jsackmann – Heavy Topspin

Matteo Berrettini and Best-of-Five Puzzles

*Matteo Berrettini is a man you don’t want to face in best-of-five. (Unless you’re Holger Rune, in which case you beat him in four sets yesterday.)*

Some players seem to play better in best-of-five matches. Maybe they are fitter than average, or they take time to get into the rhythm of a match, or they are particularly good at managing their preparation to peak for grand slams. There are many plausible explanations. I suspect most fans assume that there’s some kind of “best-of-five” factor that makes certain players better or worse than usual at the majors.

I took a first crack at the question during the 2014 Australian Open. Then, Jo-Wilfried Tsonga stood out as a best-of-five specialist. With more data and better tools, it’s time to try again. Gill Gross and Alex Gruskin teed it up:

Challenge accepted!

Note that the stat here is awfully specific: hard-court matches since 2019. Matteo also lost to Holger Rune in the second round yesterday, so the numbers have slightly changed. We’ll come back to this particular puzzle in a bit.

First, it’s important to remember that many players should have better records in best-of-five than in best-of-three. It’s the same reason that in other sports, best-of-five (or best-of-seven) series are more likely to go to the favorite than more luck-bound best-of-threes. Given more sets, players have more time to turn things around. The stronger of the two competitors is more likely to do so.

A fantastic illustration of this comes, appropriately enough, from the (all-surface) career numbers of Berrettini himself:

Format      Set%   Win%  
Best of 5  62.1%  68.9%  
Best of 3  62.0%  63.8%

The Italian wins sets at almost exactly the same rate, regardless of format. But the match win percentage is different. There’s still something to be explained: A player who wins 63.8% of best-of-three matches should, all else equal, win approximately 67% of best-of-five matches. Matteo has done better than that, but the format alone explains much of the gap.

Better at best of five?

Identifying players who outperform expectations requires that we define “expectations.” As usual, Elo makes it easy to do this. For every individual match, we can use the Elo ratings of the two men to generate probabilities that each will win.

For Berrettini at hard-court slams since 2019–excluding retirements and the 2025 Australian Open–I have him at 27-9, good for a 75% win rate. Based on pre-match Elo ratings, though, he “should” have gone just 22-14 (technically, 22.5-13.5), taking 62% of the contests.

That’s noteworthy but not astonishing: A player expected to win 62% of the time has about an 8% chance of winning at least 27 of 36 matches. Given the number of ATP tour regulars, it stands to reason that somebody would post a stat like that. We can’t cast aside the Italian’s case yet, because we haven’t talked about his best-of-three results. Again, I’m going to kick it down the page because I want to show you some other numbers first.

Before looking at the narrow set of 2019-24 hard-court numbers, let’s see how everybody fared at grand slams, on all surfaces, since 2000. Out of 154 men with at least 50 grand slam matches this century, here are the top dozen overperformers:

Player               W-L     W%   Exp%  Ratio  
Pablo Andujar      23-39  37.1%  27.9%   1.33  
Denis Istomin      34-41  45.3%  34.7%   1.31  
Frances Tiafoe     45-34  57.0%  46.2%   1.23  
Mario Ancic        40-20  66.7%  54.4%   1.23  
Victor Hanescu     29-35  45.3%  38.6%   1.17  
Karen Khachanov    59-30  66.3%  56.6%   1.17  
Simone Bolelli     25-32  43.9%  37.6%   1.17  
Leonardo Mayer     33-38  46.5%  40.1%   1.16  
Marat Safin        79-32  71.2%  61.5%   1.16  
Nick Kyrgios       54-28  65.9%  57.0%   1.16  
Bernard Tomic      40-35  53.3%  46.7%   1.14  
Matteo Berrettini  49-20  71.0%  62.6%   1.13

The first percentage is actual win percentage, followed by expected win rate (based on Elo ratings). The ‘Ratio’ column is simply the ratio of actual to expected. These are the guys who have played better at slams than their track records would have implied.

This ratio starts to identify overperformers, but we can go one step further. Sample size really counts here. It’s one thing to win seven of ten matches when you’re expected to win five. It’s wildly different to win 70 of 100 when you’re expected to win 50. The odds of the first are 17%, while the chances of the second are a fraction of one percent.

Since this next metric accounts for sample size, I’ve expanded our view to the 334 men with at least 20 slam matches since 2000. Here are the twenty players who have most defied the odds with their overperformance in best-of-five:

Player                   Record     W%   Exp%  Ratio  Odds  
Novak Djokovic           364-45  89.0%  84.3%   1.06  0.4%  
Rafael Nadal             304-41  88.1%  82.9%   1.06  0.5%  
Tennys Sandgren           16-17  48.5%  27.6%   1.76  0.9%  
Marin Cilic              133-56  70.4%  62.4%   1.13  1.4%  
Stan Wawrinka            151-66  69.6%  62.4%   1.12  1.6%  
Marat Safin               79-32  71.2%  61.5%   1.16  2.2%  
Frances Tiafoe            45-34  57.0%  46.2%   1.23  3.5%  
Mario Ancic               40-20  66.7%  54.4%   1.23  3.6%  
Denis Istomin             34-41  45.3%  34.7%   1.31  3.7%  
Jo-Wilfried Tsonga       120-43  73.6%  66.8%   1.10  3.7%  
Karen Khachanov           59-30  66.3%  56.6%   1.17  4.0%  
Carlos Alcaraz            57-10  85.1%  76.5%   1.11  6.1%  
Nick Kyrgios              54-28  65.9%  57.0%   1.16  6.4%  
Tomas Martin Etcheverry   12-12  50.0%  33.1%   1.51  6.4%  
Lukasz Kubot              20-20  50.0%  37.3%   1.34  6.9%  
Pablo Andujar             23-39  37.1%  27.9%   1.33  7.3%  
Andrey Kuznetsov          18-21  46.2%  33.8%   1.37  7.4%  
Thomas Fabbiano           10-13  43.5%  27.7%   1.57  7.6%  
Matteo Berrettini         49-20  71.0%  62.6%   1.13  9.2%  
Joachim Johansson          15-8  65.2%  49.4%   1.32  9.5%

I’ll admit it, I mostly lowered the match minimum so that we could have a top five consisting of four slam winners and one Tennys Sandgren. Djokovic and Nadal don’t stand out in the “Ratio” category: They were expected to wins lots of matches, and they did. But not only that, they slightly exceeded expectations for a very, very long time. There’s only a one-in-two-hundred chance that a player expected to win 83% of matches would win 88% over such a long stretch.

Enter the skeptic

Even highlighting these outlier performances–many of them in the hands of players we’d expect to see on the list–it’s not clear whether there’s really a best-of-five factor. As noted, we’re working with a population of over 300 players. Three of them gave us one-in-one-hundred performances. Fewer than 10% turned in one-in-ten performances. Isn’t that what we’d expect?

This isn’t a laboratory: We can’t run tests on Novak Djokovic to see if he would keep winning at the same rate in his next 410 best-of-five matches. We certainly can’t do it 100 times to be sure. We can, however, wring a bit more from the data we have.

If there is a special best-of-five skill–above and beyond a player’s general tennis ability–we’d expect players to show it with some consistency. (If they didn’t, could we call it a skill?) Here are two tests to check whether it’s a skill:

Career halves: Split each player’s list of best-of-five matches into halves. Tommy Paul, for instance, went 13-13 in his first 26 best-of-five matches–worse than expected. Since then, he’s won 19 of 27–better than expected. If there’s a best-of-five skill, we’d expect those numbers to be persistent. Sometimes they are, but in general, they are not. Statistically, there’s virtually zero correlation.
Odd and even matches: Maybe career halves are the wrong way to do it: Players improve and tendencies change with age. Instead, take each player’s list of best-of-fives and put them in two buckets, one for the first, third, fifth, etc. matches on the list, the other for the second, fourth, etc. Different tack, same results: no reliable relationship.

To be clear, this doesn’t tell us that everyone’s results are a luck-driven mirage, or that no one has any noteworthy best-of-five skill. Across 350 or 400 matches, I’d bet that Djokovic and Nadal probably do. (Heuristic: If a trait is good, they probably have it.) But in general, a player who is winning more best-of-fives than expected is probably due for a correction. There’s no basis to expect the trend to continue.

The Berrettini double

With that bucket of cold water thrown on our dreams, let’s return to the head-scratcher we started with.

Hard-court matches since 2019. Here are the best-of-five overperformers, minimum 20 slam matches:

Player               Record     W%   Exp%  Ratio   Odds  
Frances Tiafoe        27-12  69.2%  49.6%   1.40   1.0%  
Matteo Berrettini      27-9  75.0%  62.4%   1.20   8.1%  
Adrian Mannarino      15-12  55.6%  41.1%   1.35   9.3%  
Alexei Popyrin        13-11  54.2%  39.9%   1.36  11.2%  
Taylor Fritz          27-12  69.2%  58.6%   1.18  11.6%  
Novak Djokovic         52-4  92.9%  87.2%   1.07  13.9%  
Rafael Nadal           31-5  86.1%  79.7%   1.08  23.4%  
Daniil Medvedev       56-11  83.6%  79.5%   1.05  25.6%  
Pablo Carreno Busta    21-9  70.0%  63.1%   1.11  27.9%  
Marin Cilic            17-8  68.0%  60.9%   1.12  30.7%

Holy Tiafoe! Elo would have predicted a 50% win rate, and instead he went 27-12. Berrettini comes next, but by this metric, it’s a distant second. Only Tiafoe really stands out in this sample.

But wait–there’s more to the Gross/Gruskin puzzle. The Italian has not only overperformed at slams, he has notably underperformed on hard courts elsewhere. Excluding Challengers, retirements, and Davis Cup, Berrettini’s record is even more mediocre than the one listed above: It works out to 42-42. Elo would have predicted a 58% win percentage, not a mere break-even rate.

Of the 35 players with at least 20 hard-court slam matches in this span, only David Goffin more severely underperformed in best-of-three. Only Goffin, Jannik Sinner, and Gael Monfils posted more unexpected numbers in best-of-three. Berrettini is as odd in best-of-three as he is in the longer format, just in the opposite direction.

Using the “Ratio” numbers, we can compare best-of-five over- (or under-) performance with best-of-three, for a kind of “super-ratio.” While Matteo is unique is the unexpectedness of his two numbers, Tiafoe still comes out ahead:

Player             bo5 Ratio  bo3 Ratio  bo5/bo3  
Frances Tiafoe          1.40       0.98     1.42  
Matteo Berrettini       1.20       0.86     1.40  
Adrian Mannarino        1.35       0.98     1.38  
Alexei Popyrin          1.36       1.07     1.27  
Marin Cilic             1.12       0.91     1.23  
David Goffin            1.04       0.86     1.21  
Daniel Evans            1.12       0.97     1.15  
Dominic Thiem           1.10       0.97     1.13  
Davidovich Fokina       1.14       1.01     1.13  
Taylor Fritz            1.18       1.05     1.13

The odds that Berrettini would give us such an unusual pair of stats are 0.3915%. Tiafoe’s number is 0.3969%. Let’s call it a tie.

Are we there yet?

After all this, I’m not sure that I’ve “explained” what’s going on here, per Gruskin’s request. We’ve seen that where best-of-five results differ from a player’s overall results, it’s mostly luck. I assume it’s the same with best-of-three. Maybe there are some additional factors in Berrettini’s case: Perhaps he’s more likely to play non-slams when he’s physically less than 100%.

There’s also this:

Tiebreak records are definitely luck-driven. These splits account for much of the difference in Matteo’s match-level results. A few points here or there, and we wouldn’t be having this conversation. Or, more likely, we’d be overreacting to unexpected numbers from somebody else.

Poor Hubi

One last thought. We’ve looked only at overperformers so far. Of course, there will always be underperformers as well. Hubert Hurkacz has disappointed a bit at slams: 34-25 before the Australian Open, compared to an Elo-expected 37-22. The subset of hard-court slams since 2019, where you’d expect the big-serving Pole to excel, has been far worse.

In a dozen majors, Hurkacz has gone 14-12, a 54% win rate compared to an expected mark of 73%. The odds of such a wide gap are 0.9%, slightly more extreme than Tiafoe’s happier results. In the same span, Hubi has outperformed in best-of-three, winning 63% of those matches instead of 58.5%. He is the anti-Berrettini.

We’ve learned today that outlying best-of-five records are probably not predictive of future results. For a statistician, such findings can be a bit disappointing. For Polish fans, though, it’s reason to rejoice. Hurkacz didn’t turn things around in Australia, winning one match and losing his second. Still, a correction remains in the cards. If apparent best-of-five specialists like Berrettini and Tiafoe can lose in the second round, a laggard like Hurkacz could–eventually–give us a deep run.

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This Is What a Dangerous Madison Keys Looks Like

*Madison Keys in 2023. Credit: Hameltion*

Last year, Madison Keys missed the Australian Open with a shoulder injury. She ended up playing barely half the season, missing more time after tearing a hamstring at Wimbledon. She still won enough matches to head to Melbourne as the 19th seed at this year’s first major.

She’s better than that.

In one sense, I’m just stating the obvious: She beat Jessica Pegula for the title in Adelaide on Saturday and moved up to 14th on the WTA computer. Beyond that, anyone who can hold on to a spot in the top 20 despite missing so many events is probably better than their ranking. Elo agrees, rating Keys ninth among women, a modest 33 points behind fifth-place Elena Rybakina.

Even more striking is the way the American won the Adelaide championship. She served as well as she has in years, indicating that the shoulder is fully healed. She played extremely aggressively, a style that she has never shied away from, but that she sometimes struggles to maintain. Finally, Keys did all that while posting excellent return numbers. The 29-year-old is a two-time semi-finalist at the Australian Open, and if she keeps this up, she could easily make it three.

The serve is back

When everything clicks, no one on tour–with the possible exception of Rybakina or Aryna Sabalenka–makes serving look so easy. Keys doesn’t just slam flat serves down the tee: She adds a bit of side spin, so her inch-perfect deliveries look like they’re sailing slightly wide until after they cross the net. Then she employs the same spin to send wide serves even wider. When she misses, she can fall back on some of the heaviest topspin seconds in the women’s game.

