Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.
Match Charting Project: The dataset has grown by more than 60 matches in the last week, from 5,376 to 5,439. New additions include a near-complete run of 1989-96 WTA Miami finals, plus many men’s and women’s grand slam semi-finals. And, of course, a lot of matches from this year’s Miami event, including the women’s final and both semi-finals.
Roger Federer could finally become the first ATP player to win multiple titles this season, but the WTA remains a tour of unique winners. In Miami, Ashleigh Barty became the 14th champion in 14 tour-level events.
To reach the final, Federer needed to beat someone more than 15 years his junior. In fact, both Miami semi-finals involved age gaps of at least one and a half decades. That hasn’t happened at an ATP event since 1979. The closest since then was in Dubai last month, when Fed-Coric and Monfils-Tsitsipas were both at least 11.9 year gaps.
Speaking of unusual semi-finals… The Bryans beat Kubot/Melo by a score of 7-6(7) 6-7(8) [14‑12], just about as long as a match can be within the constraints of the modern doubles format of no-ad with a third-set super-tiebreak. It lasted 187 points. While match stats are hard to come by for doubles, I do have a reasonably complete set for tour-level doubles since 2017. In that span, 187 points is the longest match under these rules. There was one other 187-pointer in 2018 and a 186-point marathon in 2017.
Thanks in part to his run in Miami, Felix Auger Aliassime won his first five career matches against top 20 players, something that’s never been done before. Mario Ancic won his first three; Felix is the only guy with more. After the semi-final loss to Isner, FAA falls to 5-1, but still has a chance to set more records. No one has won more than 7 of their first 10 matches against the top 20, a feat accomplished by Gustavo Kuerten and Andrei Medvedev.
Episode 54 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, catches up on the Miami early rounds, beginning with some rocky starts for Roger Federer and Novak Djokovic. We also look at the slew of early upsets and the threats who remain in the draw. We talk about how to evaluate Bianca Andreescu’s feats at her young age, and the wacky wild cards that always find their way into the Miami main draw.
Thanks for listening!
(Note: this week’s episode is about 64 minutes long; in some browsers the audio player may display a different length. Sorry about that!)
Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.
We’re still waiting for our first multiple-title winner of the 2019 season. On the ATP side, that’s 19 champions in 19 events, a new record.
The player to break that streak will not be Dominic Thiem, who lost his first match in Miami against Hubert Hurkacz. Thiem is the first Indian Wells titlist to fail to win a Miami match since 2010, when Ivan Ljubicic lost to Benjamin Becker. It’s not bad company for Thiem, though, as the other three IW champions to lose their first match in Miami are Novak Djokovic, Lleyton Hewitt, and Alex Corretja.
Conceivably, the man who breaks the unique-titlist streak could be Reilly Opelka, who beat Diego Schwartzman despite being out-aced by El Pique in the first set. Opelka didn’t record a single ace in the first set, and it was only his second tour-level match in which less than 10% of his service points went for aces. (The other was his 2017 first-round encounter with Tommy Haas in Houston, and his career rate is 22.3%.)
Kei Nishikori is king of deciding sets no more. After dropping a third set to Dusan Lajovic in his first outing in Miami, he loses the top spot on the deciding-set winning percentage leaderboard, to Djokovic.
Yesterday, Naomi Osaka won the first set against Su-Wei Hsieh, but Hsieh came back to win the match. It’s the first time since 2016 that Osaka failed to convert a one-set advantage, a streak I wrote about a couple of months ago. She fell only 156 matches short of Chris Evert’s record.
Episode 53 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, focuses on the breakthrough hard-court title for Dominic Thiem, who claimed his first non-clay Masters trophy yesterday in Indian Wells. We talk a bit about his tactics, his draw, and–we just can’t help it, apparently–whether we’re in a weak era.
On the women’s side, we use the shock victory of 18-year-old wild card Bianca Andreescu to consider the strength of her generation, with names like Osaka, Sabalenka, and now Andreescu poised to sweep away their elders. Yet still in the mix, and representing our last topic, are Serena, Venus, and Vika, who remain a threat against just about anyone.
Thanks for listening!
(Note: this week’s episode is about 60 minutes long; in some browsers the audio player may display a different length. Sorry about that!)
Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.
Match Charting Project: The dataset has grown by 70 matches in the last week, from 5,256 to 5,326. We’ve added a slew of men’s and women’s matches from Indian Wells, several more 90’s Wimbledon semi-finals, and best of all, three long-sought Roland Garros women’s finals. We have now charted all men’s and women’s French Open finals back to 1980.
Trivia
Belinda Bencic continues to rack up top-ten wins, and despite her semi-final loss to Angelique Kerber on Friday, her record against the top ten is above .500, at 19-16. That’s something that few of her peers can claim, even many players we consider to be elites.
Sara Errani hit a whopping 57 double faults in her last four matches, including 22 in the Guadalajara first round against Irina Camelia Begu. And she won! 57 double faults is more than she hit in the entire 2017 or 2018 seasons.
