Author: jsackmann

Around the Net, Issue 2

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

Articles and Papers

Multimedia

Data

  • Match Charting Project: The dataset has grown by 60 matches in the last week, from 5,083 to 5,143. Highlights include the 100th charted Petra Kvitova match, making her the 7th woman to become so well represented. We’ve also continued filling out the historical record of grand slam semi-finals, including a 1981 clash between Jimmy Connors and Bjorn Borg.

Trivia

  • Last week’s New York semi-final between John Isner and Reilly Opelka set plenty of records, the number of which is probably limited only by our imaginations. First, their 59 tiebreak points tied a best-of-three record. Unsurprisingly, Isner (and Jeremy Chardy) held the previous record as well.
  • The Isner-Opelka tilt also set the record for most aces (81) in a best-of-three match–breaking another of Isner’s marks–and was also the first best-of-three match in which both players hit at least 37 aces.
  • Marco Cecchinato has somehow won three tour-level titles (and reached a Roland Garros semi-final!) with only 33 tour-level match wins. By contrast, Julien Benneteau won 273 tour-level matches but nary a title.
  • Since 2008, Fabio Fognini has played at least part of the South American golden swing every year but one. But 2019 was the first time he suffered three straight first-round exits, despite entering each event as a top-two seed.

Beyond the net

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

Dominic Thiem, Tennys Sandgren, and Playing Your Way In

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

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

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

Present and prepared

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

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

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

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

What about Tennys?

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

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

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

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

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

Positive results, anyone?

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

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

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

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

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

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

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

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

Thanks for listening!

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

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

Around the Net, Issue 1

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

Articles

Podcasts

Data

Trivia

Miscellaneous

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

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.

Do Rallies Get Longer as Matches Progress?

Italian translation at settesei.it

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

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

Set  Avg Rally  
1          3.2  
2          4.0  
3          4.9

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

Theorizing

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

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

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

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

More matches

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

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

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

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

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

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

WTA to the rescue

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

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

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

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

Is Doubles As Entertaining As We Think?

For as long as I’ve been following tennis, there’s been a tension between the amount of doubles available to watch and the amount of doubles that fans say they want to watch. In-person spectators flock to doubles matches at grand slams and aficionados pass around GIFs of the most outrageous, acrobatic doubles points. Yet broadcasters almost always stick with singles, leaving would-be viewers chasing down online streams, often illegal ones.

There are some good reasons for that, foremost among them the marquee drawing power of the best singles players. Broadcasters are convinced that their audiences would rather watch a Fed/Rafa/Serena/Pova blowout than a potentially more entertaining one-on-one contest between unknowns, let alone a doubles match. And they’re probably right–at least, they’ve got ratings numbers to back them up. So we’re left with a small population of hipster doubles fans, confident that two-on-two is the good stuff, even if most of us rarely watch it.

It’s probably impossible to quantify entertainment value, but that doesn’t mean we shouldn’t try. What can the numbers tell us about the watchability of doubles?

Hip to be rectangular

There’s plenty of room for a diversity of preferences–one fan’s Monfils may be another fan’s Isner. But there are some general principles that seem to define entertaining tennis for most spectators. Winners are better than errors, for one. Long rallies are better than short ones, at least within reason. And you can never go wrong with more net play.

If net play were the only criterion, doubles would beat singles easily. But what about other factors? I started wondering about this while researching a recent post on gender differences in mixed doubles, when I came across a match in which every rally was four shots or fewer. For every brilliant reflex half-volley, doubles features a hefty dose of big serving and tactically high-risk returning. Especially in men’s doubles, that translates into a lot of team conferences and not very much shotmaking.

Let’s see some numbers. For each of the five main events at the 2019 Australian Open–men’s and women’s singles, men’s and women’s doubles, and mixed doubles–here is the average rally length, the percentage of points ended in three shots or less, and the percentage of points that required at least ten shots:

Event            Avg Rally  <3 Shots  10+ Shots  
Men's Singles          3.2     72.6%       5.1%  
Women's Singles        3.4     67.9%       5.4%  
Men's Doubles          2.5     81.6%       1.1%  
Women's Doubles        2.9     76.7%       2.4%  
Mixed Doubles          2.8     74.0%       1.8%

There's a family resemblance in these numbers, but it's clear that doubles points are shorter. Men's doubles is the most extreme, at 2.5 shots per point. By comparison, only 8% of the men's singles matches in the Match Charting Project database have an average rally length lower than that. More than four out of every five men's doubles points ends by the third shot, and with barely one in one hundred points lasting to ten shots, you'd be lucky to sit through an entire match and see more than one such exchange.

Quantity and quality

Shorter points are the nature of the format. Even recreational players can find it hard to keep the ball in play when half of each team is patrolling the net, looking for an easy putaway. Short-rally tennis can still be entertaining, as long as the quality of play offsets the unfavorable watching-to-waiting ratio.

I've mentioned my perception that men's doubles features a lot of unreturned serves. The numbers suggest that I spoke too soon. For the five events, here are the percentage of points in which the return doesn't come back in play:

Event            Unret%  
Men's Singles     31.7%  
Women's Singles   24.3%  
Men's Doubles     32.1%  
Women's Doubles   21.6%  
Mixed Doubles     29.3%

For men, singles and doubles are about the same. Perhaps the singles servers are a bit stronger, but the doubles returners are taking more chances, trying to avoid feeding weak returns to aggressive netmen. With women, you're more likely to see a return in play in a doubles match than in singles. Unless you're a connoisseur of powerful serves, you'll probably find higher rates of returns in play to be more enjoyable to watch.

The same applies to winners, compared to unforced errors. (Forced errors are a bit tricky--sometimes they are as exciting and indicative of quality as a winner; other times they're just an out-of-position unforced error.) Let's see what fraction of points end in various ways, for each of the five events:

Event            Unforced%  Forced%  Winner%  
Men's Singles        25.6%    16.2%    21.3%  
Women's Singles      28.9%    16.0%    23.4%  
Men's Doubles        12.8%    17.2%    29.9%  
Women's Doubles      20.9%    18.0%    32.1%  
Mixed Doubles        14.5%    17.0%    29.5%

Here, doubles is the clear winner. For both men and women, more doubles points than singles points end in winners, and fewer points end in unforced errors. Some of that reflects the much higher rate of net play, since it's easier to execute an unreturnable shot from just a few feet behind the net. There are a few more forced errors in doubles, perhaps representing failed attempts to handle volleys that almost went for winners, but no matter how we interpret them, the difference in forced errors is not enough to offset the differences in winners and unforced errors.

The hipsters weren't wrong

The numbers aren't as conclusive as I expected them to be. Yes, doubles points are shorter, but not so much so that the format is reduced to only serving and returning. (Though some men's matches are close.) As usual, our data has limitations, but the information available for each point suggests that there's plenty of high-quality, entertaining tennis to be seen on doubles courts, even if it's usually limited to four or five shots at a time.

Juan Ignacio Londero’s First Five ATP Match Wins

Italian translation at settesei.it

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

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

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

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

A perfect 25

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

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

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

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

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

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

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

Thanks for listening!

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

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

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.