggest he is not only a top-70 player overall, but he is just outside the top 40 on clay courts. Londero’s title truly came out of nowhere, but there’s no reason to be suprised the next time he posts an excellent result on clay.

Podcast Episode 48: Fed Cup and a Survey of Tennis Analytics

Episode 48 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, recaps the weekend’s Fed Cup action, starting with Romania’s upset of the defending champion Czechs. We also look at the long list of stars languishing in Group I and consider whether Fed Cup would benefit from a Davis Cup-style revamp.

In the second half, we flip the script, with Carl interviewing Jeff about his recent posts, including an attempt at better Davis Cup rankings, a major milestone for the Match Charting Project, a look at men’s break point serving tactics, and last week’s disappointing results for top seeds at ATP events.

Thanks for listening!

(Note: this week’s episode is about 65 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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

Break Point Serve Tendencies on the ATP Tour

Italian translation at settesei.it

Every player has their “go-to” serve, their favorite option for high-pressure moments. At the same time, their opponents notice patterns, so no server can be too predictable. Let’s dive into the numbers to see who’s serving where, how it’s working out for them, and what it tells us about service strategies on the ATP tour.

Specifically, let’s look at ad-court first serves, and where servers choose to go on break points. For today’s purposes, we’ll focus on a group of 43 men, the players with at least 20 charted matches from 2010-present in the Match Charting Project dataset. For each of the players, we have at least 85 ad-court break points and another 800-plus ad-court non-break points. (I’ve excluded points in tiebreaks, because many of those are high-pressure as well, but it’s less clear cut than in other games.) For most players we’ve logged a lot more, including nearly 1,000 ad-court break points each for Novak Djokovic and Rafael Nadal.

First question: What’s everybody’s favorite break point serve? On average, these 43 men hit about 20% more “wide” first serves than “T” first serves on break points. (Body serves are a factor as well, but they make up only about 10% of total first serves, and comparing two options is way more straightforward than three.) That 20% difference isn’t quite as big as it sounds, since on non-break points in the ad court, players go wide about 10% more often. So while the wide serve is the typical favorite, it’s only a bit more common than on other ad-court points.

Tour-wide averages don’t tell us the whole story, so let’s look at individual players. Here are the ten men who favor each direction the most when choosing an ad-court first serve on break point:

Player                       BP Wide/T  
Philipp Kohlschreiber             2.58  
Pablo Cuevas                      2.46  
Denis Shapovalov                  1.94  
Rafael Nadal                      1.87  
Jack Sock                         1.84  
David Goffin                      1.78  
Nick Kyrgios                      1.69  
Alexandr Dolgopolov               1.66  
Dominic Thiem                     1.64  
Pablo Carreno Busta               1.58  
…                                       
Gilles Simon                      0.94  
Alex De Minaur                    0.94  
Gael Monfils                      0.90  
Feliciano Lopez                   0.83  
Tomas Berdych                     0.83  
Karen Khachanov                   0.82  
David Ferrer                      0.81  
Fabio Fognini                     0.77  
Diego Schwartzman                 0.69  
Borna Coric                       0.67

You’re probably as unsurprised as I was to find Rafael Nadal near the top of the list. The combination of Rafa and Denis Shapovalov suggests that lefties all follow the same pattern, but Feliciano Lopez swats away that hypothesis, as one of the players who most favors the T serve on break points. The other two lefties in our 43-player set, Adrian Mannarino and Fernando Verdasco, both hit more wide serves than average, so perhaps Feli is the odd man out here. We don’t have a lot of data on other contemporary lefties, so it’s tough to be sure.

Second question: How do break point tendencies compare to ad-court tendencies in general? We’ve already seen that players opt for wide first serves about 10% more than T deliveries in non-break point ad-court situations. That difference doubles on break points. These modest shifts lend themselves to an easy explanation: Most players serve a little better wide to the ad court, and under pressure, they’re a bit more likely to go with their most reliable option.

For some guys, though, there’s no “little” about it. We’ve already seen that Philipp Kohlschreiber goes wide every chance he gets on break points, more often than anyone else in our group. Yet on non-break points in the ad court, he splits his deliveries almost fifty-fifty. That’s a huge difference between break point and non-break point tendencies. He’s not alone. Borna Coric is similar (albeit less extreme) in the opposite direction, splitting his ad-court first serves about fifty-fifty in lower-pressure situations, then heavily favoring T serves when facing break point.

The next table shows the players who shift tactics most dramatically on break points. The first two columns show the ratio of wide serves to T serves on break points and on other ad-court points. The rightmost column shows the ratio between those two. At the top of the list are the men like Kohlschreiber, who go wide under pressure. At the bottom are the men like Coric. I’ve included the top ten in both directions, as well as the three members of the big four who aren’t in either category. Djokovic, for example, doesn’t let the situation alter his tactics, at least in this regard.