The next generation of WTA teens is coming fast: 16-year-old Clara Tauson won this week’s ITF Shenzhen $60K title, and 15-year-old Dasha Lopatetskaya won her fifth pro title. At least two more teens won ITF titles this week, with three more playing finals today.
With his defeat of Novak Djokovic, Philipp Kohlschreiber became the 4th-oldest player to beat an ATP No. 1.
Ivo Karlovic turned 40 three weeks ago, and celebrated by winning three matches at Indian Wells, the first time since 2011 (also at Indian Wells) that he won three or more matches at a Masters event.
Episode 52 of the Tennis Abstract Podcast features guest co-host Jeff McFarland, the man behind Hidden Game of Tennis. We start with Jeff M’s origin story as a baseball analyst shifting to tennis, and then dive into a slew of WTA topics as we enter the second week of Indian Wells.
We start with Aliaksandra Sasnovich’s unusual proclivity for losing 6-0 sets and the serve-return balance in women’s tennis that results in more lopsided set scores. Next, we consider Sloane Stephens’s latest early-round ouster, dismissing some of the theories that are often thrown around after such upsets. We also talk Osaka, and finish up with a speed round on Serena, Vika, and Danielle Collins.
Thanks for listening!
(Note: this week’s episode is about 70 minutes long; in some browsers the audio player may display a different length. Sorry about that!)
Around the Net is my attempt to provide a clearinghouse for tennis analytics on the web. Each week, you’ll find a summary of recent articles, podcasts, papers, and data sources, as well as trivia and the occasional bit of interesting non-tennis content. If you would like to suggest something for a future issue, drop me a line.
Match Charting Project: The dataset has grown by more than 60 matches in the last week, from 5,194 to 5,256. We completed a run of Indian Wells women’s finals back to 2004, along with 1999 and 2000. We also added all of last week’s finals, Kyrgios’s last four matches in Acapulco, and another handful of Pete Sampras’s grand slam semi-finals.
MCP Most Wanted Video: We’re really close to completing some noteworthy subsets, but we’re missing video for several key matches. Please help!
Trivia
Laslo Djere is the 30th seed at this year’s BNP Paribas Open. This is only the second time ever that a wild card is also seeded at a mandatory ATP Masters tournament since the introduction of mandatory ATP Masters tournaments at around 2000. Djere has never won a match at Masters level, and has won only four tour-level hard-court matches.
Donald Young also got a wild card into the Indian Wells main draw, his 29th career tour-level main draw WC, and his fourth at this event. His first was 14 years ago.
Yesterday, 104th-ranked Stefanie Voegele upset 4th-seeded Sloane Stephens. “Upset” is hardly the right word, since Voegele had won four of their previous five meetings, all but one since Stephens cracked the top 15.
Tennis Abstract readers and listeners are amazing: Luke Burrage built upon the sport of juggling combat and organized a tour, Fight Night Combat, with ranking and tour-structure concepts borrowed from tennis.
Earlier this week, we looked at whether Nick Kyrgios is unusually inconsistent. That is, is he more likely to upset higher-ranked players and lose to lower-ranked players than his peers? The numbers say he isn’t.
But that isn’t all we mean when we talk about Kyrgios’s unreliability. He often undergoes dramatic shifts within matches. At times, he is visibly distracted; during his Delray Beach match against Radu Albot, he even shouted that he wanted to get off the court. Other times, he comes up with breathtaking serving and shotmaking at the most crucial moments. He seems motivated by both packed grandstands and on-court pressure. Unfortunately, both of those are missing from a lot of professional tennis.
We already have some evidence for the better-under-pressure hypothesis. In his five matches in Acapulco last week, he won a mere 50.4% of points, one of the lowest totals ever for a title-winner. In three of the five matches, he won return points at a lower rate this opponent, resulting in Dominance Ratios (DRs) below 1.0. Winning a match with a sub-1.0 DR (or fewer than 50% of total points won) isn’t unheard of, but it’s not a reliable way to rise to the top of the sport. Such contests are called “lottery matches” for a reason–there’s a lot of luck involved in winning with such fine margins, and fortune tends to even out.
Yet Kyrgios’s “luck” keeps nudging his results in the same direction. He has played 15 career tour-level matches in which his DR is between 0.9 and 0.99–close matches in which he was slightly outplayed, at least in the points column. With stats like that, players tend to win about one-third of the time. Kyrgios, however, has won eleven of those 15 matches. His good fortune doesn’t cancel out when he narrowly edges out an opponent: In 13 matches with DRs between 1.0 and 1.1, he has lost only two. The Australian is doing something right.
Big points are big
You probably already know what’s going on here, even if you haven’t listened to commentators speculate during Nick’s matches. The key to such narrow victories is converting the “big” points–break points, deuces, tiebreaks, and so on. It doesn’t matter if you throw away a point or two when serving at 40-love. Other situations have considerably more leverage, and that’s when Kyrgios brings his best tennis.
