Category: Elo Ratings

Andreescu, Medvedev, and the Future According to Elo

With the US Open title added to her 2019 trophy haul, Bianca Andreescu is finally a member of the WTA top 10, debuting at fifth on the ranking table. Daniil Medvedev, the breakout star of the summer on the men’s side, only cracked the ATP top 10 after Wimbledon. He’s now up to fourth. The official ranking algorithms employed by the tours take some time to adjust to the presence of new stars.

Elo, on the other hand, reacts quickly. While the ATP and WTA computers assign points based on a year’s worth of results (rounds reached, not opponent quality), Elo gives the most weight to recent accomplishments, with even greater emphasis placed on surprising outcomes, like upsets of top players. If your goal in using a ranking system is to predict the future, Elo is better: Elo-based forecasts significantly outperform predictions based on ATP and WTA ranking points.

Andreescu’s first Premier-level title came at Indian Wells in March, when she beat two top-ten players, Elina Svitolina and Angelique Kerber, in the semi-final and final. The WTA computer reacted by moving her up from 60th to 24th on the official list. Elo already saw Andreescu as a more formidable force after her run to the final in Auckland, so after Indian Wells, the algorithm moved her up to seventh. Three more wins in Miami, and the Canadian teen cracked the Elo top five.

Tennis fans are accustomed to the slow adjustments of the ranking system, so seeing a “(22)” or a “(15)” next to Andreescu’s name at Roland Garros and the US Open wasn’t particularly jarring. And there’s something to be said for withholding judgment, since tennis has had its share of teenage flashes in the pan. But Elo is usually right. The betting market heavily favored Serena Williams in the US Open final, but Elo saw the Canadian as the superior player, giving her a slight edge. After the latest seven match wins in New York, the algorithm rates Andreescu as the best player on tour, very narrowly edging out Ashleigh Barty. Would you dare disagree?

The launching (Ar)pad

When Medvedev first reached the top ten on the Elo list last October, I ran some numbers to compare the two ranking systems. Most players who earn a spot in the Elo top ten eventually make their way into the ATP top ten as well, but Elo is almost always first. On average, the algorithm picks top-tenners more than a half-year sooner than the tour’s computer. The 23-year-old Russian is a good example: He reached eighth place on the Elo list last October, but didn’t match that mark in the ATP rankings for another 10 months, after reaching the Montreal final.

Andreescu closed the gap faster than Medvedev did, needing a more typical six months to progress from Elo top-tenner to a single-digit WTA ranking. It may not take much longer before her Elo and WTA rankings converge at the top of both lists.

We no longer need Elo to tell us that Andreescu and Medvedev are likely to keep winning matches at the highest level. But having acknowledged the accuracy with which Elo glimpses the future, it’s worth looking at which players are likely to follow in their footsteps.

After the US Open, Elo’s boldest claim regards Matteo Berrettini, ranked sixth. The ATP computer sites him at 13th, and he only made one brief stop this summer inside the top 20. The Flushing semi-finalist has been inside the Elo top 10 since mid-June, and the algorithm currently puts him ahead of such better-established young players as Alexander Zverev and Stefanos Tsitsipas.

The women’s Elo list doesn’t feature any similar surprises in the top 10, but that hardly means it agrees with the WTA computer. Karolina Muchova, currently at a career-high WTA ranking of 43rd, is 23rd on the Elo table. Two veteran threats, Victoria Azarenka and Venus Williams, are also marooned outside the official top 40, but Elo sees them as 18th and 28th best on tour, respectively. In terms of predictiveness, quality is more important than quantity, so a limited schedule isn’t necessarily seen as a drawback. Elo is also optimistic about Sofia Kenin, rating her 13th, compared to her official WTA standing of 20th.

Half a year from now, I’d bet Berrettini’s official ranking is closer to 6 than to 13, and that Muchova’s position is closer to 23 than 43. It’s impossible to tell the future, but if we’re interested in looking ahead, Elo gives us a six-month head start on the official rankings. We’ll have to wait and see whether the rest of the women’s tour can keep Andreescu away from the top spot for that long.

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

Italian translation at settesei.it

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

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

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

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

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

Rafa broke Elo

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

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

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

Now, about those conditions

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

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

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

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

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

Introducing Elo Ratings for Mixed Doubles

Scroll down for Wimbledon updates, including a forecast for the title match.

With Andy Murray and Serena Williams pairing up in this year’s Wimbledon mixed doubles event, more eyes than ever are on tournament’s only mixed-gender draw. Mixed doubles is played just four times a year (plus the Olympics, the occasional exhibition, and the late Hopman Cup), so most partnerships are temporary, and it’s tough to get a sense of who is particularly good in the dual-gender format.

That’s where math comes into play. Over the last few years, I’ve deployed a variation of the Elo rating algorithm for men’s doubles. It treats each team as the average of the two members, and after every match, it adjusts each player’s rating based on the result and the quality of the opponent. Doubles Elo–D-Lo–is even better suited for mixed than for single-gender formats, because players rarely stick with the same partner. The main drawback of D-Lo for men’s or women’s doubles is that it doesn’t help us tease out the individual contributions of long-time teams such as Bob and Mike Bryan. By contrast, mixed doubles draws often look like a game of musical chairs from one major to the next.

The rating game

Let’s jump right in. The Wimbledon mixed doubles draw consists of 56 teams. Here are the 10 highest-rated of those 112 players, as of the start of the fortnight:

Rank  Player                 XD-Lo  
1     Venus Williams          1855  
2     Serena Williams         1847  
3     Bethanie Mattek-Sands   1834  
4     Jamie Murray            1809  
5     Ivan Dodig              1793  
6     Latisha Chan            1785  
7     Bruno Soares            1776  
8     Leander Paes            1771  
9     Heather Watson          1770  
10    Gabriela Dabrowski      1760

Serena and Venus Williams require a bit of an asterisk, since both are playing mixed for the first time after a long break. Venus last played at the 2016 Olympics, and Serena last competed in mixed at the 2012 French Open. Maybe they’re rusty. My XD-Lo algorithm doesn’t include any kind of adjustment for injuries or other layoffs, so it’s possible that we should expect them to perform at a lower level. On the other hand, they are among the greatest doubles players of all time, and players tend to age gracefully in doubles. Venus lost her opening match, but perhaps we should blame that on Francis Tiafoe (XD-Lo: 1,494). The sisters will probably trade places at the top of the list once Wimbledon results are incorporated.

Murray’s rating is a decent but more pedestrian 1,648, so Murray/Williams is not the best team in the field. But they’re close. The strongest pair is Jamie Murray and Bethanie Mattek Sands–3rd and 4th on the list above–followed by Ivan Dodig and Latisha Chan, 5th and 6th on the individual list. Due to the vagaries of ATP and WTA doubles rankings and the resulting seedings, Dodig/Chan entered the event as the narrow favorites, because they got a first-round bye and Murray/Mattek-Sands did not.

Here are the top ten teams in the draw:

Rank  Team                                XD-Lo  
1     Bethanie Mattek-Sands/Jamie Murray   1822  
2     Ivan Dodig/Latisha Chan              1789  
3     Bruno Soares/Nicole Melichar         1762  
4     Serena Williams/Andy Murray          1748  
5     Gabriela Dabrowski/Mate Pavic        1734  
6     Leander Paes/Samantha Stosur         1731  
7     Heather Watson/Henri Kontinen        1708  
8     Venus Williams/Frances Tiafoe        1674  
9     Abigail Spears/Marcelo Demoliner     1653  
10    Neal Skupski/Chan Hao-ching          1634

The top five have survived (though Murray/Mattek-Sands and Pavic/Dabrowski will complete their second-round match this afternoon, leaving only four), and of the last 18 teams standing, only one other one–John Peers and Shuai Zhang–is rated above 1,600.

Forecasting SerAndy

Using my ratings, Murray/Williams entered the tournament with a 9.8% chance of winning. That made them fourth favorite, behind Dodig/Chan (17.1%), Murray/Mattek-Sands (16.3%), and the big-serving duo of Bruno Soares and Nicole Melichar (14.5%). I’ll update the forecast this evening, when the second round is finally complete.