Player                 BP W/T  Other W/T  Wide BP/Other  
Philipp Kohlschreiber    2.58       1.04           2.49  
Nick Kyrgios             1.69       0.74           2.28  
Juan Martin del Potro    1.52       0.81           1.87  
Jack Sock                1.84       1.05           1.75  
Pablo Cuevas             2.46       1.50           1.64  
Kevin Anderson           1.18       0.74           1.59  
David Goffin             1.78       1.13           1.58  
John Isner               1.43       0.91           1.58  
Grigor Dimitrov          1.41       0.94           1.49  
Dominic Thiem            1.64       1.11           1.48  
…                                                        
Andy Murray              1.19       0.86           1.39  
Rafael Nadal             1.87       1.51           1.24  
Novak Djokovic           1.20       1.16           1.03  
…                                                        
Stan Wawrinka            0.99       1.15           0.87  
Roberto Bautista Agut    1.38       1.60           0.86  
Fabio Fognini            0.77       0.91           0.85  
Roger Federer            1.08       1.35           0.80  
Benoit Paire             1.36       1.73           0.78  
Adrian Mannarino         1.45       1.86           0.78  
Diego Schwartzman        0.69       0.89           0.78  
Feliciano Lopez          0.83       1.09           0.76  
Borna Coric              0.67       0.97           0.69  
Karen Khachanov          0.82       1.25           0.66

Some of the tour’s best servers feature near the top of the list. While many of them favor the ad-court T serve in general, they go wide more often under pressure. This tactic offers an explanation of why some players outperform (at least sometimes) on break points and in tiebreaks. Nick Kyrgios, for instance, is deadly serving in all directions, but in the ad court, he’s even better out wide. Overall, he wins 78.8% of his wide first serves in the ad court, against 75.8% of his T first serves. By “saving” the wide serves for big moments, he is able to defend more break points than his overall ad-court record would suggest. The same theory applies to tiebreaks, where a player could deploy their favored serve more often.

Third question: Could these tactics be improved? I usually start with the assumption that players know what they’re doing. If Kyrgios goes down the middle most of the time and then out wide more often on break points, it probably isn’t a random choice. There’s an easy rule of thumb to check whether servers are making optimal choices, which my co-podcaster Carl Bialik described a few years ago:

If your T serve is better than your wide serve, hit the T serve more. But don’t hit it 100 percent of the time because if you do, your opponent knows you’ll hit it and can stand in the middle of the court waiting for it instead of guarding against the wide serve. So how often should you hit it? Exactly as often as it takes to make it just as successful, but no more, than when you hit a wide serve. If your success rates on different choices are different, you’re not serving optimally.

For instance, facing break point in the ad court, Kyrgios wins 79.7% of his wide first serves and 76.1% of his T first serves. By Carl’s game-theory-derived logic, Kyrgios should be going wide even more often. His win rate on wide serves will go down a bit, as returners find him more predictable, but the average result of all of his break point serves will go up, as he trades a few T serves for more successful wide deliveries.

On average, our 43 players have a 4% gap between their break point win percentages on wide and T serves. Some of that is probably just noise. We’ve logged only 94 break points served by Alexandr Dolgopolov, so his 15% gap isn’t that reliable. Still, some gaps appear even for those players with considerably more data.

The following table shows the ten players with the most break points faced in the dataset. The third column–“BP Wide/T”–shows how much they favor the wide serve on break points. The next two columns show their winning percentages on break point first serves in the two primary directions. Finally, the last column shows the difference between those winning percentages, also in percentage terms. The closer the gap to 0%, the closer to an optimal strategy.

Player             BPs  BP Wide/T  Wide W%   T W%    Gap  
Novak Djokovic     973       1.20    73.1%  72.9%   0.3%  
Rafael Nadal       971       1.87    67.3%  76.7%  12.2%  
Roger Federer      865       1.08    77.1%  77.1%   0.0%  
Andy Murray        730       1.19    71.1%  72.2%   1.6%  
Alexander Zverev   493       1.04    72.4%  76.6%   5.5%  
Stan Wawrinka      379       0.99    72.7%  71.9%   1.2%  
Kei Nishikori      366       1.18    59.5%  69.6%  14.5%  
David Ferrer       347       0.81    59.7%  63.7%   6.2%  
Diego Schwartzman  338       0.69    72.2%  67.8%   6.5%  
Dominic Thiem      294       1.64    71.8%  73.9%   2.8%

Djokovic, Roger Federer, Andy Murray, and Stan Wawrinka are close to the tactical optimum. Nadal is … not. He loves the wide serve on break points, yet he is considerably more successful when he lands his first serve down the T.

But again, we need to work from the assumption that the players know what they’re doing–especially when that player is as accomplished and otherwise strategically sound as Rafa. My focus throughout this post has been on first serves. In general, players make first serves at about the same rate regardless of which direction they choose. In the ad court, down-the-middle attempts are a bit more likely to land in than wide deliveries. But for Rafa, it’s a different story. His wide serve isn’t particularly deadly, but it is the picture of reliability. His ad-court first serve wide hits the mark 77.8% of the time, compared to a mere 59.5% down the middle. The T serve is effective when it lands in, but that in itself is not sufficient reason to make more attempts.

The same reasoning can’t save Kei Nishikori. He has an even bigger gap than Rafa’s, winning about 70% of his break point first serves down the T but only 60% when he goes wide. This is almost definitely not luck: Assuming 180 serves in each direction and the average success rate of about 65%, the chances of either number being at least five percentage points above or below the mean is about 18%. The probability that both are so extreme is roughly 3.5%, so the odds that they are extreme in opposite directions is less than 2%, or one in fifty.

Like Nadal, he is one of the few players who makes a lot more first serves in one direction than the other. But unlike Nadal, his first-serve-in discrepancy makes the gap even more pronounced! In the 366 break points we’ve logged, he landed 48.8% of his break point wide first serve attempts and 62.8% of his tries down the T. He lands more first serves down the middle and those serves are more likely to result in points won. Nishikori needs to hit a lot more of his break point serves down the T. His T-specific winning percentage will probably decrease as opponents discover the more pronounced tendency, but his overall results would likely improve.

At the most basic level, players should be aware of their opponents’ serving tendencies, whether by rumor, advance scouting, or data like the Match Charting Project. Beyond that, we’ve seen that there’s even more potential in the data, showing that some men are leaving break points on the table. Most elite tennis players have a good intuitive grasp of game theory, but even elite-level intuition gets it wrong sometimes.