Whether the shoulder was still shaky or the hamstring compromised her motion, the American struggled to maximize her serve as late as last year’s US Open. In her third-round loss to Elise Mertens, her average first-serve speed was just under 99 miles per hour. Out of nearly 100 grand slam matches for which I have serve speed data, it was only the second time–the other was 2017 Roland Garros–that she hit firsts so slowly.

Today in her Melbourne opener against Ann Li, her average first serve was 109 miles per hour. That’s the fastest I have on record for her since 2015.

I don’t have serve speeds for Keys’s victories last week in Adelaide, but the results hint at numbers well into triple digits. In the final against Pegula, she hit 10 aces, good for 13% of her serve points. Facing Liudmila Samsonova in the semis, she smacked 12 aces–17% of serve points. In a short quarter-final against Daria Kasatkina, she tallied 11 aces, an eye-popping 21% of serve points. It was only the fourth time in the 2020s that Keys topped the 20% mark and the only time in her career she managed it against a top-ten opponent.

Adelaide marked the first time since 2019 that the American aced at least 10% of her service points in three consecutive matches. She hadn’t done so at a single event since 2016.

Aces are great in themselves, but the stat is particularly useful for representing the serve’s effect on even more points. Yes, Keys won 13% of her serve points against Pegula with aces, but 41% didn’t come back. That’s another sign of a revival: In dozens of Match Charting Project-logged matches, it’s the first time she’s topped 40% in that category since the Australian Open in 2022–her most recent semi-final run Down Under.

The American mitigated her shoulder woes last year by starting points more conservatively. She wasn’t as deadly with her first serve, but she landed more of them. Among the WTA top 50, only Elina Avanesyan and Yulia Putintseva missed fewer first serves. If Adelaide is any indication, it’s back to business as usual, taking a few more risks and wreaking absolute first-serve devastation:

Span        SPW  1stIn  1st W%  2nd W%  
Adelaide  65.4%  63.4%   71.6%   54.8%  
2024      60.6%  68.2%   66.7%   47.4%

It’s not an apples-to-apples comparison, because the 2024 line contains plenty of clay-court matches, including two against Iga Swiatek. But the difference is sufficient to tell the story anyway. 60.6% of serve points won was good for 8th-best on tour last year. 65.4%, on the other hand, is almost two percentage points better than anyone posted on hard courts. The 71.6% first-serve win rate would have put her in the top five, and no one came close to winning 54.8% of their second serves.

I don’t want to put too much emphasis on a single tournament–everybody looks good if you turn the microscope on a great week. But it’s worth offering one more tidbit in Keys’s favor. She posted those numbers against extremely strong opposition. Her five victims in Adelaide were ranked 16th, 17th, 9th, 26th, and 7th, respectively. That’s a tougher schedule that any player faces over the course of an entire season. If Madison does reached the Melbourne semis, it’ll be an easier path than she faced to collect the trophy in Adelaide.

Swinging freely

Keys has improved her return game over the years, and she’s gotten more comfortable playing long rallies. One of the more surprising numbers on her stat sheet is that she has a better winning percentage on clay than on hard courts.

Still, she’s an aggressor at heart. Her serve isn’t the only shot she can hit as hard as anyone, nor is it the only weapon she can land on the line. Generally speaking, the more aggressive she is, the better her results. The shoulder and hamstring injuries forced her to play more conservatively. That is now over.

In less than an hour on court with Kasatkina, she crushed, by one count, 38 winners. Facing Pegula on Saturday, she tallied 40. I have winners and unforced errors for about one-quarter of her career matches, and the Adelaide final was the first time she cracked 40 winners since 2019. It wasn’t uncontrolled either. The opposite side of the ledger was a respectable 27 unforced errors, good for a ratio of 1.5. Even in her Auckland loss to Clara Tauson the previous week, she recorded 38 winners against 30 unforced, a ratio that would win most WTA matches.

The best indicators of the American’s renewed attack are the various metrics for aggression. By Rally Aggression Score–a measure of how often a player ends points for good or ill after the return of serve–she rated +147. (Average is 0, and almost all players fall between -100 and 100.) Return Aggression Score–the same idea, but strictly for returns of serve–put her at +137. Her career averages are around +100, but in 2024, she fell below +60 in both.

The last time that Keys reached +137 or higher by both measures was the 2019 Cincinnati quarter-final, when she beat Venus Williams en route to the title.

We keep finding things that Keys has done for the first time since 2019. They almost all go back to that week in Cincinnati. (Coincidentally, she straight-setted Kasatkina there, too.) With the possible exception of her 2017 US Open final run, the Cincinnati effort was the best of her career. She has found that form again.

Keys to the match

One difference between 2019 Cincinnati and 2025 Adelaide: The American returned a whole lot better last week. She won 48.1% of return points in Adelaide, compared to 43.7% in Cinci.

It’s rare for players to substantially improve their return game once they arrive on tour. The rest of the tour learns how to beat you, the opposition gets stronger, and age slows you down. Yet Keys, in her late 20s, has gotten better:

While the 2025 data point probably won’t stick above 47%, the 2023 and 2024 results demonstrate the trend. Last year, Keys’s 44% mark was better than half of the top 50, a strong showing for a serve-first player. Return points are an extreme case of tennis’s small margins. By top-50 standards, 43% of return points is weak, 44% is adequate, and 45% is strong.

47%–the American’s success rate in Auckland and Adelaide–is beyond elite. Only two players–Coco Gauff and Marketa Vondrousova–did better than that on hard courts last year.

It will take some time before we know whether Adelaide was an outlier or a harbinger of a resurrected career. Keys’s 2025 season will surely fall somewhere in the middle, at least if she remains healthy. There are certainly reasons for optimism. For the most part, she’s done all of this before, serving and attacking her way into the top ten as far back as 2016, and returning better in the last two years. If those two halves come together, we won’t see a (19) next to her name again for a long time.

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Trivia Notebook #1: Ranking Leaps and Marathon Men

*Tomas Martin Etcheverry, three-set marathon expert*

I run a lot of queries, and people often ask me arcane trivia questions. Has this ever happened before? Is that a record? My beat seems to be the super-niche stuff that no one would ever bother to include in the official media notes.

The Trivia Notebook is my attempt to put more of the answers in one place. I’m thinking I’ll do one of these every two or three weeks. If you have a question or topic you think would fit well here, please send it. No promises–most of my ideas don’t end up making the cut.

It’s a new year, so we’re all bursting with energy to start new projects. Most of them are long gone by April, and odds are the same thing will happen to this one. But hey, you never know, right?

In this first installment, we’ll look at 100-point ranking leaps, marathon man Tomas Martin Etcheverry, seedless quarter-final lineups, and single-country duos that conquered a tournament.

100-spot ranking leaps

Joao Fonseca ended 2024 ranked 145th in the world. My Elo ratings put him 45th, and after the Canberra title last week, his place on that list climbed to 27th.

As with many trivia questions, we’ll need to be a bit more specific. Tons of players move up 100 places each year, but going from 845th to 745th–while impressive!–is presumably not the sort of thing we’re looking for. Same thing with injury recoveries. While Pablo Carreno Busta finished 2024 ranked 196th, it won’t be momentous if the former top-tenner bounces back to the top 100.

A narrower question, then: Which players have jumped at least 100 ranking places in a single year, ending with their first year-end top-100 finish?

Here are the biggest single-year improvements that ended with a top-100 debut:

Player               Year  Prev YE  New YE  Jump  
Kenneth Carlsen      1992      835      69   766  
Leonardo Lavalle     1985      745      87   658  
Guillermo Coria      2000      722      88   634  
Pablo Carreno Busta  2013      654      64   590  
Marco Chiudinelli    2009      605      56   549  
Jacob Fearnley       2024      645      99   546  
Josef Cihak          1987      613      77   536  
Andreas Vinciguerra  1999      633      98   535  
Andre Agassi         1986      618      91   527  
Alex Michelsen       2023      599      97   502  
Arnaud Di Pasquale   1998      572      81   491  
Radek Stepanek       2002      542      63   479  
Ben Shelton          2022      573      96   477  
Fritz Buehning       1979      555      81   474  
Jannik Sinner        2019      551      78   473

Pablo made it! A few other names there you might recognize, too.

If Fonseca skips forward 100 spots, he’ll do something that sets him apart from everyone on that list: He’ll leap into the top 50. Still, a 100-spot move is hardly historic:

Player               Year  Prev YE  New YE  Jump  
Marc Rosset          1989      474      45   429  
Ronald Agenor        1985      418      49   369  
Goran Ivanisevic     1989      371      40   331  
Vincent Van Patten   1979      374      43   331  
Sergi Bruguera       1989      333      26   307  
Juan Carlos Ferrero  1999      346      42   304  
Jim Courier          1988      346      43   303  
Horst Skoff          1986      299      42   257  
John McEnroe         1977      264      18   246  
Ulf Stenlund         1986      274      34   240  
Mark Philippoussis   1995      274      38   236  
Peter Lundgren       1985      265      31   234  
Ricardo Cano         1975      274      42   232  
Jack Draper          2022      265      42   223  
Mel Purcell          1980      245      27   218

About 80 players have made a 100-plus-spot jump into the top 50. It’s harder to do so now than it was in the days of McEnroe or Courier, but men still manage it with some regularity. Fonseca will have to settle for breaking other records.

Marathon men

This was the Adelaide second round. Thanasi Kokkinakis decided this was enough for his Australian Open prep, as he withdrew from the quarters. Headlines about this match tended to focus on Thanasi’s penchant for marathons. He’s well-known for his 5h45 battle with Andy Murray two years ago in Melbourne. Last year, he went 3h15 against Aleksandar Kovacevic in Houston, then 3h29 a week later at the Sarasota Challenger against Gabriel Diallo.

But… the name that caught my eye was Tomas Martin Etcheverry. While he doesn’t have a marquee marathon to his name like the Murray tilt, he spends a lot of time on court. Just three months ago, he muscled through three hours and 43 minutes to beat Botic van de Zandschulp in Shanghai.

Etcheverry doesn’t have a ton of slam experience, and the best-of-five format lends itself to memorable marathons. But in best-of-three matches, the Argentinian has now crossed the three-hour mark more than any other active player:

Rank  Player                   Bo3 Marathons  
1     Tomas Martin Etcheverry             27  
2     Albert Ramos                        26  
3     Novak Djokovic                      25  
4     Pedro Martinez                      24  
5     Carlos Taberner                     23  
6     Thiago Monteiro                     22  
7     Roberto Carballes Baena             20  
8     Mikhail Kukushkin                   19  
8     Timofey Skatov                      19  
8     Juan Pablo Varillas                 19  
8     Thanasi Kokkinakis                  19  
12    Lorenzo Giustino                    17  
13    Jordan Thompson                     16  
13    Alessandro Giannessi                16  
13    Marton Fucsovics                    16

This is an imprecise measure, because it’s really “three-hour matches I know about.” It includes tour-level matches back to 1991, tour qualies and Challengers going back a decade, and Challenger qualies for the last few years. So it’s biased a bit toward younger players, who have played more in the “Jeff knows about their match times” era. Still, it’s an impressive tally for Etcheverry–and he’s only 25 years old.

The Kokkinakis match also tied Etcheverry for first place on the all-time list with Nicolas Massu. Here’s that leaderboard, again with the caveat that older players do not have Challenger matches counted:

Rank  Player                    Bo3 Marathons  
1     Tomas Martin Etcheverry              27  
1     Nicolas Massu                        27  
3     Albert Ramos                         26  
3     Carlos Berlocq                       26  
5     Novak Djokovic                       25  
5     Rafael Nadal                         25  
5     Andy Murray                          25  
8     Pedro Martinez                       24  
9     Carlos Taberner                      23  
10    Thiago Monteiro                      22  
10    Paolo Lorenzi                        22  
12    Adrian Menendez Maceiras             21  
13    Roberto Carballes Baena              20  
14    Mikhail Kukushkin                    19  
14    Timofey Skatov                       19  
14    Juan Pablo Varillas                  19  
14    Thanasi Kokkinakis                   19  
18    Gilles Simon                         18

Legends one and all. It’s continually amusing to me that Djokovic, Murray, and Nadal have landed on the same number. Roger Federer, for his part, only reached three hours in six of his short-form matches.

Seedless quarter-finals

At the Nonthaburi Challenger this week in Thailand, the quarter-finals featured a wild card, two qualifiers, and an alternate… but no seeds. One of the seeds withdrew, five lost in the first round, and the remaining two fell in the second.

Let’s say it together: Has that ever happened before?

In fact, there were seedless quarter-finals five times at Challenger level last year, including once in Nonthaburi! Altogether, there have been more than 80 such tournaments in Challenger history. Even “two seeds in the second round” isn’t that special–it happened at Amersfoort last year.

What about one seed in the second round? For that, we have to go back to 2018 in Lyon, where a 17-year-old Felix Auger-Aliassime defended his title. Gotta love the Wikipedia summary of how things went for the seeds:

As far as I can tell, that’s the closest we’ve come to a Challenger with no seeds in the second round. Credit to Pablo Andujar, he did his best.

Lonely countrymen

Last one, this time from the archives. Last year at the Dobrich Challenger, two Dutchmen–Jelle Sels and Guy den Ouden–met in the final. It isn’t unusual to have players from the same country in a Challenger final, even outside their home country. What was odd about Dobrich is that Sels and den Ouden were the only Dutch men in the main draw.

You know the drill: Has that ever happened before?

I should know by now: Ask that question about the varied history of the Challenger tour, and the answer is almost always yes. It happened again in September, when two Japanese men met for the Columbus final. It also arose twice in 2023. Two Bosnians played for the championship in Sibiu, and the San Benedetto title match was contested between Benoit Paire and Richard Gasquet, the only Frenchmen in the draw.

Altogether, there have been 32 such Challengers. There were none in the first decade of the tour, but they’ve clicked off about once per year since. My favorite of the bunch is the 1998 Fürth Challenger, where Christian Ruud and Jan Frode Andersen saw off all of their non-Norwegian foes.

This scenario has also come up about as often at tour level. More than half of them were before 1980, and they’ve gotten progressively rarer. But we got one in 2024! Arthur Fils and Ugo Humbert were the only two Frenchmen in the Tokyo draw, and they were the last two men standing. That was the first such tournament in a decade, since Monte Carlo in 2014, where Stan Wawrinka upset Roger Federer for the title.