Murray/Williams’s second-round match is against Fabrice Martin and Racquel Atawo. They are both excellent doubles players, though neither has excelled in mixed. Atawo, especially, has struggled. Her XD-Lo is 1,304, the third-lowest of anyone who has entered a mixed draw since 2012. (Shuai Peng is rated 1,268, and Marc Lopez owns last place with 1,252.) A player with no results at all enters the system with 1,500 points, so falling to 1,300 requires a lot of losing. The combined ratings translate into a 89% chance of a Murray/Williams victory.

The challenge comes in the third round. Soares/Melichar are the top seed, and they have already advanced to the round of 16, awaiting the winner of Murray/Williams and Martin/Atawo. Thus two of of the top four teams will likely play for a place in the quarter-finals, with Soares/Melichar holding a narrow, 52% edge.

Historical peaks

Generating these current ratings required amassing a lot of data, so it would be a waste to ignore the history of the mixed doubles format. Here are the top 25 female mixed doubles players, ranked by their peak XD-Lo ratings:

Rank  Player                   Peak  
1     Billie Jean King         2043  
2     Greer Stevens            2035  
3     Margaret Court           2015  
4     Rosie Casals             2000  
5     Martina Navratilova      1998  
6     Helena Sukova            1991  
7     Anne Smith               1989  
8     Betty Stove              1985  
9     Jana Novotna             1977  
10    Martina Hingis           1964  
11    Wendy Turnbull           1956  
12    Kathy Jordan             1948  
13    Elizabeth Smylie         1947  
14    Arantxa Sanchez Vicario  1946  
15    Serena Williams          1942  
16    Venus Williams           1937  
17    Francoise Durr           1934  
18    Jo Durie                 1929  
19    Kristina Mladenovic      1922  
20    Zina Garrison            1901  
21    Samantha Stosur          1898  
22    Larisa Neiland           1891  
23    Lindsay Davenport        1888  
24    Victoria Azarenka        1887  
25    Natasha Zvereva          1886

Venus really can’t catch a break. She’s one of the best players of all time, and Serena is always just a little bit better.

And the top 25 men:

Rank  Player               Peak XD-Lo  
1     Owen Davidson              2043  
2     Bob Hewitt                 2042  
3     Marty Riessen              2016  
4     Todd Woodbridge            2000  
5     Frew McMillan              1999  
6     Kevin Curren               1997  
7     Jim Pugh                   1995  
8     Ilie Nastase               1975  
9     Tony Roche                 1962  
10    Bob Bryan                  1949  
11    Rick Leach                 1938  
12    Mahesh Bhupathi            1933  
13    Mark Woodforde             1929  
14    Justin Gimelstob           1929  
15    Max Mirnyi                 1926  
16    John Lloyd                 1922  
17    Emilio Sanchez             1918  
18    Ken Flach                  1909  
19    Jeremy Bates               1908  
20    John Fitzgerald            1906  
21    Cyril Suk                  1902  
22    Wayne Black                1889  
23    Dick Stockton              1881  
24    Jean-Claude Barclay        1879  
25    Mike Bryan                 1875

Owen Davidson won eight mixed slams with Billie Jean King, plus three more with other partners. Bob Hewitt won six, spanning 18 years from 1961 to 1979. (We can’t erase his accomplishments from the history books, but any mention of Hewitt comes with the caveat that he is a convicted rapist who has since been expelled from the International Tennis Hall of Fame.)

It is interesting to see two famous pairs represented on the men’s list. Bob Bryan ranks 10th to Mike’s 25th, and Todd Woodbridge comes in 4th to Mark Woodforde’s 13th. We probably can’t conclude from mixed doubles results that one member of the team was a superior men’s doubles player, but it is one of the few data points that allows us to compare these partners.

The ignominious Spaniards

Finally, I can’t spend this much time with mixed doubles ratings without revisiting the case of David Marrero. You may recall the 2016 Australian Open, when Marrero’s first-round match with Lara Arruabarrena triggered “suspicious betting patterns.” As I wrote at the time, the most suspicious thing about it was that Marrero–who was terrible at mixed doubles and admitted that he played differently with a woman across the net–could still find a partner.

He entered that match with an XD-Lo rating of 1,349–the worst of any man in the draw, though Anastasia Pavlyuchenkova was a few points lower–and left it at 1,342. He played his last mixed doubles match at Wimbledon that year, and–surprise!–he lost. One hopes he’ll stick to men’s doubles for the remainder of his career, sticking with an XD-Lo rating of 1,326.

Marrero’s only saving grace is that he’s better than his compatriot Marc Lopez. Lopez has been active in mixed doubles more recently, entering the US Open last year with Arruabarrena. After that loss, he fell to his current rating of 1,252, the lowest mark recorded in the Open Era.

Fortunately for us, this year’s Wimbledon draw includes both Williams sisters, both Murray brothers, a healthy Mattek-Sands … and very few players as helpless in the mixed doubles format as Marrero or Lopez.

—

Update: Murray/Williams won their second-rounder, setting up the final 16. Mixed doubles isn’t the top scheduling priority, so it didn’t exactly work that way–by the time Muzzerena advanced, two other teams had already secured places in the quarter-finals. Ignoring those for the moment, here is the last-16 forecast:

Team                      QF     SF      F      W  
Soares/Melichar        52.5%  44.5%  33.2%  18.8%  
Murray/Williams        47.5%  39.7%  29.0%  15.8%  
Middelkoop/Yang        55.5%   9.5%   3.6%   0.8%  
Daniell/Brady          44.5%   6.3%   2.1%   0.4%  
Peers/Zhang            61.6%  36.9%  13.8%   5.2%  
Lindstedt/Ostapenko    38.4%  18.7%   5.2%   1.5%  
Skugor/Olaru           56.2%  26.3%   8.3%   2.6%  
Mektic/Rosolska        43.8%  18.0%   4.8%   1.3%  
                                                   
Player                    QF     SF      F      W  
Koolhof/Peschke        42.6%  10.1%   2.4%   0.6%  
Qureshi/Kichenok       57.4%  16.7%   4.9%   1.5%  
Sitak/Siegemund        27.4%  16.0%   5.3%   1.8%  
Pavic/Dabrowski        72.6%  57.2%  30.8%  17.5%  
Dodig/Chan             75.9%  64.6%  44.1%  28.1%  
Roger-Vasselin/Klepac  24.1%  15.5%   6.6%   2.5%  
Hoyt/Silva             54.1%  11.3%   3.5%   1.0%  
Vliegen/Zheng          45.9%   8.6%   2.5%   0.6%

The two teams already in the quarters are Skugor/Olaru and Hoyt/Silva. Since both of their matches were close to 50/50, you can roughly double their odds, and the odds of the other teams are only a tiny bit less. The remaining six third-round matches are scheduled for Wednesday, and I’ll try to update again when those are in the books.

Update 2: Murray/Williams are out, so the number of people interested in mixed doubles has fallen from double digits back to the typical level of single digits. The departure of the singles stars also leaves one clear favorite in each half. Here is the updated forecast:

Team                    SF      F      W  
Soares/Melichar      83.4%  64.3%  36.4%  
Middelkoop/Yang      16.6%   6.7%   1.5%  
Lindstedt/Ostapenko  46.0%  12.6%   3.7%  
Skugor/Olaru         54.0%  16.4%   5.2%  
Koolhof/Peschke      37.5%   7.3%   1.8%  
Sitak/Siegemund      62.5%  17.2%   6.0%  
Dodig/Chan           84.4%  68.3%  43.3%  
Hoyt/Silva           15.6%   7.2%   2.0%

All four quarter-finals are scheduled for Thursday, so I’ll post another update tomorrow evening.

Update 3: We’re down to four teams. Of the Elo favorites in the quarter-finals, only Dodig/Chan survived, leaving them as the overwhelming pick to take the title. Here’s the full forecast:

Team                     F      W  
Middelkoop/Yang      42.3%   8.2%  
Lindstedt/Ostapenko  57.7%  14.1%  
Koolhof/Peschke      14.1%   6.3%  
Dodig/Chan           85.9%  71.4%

Update 4: Both favorites won in Friday’s semi-finals, so we’ve got a final between Lindstedt/Ostapenko and Dodig/Chan. The first team didn’t get an opening-round bye, so they won one more match to get here. They also have a better story, since Ostapenko keeps hitting her partner in the head. Dodig/Chan entered as the 8th seeds, despite being the second-best team according to XD-Lo.