I tallied up Kyrgios’s return points won over the course of his career, based on the point score of each one. (I don’t have the point-by-point sequence of every one of his tour-level matches, but most of them are included, more than enough to constitute a reliable sample.) Here are the five games scores when he wins the most return points, starting with the most effective:
0-40, 40-AD, 15-30, 30-40, 40-40
And the five scores, again in order, starting with least effective:
30-0, 40-0, 40-15, 0-15, 0-0
In other words, when he has a chance to break, he’s great. In my sample of matches, he won 31.5% of return points; when the opposing server is facing him at 0-40, he wins the point 45.0% of the time. At 40-AD, it’s 41.9%. When his opponent serves with a 30-0 advantage, Kyrgios wins a mere 27.3% of return points.
Everybody does it (a little)
Astute readers will realize that I haven’t accounted for a key variable. In a data set of dozens of matches, scores that favor the returner will occur more often against weaker servers. Kyrgios didn’t get many 0-40 or even 40-AD chances against John Isner last week, but he can expect to get more against the likes of Albot. So to some extent, we should expect players to win more return points at these moments. In the last 52 weeks, ATPers have won 37.3% of return points, but 40.1% of break points.
Everybody does it, but Nick does it more. The following table shows the ratio of return points won at each game score to average return points won. The middle column shows Kyrgios’s ratios and the right-most column shows the 2018 ATP tour average:
Most players take advantage in 0-40 situations, and to a lesser extent at break points, but Kyrgios is on another planet. The average player wins roughly 10% more return points in break situations; Kyrgios triples the ratio.
Leverage
We’ve taken a big step toward explaining Kyrgios’s pattern-breaking results and his in-match inconsistency. But even game scores don’t tell the whole story. A deuce point at 5-0 usually matters a great deal more than a break point when the returner is already up a set and a break.
To account for those differences, we’ll turn to the leverage metric. (You’ll also see it referred to as “volatility” or “importance.”) Here’s the idea: Given what we know about two players, we can calculate the probability that one of them will win the match, based on the current situation. If the server wins, that probability shifts in his favor. If the returner wins, it shifts in the opposite direction. Leverage is the sum of those two shifts: the amount of win probability that is at stake at any given point.
For today’s purposes, there are no specific numbers; you need only to understand the concept. The higher the leverage, the more the point matters. Players might disagree with some of the details that a purely math-based approach spits out, but for the most part, the equations capture our intuition about which points matter, and how much.
I calculated the leverage for every point of the 2018 ATP season and split the points into ten categories, from least important (1) to most important (10). The following graph shows the tour average rate of return points won (RPW) for each of those ten categories:
If we ignore the leftmost and rightmost data points, there’s something of a trend here. From the second-to-least-important category to the second-to-most-important, players increase their return points won from about 36.0% to 37.5%. Some of that shift can be explained by a phenomenon I’ve already mentioned: returners find themselves in crucial situations (such as break points) more often against weaker servers.
Here’s the same graph, now with a second line showing Kyrgios’s RPW in the ten categories, from least important to most important. I’ve kept the ATP average trendline for comparison:
Remember that 36.0% to 37.5% increase I mentioned a minute ago? For Kyrgios, the same shift is 27.0% to 35.2%–eight percentage points instead of less than two. It appears that the Australian is extremely sensitive to what’s at stake throughout matches, and when the rewards are high enough, he turns into a credible returner.
Some of you are probably thinking, “of course, I knew that all along.” First of all, I hate it when people say that, because what they really mean is, “I suspected that all along,” and they didn’t really know. Some of the other things such people “know” are actually wrong.
Second, I need to underline just how unusual this is. I’ve been playing around with point-by-point data for a few years now, looking for in-match patterns, for specific players and for the sport overall. Such patterns exist: points and games aren’t entirely independent of each other. But usually they are minor–a percentage point or two, not the kind of thing you could spot even in a fortnight’s worth of matches. Kyrgios breaks the mold. When it comes to the mercurial Australian, the assumptions that are adequate to account for most of professional tennis simply fail.
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.
Episode 51 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, starts with a discussion of Roger Federer’s 100th title, and the chances that he’ll reach Jimmy Connors’s record of 109. We also consider the odds that Rafael Nadal and Novak Djokovic will join him in the triple-digit title club.
We continue with a lot of Nick Kyrgios talk, attempting to make sense of his ability to win matches despite losing the majority of points, as well as his abilities to unsettle opponents and cause commentators to say questionable things. We also do a bit of an Indian Wells preview to close out the hour.
Finally: My apologies for the poor editing of last week’s episode. You won’t run into any similar issues with this one.
Thanks for listening!
(Note: this week’s episode is about 62 minutes long; in some browsers the audio player may display a different length. Sorry about that!)