That, I think, is enough tennis trivia for one day. We’ll have some more–maybe!–in a couple weeks.

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Can Clara Tauson Withstand the Winners?

*As long as she’s hitting this shot, Clara Tauson will be fine.*

Clara Tauson picked up her third career title in Auckland on Sunday. She claimed the trophy after losing the first set when her opponent, Naomi Osaka, retired with an abdominal injury. It isn’t the way she would have liked to have won it, and based on the first 45 minutes of play, she probably wouldn’t have beaten a healthy Osaka. But she earned her spot in the championship match, ousting top-seeded Madison Keys in the quarter-finals.

It was a long wait for the 22-year-old from Denmark. She won two titles and reached a third final in 2021, ultimately climbing to a peak rank of 33 in early 2022. Back and foot injuries derailed her progress, and she languished outside the top 50 for more than two years. Once one of the game’s leading prospects, she still has plenty left to prove.

Tauson’s signature is what tennis people call easy power. She whips through the ball, especially on the forehand, with such impeccable technique that her strokes shoot through the court faster than it looks like they ought to. The Kiwi crowd saw an excellent display of easy power on Sunday, as Osaka possesses the same magic to an even greater extent. Both players hit angled bullets that made their opponent look lazy. But there’s little point in chasing a well-placed groundstroke off the Tauson (or Osaka) racket.

The Dane is nearly as effective from the service line. Standing six feet tall with excellent control, she can break a returner’s spirit with one ball after another on the center line. On Sunday, she served three consecutive down-the-tee aces against Osaka. A fully fit opponent might have gotten a racket on one or two of them, but only a handful of women could have put them back in play. In the last 52 weeks, only Osaka, Qinwen Zheng, and Elena Rybakina have hit aces at a better clip.

The challenge for Tauson is, well, everything else. While her backhand sometimes looks strong, the results on that wing are middling. Her second serve does not befit a six-footer. Most problematic of all, she doesn’t defend well. She piles up plenty of winners, but the women across the net hit more.

Winners take it all

It’s tempting to say that Clara’s big-swinging game is in the high-risk, high-reward Kvitova/Ostapenko/Alexandrova mold. (Okay, no one could ever be like Jelena Ostapenko, but you know what I mean.) It’s easy to picture her smacking a forehand winner or sending a groundstroke wildly astray.

The numbers, however, don’t bear it out. My go-to metric for WTA game style is Aggression Score, which tells us how often–on a per-shot basis–a player ends the point for good or ill. Tauson comes in at +18, more aggressive than average, but barely. (Average is zero, with most players falling between -100 and +100.) That’s only marginally ahead of Jasmine Paolini (+10), in a different territory entirely from Osaka (+108) or Ostapenko (+251) over the last year.

We gain some insight by breaking that number down into its components. The 22-year-old scores often enough on her favorite wing, ending points with a winner (or forced error) with nearly one in five forehands she hits. Over the last 52 weeks, that’s good for 13th out of 75 women for whom we have sufficient data:

Player                    FH Wnr%  
Jelena Ostapenko            33.1%  
Naomi Osaka                 24.8%  
Lulu Sun                    22.2%  
Aryna Sabalenka             22.0%  
Ekaterina Alexandrova       21.6%  
Amanda Anisimova            20.8%  
Katie Boulter               20.5%  
Diana Shnaider              20.4%  
Danielle Collins            20.4%  
Anastasia Pavlyuchenkova    20.3%  
Donna Vekic                 20.1%  
Liudmila Samsonova          19.6%  
Clara Tauson                19.4%

She’s barely above average, however, by the backhand version of the same metric. Many of the women near the top of the forehand list take the same tactical approach off both sides. Ostapenko, Sabalenka, and Anisimova (among others) appear in the top ten by backhand winner rate, as well. Tauson, on the other hand, puts away backhands about as often as Bianca Andreescu or Jessica Pegula.

On the positive side, the Dane’s Aggression Score lags because she doesn’t hit a huge number of unforced errors. She takes more risks than the average WTAer on forehand–13% UFEs versus 11% for the tour as a whole–but fewer on the backhand–9% against 10%. Anisimova, by comparison, hits unforced errors on 14% of her forehands and 13% of her backhands.

Fewer errors is better, all else equal. But when Tauson works the point, her opponents tend to reap the rewards. She is much more successful in short points than long ones: Only a handful of players win fewer points in the 7- to 9-shot category. Prolonging the point has only so much value when you’re unlikely to win it. For someone with the 22-year-old’s skillset, better to swing away, accept more errors, and pick up that many more quick winners in exchange.

Flat-footed

The best illustration of what happens to Tauson in those (relatively) long points is the rate at which players hit winners against her. On average, about 29% of points end with a clean winner by either player, so the typical woman sees a winner fly by about 14.5% of the time. The Dane is near the top of the list:

Player              vs Wnr/Pt  
Angelique Kerber        22.5%  
Clara Tauson            21.0%  
Marie Bouzkova          20.9%  
Emma Raducanu           19.8%  
Elise Mertens           19.3%  
Caroline Wozniacki      18.8%  
Daria Kasatkina         18.6%  
Victoria Azarenka       18.2%  
Yulia Putintseva        18.2%  
Elina Avanesyan         18.0%

This isn’t bad company, exactly. But for a free-swinging forehand expert, it’s the wrong crowd. With the possible exception of Raducanu, these are players who cough up winners because they try to build points and sometimes fail. (Or in some of these cases, opponents feast on weak second–or even first–serves.)

The biggest hitters generally allow their opponents fewer winners than average, even if they aren’t the best movers or their defensive rally skills are subpar. Here are the tour’s top ten in winners per point over the last year. I’ve shown their winners against (vs Wnr/Pt) as well, and added Tauson to the list for comparison:

Player                 Wnr/Pt  vs Wnr/Pt  
Jelena Ostapenko        21.6%      11.5%  
Aryna Sabalenka         20.7%      12.0%  
Naomi Osaka             20.1%      14.8%  
Lulu Sun                20.1%      13.5%  
Elena Rybakina          19.1%      13.4%  
Ekaterina Alexandrova   19.1%      13.2%  
Ons Jabeur              18.9%      11.5%  
Danielle Collins        18.5%      13.1%  
Leylah Fernandez        18.2%      13.5%  
Donna Vekic             18.0%      15.5%  
…                                         
Clara Tauson            15.7%      21.0%

Of the top ten, only Osaka (barely) and Vekic allow more winners than average against them. None is even close to the previous list. One of the main benefits of an aggressive game style is that it takes the racket out of the other woman’s hand, even at the cost of some mishit service returns and wild groundstroke misses. Tauson, so far, has been unwilling to make that trade. It’s unclear whether she has the ability to post consistent wins with her more conservative approach.

Take the gamble

The Dane has some room to build on her current WTA rank of 41, but without unleashing more of her weapons, more of the time, I suspect she’ll get stuck on the wrong side of the top 20. Even if she can’t match the all-around barrage of someone like Sabalenka, she’d do better to follow that example than let herself turn into the next Elise Mertens.

In the 30 Tauson matches logged by the Match Charting Project, her Aggression Score in losses was +16. In victories, it was +36. (I grouped the Auckland final with the losses.) That’s not a slam-dunk case, especially since it lumps together matches from 2019 to today. But it hints that the 22-year-old plays better when she goes bigger.

One counter-example to that trend is instructive. Last week’s quarter-final against Keys was an unusually passive victory: Tauson won in straights despite an Aggression Score of -38. The American was more likely to seize the initiative, hitting exactly twice as many winners (and twice as many unforced errors) as the Dane. Tauson’s salvation was that Keys couldn’t do much against second serves. Keys won a dire 32% of second-serve return points–worse than she did against the Tauson first serve, and enough of a liability to swing the match in her opponent’s favor.

That kind of rescue is something that Clara rarely enjoys. Among the WTA top 50, she ranks 14th by first-serve win percentage. By second-serve win percentage, though, she’s fifth from the bottom. The percentage-point difference between her two numbers ranks with the biggest gaps on tour:

Player                  1st%   2nd%   Diff  
Qinwen Zheng           75.5%  46.8%  28.7%  
Coco Gauff             71.7%  43.8%  27.9%  
Ekaterina Alexandrova  69.5%  42.6%  26.9%  
Katie Boulter          70.8%  45.1%  25.7%  
Clara Tauson           69.2%  43.5%  25.7%  
Donna Vekic            70.7%  45.4%  25.3%  
Danielle Collins       70.5%  45.8%  24.7%  
Karolina Pliskova      67.4%  42.8%  24.6%  
Elena Rybakina         72.0%  48.6%  23.4%  
Naomi Osaka            74.5%  51.6%  22.9%

Qinwen is known for the disconnect between her two deliveries; Gauff spent much of the 2024 season struggling with double faults. More to the point, most of this group cleans up on first serve. Sure, Zheng or Rybakina would love to win more second-serve points, but their first-serve win rates are so high that it hardly holds them back.

Tauson’s first-serve success–even though it pales against that mostly-elite group–implies that she should be able to muster something better off the second delivery. She could take more chances: While her double-fault rate is above average, it is not worryingly high. More likely, she could use her height to push returners wide, then attempt to finish the job on the next shot. She shows us textbook examples of that play in almost every match, just not often enough.

The Dane has physical tools that will take her far in pro tennis. She’s doesn’t, however, have such extravagant gifts that she can get away with suboptimal tactics. Even in the tall-women’s club of the WTA, there aren’t many six-footers. It’s time that Tauson plays like one.

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The 52-Week Ranking Forecast

*A healthy Karolina Muchova is a top-tenner. Credit: Hameltion*

What will the men’s and women’s ranking lists look like at the end of the 2025 season? A few days ago, I attempted to predict which players would crack the top 100. Today, we’re playing for bigger stakes: The names at the top the table.

As with the top-100-breakthrough forecast, the most important inputs are current Elo rank and current ATP or WTA rank. Elo tells us how well someone is playing, and the official ranking tells us how well that translated into points. After all, ranking points are what will determine the list in a year’s time, too.

The cumulative ATP and WTA rankings reflect whether a player missed time in the previous year; while that isn’t always indicative of whether he or she will be absent again, injuries often recur and some pros have a hard time staying on court. The official ranking also gives some players a head start over others: The 32nd seed at the Australian Open is more likely to reach the second week than the best unseeded player, even if they have roughly the same skill level.

Age is crucial, as well. The younger the player, the more we expect him or her to improve over the course of the year. Later than the mid-20s, however, results trend (usually!) in the other direction.

I tested the usefulness of myriad other variables, including height, handedness, and surface preference. None unambiguously improved the model. I ended up using just one more input: last year’s Elo rank. Current ranks have more predictive value, but last year’s position helps, as it offers a clue as to whether a player’s current level is sustainable.

Enough chatter–let’s start with the forecast for the 2025 year-end women’s rankings:

YE 25    Player                     Age  YE 24  Elo 24  Elo 23  
1        Aryna Sabalenka           26.7      1       1       3  
2        Iga Swiatek               23.6      2       2       1  
3        Coco Gauff                20.8      3       3       2  
4        Qinwen Zheng              22.2      5       4       8  
5        Elena Rybakina            25.5      6       6       5  
6        Jasmine Paolini           29.0      4       9      28  
7        Jessica Pegula            30.9      7       8       4  
8        Paula Badosa              27.1     12       5      24  
9        Emma Navarro              23.6      8      16      53  
10       Mirra Andreeva            17.7     16      15      26  
11       Diana Shnaider            20.7     13      12     100  
12       Daria Kasatkina           27.7      9      19      16  
13       Karolina Muchova          28.4     22       7       6  
14       Barbora Krejcikova        29.0     10      22      14  
15       Marta Kostyuk             22.5     18      20      38  
16       Anna Kalinskaya           26.1     14      23      31  
17       Madison Keys              29.9     21      11      12  
18       Beatriz Haddad Maia       28.6     17      17      18  
19       Jelena Ostapenko          27.6     15      29      13  
20       Marketa Vondrousova       25.5     39      10       9  
21       Danielle Collins          31.0     11      31      22  
22       Linda Noskova             20.1     26      35      42  
23       Donna Vekic               28.5     19      27      41  
24       Liudmila Samsonova        26.1     27      26      11  
25       Leylah Fernandez          22.3     31      30      20  
                                                                
YE 2025  Player                     Age  YE 24  Elo 24  Elo 23  
26       Victoria Azarenka         35.4     20      13      29  
27       Elina Svitolina           30.3     23      24      19  
28       Ons Jabeur                30.3     42      14       7  
29       Maria Sakkari             29.4     32      21      15  
30       Katie Boulter             28.4     24      33      62  
31       Amanda Anisimova          23.3     36      28       
32       Anastasia Potapova        23.8     35      36      36  
33       Emma Raducanu             22.1     56      18       
34       Yulia Putintseva          30.0     29      25      55  
35       Magdalena Frech           27.0     25      51      85  
36       Elise Mertens             29.1     34      37      33  
37       Xin Yu Wang               23.3     37      59      57  
38       Ekaterina Alexandrova     30.1     28      48      25  
39       Anastasia Pavlyuchenkova  33.5     30      32      35  
40       Marie Bouzkova            26.4     44      44      30  
41       Elina Avanesyan           22.3     43      60     131  
42       Lulu Sun                  23.7     40      56     182  
43       Peyton Stearns            23.2     47      53     113  
44       Katerina Siniakova        28.6     45      38      40  
45       Olga Danilovic            23.9     51      50      82  
46       Ashlyn Krueger            20.7     64      54      67  
47       Camila Osorio             23.0     59      49      56  
48       Dayana Yastremska         24.6     33     104      96  
49       Clara Tauson              22.0     50      83      64  
50       Karolina Pliskova         32.8     41      40      39

No big surprises here–that’s the nature of a model like this. Where players are predicted to move up or down, it’s usually because their Elo rank is notably higher or lower than their official position, like Muchova or Paolini. Mirra Andreeva, the youngest woman in the top 175, is expected to gradually work her way into the top ten.

Getting fuzzier

Of course, there’s considerable uncertainty. When we check in at the end of the 2025 season, we’ll find some substantial moves, like Paolini in 2024. We can get a better idea of that uncertainty by forecasting the likelihood that players reach certain thresholds.