Consequently, Dodig/Chan get the edge here, with an 81% of winning the 2019 Wimbledon Mixed Doubles title.

How Good is Cori Gauff Right Now?

Italian translation at settesei.it

15-year-old sensation Cori Gauff holds a WTA ranking of No. 313. She has played only a limited number of events that are considered by the WTA’s system, so even before her impressive run began, we could’ve predicted that her ranking was an understatement. But by how much?

Gauff doesn’t show up yet on my Elo ratings list because, before Wimbledon qualies, she hadn’t played at least 20 matches at the ITF $50K level or higher in the last year. However, she still had a rating: 1,488, good for 194th place among those who had met the playing time minimum. A rating in that range translates to about a 3% chance of upsetting current top-ranked player Ashleigh Barty, and a 10% chance of beating someone around 20th, such as Donna Vekic. Given how little data we had to work with at that point, that seemed like a reasonable assessment.

Since she arrived in London, she has won six matches: Three in qualifying and three in the main draw, with wins over Venus Williams, Magdalena Rybarikova, and Polona Hercog. Not bad for a teenager who had previous won only one slam qualifying match and one tour-level main draw match in her young career!

194th place doesn’t seem like such a fair judgment anymore. Any player who comes through qualifying and reaches the fourth round at a major deserves some reassessment, and that’s even more applicable to a player about whom we knew so little two weeks ago. The tricky part is figuring out how much to adjust. Is Gauff now a top-100 player? Top 50? Top 20?

Revising with Elo

The Elo algorithm does a good job of approximating how humans make those reassessments: The more data we already have about a player, the less we will adjust her rating after a win or loss. The previous player to defeat Hercog was Simona Halep, at Eastbourne. We already have years’ worth of match results for Halep, and she was heavily favored to win the match. Thus, the fact that she recorded the victory alters our opinion of her by only a small amount. In Elo terms, it was an increase from 2,100 points to 2,102–basically nothing.

Gauff is a different story. Entering her third-round clash with Hercog, not only did we know very little about her skill level, it wasn’t even clear if she was the favorite. The result caused Elo to make a considerably larger adjustment, increasing her rating from 1,713 to 1,755, a rise 21 times greater than what Halep received after beating the same opponent. The 42-point jump caused her to leapfrog 16 players in the rankings.

Here is Gauff’s Elo progression, from the moment she arrived at Wimbledon to middle Sunday. After each match, I show her overall Elo (the numbers I’ve been discussing so far), her grass-specific Elo, and her grass-weighted Elo, a 50/50 blend of overall and grass-specific that is used for forecasting. For each of the three ratings, I also show her ranking among WTA players with at least 20 matches in the last 52 weeks.

Match          Overall   Rk  Grass   Rk  Weighted   Rk  
Pre-Wimbledon     1488  194   1350  163      1419  187  
d. Bolsova        1540  171   1405  132      1473  155  
d. Ivakhnenko     1566  157   1447  107      1507  131  
d. Minnen         1614  132   1514   57      1564   95  
d. Venus          1670  108   1578   40      1624   73  
d. Rybarikova     1713   83   1650   21      1682   41  
d. Hercog         1755   67   1686   17      1721   31

Over the course of only six matches, Gauff has jumped from 194th in the overall Elo rankings to 67th. For forecasting purposes, her grass court rating has soared from 187th to 31st. Her current weighted rating of 1,721 is better than that of three other women in the round of 16: Karolina Muchova, Carla Suarez Navarro, and Shuai Zhang. She trails another surviving player, Elise Mertens, by only 20 points.

So you’re telling me there’s a chance

Unfortunately, none of those relatively weak grass-court players are Gauff’s next opponent. Instead, the 15-year-old will face Halep, the third-best remaining player (behind Barty and Karolina Pliskova), and a three-time quarter-finalist at the All England Club. Halep’s weighted Elo rating is 229 points higher than Gauff’s, implying that the veteran has a 79% chance of winning on Monday. The betting market concurs, suggesting that the probability of a Halep victory is about 80%.

It doesn’t usually have much of an effect on forecasts to update Elo ratings throughout a tournament. While anyone reaching the 4th round has a higher rating than they did before the event, the differences are typically small. And since forecasts are based on the difference between the ratings of two players, the forecast isn’t affected if both players’ ratings have increased by roughly the same amount.

As a teenager with such limited match experience, Gauff breaks the mold. Her pre-Wimbledon 1,488 Elo rating is only two weeks old, and it is already completely unrepresentative of what we know of her skill level. She’ll have ample time to prove us right or wrong in the upcoming years, but for now, we have good reason to estimate that she belongs–even more than some of the older players who have reached the second week at Wimbledon.

Forecasting Andy Murray, Doubles Specialist

We are three weeks into the mostly-triumphant doubles comeback of Andy Murray. In his first week back, he raced to the Queen’s Club title with Feliciano Lopez. A week later, he paired Marcelo Melo and lost in the first round. At Wimbledon, he is partnering Pierre-Hugues Herbert, with whom he has already defeated the only-at-a-slam duo of Marius Copil and Ugo Humbert.

Today in the second round, Herbert/Murray face a sterner test: sixth-seeded team Nikola Mektic and Franco Skugor. The betting markets heavily favored Herbert/Murray going into the contest, but we have to assume that punters (including an unusually high number of casual ones) are probably overrating the familiar name on his home turf.

D-Lo to the rescue

Let’s see what D-Lo (Elo for doubles!) says about today’s match. D-Lo treats each team as a 50/50 mix of the two players, and adjusts each player’s rating after every match, depending on the quality of the opponent. It also very slightly regresses both partners to the team average after each match, because it’s impossible to know how much each player contributed to the result.

Herbert is D-Lo’s top doubles player in the world on hard and clay courts, though he falls to 6th in the 50/50 blend of overall and grass-specific ratings used for forecasting. Murray, thanks to his run at Queen’s, is up to 54th in the blend, though that’s really more like 40th among players in the draw, since several injured and recently-retired players are clinging to high D-Lo ratings.

Mektic and Skugor are credible specialists, as indicated by their ATP ranking. They are 24th and 26th in the D-Lo, respectively. Combined, the two teams’ ratings are quite close: 1773 for Herbert/Murray to 1763 for Mektic/Skugor. In a best-of-three match, a difference of 10 points translates to a 51.4% edge for the favorites. In best-of-five, the better team is always more likely to come out on top, though with such a small margin it barely matters. Here, the best-of-five number is 51.6%.

Versus the pack

How does a team rating of 1773 compare to the rest of the remaining field? Entering Saturday’s play, 22 men’s doubles pairs were still in the draw. As I write this, Lopez and Pablo Carreno Busta are the only additional team to have been eliminated, reducing the field to 21.

Here are the combined D-Lo ratings of these teams. The rank shown for each player is based on the 50/50 blend of overall and grass rating used for forecasting.

Team D-Lo  Rank  Player             Rank  Player             
1873       2     Mike Bryan         3     Bob Bryan          
1858       4     Lukasz Kubot       7     Marcelo Melo       
1836       9     Raven Klaasen      10    Michael Venus      
1817       8     John Peers         17    Henri Kontinen     
1802       12    Nicolas Mahut      22    E Roger-Vasselin   
1788       18    J S Cabal          19    Robert Farah       
1773       6     P H Herbert        54    Andy Murray        
1764       15    Oliver Marach      36    Jurgen Melzer      
1763       24    Nikola Mektic      26    Franco Skugor      
1757       20    Rajeev Ram         33    Joe Salisbury      
1747       23    Horia Tecau        41    Jean Julien Rojer  
1709       42    Maximo Gonzalez    46    Horacio Zeballos   
1695       29    Ivan Dodig         88    Filip Polasek      
1681       58    Marcus Daniell     62    Wesley Koolhof     
1677       50    Frederik Nielsen   77    Robin Haase        
1644       81    Marcelo Demoliner  90    Divij Sharan       
1637       84    A Ul Haq Qureshi   99    Santiago Gonzalez  
1596       106   Philipp Oswald     123   Roman Jebavy       
1575       101   Mischa Zverev      184   Nicholas Monroe    
1533             Jaume Munar        216   Cameron Norrie     
1517       177   Marcelo Arevalo    214   M Reyes Varela

Herbert/Murray rank 7th among the surviving pairs. The combined rating of 1773 makes them competitive against anyone. The 100-point difference separating them and the Bryans gives them a 33% chance of pulling off a best-of-five upset, while the 29-point gap between them and Nicolas Mahut/Edouard Roger Vasselin translates to a 45/55 proposition.