Here is each top player’s probability of becoming the 2025 year-end number one:

Player              p(#1)  
Aryna Sabalenka     42.3%  
Iga Swiatek         32.6%  
Coco Gauff          21.1%  
Qinwen Zheng         6.9%  
Elena Rybakina       4.3%  
Jasmine Paolini      2.8%  
Jessica Pegula       2.4%  
Emma Navarro         0.9%  
Paula Badosa         0.9%  
Daria Kasatkina      0.9%  
Barbora Krejcikova   0.7%  
Mirra Andreeva       0.7%  
Diana Shnaider       0.5%  
Karolina Muchova     0.5%

This is not the list I would have made. Again, this type of model isn’t going to give you big surprises, and there’s no consideration for things like playing styles. Intuitively, a big breakthrough from Andreeva (or Shnaider) seems more likely than a belated push from Kasatkina, or even Pegula.

In any event, we get an idea of how much the ranking list can shuffle itself in a year’s time. Even beyond these 14 names, the model gives another 20 women at least a one-in-a-thousand chance to end the year at the top.

We can run a similar exercise to get the odds that each player ends the season in the top 5, 10, or 20:

Player                    p(top 5)  p(top 10)  p(top 20)  
Aryna Sabalenka              82.4%      95.8%      99.3%  
Iga Swiatek                  81.0%      94.9%      98.9%  
Coco Gauff                   75.5%      92.7%      98.3%  
Qinwen Zheng                 50.3%      80.3%      95.5%  
Elena Rybakina               32.5%      65.5%      90.3%  
Jessica Pegula               15.5%      42.0%      78.4%  
Paula Badosa                 15.2%      41.5%      81.7%  
Mirra Andreeva               13.7%      34.5%      68.3%  
Jasmine Paolini              13.1%      38.4%      77.7%  
Karolina Muchova             10.6%      30.2%      69.8%  
Diana Shnaider                8.8%      25.7%      64.6%  
Emma Navarro                  7.9%      24.0%      60.2%  
Marketa Vondrousova           6.6%      19.2%      53.8%  
Daria Kasatkina               5.8%      18.3%      49.6%  
Marta Kostyuk                 4.9%      14.9%      43.4%  
Madison Keys                  4.9%      15.8%      49.7%  
Barbora Krejcikova            4.2%      13.5%      40.2%  
Beatriz Haddad Maia           3.8%      12.1%      39.7%  
Anna Kalinskaya               3.5%      11.2%      35.8%  
Jelena Ostapenko              3.0%       9.4%      28.8%  
Leylah Fernandez              2.9%       8.5%      25.8%  
Liudmila Samsonova            2.8%       8.6%      27.0%  
Linda Noskova                 2.8%       8.2%      24.9%  
Ons Jabeur                    2.8%       8.7%      31.7%  
Maria Sakkari                 1.9%       6.1%      23.1%  
                                                          
Player                    p(top 5)  p(top 10)  p(top 20)  
Danielle Collins              1.9%       6.3%      22.5%  
Elina Svitolina               1.7%       5.7%      21.6%  
Donna Vekic                   1.7%       5.4%      21.1%  
Victoria Azarenka             1.6%       5.9%      28.2%  
Anastasia Potapova            1.5%       4.5%      15.8%  
Emma Raducanu                 1.5%       4.7%      21.6%  
Amanda Anisimova              1.1%       3.5%      15.4%  
Yulia Putintseva              1.0%       3.4%      15.1%  
Katie Boulter                 1.0%       3.3%      13.5%  
Marie Bouzkova                0.8%       2.4%       8.8%  
Elise Mertens                 0.8%       2.5%      10.1%  
Xin Yu Wang                   0.8%       2.3%       7.8%  
Ashlyn Krueger                0.8%       2.1%       7.3%  
Camila Osorio                 0.7%       2.0%       7.4%  
Ekaterina Alexandrova         0.7%       2.1%       7.9%  
Magdalena Frech               0.6%       2.0%       8.0%  
Katerina Siniakova            0.6%       2.0%       8.1%  
Olga Danilovic                0.6%       1.8%       6.8%  
Peyton Stearns                0.6%       1.7%       6.6%  
Anastasia Pavlyuchenkova      0.6%       1.9%       8.9%  
Elina Avanesyan               0.6%       1.7%       6.2%  
Clara Tauson                  0.5%       1.4%       4.3%  
Lulu Sun                      0.5%       1.5%       5.9%  
Eva Lys                       0.4%       1.2%       4.8%  
Elisabetta Cocciaretto        0.4%       1.2%       4.1%

Most interesting to me in this table is where the columns diverge. Andreeva, with her unrealized potential, ranks higher on the top-5 list than by top-10 or top-20 probability. Azarenka, though she has little chance of returning to the top ten, is more likely than her list-neighbors to hang inside the top 20.

The same variation means that there are some new names in the table. Eva Lys, for instance, is forecast to land at #65 ahead of the 2026 season. But because she is young and has already posted multiple top-100 seasons by Elo rating, she has an outsized chance of a major breakout. The women who were displaced are either fringy veterans, like Pliskova, or those whose Elo ratings didn’t match their WTA rank, such as Yastremska.

(These forecasts are probably more accurate than the year-end-number-one table above. There haven’t been many year-end number ones, by definition, so there’s less data to draw upon.)

Long may Sinner reign

Now for the men. I’ve extended this list to 51 for obvious reasons:

YE 25  Player                  Age  YE 24  Elo 24  Elo 23  
1      Jannik Sinner          23.4      1       1       2  
2      Carlos Alcaraz         21.7      3       3       3  
3      Alexander Zverev       27.7      2       4       5  
4      Taylor Fritz           27.2      4       6      10  
5      Daniil Medvedev        28.9      5       5       4  
6      Novak Djokovic         37.6      7       2       1  
7      Holger Rune            21.7     13      10      12  
8      Jack Draper            23.0     15       8      19  
9      Casper Ruud            26.0      6      21      16  
10     Alex de Minaur         25.9      9      16      11  
11     Andrey Rublev          27.2      8      18       6  
12     Stefanos Tsitsipas     26.4     11      14       9  
13     Tommy Paul             27.6     12      11      18  
14     Hubert Hurkacz         27.9     16       9       8  
15     Grigor Dimitrov        33.6     10       7       7  
16     Ugo Humbert            26.5     14      17      13  
17     Lorenzo Musetti        22.8     17      20      50  
18     Arthur Fils            20.6     20      25      38  
19     Ben Shelton            22.2     21      22      17  
20     Sebastian Korda        24.5     22      15      22  
21     Tomas Machac           24.2     25      12      33  
22     Karen Khachanov        28.6     19      19      23  
23     Felix Auger Aliassime  24.4     29      28      15  
24     Frances Tiafoe         26.9     18      33      26  
25     Matteo Berrettini      28.7     34      13      14  
                                                           
YE 25  Player                  Age  YE 24  Elo 24  Elo 23  
26     Alexei Popyrin         25.4     24      27      75  
27     Jiri Lehecka           23.1     28      39      46  
28     Flavio Cobolli         22.7     32      30     136  
29     Alex Michelsen         20.4     41      35     134  
30     Jakub Mensik           19.3     48      37     119  
31     Mpetshi Perricard      21.5     31      43     192  
32     Francisco Cerundolo    26.4     30      36      25  
33     Matteo Arnaldi         23.9     37      48      31  
34     Sebastian Baez         24.0     27      67      40  
35     Brandon Nakashima      23.4     38      42      70  
36     Jordan Thompson        30.7     26      29      51  
37     Juncheng Shang         19.9     50      52       
38     Tallon Griekspoor      28.5     40      32      24  
39     Alejandro Tabilo       27.6     23      54     121  
40     Denis Shapovalov       25.7     56      34      34  
41     T M Etcheverry         25.5     39      58      65  
42     Alexander Bublik       27.5     33      59      44  
43     Davidovich Fokina      25.6     61      46      28  
44     Roman Safiullin        27.4     60      38      27  
45     Nicolas Jarry          29.2     35      63      20  
46     Nuno Borges            27.9     36      53      88  
47     Thanasi Kokkinakis     28.7     77      24      61  
48     Luciano Darderi        22.9     44     106     122  
49     Miomir Kecmanovic      25.3     54      65      71  
50     Jan Lennard Struff     34.7     42      26      35  
51     Joao Fonseca           18.4    145      45

The men’s ranking model is more accurate than the women’s version, though that may be because it is built, in part, on the unusually stable Big Three/Big Four era. That stability might be gone, taking the reliability of this model with it. (The men’s model predicted the log of next year’s ranking with an adjusted r-squared of .631, compared to .580 for the women.) So again, if it looks boring, that’s the nature of the beast.

Still: We have Carlos Alcaraz taking back the number two spot, Holger Rune returning to the top ten, and Jack Draper following him in. In the other direction, we see Grigor Dimitrov’s age catching up to him, dropping five spots from his current position.

At the bottom of the list, we find Joao Fonseca bounding up nearly 100 ranking spots in a single season. That already feels conservative, less than one week into his season. All of these numbers are based on 2024 year-end rankings, yet Fonseca is up 18 places in the live rankings with his run to the Canberra Challenger final. He’d gain another 14 with a win tomorrow.

What about Novak?

The table above shows Novak Djokovic in 6th place, a prediction that aggregates a vast range of possibilities. Here are the odds of various players ending 2025 at the top of the list:

Player             p(#1)  
Jannik Sinner     56.4%  
Carlos Alcaraz    22.5%  
Novak Djokovic    14.6%  
Alexander Zverev   3.8%  
Daniil Medvedev    3.4%  
Taylor Fritz       1.3%  
Holger Rune        1.2%  
Jack Draper        1.2%  
Hubert Hurkacz     1.0%  
Grigor Dimitrov    0.7%

No one else is even half as likely as Dimitrov to end the season ranked #1. Sinner is the clear favorite, with virtually every stat in his favor. Alcaraz is expected to improve. Djokovic, though, is the clear number three, far ahead of the other players above him in the previous table.

This is partly to be expected: He ended 2024 in second place on the Elo list. He didn’t play a full schedule, but he posted great results much of the time he played, and Alcaraz wasn’t consistent enough to capitalize on the veteran’s step back. Beyond that, remember that the model considers last year’s Elo rank as well. Twelve months ago, Djokovic still had a strong claim to be the best player in the world. His age counts against him, but he is one of only a few players in the 2025 field who has proven he can reach the top.

Novak’s 6th-place forecast, then, averages a disproportionately high probability of a resurgence with all the things that can happen to 37-year-old athletes. He’s more likely than, say, (projected) #5 Medvedev or #7 Rune to claim the top spot, but he’s also more likely to fall down the list due to injury or lack of interest.

Djokovic looks like less of an outlier when we see the chances of top-5, top-10, and top-20 finishes this year:

Player                  p(5)  p(10)  p(20)  
Jannik Sinner          95.6%  98.9%  99.8%  
Carlos Alcaraz         84.5%  95.7%  99.2%  
Alexander Zverev       61.7%  88.4%  97.5%  
Daniil Medvedev        38.5%  71.8%  92.6%  
Taylor Fritz           34.1%  72.0%  92.9%  
Novak Djokovic         32.4%  59.8%  86.4%  
Holger Rune            20.3%  52.8%  86.1%  
Jack Draper            15.6%  46.3%  82.2%  
Hubert Hurkacz          9.8%  29.9%  68.2%  
Andrey Rublev           9.8%  31.8%  70.8%  
Stefanos Tsitsipas      9.6%  31.6%  70.6%  
Alex de Minaur          9.5%  32.9%  72.1%  
Grigor Dimitrov         8.3%  27.0%  63.1%  
Casper Ruud             7.7%  31.1%  70.6%  
Tommy Paul              7.1%  26.8%  65.0%  
Ugo Humbert             5.3%  20.2%  56.9%  
Ben Shelton             4.8%  18.5%  55.8%  
Sebastian Korda         4.5%  17.8%  53.5%  
Tomas Machac            4.4%  18.3%  54.3%  
Arthur Fils             3.7%  17.0%  54.0%  
Lorenzo Musetti         3.4%  16.6%  52.3%  
Matteo Berrettini       2.4%   8.7%  32.2%  
Felix Auger Aliassime   2.1%   8.2%  32.7%  
Karen Khachanov         2.0%   8.8%  32.8%  
Frances Tiafoe          1.3%   6.3%  25.9%  
                                            
player                  p(5)  p(10)  p(20)  
Jiri Lehecka            1.0%   5.0%  22.7%  
Alexei Popyrin          0.9%   5.4%  23.1%  
Francisco Cerundolo     0.8%   3.8%  17.3%  
Flavio Cobolli          0.7%   4.5%  20.7%  
Jakub Mensik            0.7%   4.1%  20.0%  
Alex Michelsen          0.7%   4.2%  20.2%  
Matteo Arnaldi          0.7%   3.0%  14.7%  
Tallon Griekspoor       0.6%   2.4%  11.0%  
Brandon Nakashima       0.5%   2.8%  13.9%  
Denis Shapovalov        0.5%   2.2%  10.5%  
Sebastian Baez          0.5%   2.5%  12.6%  
Mpetshi Perricard       0.5%   3.3%  16.2%  
Jordan Thompson         0.4%   2.3%  10.3%  
Davidovich Fokina       0.4%   1.5%   7.6%  
Roman Safiullin         0.4%   1.5%   7.1%  
Juncheng Shang          0.3%   2.0%  10.8%  
Nicolas Jarry           0.3%   1.2%   5.7%  
Thanasi Kokkinakis      0.3%   1.2%   5.7%  
Alexander Bublik        0.3%   1.3%   6.6%  
T M Etcheverry          0.3%   1.4%   7.1%  
Alejandro Tabilo        0.2%   1.5%   7.5%  
Jan Lennard Struff      0.2%   1.0%   4.3%  
Joao Fonseca            0.2%   1.0%   5.7%  
Nuno Borges             0.2%   1.0%   5.2%  
Miomir Kecmanovic       0.2%   0.9%   4.5%

The various models don’t quite agree: It can’t really be the case that if Djokovic cracks the top five (32.4% here), it’s nearly 50/50 whether he ends the season at number one. From outside of the models, we can be particularly skeptical, since we know that Novak isn’t likely to play a full schedule. Still, we can glean something from the juxtaposition: There’s not a lot of middle ground for the all-time-great.