Fortunately for the French-British pair, they won’t have to play a higher-rated team for some time. If they win today, they’ll face the winner of Dodig/Polasek vs Zverev/Monroe. The first of those teams is rated 80 points lower than Herbert/Murray (64% odds for the favorites), and the second is 200 points lower (81% for the faves). The three teams that could advance to become the quarter-final opponent for Herbert/Murray are all rated lower than Dodig/Polasek.

The draw certainly favored Sir Andrew. Yes, the 1859-rated Pavic/Soares duo crashed out in their section, but even before that, three of the best teams–Bryan/Bryan, Kubot/Melo, and Mahut/Roger-Vasselin–were stuck together in another quarter. While no men’s doubles match is a sure thing, the path is clear for Herbert/Murray to reach the final four.

Beyond Wimbledon

Does Murray have what it takes to become a full-time doubles specialist? Taking his Queen’s Club title into account, his overall D-Lo is already up to 36th best on tour, just ahead of Skugor, and several places better than Roland Garros co-champ Kevin Krawietz. Jurgen Melzer, another excellent singles player making of a go of it on the doubles circuit, is ranked 20 places lower, with a D-Lo 40 points less than Murray’s.

The short answer, then, is yes. It must be noted, though, that he isn’t the best choice among the big four to have a successful post-singles career as part of a team. That honor goes overwhelmingly to Rafael Nadal. Nadal’s career peak D-Lo is 100 points higher than Murray’s, and even his grass-court rating–based, admittedly, on some old results–is 70 points higher. Aside from the injured doubles wizard Jack Sock, Nadal is the best active player absent from the Wimbledon draw.

So, Murray/Nadal, Wimbledon 2021 champions? Sounds good to me–as long as Herbert relinquishes his new partner and finally commits to focusing on singles.

Forecasting Future Felix With ATP Aging Patterns

Italian translation at settesei.it

It’s been an exceptional six weeks for Felix Auger-Aliassime. He broke into the top 100 with a runner-up performance on clay in Rio de Janeiro, won two matches each at Sao Paulo and Indian Wells (including an upset of Stefanos Tsitsipas), and raced to a semi-final at the Miami Masters, the youngest player ever to make the final four of that event. Four months away from his 19th birthday, his ranking is up to 33rd in the world, and he has few points to defend until June.

Felix is the youngest man in the top 100, and he’s reaching milestones early enough to draw comparisons with some of the best young players in the sport’s history. Will he follow in the footsteps of past wunderkinds such as Rafael Nadal and Lleyton Hewitt? To answer that question, let’s take a look at typical ATP aging patterns, what they say about when players hit their peaks, and what they can show us about the fate of the best 18 year olds.

The standard curve

Last week, I looked at WTA aging curves and found that women tend to peak around age 23 or 24, an age that has not changed even as the sport has gotten older. I also discovered that there is a surprisingly modest gap–about 70 Elo points–between 18-year-old performance and a woman’s peak level. The men’s results are different.

To calculate the average ATP aging curve, I found over 700 players who were born between 1960 and 1989 and played at least 20 tour-level, tour qualifying, or challenger-level matches in each of five seasons. Overall, peak age was 25, though the difference from age 24 to 27 is only a few Elo points, so small as to be negligible.

As the tour has gotten older, the men’s peak age has also increased. Of the nearly 300 players born between 1980 and 1989, peak age is 26-27, with ages 28 and 29 also within 10 Elo points of the age 26-27 peak. Plenty of players are peaking at older ages, and many of those who aren’t are remaining close to their best levels into their late twenties. The peak age could be even higher still–a few of the players in the 1980-89 cohort turn 30 this year, and could conceivably still improve on their career bests.

The following graph shows the trajectory of the average player (with peak year-end Elo set to 1,850) born in the 1960s and the pattern of the average player born in the 1980s:

It’s a long ascent from the performance level at age 18 to the typical peak, especially for more recent players. There’s even a hefty bit of selection bias that should inflate the level of 18 year olds, since only about 10% of the players in the overall sample qualified for a year-end Elo rating when they were 18. The ones who did were, in general, the best of the bunch.

Felix forward

Through the Miami semi-final, Auger-Aliassime’s Elo rating is 1,848. The average player in the entire dataset who played at least 20 matches in their age-18 season went on to add another 281 Elo points to their rating between the end of their age-18 season and their peak. In the narrower, more recent cohort of 1980-89 births, the players with year-end ratings as 18 year olds improved their Elos by a whopping 369 points before reaching their peaks.

Adding either of those numbers to Felix’s current rating gives us quite the rosy forecast:

Cohort   Current  Increase  Proj. Peak  
1960-89     1848       281        2129  
1980-89     1848       369        2217

There’s a bit of slight of hand in how I’m doing this, since my study uses players’ year-end ratings, and I’m using Felix’s rating in April. However, there’s no natural law that says one artificial 12-month span is better than another, and Felix’s current age of 18.6 is roughly in the middle of the ages of the year-end 18-year-olds with whom I’m comparing him.

An Elo rating of 2,129 would be good enough for fourth place on the current list, behind only the big three. The rating of 2,217 is better than any of the big three can boast at the moment, and would be the fourth-best peak year-end rating among active players, again trailing only the big three. (And Andy Murray, if you consider him active.) Only 15 Open era players have managed year-end Elo peaks above 2,217.

No comparisons

It’s tough to say whether this method, of finding the typical difference between 18-year-old and peak Elo ratings, is adequate to handle the extremes. Some players peak earlier than average, and it stands to reason that the best young talents are more likely to do so. Boris Becker posted a whopping 2,212 Elo rating at the end of his age-18 season, which didn’t leave much room for improvement. He gained another 90 points before the end of his age-19 season, which was his career best.

Becker’s career path is not particularly helpful to our effort to forecast Felix’s, in part because the German was so unique, and also because his experience reflects such a different era. But even among less unique players, there are few useful comparables. No one born since 1987 managed a better age-18 Elo rating than Felix’s 1,848, and only a handful of active or recently-retired players even reached 1,750 by that age.

Lacking the data for a more precise approach, let’s repeat what I did for Bianca Andreescu last week, and see how the nearest 18-year-old comparisons fared. Of the players whose age-18 year-end Elos were closest to Felix’s 1,848, here are the 10 above him and the 10 below him on the list:

Player               BirthYr  18yo Elo  Incr  Peak Elo  
Stefan Edberg           1966      1916   350      2266  
John Mcenroe            1959      1912   496      2408  
Guillermo Coria         1982      1909   145      2055  
Pat Cash                1965      1907   151      2058  
G. Perez Roldan         1969      1884    41      1925  
Andy Murray             1987      1878   465      2343  
Roger Federer           1981      1871   487      2359  
Thomas Enqvist          1974      1865   216      2081  
Rafael Nadal            1986      1862   452      2314  
Jim Courier             1970      1849   283      2132  
…                                                       
Jimmy Brown             1965      1834     0      1834  
Andy Roddick            1982      1815   291      2106  
Aaron Krickstein        1967      1812   246      2058  
Yannick Noah            1960      1812   299      2112  
Fabrice Santoro         1972      1805    85      1890  
Andreas Vinciguerra     1981      1803    16      1819  
Novak Djokovic          1987      1792   645      2436  
Sergi Bruguera          1971      1790   265      2055  
Thomas Muster           1967      1788   329      2117  
Dominik Hrbaty          1978      1779   133      1913

The average increase among this group is 270 Elo points, close to the overall average for players who qualified for a year-end Elo rating at age 18. The youngest members of this list are encouraging: the big four, Andy Roddick, and Andreas Vinciguerra. Most promising youngsters would happily take a two-in-three shot at having a career at the level of the big four.

Perhaps the best comparison for Felix is a player who didn’t quite make that list, Alexander Zverev. The 21-year-old German posted a year-end Elo of 1,768 as an 18 year old, and already boosted that number by more than 300 points at the end of his 2018 campaign. Zverev is only an approximate comparison, he’s just a single data point, and we don’t know where he’ll end up, but his experience is a decade more recent than those of Novak Djokovic, Murray, and Nadal.

Forecasting the career performance of young tennis players is an inexact science, at best. Potential outcomes for Auger-Aliassime range from teenage flameout to double-digit major winner. Based on the limited information he’s given us so far, the latter seems within reach. What we know for sure is that he’s playing better tennis than any 18 year old we’ve seen in a decade. If that’s not reason for optimism, I don’t know what is.

Nick Kyrgios is More Predictable Than We Think

Italian translation at settesei.it

There is a persistent belief among tennis fans and commentators that some players are particularly inconsistent. For today’s purposes, I’m talking about match-to-match results, the players who have a knack for upsetting higher-ranked opponents but are also particularly susceptible to losses against weaker players. We have a range of words for this, like unpredictable, dangerous, tricky, and the preferred term for Nick Kyrgios: mercurial.

So far in 2019, Kyrgios has provided a perfect example of the inconsistent type. After early losses to Jeremy Chardy and Radu Albot, he bounced back to win last week’s ATP 500 in Acapulco, knocking out Rafael Nadal, Stan Wawrinka, John Isner, and Alexander Zverev. There’s no question that the Australian possesses more talent than his ranking would suggest. This is a guy who has yet to crack the top ten, but holds a .500 record in completed matches against the Big 3, a feat managed by no other active player (minimum 5 matches, excepting Nadal and Novak Djokovic themselves).

He sounds inconsistent. His results look unpredictable. But compared to the uncertainty that comes with every tennis match between highly-ranked professionals, how does he stack up? As my headline suggests, it’s not as clear-cut as it seems.

Measuring predictability

Consider the opposite type, a player who reliably beats lower-ranked opponents and usually loses against his betters. Roberto Bautista Agut has this type of reputation. As we’ll see, the numbers bear it out, notwithstanding his Doha upset of Djokovic a couple of months ago. If someone really is so predictable, that should show up in a comparison of his pre-match forecasts to his results. For a Bautista Agut type, the forecasts would be particularly accurate, while for a Kyrgios type, the forecasts would be much less reliable.

We already have a metric for this. Brier Score measures the accuracy of forecasts, considering not just how often predictions proved correct, but how close they came. For instance, after Kyrgios beat Zverev in Saturday’s Acapulco final, those prognosticators who gave the Aussie a 90% chance of winning were “more” correct than those who gave him a 60% shot. On the other hand, too much confidence runs the risk of a worse Brier Score–if you’re always giving tennis favorites a 90% chance of winning, you’ll often be wrong. Brier Score is the average of the squared difference between the pre-match forecast (e.g. 90%) and the result (1 or 0, depending if the pick was correct).

Brier Scores for ATP forecasting hover around the 0.2 mark. A lower Brier Score is better, representing less difference between prediction and results, so if you can come in much lower than 0.2, you should be making money betting on matches. If you’re much higher than 0.2, you might as well be flipping a coin. If we use random, 50/50 pre-match predictions, the resulting Brier Score is 0.25.

Brier-gios

If a player is truly unpredictable, the Brier Score for his matches should approach the 0.25 mark, and it should definitely exceed the tour-typical 0.2. To measure the reliability of pre-match forecasts for Kyrgios and other players, I used my surface-weighted Elo ratings for every completed tour-level main draw match since 2000 and generated percentage forecasts for each one. By this method, Zverev had a 67.4% probability of winning the Acapulco final.

So far in 2019, Kyrgios does look truly unpredictable. The Brier Score of his ten match results is 0.318, meaning that we’d have done better by simply flipping a coin to forecast the result of each of his matches. Even if we retroactively increase his chances of winning each match to account for the fact that he’s playing better than his Elo rating predicted, the Brier Score is 0.277, still worse than coin flips.

On the other hand, it’s just ten matches. Several other players have 2019 Brier Scores well over the 0.25 threshold, including Frances Tiafoe, Joao Sousa, Juan Ignacio Londero, and Felix Auger Aliassime. In a handful of tournaments, you’ll always get a few oddball results, either because of marked improvements (as is likely with Auger Aliassime) or extreme good or bad luck. Unless we’re willing to say that Sousa and Londero are remarkably unpredictable players, we shouldn’t draw the same conclusion based on Kyrgios’s last ten matches.

What you predict is what you get

The Brier Score for Elo-based forecasts of Kyrgios’s career matches at tour level is 0.219. That’s higher–and thus less predictable–than average, but not by that much. Of the 280 players with at least 100 tour-level matches this century, Kyrgios ranks 84th, more reliable than 30% of his peers. In 2017, his results were quite unpredictable, with a Brier Score of 0.244, but in 2015 and 2016 they generated a more pedestrian 0.210, and last year they looked downright predictable, at 0.177.

The Australian may be quite unpredictable in tactics, point-to-point performance, or on-court behavior, but his results just aren’t that unusual. The following table shows the 15 most unpredictable active players, as measured by Brier Score, along with Kyrgios, followed by the 15 most predictable active players:

Player                 Matches  Brier  
Lucas Pouille              189  0.247  
Andrey Rublev              106  0.245  
Benoit Paire               377  0.239  
Ivo Karlovic               650  0.239  
Stefanos Tsitsipas         100  0.232  
Karen Khachanov            154  0.231  
Peter Gojowczyk            102  0.231  
Federico Delbonis          225  0.227  
Marius Copil               108  0.227  
Damir Dzumhur              173  0.227  
Ernests Gulbis             420  0.226  
Pablo Cuevas               338  0.226  
Mischa Zverev              297  0.226  
Joao Sousa                 323  0.226  
Borna Coric                210  0.226  
...                                       
Nick Kyrgios               191  0.219  
...                                       
Matthew Ebden              171  0.188  
David Goffin               344  0.188  
Marin Cilic                684  0.186  
Richard Gasquet            770  0.183  
Tomas Berdych              911  0.182  
Milos Raonic               448  0.178  
David Ferrer              1048  0.177  
Jo Wilfried Tsonga         600  0.175  
Roberto Bautista Agut      384  0.172  
Kei Nishikori              517  0.167  
Juan Martin Del Potro      560  0.160  
Andy Murray                802  0.146  
Roger Federer             1350  0.121  
Novak Djokovic             951  0.117  
Rafael Nadal              1060  0.114

Lucas Pouille’s results have been almost impossible to forecast. The Brier Score generated by his 2018 results was nearly 0.3, suggesting it would have been smarter to calculate a forecast and then bet against it! Ivo Karlovic also shows up among the less reliable players, though it’s not clear whether that’s due to his unusual game style. Isner, the only decent parallel we have, is as reliable as the tour in general, with a career Brier Score of 0.201. Reilly Opelka, the other towering ace machine in the ATP top 100, has defied the odds so far in 2019, but he hasn’t yet amassed enough data to draw any conclusions.

At the other end of the spectrum, the most reliable players are many of the best. That adds up: A dominant player not only wins most of the matches he should, but his performance also allows us to make more aggressive forecasts. Nadal often enters matches with a 90% or better probability of winning, and confident predictions like that–as long the player converts them into wins–are what generate the lowest Brier Scores.

Consistent consistency results

We all tend to read too much into unusual results. Kyrgios has given us plenty of those, and we’ve repaid the favor by making him out to be even more of a wild card than he is. A couple of weeks ago, I took on a similar question and found that ATPers don’t really “play their way in” to tournaments, earning better or worse results in different rounds. This isn’t quite the same issue, but it all comes back to similar truths: Existing forecasts are pretty good, there’s always going to be a lot of randomness in the results, and the stories we invent to account for the randomness don’t really explain much at all.