Again, it’s worth peeking at the bottom of the list. Fonseca makes this one, too, with a nearly 6% chance of a top-20 debut this year. (Actually, a debut is even more likely, since this is the stricter probability of a year-end top-20 finish.) It seems a bit crazy to say that the 18-year-old has the same top-20 chances as Nicolas Jarry. On the other hand, Fonseca leads Jarry on the Elo table by a healthy margin. He may already be the stronger player.

Few pros are likely catapult up or down the rankings like Fonseca. Plenty will make moves that these models don’t foresee. With the information available at the beginning of the season, we can get a general sense of how things will change over the next twelve months. Now for the good part: We get to find out how the models were wrong.

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The Pending Breakthroughs of 2025

*Eva Lys, probably a top-100 player in 2025*. *Credit: Nuta Lucian*

Every year, Challenger maven Damian Kust lists the players he thinks are likely to join the ATP top 100 in the coming year. He did a typically good job last year, picking 14 of the 20 players who reached the threshold in 2024. We can forgive him for missing Jacob Fearnley, who rose from 646th to the top 90 in less than twelve months.

I’ve yet to meet a forecast that I didn’t want to mathematically model, and this is no exception. An algorithm probably isn’t going to do better than Damian does, as it will miss all kinds of details accumulated by a full-time tour watcher. But the exercise will give us a better idea of what factors make it more or less likely that a player joins the top-100 club.

Let’s get straight to the forecast:

Rank  Kust  Player               Rank  Elo Rk   Age  p(100)  
1     3     Joao Fonseca          145      45  18.4   96.5%  
2     4     Learner Tien          122      74  19.1   92.4%  
3     1     Hamad Medjedovic      114      91  21.5   89.1%  
4     5     Nishesh Basavareddy   138      84  19.7   84.2%  
5     9     Raphael Collignon     121      97  23.0   82.5%  
6     8     Martin Landaluce      151      99  19.0   82.1%  
7     6     Jerome Kym            134     111  21.9   79.6%  
8           Leandro Riedi         135     108  22.9   71.9%  
9     15    Jaime Faria           123     146  21.4   69.0%  
10    7     Jesper de Jong        112     117  24.6   66.8%  
11    12    Tristan Boyer         133     116  23.7   64.0%  
12    2     Francesco Passaro     108     147  24.0   60.9%  
13          Harold Mayot          116     154  22.9   57.6%  
14    10    Alexander Blockx      203     102  19.7   56.8%  
15    16    Valentin Vacherot     140     110  26.1   55.2%  
16    11    N Moreno de Alboran   110     132  27.5   52.5%  
17          Lukas Klein           136     126  26.8   47.0%  
18    19    Elmer Moeller         160     160  21.5   37.4%  
19    18    Duje Ajdukovic        142     171  23.9   36.6%  
20          Terence Atmane        158     174  23.0   35.5%  
21          R A Burruchaga        156     177  22.9   28.1%  
22          Matteo Gigante        141     203  23.0   26.8%  
23    13    Vit Kopriva           130     150  27.5   26.3%  
24          Gustavo Heide         172     190  22.8   24.3%  
25          Coleman Wong          170     238  20.6   24.3%  
            …                                                
35    14    Mark Lajal            229     187  21.6   13.4%  
            …                                                
41    17    Dino Prizmic          292     167  19.4   10.6%  
42    20    James Trotter         193     175  25.4   10.4%

The table shows the 25 men who are most likely to make their top-100 debut this year, plus a few more from Damian’s list. I’ve included Damian’s rankings*, as well as each player’s year-end ATP ranking, year-end ranking on my Elo list, and their current age. The final column, “p(100),” is their probability of reaching the ranking milestone sometime in 2025.

* Damian points out that his numbering wasn’t intended as an explicit ranking, though he did end up picking the more obvious players first, with the long shots at the end.

The three columns between the players and their probabilities are the main components of the logistic-regression model. Age, unsurprisingly, is key. The younger the player, the more likely he’ll improve. Plus, the youngest men may have played limited schedules, causing their official rankings to underestimate their ability levels.

It’s a bit unusual to include both ATP rank and Elo rank, since they are simply different interpretations of the same underlying match results. In this case, though, it makes sense. Elo is better at predicting a player’s performance tomorrow, and it outperforms the official list as a way of predicting rankings a year from now. However, we’re trying to forecast ranking breakthroughs less than a year from now. If Fonseca has a good month Down Under, he’ll crack the top 100 in large part thanks to his eleven months’ worth of ranking points from 2024. In this model, then, the ATP ranking tells us how close a player is to the point total he needs. Elo tells us more about how likely he is pile up the remaining wins.

A player’s existing stock of points turns out to be somewhat more important than his underlying skill level. The model weights ATP ranking about half-again as heavily as Elo rank.

There are innumerable other variables we could include. I tested a lot of them. The only other input I kept was height. Height is only a minor influence on top-100 breakthroughs, but it’s definitely better to be taller. De Jong, for instance, is five feet, eleven inches tall. He ranks eighth on the 2025 list when height is omitted, and falls to tenth when height is included.

This tallies with the Challenger-to-tour conversion stats I worked out for my recent post about Learner Tien. Both short players and left-handers have a harder time making the jump than their taller, right-handed peers. Those conversions don’t address quite the same thing, since it’s possible to crack the top 100 with little to no success at tour level–it just means winning lots of Challengers. For that reason, left-handedness is probably an advantage for players aiming to jump from, say, 122nd to the top 100, as Tien is now. The relationship between left-handedness and breakthrough likelihood was less clear-cut than height, though, so I left it out.

J-wow

Enough mechanics–back to the forecasts. Fonseca’s 96.5% probability might strike you as crazily high or outrageously conservative. It’s certainly confident, but then again the Brazilian is a special player. Barring injury–and immediate injury, at that–a breakthrough seems likely to happen soon.

Whether high or low, the Fonseca forecast is unusual. Like his forehand, it puts him in classy company. Going back to 2000, here are the players about whom the model would have been most optimistic:

Year  Player                 Rank  Elo    Age  p(100)  Y+1  
2021  Holger Rune             103    50  18.7   98.7%   10  
2020  Sebastian Korda         118    48  20.5   97.9%   38  
2024  Joao Fonseca            145    45  18.4   96.5%       
2010  Grigor Dimitrov         106    75  19.6   96.3%   52  
2020  Carlos Alcaraz          141    51  17.7   96.1%   32  
2018  Felix Auger Aliassime   108    89  18.4   95.8%   17  
2023  Hamad Medjedovic        113    66  20.5   95.4%  105  
2000  Andy Roddick            156    52  18.3   94.5%   14  
2020  Lorenzo Musetti         128    68  18.8   94.0%   57  
2019  Emil Ruusuvuori         123    64  20.7   94.0%   84

It’s not so remarkable that eight of the nine other players on the list succeeded in reaching the top 100. The forecast would have expected (at least) that. But even including Medjedovic’s disappointing finish to 2024, the average ranking of these nine guys at the end of the following season (“Y+1”) is 45. Three broke into the top 20. And Fonseca’s forecast places him ahead of most of them.

Medjedovic’s near-miss was due in part to illness. It’s worth remembering that this model only predicts a single year; the young Serbian is still set up for a nice career. (Including, probably, a top-100 debut in 2025.) The model would have given Francisco Cerundolo a 90% chance of breaking through in 2021. He didn’t make it, yet he reached the top 20 a couple of years later. Fernando Gonzalez failed to convert an 80% chance in 2001, but after a few more years, he made the top ten.

Using a simple model–instead of the expert opinion of someone like Damian–exposes us to another type of error. The model is optimistic about the 2025 chances of 22-year-old Leandro Riedi, who possesses both official and Elo ranks on the cusp of the top 100. On paper, he’s a great pick. But he had knee surgery in September. Instead of defending points from two Challenger titles in January, he’s continuing to recover. He may ultimately surpass many of the other guys on the list, but even just regaining his pre-injury form this year is a big ask.

Waiting for Eva

Let’s run the same exercise for the women’s game. Unfortunately I don’t have enough height data, so we can’t use that. The resulting model is less predictive than the men’s forecast (even apart from the lack of player heights), but with year-end WTA rank, Elo rank, and age, it’s almost as good.

Patrick Ding took up the task of a Kust-style list for women. It’s unordered, so I’ve added a “Y” in the “PD” column next to his picks:

Rank  PD  Player                Rank  Elo   Age  p(100)  
1     Y   Eva Lys                131   43  23.0   80.1%  
2     Y   Anca Todoni            118  100  20.2   74.9%  
3     Y   Maya Joint             116  173  18.7   65.8%  
4         Aoi Ito                126  109  20.6   65.4%  
5     Y   Marina Stakusic        125  131  20.1   62.3%  
6     Y   Polina Kudermetova     107  159  21.6   61.8%  
7     Y   Alina Korneeva         177   80  17.5   61.8%  
8     Y   Robin Montgomery       117  155  20.3   61.1%  
9     Y   Sara Bejlek            161   88  18.9   59.9%  
10        M Sawangkaew           130   94  22.5   58.8%  
11        Anastasia Zakharova    112  145  23.0   54.1%  
12    Y   Sijia Wei              134  135  21.1   49.9%  
13    Y   Celine Naef            153  124  19.5   48.8%  
14    Y   Antonia Ruzic          143  105  21.9   48.7%  
15        Maja Chwalinska        128  119  23.2   47.7%  
16    Y   Sara Saito             150  182  18.2   43.1%  
17        Alexandra Eala         148  162  19.6   41.6%  
18    Y   Darja Semenistaja      119  192  22.3   41.5%  
19    Y   Dominika Salkova       151  150  20.5   38.1%  
20        Talia Gibson           140  185  20.5   37.2%  
21        V Jimenez Kasintseva   156  170  19.4   36.3%  
22    Y   Ella Seidel            141  205  19.9   36.2%  
23    Y   Iva Jovic              189  157  17.1   33.8%  
24        Daria Snigur           139  161  22.8   32.0%  
25        Francesca Jones        152  106  24.3   31.5%  
26    Y   Solana Sierra          163  156  20.5   30.2%  
27    Y   Ena Shibahara          137  103  26.9   29.1%  
28        Lois Boisson           204   95  21.6   23.9%  
29        Elsa Jacquemot         159  191  21.7   21.8%  
30    Y   Taylah Preston         170  246  19.2   20.0%  
31    Y   Tereza Valentova       240  127  17.9   19.6%  
32        Elena Pridankina       186  201  19.3   18.9%  
33        Lola Radivojevic       185  186  20.0   18.9%  
34    Y   Oksana Selekhmeteva    176  176  22.0   16.8%  
35        Barbora Palicova       180  202  20.8   16.2%

This isn’t quite a fair fight with Patrick, because he made his picks in early October. Two of his choices (Suzan Lamens and Zeynep Sonmez) have already cleared the top-100 hurdle. He would presumably consider Ito more carefully now, since she reached a tour-level semi-final two weeks after he made his list. I should also note that Patrick picked two prodigies outside the top 300: Renata Jamrichova and Mia Ristic. My model didn’t consider players ranked that low. I had to draw the line somewhere, and Fearnley aside, single-year ranking leaps of that magnitude are quite rare.

The mechanics of the algorithm are pretty much the same as the men’s version. The women’s list looks a bit more chaotic, pitting players with strong Elo positions (such as Lys and Korneeva) against others who are close to 100 without the results that Elo would like to see (Joint, Kudermetova, etc).

Eva Lys is fascinating because this is her third straight year near the top of the list. She finished 2022 ranked 127th, standing 71st on the Elo table. Just short of her 21st birthday, that was good for a 76% chance of reaching the top 100 the following year–second on the list to Diana Shnaider. She rose as high as 112, but no further.

A year older, Lys was fourth on the 2023 list. Her WTA ranking of 136 and her nearly-unchanged Elo position of 72 worked out to a 67% chance of a 2024 breakthrough. Only three players–Brenda Fruhvirtova, Erika Andreeva, and Sara Bejlek–scored higher. She came within one victory of the milestone in September but finds herself back on the list for 2024.

Even beyond Lys’s 80% chance of finally making it in 2025, history is encouraging. I went back 25 years for this study, and only two other players would have been given a 50% or better chance of reaching the top 100 for three consecutive years. Stephanie Dubois was on the cusp from 2005 to 2007, finishing the third year ranked 106th. She finally made it in 2008. More recently, Wang Xiyu was within range from 2019-21. (Covid-19 cancellations and travel challenges didn’t help.) She not only cleared the hurdle in 2022, she did it with style, climbing to #50 by the end of that season.

The same precedents bode well for Bejlek, who had a 52% chance of breaking through in 2023, a 77% chance last year, and a 60% probability for 2025.

Mark your calendars

In twelve months, we can check back and see how the model fared against Damian and Patrick. The algorithm has the benefit of precision, and it is less likely to get overexcited about as-yet-unfulfilled potential. The flip side is that it doesn’t consider the innumerable quirks that might bear on the chances of a particular player.

For now, I’m betting on the humans.

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Joao Fonseca and Damage Down the Line

Joao Fonseca made easy work of last week’s Next Gen Finals. He went undefeated against the world’s best under-21s, dropping just one set in best-of-five semi-finals and finals. The youngest player in the field, he joins Jannik Sinner and Carlos Alcaraz as 18-year-old champions in the seven-year history of the event.

There’s no secret to Fonseca’s success. He already possesses a devastating forehand, a shot with power that invites comparisons to fellow South Americans Juan Martin del Potro and Fernando Gonzalez. Outrageous as it sounds, Fonseca’s might turn out to be even better. The Brazilian has a more compact stroke, making his forehand more flexible–and perhaps ultimately more reliable–than the long-levered Delpo motion.

In his round-robin match against Jakub Mensik, the TennisTV broadcast flashed a stat meant to indicate one way Fonseca excels:

The TennisTV commentators were flabbergasted by these numbers: They’d never seen anything like it. Hugh, responsible for the screenshot here, got straight to the point. If a player with a forehand like Fonseca’s can consistently send the ball to his opponent’s weaker side, it’s game over.