Kyrgios is an immensely interesting player–I joked in yesterday’s podcast that readers should prepare themselves for a ten-part series–and digging into his point-by-point stats could reveal characteristics that are unique among tour players. That is still true. But at the match level, the likelihood that his contests will end in upsets isn’t unique at all–even if he is the proud new owner of a sombrero that says otherwise.

Belinda Bencic Won a Historically Difficult Title, Just Not Last Week

Italian translation at settesei.it

Belinda Bencic is back among the WTA elites. Last week in Dubai, she won her first Premier-level title since 2015, knocking out four top-ten players in the process. They were hardly dominant victories, with all four going to deciding sets and two of the four culminating in final-set tiebreaks, but there is no question that the 21-year-old Swiss is once again a threat at the tour’s biggest events.

Her string of top-ten victories leaves us to wonder how her title stacks up against similar feats in the past. Most relevant is the path Bencic took to her last Premier title, the 2015 Canadian Open. Four years ago in Toronto, she defeated four members of the top six, including then-top-ranked Serena Williams in the semi-final and Simona Halep in the championship match. Even the two lower-ranked opponents she faced that week were dangerous players then ranked in the top 25, Eugenie Bouchard and Sabine Lisicki. Those two presented more serious challenges than Bencic’s first two matches last week against Lucie Hradecka and Stefanie Voegele.

Spoiler alert: Toronto was the tougher path. It wasn’t the most difficult of all time, but it’s in the conversation. Bencic’s Dubai title surely wasn’t easy, but it wasn’t quite as unusual as last weekend’s press made it out to be.

Quantifying path difficulty

This is something we’ve done before. I’ve written several articles comparing the quality of opposition faced in slams, particularly as it applies to the ATP’s big three. It’s more complicated to compare all WTA events, in part because there are so many different levels of tournament, and the categorizations have changed over the years. But we can wave some of that aside for today’s purposes.

Here’s the simple algorithm to measure the difficulty of a player’s path to a title:

  • Pick a standard Elo rating for the type of tournament won. (In this case, we’re using 1900 for hard-court wins. We’d use lower numbers for clay and grass, but it gets complicated, and it’s more practical for today’s purposes to focus solely on hard-court events.)
  • Find the surface-weighted Elos of each opponent she played in the tournament
  • For each opponent, calculate the odds using the standard Elo rating and the opponent’s Elo rating.
  • Calculate the difficulty for each match as one minus the odds in the previous step.
  • Sum the single-match difficulties.

In the grand slam exercises I’ve done in the past, I’ve taken a final step of normalizing the results so that an average major title is exactly 1.0. Here, the idea of ‘average’ is more nebulous, so we’ll leave our results un-normalized.

The average difficulty of a hard-court title (excluding majors and year-end championships) is about 1.8. Bencic’s 2015 Toronto run was 3.64, and her path last week was 3.01.

It’s hotter in Miami (and Indian Wells)

One of the variables that influences path difficulty is number of matches. Bencic played six last week (as she did at the 2015 Canadian Open), but the top eight seeds played only five. At Indian Wells and Miami, the top 32 seeds play up to six matches, but those might be expected to present more challenges than Bencic’s six in Dubai, since the round-of-64 opponent has already won a match.

Certainly it has turned out that way. Here are the top ten most difficult hard-court WTA title paths since 2000:

Year  Event          Winner             Matches  Difficulty  
2010  Miami          Kim Clijsters            6        3.80  
2011  Miami          Victoria Azarenka        6        3.78  
2007  Miami          Serena Williams          6        3.65  
2015  Canadian Open  Belinda Bencic           6        3.64  
2012  Indian Wells   Victoria Azarenka        6        3.59  
2018  Cincinnati     Kiki Bertens             6        3.54  
2000  Miami          Martina Hingis           6        3.46  
2002  Miami          Serena Williams          6        3.45  
2008  Miami          Serena Williams          6        3.37  
2013  Miami          Serena Williams          6        3.35

Seven of the ten are from Miami, an event with a grand-slam-like field. Indian Wells is similar, but featured a weaker draw for most of the 21st century because Serena and Venus Williams chose not to play there. Bencic’s Toronto run is one of only two in the top ten outside of the March sunshine swing. The other is Kiki Bertens’s path to last year’s Cincinnati title, in which she also defeated Halep, Petra Kvitova, and Elina Svitolina, albeit not quite in the same order than Bencic did last week.

Also hot in Dubai

I calculated title difficulty for about 600 hard-court champions going back to 2000. Bencic’s Dubai path doesn’t register among the very most challenging, but it still stands above most of the pack. Here are the next 25 toughest routes, including every path rated a 3.0 or above:

Year  Event         Winner              Matches  Difficulty  
2016  Wuhan         Petra Kvitova             6        3.32  
2000  Indian Wells  Lindsay Davenport         6        3.32  
2014  Beijing       Maria Sharapova           6        3.30  
2008  Olympics      Elena Dementieva          6        3.27  
2009  Indian Wells  Vera Zvonareva            6        3.27  
2007  Indian Wells  Daniela Hantuchova        6        3.23  
2002  Filderstadt   Kim Clijsters             5        3.23  
2013  Beijing       Serena Williams           6        3.21  
2018  Doha          Petra Kvitova             6        3.18  
2002  Los Angeles   Chanda Rubin              5        3.18  
2000  Los Angeles   Serena Williams           5        3.16  
2009  Miami         Victoria Azarenka         6        3.15  
2003  Miami         Serena Williams           6        3.13  
2002  Indian Wells  Daniela Hantuchova        6        3.10  
2018  Wuhan         Aryna Sabalenka           6        3.08  
2008  Indian Wells  Ana Ivanovic              6        3.08  
2012  Tokyo         Nadia Petrova             6        3.08  
2010  Sydney        Elena Dementieva          5        3.06  
2010  Indian Wells  Jelena Jankovic           6        3.03  
2000  Sydney        Venus Williams            6        3.02  
2000  Sydney        Amelie Mauresmo           4        3.02  
2019  Dubai         Belinda Bencic            6        3.01  
2009  Tokyo         Maria Sharapova           6        3.00  
2002  San Diego     Venus Williams            5        3.00  
2001  Sydney        Martina Hingis            4        2.99

There’s Belinda again, at 32nd overall. Historically, the February tournaments in the Gulf haven’t been the toughest on the calendar, at least compared with Indian Wells, Miami, and Sydney. Yet Kvitova took an even more difficult path to the title last year in Doha. (Dubai and Doha trade tournament levels each year. As a Premier 5, Doha was worth more points in 2018; Dubai took over the status and was worth more points in 2019.) She also plowed through four top-ten opponents, and she needed to beat 33rd-ranked Agnieszka Radwanska just to earn a place in the round of 16.

Strong but weaker

Again, Bencic’s Dubai title was an impressive feat. But as we’ve seen, it pales in comparison with her previous Premier title. I suppose she might have won anyway if faced with more difficult competition, but that pair of third-set tiebreaks suggests she was pushed to the limit as it was.

While the current WTA field is extremely deep, packed with very good players, the lack of one historically great superstar (or more!) shows up in the Elo ratings. Of the 35 champions shown in the two tables above, 12 had to beat a player with a surface-weighted rating of 2240 or higher, and 12 more needed to get past an opponent rated 2100 or above. Bencic’s toughest task last week was Halep, at 2054. While it isn’t easy to knock off several consecutive foes in the 2000 range, it’s not the same as including one victory over a superstar like Serena, Venus, Maria Sharapova, or Victoria Azarenka at her peak.

At the 2015 Canadian Open, Bencic counted Serena among the vanquished. Maybe in another four years, when the Swiss is due for her next odds-defying Premier title, she’ll face down a couple of new young superstars and earn a place at the top of this list.

Forecasting the Davis Cup Finals

It took more than a year to decide on a new format, but barely a week to make the draw. With 12 countries qualifying for the inaugural Davis Cup Finals in home-and-away ties earlier in month, the field of 18 is set. Using the ITF’s own system to rank countries, the 18 teams were divided into three “pots,” then assigned to the six round-robin groups that will kick off the tournament this November in Madrid.

The new format sounds complicated, but as round-robin events go, it’s easy enough to understand. Each of the six round-robin groups will send a winning team to the quarter-finals. Two second-place sides will also advance to the final eight, as determined by matches won, then sets won, and so on as necessary, until John Isner and Ivo Karlovic stand back to back to determine which one is really taller. From that point, it’s an eight-team knock-out tournament.

Here are the groups, as determined by yesterday’s draw, with seeded countries indicated:

  • Group A: France (1), Serbia, Japan
  • Group B: Croatia (2), Spain, Russia
  • Group C: Argentina (3), Germany, Chile
  • Group D: Belgium (4), Australia, Colombia
  • Group E: Great Britain (5), Kazakhstan, Netherlands
  • Group F: United States (6), Italy, Canada

The ITF ranking system considers the last four years of Davis Cup results, so Spain’s brief exit from the World Group makes the seedings a bit wonky. As it turns out, not only is it a top team (Croatia) who will have to deal with early ties against the Spaniards, the entire Group B trio constitutes a group of death. Russia would be an up-and-coming squad in any format, and it is clearly the most dangerous of the six lowest-ranked sides.

Madrid to Monte Carlo

Last week, I introduced a more accurate, predictive rating system for Davis Cup, involving surface-specific Elo ratings for the players likely to compete. Those rankings put Spain at the top, Croatia second, Russia fifth, and fourth-seeded Belgium 14th in the 18-team field.

Now that we have a draw, we can use those ratings to run Monte Carlo simulations of the entire Davis Cup carnival Finals. As in my post last week, I’m estimating that singles players have a 75% chance of playing at any given opportunity and doubles players have an 85% chance. Those are just guesses–there’s no data involved in this step. Surely some teams are more fragile than others, perhaps because their stars are particularly susceptible to injury or just uninterested in the next event. I’ve excluded Andy Murray, but for the moment, I’m keeping Novak Djokovic and Alexander Zverev in the mix.

(We’re using Elo ratings for each individual player, which means the simulation is telling us what would be likely to happen if it were played today. Things will change between now and November, even if every eligible player shows up. A proper forecast that takes the time lag into account would probably give a slight boost for younger teams [whose players will have nine months to mature] and a penalty for older ones [who are more likely to be hit by injury]. And overall, it would shift all of the championship probabilities a bit toward the mean.)

Here are the results of 100,000 simulations of the draw, with percentages given for each country’s chance of winning their group, then reaching each of the knock-out rounds:

Country  Group     QF     SF      F      W  
ESP      46.1%  59.1%  41.9%  30.3%  19.3%  
FRA      54.2%  66.6%  40.6%  25.1%  14.6%  
AUS      74.5%  84.4%  46.0%  23.8%  12.1%  
USA      53.0%  65.5%  36.8%  19.7%  10.4%  
CRO      31.0%  43.0%  27.2%  17.8%   9.8%  
GER      52.5%  67.9%  39.7%  17.6%   7.7%  
RUS      22.9%  33.1%  19.5%  12.0%   6.1%  
SRB      33.0%  47.9%  24.1%  12.6%   6.0%  
GBR      66.8%  78.7%  35.9%  12.5%   4.4%  
ARG      39.7%  56.6%  28.6%  10.4%   3.8%  
ITA      24.3%  35.9%  14.6%   5.5%   2.1%  
CAN      22.7%  33.4%  13.1%   4.9%   1.8%  
JPN      12.8%  19.5%   7.2%   2.8%   0.9%  
BEL      20.3%  32.0%   8.5%   2.1%   0.6%  
NED      21.7%  35.5%   8.6%   1.7%   0.3%  
CHI       7.8%  12.9%   3.4%   0.6%   0.1%  
KAZ      11.5%  19.0%   3.2%   0.5%   0.1%  
COL       5.1%   8.9%   1.2%   0.1%   0.0%

Spain is our clear favorite, despite their path through the group of death. Five teams have a better chance of winning their group and reaching the quarters than the Spaniards do, but their chances in the single-elimination rounds make the difference. At the other extreme, Australia seems to be the biggest beneficiary of draw luck. My rankings put them sixth, and they landed in a group with Belgium (the lowest-rated seed) and Colombia (the weakest team in the field). Their good fortune makes them the most likely country to reach the final four, even if Spain and France have a better chance of advancing to the championship tie.

Less randomness, more Spain

What if we run the simulation one step earlier in the process? That is to say, ignore yesterday’s draw and see what each country’s chances were before their round-robin assignments were determined. For this simulation, we’ll keep the ITF’s seeds, so Spain is still a floater. Here’s how it looked ahead of the ceremony:

Country  Group     QF     SF      F      W  
ESP      63.0%  75.9%  52.9%  35.0%  22.6%  
FRA      56.8%  70.8%  43.9%  25.7%  14.5%  
CRO      55.5%  69.4%  42.2%  25.1%  13.5%  
USA      51.3%  65.6%  38.5%  19.8%  10.0%  
AUS      48.3%  62.9%  34.8%  17.7%   8.5%  
RUS      40.6%  53.5%  30.2%  15.8%   7.9%  
SRB      42.9%  55.8%  28.3%  13.5%   5.9%  
GER      42.0%  55.7%  27.3%  12.5%   5.4%  
ARG      35.9%  49.1%  20.9%   7.9%   2.8%  
ITA      33.6%  47.1%  19.2%   7.2%   2.5%  
GBR      34.9%  48.3%  20.3%   7.5%   2.5%  
CAN      24.5%  35.5%  14.3%   5.3%   1.9%  
JPN      19.8%  29.4%  10.6%   3.6%   1.1%  
BEL      20.9%  30.4%   7.5%   1.8%   0.4%  
NED       9.5%  15.5%   3.5%   0.7%   0.1%  
CHI       7.9%  13.3%   2.6%   0.4%   0.1%  
KAZ       8.4%  14.1%   2.1%   0.3%   0.0%  
COL       4.3%   7.5%   1.1%   0.2%   0.0%

With the “group of death” out of the picture, Croatia jumps from fifth to third, swapping places with Australia. The defending champs lost the most from the draw, while Spain suffered a bit as well.

Elo in charge

Another variation is to ignore the ITF rankings and generate the entire draw based on my Elo-based ratings. In this case, the top six seeds would be Spain, Croatia, France, USA, Russia, and Australia, in that order. Argentina and Great Britain would fall to the middle group, and Belgium would drop to the bottom third. Here’s how that simulation looks:

Country  Group     QF     SF      F      W  
ESP      71.6%  82.8%  57.3%  38.0%  24.1%  
FRA      64.6%  77.6%  45.8%  26.7%  14.4%  
CRO      63.1%  76.3%  45.8%  25.6%  13.6%  
USA      59.7%  73.3%  41.1%  20.2%  10.2%  
RUS      58.6%  71.2%  37.0%  19.7%   9.5%  
AUS      57.7%  71.4%  37.7%  17.7%   8.8%  
SRB      37.1%  53.0%  26.1%  12.1%   5.3%  
GER      35.3%  52.3%  24.5%  10.9%   4.6%  
ARG      28.0%  44.2%  17.5%   6.4%   2.2%  
ITA      27.4%  43.6%  16.9%   6.2%   2.1%  
GBR      27.0%  43.1%  16.5%   6.0%   2.0%  
CAN      26.7%  41.8%  16.0%   5.8%   2.0%  
JPN      15.9%  23.6%   8.1%   2.6%   0.8%  
BEL       9.4%  15.1%   3.9%   0.9%   0.2%  
NED       6.5%  10.8%   2.3%   0.5%   0.1%  
CHI       5.3%   9.0%   1.8%   0.3%   0.1%  
KAZ       3.2%   5.8%   0.9%   0.1%   0.0%  
COL       3.1%   5.2%   0.8%   0.1%   0.0%

The big winners in the Elo scenario are the Russians, who gain a seed and avoid a round-robin encounter with either Spain or Croatia. Australia gets a seed as well, but the benefit of protection from the powerhouses isn’t as valuable as the luck than shone on the Aussies in the actual draw.