The easiest direction to hit a groundstroke is back the way it came. That’s how we end up with protracted cross-court rallies. It takes impeccable timing to react to a elite-level cross-court forehand and change direction. (Pros drill that specific sequence, but no amount of practice makes it easy.) Down-the-line shots are doubly difficult because the net is higher as it nears the posts. The timing needs to be near-perfect, and there’s a smaller margin for error.

Here’s another way to get a sense of the dangerous risk level of down-the-line groundstrokes. Novak Djokovic, king of the down-the-line backhand, doesn’t hit that many of them. The inherent limitations of tennis rackets and the dimensions of the court are unforgiving. Fonseca, if he can really hit more than half of his forehands down the line, could defy those limitations.

Fact-check

Normally, I’d give you all sorts of numbers to help anchor Fonseca’s stats. How much does his 56% clip exceed tour average, or compare to someone like Sinner? That’s my goal, but first, we need to get into the weeds a bit.

The Match Charting Project has 14 Fonseca matches in the database, including all five of his NextGen Finals contests. Here’s the breakdown of groundstroke direction for the Mensik match:

This is… not the same as the broadcast graphic. We should expect minor differences, both because the 44/56 stat was shown midway through the match, and because reasonable people can disagree about how to classify shots that don’t obviously belong to a specific category. But that’s not what’s going on here. There’s virtually no way to take these numbers and conclude that Fonseca hit more than half of his forehands down the line.

What’s more, Fonseca’s forehand-direction profile is rather pedestrian. That’s not to say that his forehand is ordinary, just that he aims in the same directions as his peers. Here is Joao’s forehand-direction distribution, based on those 14 charted matches, compared with tour average:

DIRECTION      Fonseca  Average  
Cross-court        40%      39%  
Middle             20%      22%  
Down the line      10%      11%  
Inside-out         26%      24%  
Inside-in           4%       4%

Ho-hum, right? Maybe he hits a few more inside-out forehands instead of going back up the middle. Even there, the two-percentage-point gaps between Fonseca and tour average could be an artifact of the matches we’ve charted. The Brazilian’s forehand does plenty of damage, but as a function of how he hits it, not where.

More weeds, sorry

I should probably let the discrepancy go, but let’s give it another minute. I don’t think the broadcast graphic was wrong, but it’s clearly measuring something different than what I count for the Match Charting Project stats. Maybe there’s some important subset of forehands that Fonseca is unusually likely to hit down the line?

One of the more difficult–and damaging–specific shots is the forehand down the line from the forehand corner. The MCP divides the court into three sectors by width: to the (right-hander’s) forehand corner, down the middle, and to the backhand. Maybe Fonseca particularly likes to change direction when he sees a ball in his own forehand corner?

A bit, but not by much. Here are the direction frequencies for Fonseca’s forehands from his forehand corner, for both the Mensik match and the average of his charted matches, along with the frequencies for his opponents:

             To FH  Middle    DTL  
vs Mensik    58.1%   22.6%  19.4%  
Fonseca Avg  49.0%   23.6%  27.5%  
Opp Avg      45.7%   30.9%  23.4%

Nothing really dramatic here, and he went down the line less often in the Mensik match than his typical opponent does.

What about when Fonseca gets a ball down the middle? We saw in the MCP stats above that he hits a lot of inside-out forehands. That includes shots from the middle to the backhand side of a right-handed opponent. Here is the same group of frequencies for the same sets of matches, this time excluding left-handed opponents because they will naturally make different choices from the middle of the court:

              To FH  Middle    DTL  
vs Mensik     29.7%   25.7%  44.6%  
Fonseca Avg   39.7%   20.0%  40.3%  
(RH) Opp Avg  34.2%   25.4%  40.4%

There’s a few more down-the-line shots in the Mensik match. But over 14 matches, Fonseca differs from his opponents only in hitting fewer balls back up the middle.

Maybe you’ve already worked out the underlying discrepancy. Since the 44/56 split adds up to 100%, there’s no “down the middle” category in the broadcast stats. The graphic splits forehands into two buckets, not three. That’s not how I think about tennis, and I suspect it’s not how you do, either. They would have done better to label the columns “to the forehand” and “to the backhand,” or something along those lines. It’s not unusual at all to hit 56% of one’s forehands to the opponent’s backhand side. But a lot of those “to the backhand” shots are not what anybody would normally call “down the line.”

The Fonseca difference

It’s not about tactics, it’s good old-fashioned power and precision. Fonseca’s forehand isn’t innovative, and it doesn’t need to be. If he hits his shots in more or less the same directions that his peers do, he’s probably doing something right. It means that at age 18, he has already internalized pro tactics. The difference is that he’s hitting those forehands harder, and he’s often landing them closer to the lines, something hinted at by his low rate of down-the-middle forehands.

He already shows up near the top of my Forehand Potency (FHP) leaderboard–though I’ll give you some caveats in a minute:

Rank  Player              FHP/100  
1     Andrey Rublev          14.0  
2     Jan-Lennard Struff     11.6  
3     Joao Fonseca           11.3  
4     Stefanos Tsitsipas     11.0  
5     Carlos Alcaraz         10.8  
6     Rinky Hijikata          9.7  
7     Jannik Sinner           9.4  
8     Casper Ruud             9.2  
9     Juncheng Shang          8.6  
10    Novak Djokovic          8.6

FHP combines forehand winners and errors, along with shots that lead to both opponent errors and winners on the player’s next shot. Given the vagaries of estimating the effect of one shot on others, Fonseca effectively sits in a tie for second place, as good or better than everyone except for Andrey Rublev. Which, I’d say, checks out.

The caveats lie in the dataset. The Brazilian has faced only one top-20 opponent, and that was a possibly-unmotivated #20 Arthur Fils last week. The charting data on which FHP numbers are based includes all the NextGen Finals matches, along with some Challenger-level matches and some early rounds at ATPs. After all, he’s 18 and that’s all he’s played. Point being, an 11.3 FHP/100 (per 100 forehands) against that level of competition probably isn’t is good as Alcaraz’s 10.8 or Sinner’s 9.4, amassed against foes like each other.

But don’t take that adjustment too far. Fonseca scored a 11.0 against Fils in Rio, when he was barely 17 and a half. Facing Botic van de Zandschulp in a Davis Cup tilt, he registered a whopping 16.7 FHP/100. The Dutchman is hardly easy pickings: When Sinner played van de Zandschulp twice early in the season, he managed just 1.2 and 7.1 on the same metric.

Damage (not just) down the line

Fonseca doesn’t hit an unusual number of forehands down the line, but when he does, opponents barely stand a chance. Among the 200-plus players with as many down-the-line forehands in the MCP database as Fonseca has, he ranks sixth in points won when he hits the shot. Admittedly, it’s an odd list:

Rank  Player                   W/FE%   UFE%  inPtsWon%  
1     Juncheng Shang           33.6%  13.1%      66.4%  
2     Nishesh Basavareddy      29.4%  10.3%      65.1%  
3     Luca Van Assche          22.9%  11.0%      62.4%  
4     Hyeon Chung              30.1%  19.9%      61.8%  
5     Bjorn Borg               26.3%   7.3%      61.6%  
6     Joao Fonseca             31.1%  17.6%      61.3%  
7     Rafael Nadal             28.9%  12.8%      61.2%  
8     Camilo Ugo Carabelli     10.3%   9.6%      60.9%  
9     Corentin Moutet          29.4%  15.6%      60.6%  
10    Zhizhen Zhang            22.8%  15.9%      60.3%  
11    Guillermo Garcia Lopez   20.3%   5.9%      60.1%  
12    Roberto Carballes Baena  10.1%   5.8%      59.7%  
13    Carlos Alcaraz           26.7%  14.3%      59.7%  
14    Grigor Dimitrov          23.8%  13.4%      59.3%  
15    Juan Martin del Potro    26.8%  12.7%      59.2%  
                                                        
      Average                  20.1%  15.2%      53.4%

The 2024 Next Gen field is bizarrely well-represented, with Shang, Basavareddy, and Van Assche leading the way. Is this the age of the deadly down-the-line forehand? Some of the same caveats apply here as with the FHP list: The youngsters have played a different sort of opponent than Nadal, Alcaraz, or (!) Borg. The clay-courters on the list also make for awkward comaprisons. For dirtballers, the down-the-line forehand is a way to build points, not end them.

It’s clear that Fonseca loves this play. He ends points in his favor (with a winner or forced error) more than anyone on this list except for Shang.

Here’s the scary thing: A few clay-court matches are severely dragging down the Brazilian’s numbers. On hard courts, he moves up to second on the list, winning 68% of points in which he hits a down-the-line forehand. The shot ends points outright an incredible 48% of the time. Delpo’s numbers, though of course against stronger competition, pale in comparison, at 60% and 39%, respectively.

Some of the difference between Fonseca and the field is that his forehand is great, period. If you’ve got an extra ten miles per hour that the average player doesn’t, that’s going to show up in every direction, not just one. And for the most part, that’s what we see in Joao’s points won when hitting each category of forehand:

FH Pts Won     Fonseca  Tour  
Cross-court        58%   54%  
Middle             52%   46%  
Down the line      59%   53%  
Inside-out         57%   57%  
Inside-in          54%   59%

He’s better than average in all but the rarest of the five forehand directions. Even that isn’t really a negative: On hard courts he does better than tour average with the inside-in forehand.

The eye-catcher on that chart is 52% of points won when hitting a down-the-middle forehand. The typical player is likely to lose a point when hitting that shot. That’s not necessarily because down-the-middle forehands are bad, but because if you need to hit one, the point probably isn’t going your way. Unlike the other categories of forehands, it’s usually a defensive shot.

Of the 220-plus players with as many down-the-middle forehands as Fonseca in the MCP database, only 23 win more than half of those points. The Brazilian ranks fourth by points won when hitting down-the-middle forehands. It’s another oddball list, with Pablo Andujar, Tim Smyczek, Gilles Simon, and Carlos Berlocq rounding out the top five. (Yes, really.) To the extent we can group them together, those four men played a different brand of tennis, winning points with conservative shots as they wore down their opponents.

The more telling stat is that Fonseca actually ends points by clubbing forehands down the middle. The average player hits a winner or induces a forced error with less than 2% of these strokes. Joao comes in at 6.4%, better than anyone else in the dataset:

Rank  Player               W/FE%  inPtsWon%  
1     Joao Fonseca          6.4%      52.8%  
2     Fernando Gonzalez     6.0%      43.2%  
3     Christopher Eubanks   5.7%      38.8%  
4     Thanasi Kokkinakis    5.0%      46.1%  
5     Tomas Machac          4.9%      43.8%  
6     Nicolas Jarry         4.7%      41.7%  
7     Alexei Popyrin        4.4%      41.8%  
8     Sam Querrey           4.3%      41.8%  
9     Max Purcell           4.0%      38.5%  
10    Lorenzo Sonego        3.9%      46.0%  
11    Matteo Arnaldi        3.8%      49.5%  
12    Lucas Pouille         3.4%      42.9%  
13    Jan-Lennard Struff    3.3%      45.6%  
14    Matteo Berrettini     3.3%      42.1%  
15    John McEnroe          3.3%      44.0%  
16    Otto Virtanen         3.2%      35.8%  
17    Boris Becker          3.2%      44.1%  
18    Nick Kyrgios          3.2%      43.2%  
19    Roger Federer         3.2%      47.6%  
20    Carlos Alcaraz        3.2%      50.2%

When you can end points with your forehand twice as often as Federer did, you’re doing something right. The only players even close to the Brazilian’s winner rate end up losing far more points, probably because they need to take many more risks to get that small sliver of positive outcomes.

One more time for the road: Fonseca’s numbers are probably inflated due to his level of competition. In 2025, we’ll see how his forehand holds up against the elites. If we revisit these numbers twelve months from now, he’ll probably come down a notch. But raw power plays at every level. No matter who stands across the net, Fonseca’s forehand is fearsome–in all directions.

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Can Learner Tien Hang With the Big Boys?

*Learner Tien at the 2024 US Open. Credit: Hameltion*

Learner Tien has done little in 2024 except win. He reeled off a 28-match streak from May to late July, collecting five titles, including his first at the Challenger level. He reached the quarter-finals at the tour event in Winston-Salem. After picking up two more Challenger crowns and another final, the young American opened his NextGen Finals campaign yesterday with a victory over top-50 player Jakub Mensik, 21st on the Elo list.

If you don’t follow prospects, you can be forgiven if you’ve only recently learned the name. Tien is only two weeks removed from his 19th birthday. He opened the year only barely inside the top 500. There were plenty of reasons to expect big things from the young man–a national 18s title at 16, two junior slam finals–but it would have been foolish to predict so much, so soon.

One reason to moderate expectations is simply age. For those not named Alcaraz or Sinner, it takes time to develop into a top player. Only one man under the age of 21–the cutoff for this week’s event in Jeddah–is ranked inside the top 40. Before Tien turned 19 this month, he was the top-ranked 18-year-old in the world, even with a triple-digit number next to his name.

The climb to the top is even more challenging for youngsters who can’t rely on pure power. Mensik, the highest-ranked teenager, is six feet, four inches tall, with weapons that make him seem bigger. Novak Djokovic recently called him “one of the best servers we have in the game.” The Czech has plenty to learn, and he will surely continue to refine his game. But to compete at the top level, he doesn’t have to.

Tien doesn’t have that luxury. He stands five inches shorter than Mensik. While he may have a bit more growth coming, five-eleven is near the bottom edge of what can be managed on the ATP tour. Only 15 members of the top 100 stand less than six feet, and even that list is skewed toward clay-court specialists. Sebastian Baez is the only five-foot-anything ranked above 45th.

The playing styles available to shorter athletes are limited, especially on hard courts. Tien has already demonstrated his mastery of many of those tactics. He can use his left-handedness to swing serve after serve wide, to a righty’s backhand. He is sturdy from the baseline, and you can take that literally: He’s unafraid of claiming territory right up to the line itself, taking advantage of both his quickness and raw speed. Fearless counterpunching has paid dividends for smaller stars from Olivier Rochus to Kei Nishikori to Alex de Minaur. As a lefty, the American has options those men didn’t.

Still, Tien’s transition from the Challenger tour to the big leagues could be rocky. Good defense and well-executed tactics are enough to clean up against top-200 competition. The combination was (just barely) sufficient against Mensik yesterday. But a full-time spot on the ATP tour requires more.

The game plan

For such a middling server, Tien wins a remarkable number of serve points. He ranks among the top quarter of Challenger tour regulars by serve points won, though his number is helped a bit by spending the entire year on hard courts. He does even better–64.6% compared to a tour norm below 62%–when aces and double faults are taken out of the equation. When the returner gets a racket on the ball, only ten players were better on hard courts.

It’s not surprising, then, that Tien excels on return. Among Challenger players with at least ten matches at the level in 2024, only two men–Dalibor Svrcina and his fellow American in Jeddah, Nishesh Basavareddy–topped Learner’s 42.5% clip. Tien is particularly effective converting second-serve return points.

He’s even better–or at least, he has been better this season–with more on the line. His rate of return points won rises to nearly 47% on break point chances, and he’s just as clutch on the other side of the ball. He saved 65.6% of the break points he faced, second at Challenger level to Mikhail Kukushkin. Here, he has already learned how to use the lefty serve, alternately forcing opponents far out wide and sticking them with uncomfortable body serves when he catches them leaning left for the slider.

The overall package is something between those of two other left-handers, Adrian Mannarino and Cam Norrie. Mannarino, also a sub-six-footer, throws the kitchen sink at opponents, keeping them off balance to compensate for his own lack of power. Norrie is considerably taller and has more firepower at his disposal. But he, too, refuses any rhythm to the man across the net. He alternates a loopy forehand with a flat backhand–except when he doesn’t, if you ever think you’ve found a groove.

Tien serves like Mannarino out of necessity. Even if he doesn’t get taller, the American will surely get stronger, so his 90-mile-per-hour first serves from this year’s US Open probably won’t tell the story of his entire career. But at the moment, he relies on angles and variety. Mannarino has overcome his limitations to the tune of a top-20 peak ranking. On the other hand, his playing style (and the comically loose string tension it relies on) is so unique he hardly provides an example to follow.

In the Las Vegas Challenger final back in September, Tien looked particularly like Norrie. Fighting the wind, he spun forehands and zinged backhands, a combination that made it impossible for the bigger-hitting Tristan Boyer to get comfortable. In other settings, though, the youngster is increasingly using his forehand as a (flatter) weapon, building points one sharp angle at a time.

The most instructive element of these comparisons, though, is the way in which the American differs from his fellow lefties. Mannarino reached Tien’s current ranking when he was 21, after more than 250 pro matches. Norrie–who ultimately peaked inside the top ten–played three years of college tennis and didn’t approach the top 100 in the world until he was 22. Tien, by contrast, is clearing all these hurdles on the first try. Deploying a brainy playing style that normally takes years to refine, the American is making it look natural.

The projection

Aside from size and serve speed, Tien’s future looks bright. The 19-year-old has won 61 of 73 matches across all levels this year. Within a few months, he is likely to crack the top 100. At Challenger level, his serve hasn’t held him back: As we’ve seen, he wins more service points than most of his peers, despite gaining fewer free points with the serve itself.

The question, then, is what effect Tien’s attributes have on career trajectory. Everyone wins fewer points at tour level than at Challengers–the competition is better, so it would be weird if it were otherwise. But the ratio isn’t uniform. Mannarino has won about 7% fewer serve points at tour level than he did in hard-court Challenger matches, while Marcos Giron (another sub-six-footer) lost less than 1% in the transition.

These Challenger-to-tour conversions offer some insight into Learner’s future. Since he has played almost all of his pro matches on hard courts, we’re going to calculate something a bit quirky. How do serve and return win rates change from hard-court Challenger matches to all tour-level matches? That’s what we want to know for the 19-year-old: He’ll need to play on all surfaces soon, probably starting in 2025. This transition he’s about to make–how did it go for other players?

The first-pass answer is that pros are able to retain something like their hard-court Challenger serve win percentage, seeing that number drop by 2%. But they lose a lot against tougher competition on return, winning 7.1% fewer return points. The following table shows those numbers (“Conv%”), along with Tien’s career record at hard-court Challengers (“Tien CH”), along with what the conversion factors suggest for his tour-level win rates (“Tien Adj”):

        Conv%  Tien CH  Tien Adj  
Serve   98.0%    63.3%     62.0%  
Return  92.9%    42.3%     39.3%

Those are awfully respectable numbers. 62% serve points is marginal for a tour regular, but combined with 39.3% return points, it’s enough. The combination is about what Francisco Cerundolo managed this year, and he’s ranked 30th in the world.

A word of caution: This type of conversion is not suggesting that Tien’s level is the same as Cerundolo’s now. The calculation involves taking each active player’s career records in tour and Challenger main-draw matches. That probably underestimates Tien’s potential, because most men play the majority of their Challenger matches after their 19th birthday. But a player’s career numbers will include their peak, which typically comes much later. At the very least, these numbers suggest Tien could reach Cerundolo’s level (or better) eventually.

The (other) adjustments

That’s just a first-pass number, because we haven’t gotten to height and handedness. Taking those into account does not help Learner’s case.

Lefties, it turns out, have a rougher transition than right-handers do. Here are the serve and return conversion factors, separated by hand:

        Lefties  Righties  
Serve     97.3%     98.1%  
Return    92.1%     93.0%

Not a huge difference, but hey, the margins in tennis are small. I suspect it is slightly harder for left-handers to move up a level for two reasons. First, the less experienced the opponent, the more valuable it is to be unusual, and lefties are certainly that, making up barely one-tenth of the player pool. At tour level, the novelty is gone: ATP regulars generally know how to handle left-handers.

Second, lefties are more likely to get by with what we might call “crafty” tennis, rather than power. (That’s related to the first reason: They’ve reached Challenger level because they’ve outsmarted inexperienced opponents thus far.) Craftiness might be enough against #180 in the world, but against, say, the Hurkacz serve, all craftiness gets you is a few more tuts of approval in the press box.

Whatever the reason, Tien’s left-handedness means we need to update our tour-level forecast:

    (L) Conv%  Tien CH  Tien Adj  
Serve   97.3%    63.3%     61.6%  
Return  92.1%    42.3%     39.0%

Not a huge hit, but ~0.4% of total points won is roughly equivalent to four places in the rankings. A small number here ultimately translates to much bigger ones when denominated by tour-level prize money.

And then, size. Here are the conversion factors for players in three height categories: under six feet, from six feet to six-foot-three, and above six-foot-three:

        under 6'0  6'0 to 6'3  over 6'3  
Serve       97.0%       97.9%     99.0%  
Return      92.0%       93.4%     92.6%

Again, craftiness doesn’t convert. Players under six feet tall lose the most points between hard-court Challengers and tour level. The tallest players remain almost as effective on serve, while the middle category retains the most of their return effectiveness.

Here’s the Tien update, using the sub-six-feet conversion rates:

        (< 6') Conv%  Tien CH  Tien Adj  
Serve          97.0%    63.3%     61.4%  
Return         92.0%    42.3%     38.9%

Not much of a difference from the left-handed numbers, though we keep going down. This is increasingly the profile of a clay-court specialist, and we might be outside the top 40 now.

Of course, Learner is both left-handed and (relatively) small. My mini-study of active players doesn't give us a big enough pool of data to extrapolate from the small group of small lefties. Instead, a back-of-the-envelope combination of the two factors gives us conversion factors of 96.3% for serve and 91.3% for return:

        (L&Sm) Conv%  Tien CH  Tien Adj  
Serve          96.3%    63.3%     61.0%  
Return         91.3%    42.3%     38.6%

For the first time, the adjusted versions of Tien's Challenger-level stats are underwater, summing to less than 100%. Winning 61% of service points would rate fourth-worst in the current ATP top 50, just ahead of Sebastian Baez. 38.6% on return is respectable, though not enough to consistently challenge for titles when combined with such a mediocre serve.

The exact numbers are not important: For one thing, we don't have enough recent data to know exactly how size and handedness interact. Maybe it's not quite that bad. Suffice it to say that both lefties and undersized players are more likely to struggle in the transition from Challengers to the full tour. A player who fits both categories should not expect a smooth trip up the ladder.

For Tien to beat these projections, all he has to do is improve more than the average pro does. As noted above, he already has something of an edge: He posted most of his excellent Challenger numbers as an 18-year-old. That's Alcaraz territory. At the same age, Mannarino was struggling at Futures level, and future top-tenner Norrie was headed off to college. If for some reason Tien plays a lot of Challenger matches in 2025, his stats will probably look better, and the tour-level predictions would change as well.

As Learner and his team are undoubtedly aware, those improvements need to center on the serve. The youngster probably already has what it takes to break serve once or twice a set on tour. But without a bigger first-strike weapon, he'll struggle to get those opportunities. Yesterday he withstood Jakub Mensik's event-record 24 aces, winning in a fifth-set tiebreak despite losing 14 more total points than Mensik did. The American played brilliant tennis, yet it took luck and brilliant timing to pull out the victory. For a five-foot-eleven left-hander among the giants of the professional game, it's not the last tightrope he'll have to walk.

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The Tensions of Exhibition Tennis

*The 2019 Laver Cup. Credit: Sportsfan77777*

2024 was a good year for exhibition tennis. The Saudi-banked Six Kings Slam set a new standard for prize money. Rafael Nadal and Carlos Alcaraz took the long tradition of Las Vegas tennis challenges to Netflix. The Ultimate Tennis Showdown made three glitzy stops. Novak Djokovic helped Argentina celebrate Juan Martin del Potro. Even Scandinavia got involved, with a home-and-home duel showcasing Casper Ruud and Holger Rune.

The tennis season is long. Put enough money on the table, though, and it can always get longer.

Exhibitions tend to highlight the gaps between the game’s haves and have-nots. Even the official tours are headed in that direction. The ATP and WTA aim to trim the number of small events to better focus attention on longer, 1000-level tournaments. Rumors persist of a Premier League-style “super tour” that would go even further.

It’s a delicate balance. You can’t hold exhibitions without bona fide stars. You can’t have stars without universally recognized events like Wimbledon. And you can’t have Wimbledon without a thriving ecosystem of tournaments that both identify contenders and allow future champions to develop. The Six Kings Slam was about as far as you could possibly get from an ATP 250 in Santiago, but one relies–however indirectly–on the other.

These tensions are not new. There has always been a scarcity of megastars whose celebrity transcended a couple dozen standard tour stops. Though the ultra-bankable Big Four is fading into history, other trends–exemplified by Saudi money and Netflix-style starmaking–will continue to raise the incentives for exhibition-style tennis. It’s too early to tell whether things will get better or worse, but they’ll almost certainly get different.

How did we get here?

For nearly as long as there have been tennis champions, there have been promoters trying to put them in front of more fans for more money. In 1926, Suzanne Lenglen, the greatest woman player up to that time, became the first superstar to go pro. Her 40-stop series was more like a modern concert tour than anything in the tennis world. She made $100,000 for three months’ work, the equivalent of nearly $2 million today.

Lenglen soon hung up her racket, but the template had been proven. For the final four decades of the amateur era, a rotating cast of standout players from Bill Tilden to Rod Laver slogged through grueling barnstorming tours. Apart from occasional appearances in New York, London, and Australia, it wasn’t glamorous. But it was a more reliable living than taking under-the-table “expense money” from organizers of amateur events.

It didn’t take long before the business model became clear. A pro tour could support four athletes: Two big names (preferably rivals), plus two more who could play a warm-up match, then later join their colleagues for doubles. The tour did best when one of the headliners was a recent Wimbledon champion. It wasn’t unusual for the newly-minted titlist at the All-England Club to sign a contract within days of collecting his trophy.

Attempts to broaden the base of professional tennis usually failed–or, at least, didn’t become any more than another quickie tour stop. To fill out a proper tournament field, promoters had to invite retired champions and teaching pros. The would-be pro “majors” had an appeal not unlike a senior tour event, giving fans a chance to see, say, Don Budge far past his prime.

Amateur officials, as you might expect, detested this state of affairs. Wimbledon was turned into a glorified qualifying tournament, the winner to receive a six-figure check to never appear at SW19 again. While they could have stopped the exodus by offering prize money, it’s easy to sympathize. The pro tour was a parasite, trading on the fame of stars it did nothing to create.

Won’t get fooled again

The Open era kicked off in 1968, quickly consigning amateur tournaments to also-ran status. A few players began to get rich, and it was possible to make a living as a second-tier tour regular. Within a decade, though, exhibition tennis threatened the burgeoning pro circuit.

The tennis boom of the 1970s created vast numbers of fans, and with the help of television, the era’s stars became more famous than ever before. The 1973 Battle of the Sexes was not just a turning point for women in sport. It proved the potential of a one-off spectacle. Why bother with a whole tournament when you could pit Jimmy Connors against Rod Laver at Caesar’s Palace?

In the early 70s, there wasn’t much tension between the tours and exhibitions, because the unified tours didn’t exist. A national federation might gripe about a big name skipping a circuit stop in favor of a bigger payday elsewhere, but federations were losing their grip on the sport. There was little they could do about it.

Soon, though, battle lines formed. World Team Tennis muscled their way onto the stage in 1974, offering players guaranteed contracts to play up to 60 dates from May to September. WTT was expansive enough to accommodate lesser names alongside the box office draws, but the very nature of the league made the pecking order clear. Superstars demanded six-figure deals and often forced trades so that they could play for a chosen franchise. WTT could be nearly as grueling as the old pro tours, but it beat the procession of smaller events between Wimbledon and the US Open.

By the end of the decade, the ATP and WTA had organized themselves into circuits that resemble what we have today. Stars like Chris Evert and Bjorn Borg were raking in prize money. The problem was, on the exhibition market, they were worth even more. Borg, in particular, would cash in at any opportunity, sometimes playing dozens of exhibition matches in a single season.

The men’s tour eventually responded by requiring that players enter a minimum number of sanctioned events each year, one factor in Borg’s early retirement at the age of 26. But most players were willing to compromise, entering a couple dozen official tournaments, then jetting from Europe to Japan and back to pad their bank accounts.