Imagine a world with no rankings

Finally, let’s see what happens if we ignore the rankings altogether. It would be unusual for the tournament to take such an approach, but if there’s ever a time to have a tennis event with no seedings, this is it. The existing rankings are far too dependent on years-old results, leaving young teams at a disadvantage. And my system, while more accurate, doesn’t quite feel appropriate either. It is based on individual player ratings, and this is a team event.

Whatever the likelihood of a ranking-free draw in the Davis Cup future, here’s what a simulation looks like with completely random assignment of nations into round-robin groups:

Country  Group     QF     SF      F      W  
ESP      62.8%  75.4%  52.4%  34.8%  22.5%  
FRA      54.8%  68.6%  42.6%  25.0%  13.9%  
CRO      53.4%  67.2%  41.0%  23.6%  13.0%  
USA      48.8%  62.9%  35.9%  19.1%   9.7%  
RUS      47.9%  61.0%  34.8%  18.5%   9.3%  
AUS      47.1%  61.1%  34.1%  17.6%   8.5%  
SRB      41.5%  54.3%  28.0%  13.5%   6.1%  
GER      40.3%  53.6%  26.7%  12.3%   5.3%  
ARG      31.9%  44.9%  18.8%   7.2%   2.6%  
ITA      31.5%  44.2%  18.6%   7.1%   2.5%  
GBR      30.7%  43.4%  17.6%   6.5%   2.3%  
CAN      30.4%  42.7%  17.4%   6.4%   2.2%  
JPN      25.9%  36.4%  13.5%   4.6%   1.4%  
BEL      17.2%  25.9%   7.2%   1.8%   0.4%  
NED      12.5%  20.0%   4.6%   0.9%   0.2%  
CHI      10.4%  16.9%   3.5%   0.6%   0.1%  
KAZ       7.0%  11.8%   1.9%   0.3%   0.0%  
COL       5.9%   9.7%   1.5%   0.2%   0.0%

Round-robin formats do a decent job of surfacing the best teams, so the fully random approach doesn’t give us wildly different results than the seeded simulations. The main effect of the no-seed version is to give the weakest sides a slightly better chance at advancing past the group stage, since there is a better chance for them to avoid strong round-robin competition.

Madrid or Maldives redux

Some top players are likely to skip the event. Zverev has said he’ll be in the Maldives, and Djokovic has hinted he may miss the tournament as well. The new three-rubber format means that teams will suffer a bit less from the absence of a singles star, assuming he also isn’t one of the best doubles options as well. Still, both Germany and Serbia would much rather head to the party with a top-three singles player on their side.

Here are the results of the intial simulation–based on the actual draw–but without Djokovic or Zverev:

Country  Group     QF     SF      F      W  
ESP      46.5%  59.5%  44.0%  33.2%  21.3%  
FRA      68.2%  79.3%  49.6%  30.6%  17.8%  
AUS      74.3%  84.5%  46.1%  24.2%  12.6%  
USA      53.4%  66.2%  37.5%  20.4%  10.8%  
CRO      30.3%  42.5%  28.4%  19.6%  10.8%  
RUS      23.2%  33.6%  21.1%  13.8%   7.0%  
GBR      67.0%  79.0%  40.9%  14.6%   5.2%  
ARG      52.1%  66.9%  35.5%  12.9%   4.9%  
GER      36.4%  52.3%  23.3%   7.2%   2.2%  
ITA      24.2%  35.9%  14.5%   5.7%   2.2%  
CAN      22.4%  33.2%  13.4%   5.2%   2.0%  
JPN      19.4%  31.7%  11.5%   4.8%   1.6%  
BEL      20.5%  32.4%   8.6%   2.3%   0.6%  
SRB      12.4%  21.1%   6.0%   1.9%   0.5%  
NED      21.6%  35.5%   9.8%   2.0%   0.4%  
CHI      11.4%  18.5%   4.9%   0.9%   0.2%  
KAZ      11.3%  19.1%   3.8%   0.5%   0.1%  
COL       5.2%   9.0%   1.2%   0.2%   0.0%

Germany’s chances of winning the inaugural Pique Cup would fall from 7.7% to 2.2%, and Serbia’s odds drop from 6.0% to 0.5%. Argentina and France, the seeded teams sharing groups with Germany and Serbia, respectively, would be the biggest gainers from such high-profile absences.

Anybody’s game

I’ve been skeptical of the new Davis Cup, and while I remain unconvinced that it’s an improvement, I find myself getting excited for the weeklong tennis hootenanny in Madrid. These simulations were even more encouraging. As always, the ranking and seeding isn’t the way I’d do it, but in this format, the differences are minimal. The event format will give us a chance to see plenty of tennis from every qualifying nation, and the high level of competition from most of these countries ensures that most teams have a shot at going all the way.

Top Seed Upsets in ATP 250s

Italian translation at settesei.it

In a typical week, no one would notice if Fabio Fognini, Karen Khachanov, and Lucas Pouille combined to go 0-3. This week is different, as those three men held the top seeds at the ATP events in Cordoba, Sofia, and Montpellier. After their first-round byes, each of them lost in the second round, to Aljaz Bedene, Matteo Berrettini, and Marcos Baghdatis, respectively. At least two of the top seeds pushed their opponents to three sets, while Fognini lasted only 71 minutes.

This is not the first time a trio of number one seeds have suffered first-match upsets in the same week. Amazingly, it’s not even the first such occurrence in this very week on the calendar. Two years ago, when the South American event was played in Quito, the results were the same: top seeds Marin Cilic, Ivo Karlovic, and Dominic Thiem all failed to win a match. Thiem’s vanquisher, Nikoloz Basilashvili, even extended the streak the following week, heading to Memphis and handing Karlovic his second straight second-round ouster.

Predictable upsets?

Focusing on these losses, it’s natural to wonder whether top seeds are particularly fragile in this sort of tournament. There’s certainly a logic to it. The number one seed at an ATP 250 is usually ranked in the top 20, and is the sort of player who might have considered taking the week off. He knows that more ranking points are available at slams and Masters, so winning a smaller event isn’t his highest priority. His opponent, on the other hand, is competing every chance he gets, and the points on offer at a smaller event could make a big difference in his standing. Further, he has already played–and won–his first-round match, so he might be performing better than usual, or the conditions might suit him particularly well.

Let’s put it to the test. Since 2010, not counting this week’s carnage, I found 267 non-Masters events at which a top seed got a first-round bye and completed his second-round match. (Additionally, there have been three retirements and one withdrawal; only one of those resulted in a loss for the top seed.) The number one seeds had a median rank of 10, and the underdogs had a median rank of 89. Based on my surface-weighted Elo ratings at the time of each match, the favorites should have won 81.5% of the time. That’s better than this week’s trio of top-seeded losers, who were 64% (Fognini), 80% (Khachanov), and 69% (Pouille) favorites.

As it happened, the unseeded challengers were more successful than expected. The favorites won only 76.8% of those matches–a rate low enough that there is only a 3% probability it is due to chance alone. It’s not an overwhelming effect–certainly not enough that we should have predicted this week’s results–but it seems that a few of the top seeds are showing up unmotivated and a handful of the underdogs are playing better than expected.

Riding the wave

What about the underdog winners? Once they’ve defeated the top seed, how many capitalize on the opportunity? Berrettini came back to beat Fernando Verdasco in his quarter-final match today, while Baghdatis and Bedene play later. My forecasts believe that, of the three, Bedene has the best chance of claiming a title, though still less than a one-in-five shot at doing so.

In our subset of 267 matches, the underdog won 66 of them. More than half the time, though, that was the end of the run. 38 of the 66 (58%) fell in the quarter-finals. Another 17 lost in the semis. Whatever works so well for these underdogs in the second round disappears afterward. In the 105 matches contested by these 66 men in the quarter-finals and beyond, Elo thinks they should have won 44.9% of them. Instead, they managed only 42.3%.

There’s still a bit of hope. Five men knocked out the top seed in the second round and went on to win the entire tournament. One of those was a challenger we’ve already mentioned: Estrella, who knocked out Karlovic and went on to hoist the trophy in Quito two years ago. Maybe there’s some magic in week six. This week’s trio of underdogs would surely love to think so.