The compromise

Six Kings aside, we’re still far from the peak era of exhibition tennis, when Borg and Ivan Lendl played one-nighters for well-heeled fans around the globe. The ATP has steadily tweaked its rules–no exhibitions that clash with bigger tour events, for instance–while upping its own prize money.

The tours have also indirectly limited exhibitions by their own natural growth. One of the biggest exho markets in the 70s and 80s was Japan, where an increasingly rich population wanted a taste of what Westerners could enjoy at home. As the tours gave more prominence to sanctioned tournaments in Tokyo, Osaka, and elsewhere, there was less demand for one-off player appearances.

That, in short(?), is how we got here. Stars are able to play non-tour events, but only sometimes. They hardly need to, since an athlete with any kind of box office value is making seven figures in prize money, not to mention endorsements. Most localities that can support a top-tier event have got one, within the framework of the official tours.

However, it wouldn’t take much to render this equilibrium unstable.

Threat models

The biggest immediate danger to the existing structure of pro tennis is Saudi money. The nation’s Public Investment Fund basically blew up golf, poaching stars for a rival tour and leaving the sport fractured.

Fortunately, tennis officials were able to watch and learn. The Saudis have been welcomed as partners, hosting the WTA Finals and the ATP NextGen Finals, as well as sponsoring both tours’ ranking systems. The Six Kings Slam doesn’t seem like so much of a threat in the context of so much collaboration.

If the Saudis decide to make a bigger move, even that will likely be in partnership with the majors–the so-called “super tour” proposal. The resulting circuit would probably have fewer, higher-paying tournaments. By extension, it would support a smaller group of players. Breaking onto the tour would be more lucrative than ever, but many currently-fringe competitors would be stuck on an expanded version of the Challenger tour.

Maybe a super tour is imminent. I have no idea. It would certainly change the face of the sport, though not beyond recognition.

The bigger threat, as I see it, is in the longer term. Sports–not just tennis–have learned to promote their biggest stars, earlier and more persistently than ever before. Think of all the “NextGen” hype tennis fans have been subjected to for more than a decade now, since Grigor Dimitrov was a teenager. Now we’ve entered the “Drive to Survive” era, where every sport wants Netflix to do what it did for Formula 1. To grow the game–the thinking goes–stars need to develop into global icons, thus attracting new fans. Can’t just sit around and wait for the next Federer to manage it himself.

The risk is that by marketing a superstar, the value accrues to the superstar, not the sport. If more people tune into Wimbledon to watch Sinner play Alcaraz, Wimbledon reaps the benefit of the ratings and sponsorships. Yes, Sinner and Alcaraz get paid well, too, and maybe prize money goes up next year. But at what point does Wimbledon have less status than the stars themselves? When Alcaraz has his own Netflix doc and Sinner is the most popular man in Italy, who cares about the strawberries and cream?

Put another way: Imagine that the Saudis were looking to elbow their way into sport in 2008. After the epic Federer-Nadal Wimbledon final, they offered both men a ten-year, billion-dollar contract to tour the globe (with frequent stops in the Gulf), playing head to head in one sold-out arena after another. Is the offer so implausible? Are we sure that Roger and Rafa wouldn’t have taken it?

Sinner and Alcaraz are hardly Federer and Nadal–at least not yet. But their agents, and the tour’s marketing team, and a film studio or three, are trying very hard to raise them to that status. If it isn’t Sinner and Alcaraz, it’s the next generation of superstars after that. Eventually, someone, or some small group of players, will be big enough that they can sell a two-man product. Team sports don’t have to worry about that; even golf would have a hard time selling much match play. But tennis has sold two-player rivalries for a century.

That, to me, is the logical extension of exhibition tennis, the worst-case scenario that guts the sport as we know it.

Events like the Six Kings Slam, Laver Cup, and UTS are fine when there is a deep, thriving tennis ecosystem for 45 weeks a year. (I’d even settle for 35!) We are quite far, I think, from the point where the number of exhibitions threatens the tour itself. But we are closer to the more dangerous point where a small group of superstars don’t need the tour at all. Any athletes who ultimately cash in their celebrity to go it alone will do very well in the deal. But the rest of us will be left with a much less compelling sport.

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Mirra Andreeva’s Many Happy Returns

*Mirra Andreeva at the 2024 Paris Olympics.*
*Credit: Like tears in rain*

Mirra Andreeva is the best teenager on the WTA tour, and it isn’t close. She’ll finish 2024 ranked 16th on the official points table, more than one hundred places ahead of her closest teenage competitor, Maya Joint. Andreeva is a year younger than Joint, and she’s two years younger than Ella Seidel, third on the under-20 list.

Players who outpace their fellow teenagers typically go on to notable careers. Here’s the list of top teenagers at the end of each season this century:

Year  Player                    Rank  
2000  Serena Williams              6  
2001  Kim Clijsters                5  
2002  Kim Clijsters                4  
2003  Vera Zvonareva              13  
2004  Maria Sharapova              4  
2005  Maria Sharapova              4  
2006  Maria Sharapova              2  
2007  Nicole Vaidisova            12  
2008  Agnieszka Radwanska         10  
2009  Caroline Wozniacki           4  
2010  Anastasia Pavlyuchenkova    21  
2011  Christina McHale            43  
2012  Sloane Stephens             38  
2013  Eugenie Bouchard            32  
2014  Madison Keys                30  
2015  Belinda Bencic              14  
2016  Daria Kasatkina             26  
2017  Catherine Bellis            46  
2018  Dayana Yastremska           58  
2019  Bianca Andreescu             5  
2020  Iga Swiatek                 17  
2021  Emma Raducanu               19  
2022  Coco Gauff                   7  
2023  Coco Gauff                   3  
2024  Mirra Andreeva              16

There’s no such thing as a can’t-miss prospect in women’s tennis, but showing up on this list gets you pretty close. Andreeva’s case is particularly extreme, because she is still just 17 years old.

In the under-18 category, the young Russian has virtually no competition. Only three other under-18s rank among the top 200, none closer than Alina Korneeva at 176th. No woman so young has finished inside the top 20 in almost two decades, going back to Nicole Vaidisova’s top-ten showing in 2006.

Here’s another way to look at what Andreeva has accomplished. With four victories to reach the Ningbo final in October, she increased her career tour-level main-draw win count to 48. Take a look at the list of all women, post-Vaidisova, to post even 30 such wins before their 18th birthday:

Wins  Player              Last Win as 17yo  
32    Victoria Azarenka         2007-07-30  
47    Caroline Wozniacki        2008-06-23  
42    Tamira Paszek             2008-09-15  
32    Donna Vekic               2014-06-23  
33    Amanda Anisimova          2019-07-29  
64    Coco Gauff                2022-03-07  
48    Mirra Andreeva            2024-10-14

Again, good company, and think of all the stars who aren’t here. You know, everybody (besides Vekic) for a decade. In this entire time span of about 17 years, Andreeva has done more at her age than anyone except Coco Gauff. The Russian might even erase that caveat. She doesn’t turn 18 until the end of April, and this year, she had won 12 matches by that time. 17 wins–enough to surpass Gauff–is hardly out of reach.

Let’s turn now to how Andreeva is achieving so much success, and why she might soon lop a digit off of her age-defying ranking.

Returns first

Forget about all this under-18 and teenager stuff for a minute. Mirra is already one of the best returners in the game. Here are the top dozen WTA tour regulars, ranked by return points won:

This isn’t a perfect measure. For one thing, Andreeva faced one of the weaker schedules of players on this list. Her median opponent was ranked 58th, compared to 30th for Iga and 42nd for Coco. It would take considerably more work to suss out whether Andreeva’s 47.3% of return points won, against her set of opponents, is better or worse than, say, Aryna Sabalenka’s 45.3% against competition nearly as stiff as Swiatek’s.

The quibbles mean that we can’t quite proclaim the Russian a top-three returner. The point, though, is that she’s in the conversation. In fact, if we narrow our view to matches against top-20 players–limiting if not eliminating the influence of each woman’s schedule–Andreeva hangs on to her position:

(We’re not talking about Iga today, but… 47% of return points won against top-20 opponents? My word.)

Where Andreeva shines even brighter is against first serves. She won first-serve return points at a higher clip than any other woman on tour this year:

Player               1st RPW%  
Mirra Andreeva          42.6%  
Coco Gauff              42.1%  
Marketa Vondrousova     40.8%  
Iga Swiatek             40.8%  
Daria Kasatkina         40.7%  
Marta Kostyuk           40.5%  
Elina Avanesyan         40.0%  
Jasmine Paolini         40.0%  
Katerina Siniakova      39.5%  
Karolina Muchova        39.5%

Put that in perspective: Andreeva wins more first-serve return points than Barbora Krejcikova (to pick one name from several) wins all return points.

Again, the Russian’s stats are influenced by her level of competition. Against top-20 opponents, Mirra falls to third place, behind Swiatek and just back of Gauff. But you get the idea. To say, “Well, actually, she’s not quite up to Gauff’s standard” is to say we’re dealing with a special player.

Precocious patience

Andreeva’s serve is good for a 17-year-old, but as we’ve seen, it’s not the side of her game that has put her in the top 20. Her returns, and by extension, her baseline play, are responsible for that.

Among top players, Mirra is currently most similar to countrywoman Daria Kasatkina. The two Russians, according to Match Charting Project data, post average rally lengths of 4.9 strokes, more than anyone else in the top 40. Both women are effective off both wings; Andreeva’s backhand is the better of the two, while Kasatkina’s forehand scores more points. The teenager is a bit more likely to force the issue: While both rank well below average in Rally Aggression Score, Mirra is closer to the norm.

A key difference shows up in their rally breakdowns. Again based on the subset of matches logged by the Match Charting Project, here are each woman’s percent of points won at various rally length categories:

Player     1-3 W%  4-6 W%  7-9 W%  10+ W%  
Andreeva    49.8%   48.6%   51.8%   53.8%  
Kasatkina   48.0%   45.6%   51.0%   52.5%

The first thing that pops out here is that Andreeva is better in every category, something that reflects both the vagaries of the uneven tennis schedule and the non-random nature of Match Charting Project samples. However you slice it, Mirra won more points, though my Elo rankings agree with the official formula that Kasatkina was the better player.

To get a better idea of what we’re looking at, let’s normalize each woman’s rally-category splits as if they won exactly half of their overall points:

Player     1-3 W%  4-6 W%  7-9 W%  10+ W%  
Andreeva    49.5%   48.3%   51.5%   53.5%  
Kasatkina   49.6%   47.1%   52.7%   54.2%

The teenager holds the edge in the 4-6-stroke category, while Kasatkina looks better in the longer rallies.

That 4-6-shot category tells us more than it lets on. Andreeva’s 48.3% (or the un-normalized 48.6%) doesn’t look very impressive. Points in this group account for one quarter of all the points she plays, and she loses more than half.

But consider her playing style. Medium-short rallies are often determined by the lingering influence of the serve: The returner might withstand a plus-one attack, only to leave a sitter for the server to put away. Or a strong return doesn’t finish the point, but the returner’s next shot–the fourth stroke of the rally–does the job. 4-6-shot rallies go disproportionately to big hitters: Aryna Sabalenka led the category this year.

For someone like Andreeva or Kasatkina, the task is to limit the damage. Get the serve back, try to neutralize the point. Place serves where aggressive returners won’t do too much damage. If a big return comes back, play the same defense that works against the serve. Kasatkina has all of those skills, but there is only so much she can do. Mirra, with her flatter strokes and somewhat bigger weapons, can keep opponents from running away with these medium-short points. She’ll lose sometimes to the likes of Sabalenka, but unless they catch her on an off day, she won’t be blown off the court.

Growth potential

If Andreeva could be characterized as a younger, somewhat more aggressive Kasatkina, that would be a pretty good compliment for a 17-year-old. But the teenager promises to become much more.

One of my favorite bits of counterintuitive tennis wisdom is that return stats rarely improve. Returning is based on a set of skills–anticipation, quickness, speed–that, on net, decline with age. Whatever tactical savvy a player picks up as she ages will, at best, cancel out the age-related decline. This isn’t an iron law, but it’s surprising how often players reach their peak return effectiveness very early in their careers.

The same is not true for the serve. 17-year-olds (or, hey, 23-year-olds) have the capacity to get stronger. Footspeed and reaction time don’t figure into the serve, so with better coaching or targeted practice (think late-career Djokovic), serve stats can improve even as the rest of a player’s game declines. A couple of examples: Maria Sakkari steadily improved her first-serve win rate from the 13th percentile to the 93rd percentile in five years. Simona Halep’s first-serve was in the top quarter of tour regulars in 2014; two years earlier, it had been one of the WTA’s worst.

The implications for Andreeva are clear. We don’t need to wishcast an improvement in her return game: She’s already one of the best returners in the game. Instead, the road to the top ten and beyond goes through her serve. Her results so far are adequate. She won 58.4% of her serve points in 2024, compared to a top-50 average of 58.7%. When we consider how much she played on clay, that number looks a bit better. On hard courts, she won more serve points than average.

Mirra, then, doesn’t face the same uphill struggle that Sakkari and Halep overcame. Her potential trajectory is more like, say, Victoria Azarenka’s. Vika arrived on the scene as a killer returner with a good-enough serve. In 2009 and 2010, she won nearly half of her return points against 58% to 59% of her service points. That combination earned her two top-ten finishes. (She was a few years older than Andreeva at that point, yet another reminder of how unique the Russian’s early success has been.)

Two years later, Azarenka boosted her rate of serve points won to 61%. Combined with the same results on return that had gotten her into the top ten, the bigger serve earned her six titles–including her first major–and the year-end number one ranking. 59% to 61% may not sound like much, but for an elite returner, that’s all it takes.

If Andreeva did the same, lifting her 58.4% serve-point win rate to 61%, she’d be the ninth-best server on tour. Remember how she’s just a tick behind Coco Gauff on return? A Vika-like serve boost would put her ahead of the American in that category, outweighing Coco’s narrow edge on return. Shorter version: She’d be a top-three player, maybe more.

None of this is guaranteed. It may not–it probably won’t!–happen right away. For every Azarenka, there’s a Nicole Vaidisova or, worse, an injury victim like Catherine Bellis. Still, few paths to the top are marked so clearly. For Mirra Andreeva, a modest, achievable set of improvements are all that stand between her and the top.

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