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.

Break Point Serve Tendencies on the ATP Tour

Italian translation at settesei.it

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Match Charting Project Reaches 5,000 Matches

Italian translation at settesei.it

Now this is a milestone. Last night, The Match Charting Project–my volunteer-driven effort to collect shot-by-shot logs of professional tennis–posted it’s 5,000th match! The magic-numbered chart was of one of last weekend’s Davis Cup Qualifiers matches, between Robin Haase and Lukas Rosol, charted by Zindaras, who just began contributing to the project. Number 5,001 is already up–a log of Sunday’s Hua Hin final between Dayana Yastremska and Ajla Tomljanovic.

MCP charts reveal data that simply isn’t available anywhere else. We track every shot–its type and direction–as well as the direction of every serve and and the depth of every return. All told, we’ve amassed these records for over 770,000 points, and almost 3 million shots. (At time of writing, we’re just over 2,992,000.) The dataset has made possible all kinds of research projects, like my recent Economist post about anti-Novak Djokovic tactics, an attempt to quantify the value of smashes, an evaluation of Kei Nishikori’s unusual return stance, and a look at the evolution of Juan Martin del Potro’s backhand.

When I launched the project in 2013, I never imagined we would amass so much information. My goal then was depth, not breadth. Now we have both. The 100 or so charters who have contributed to the project have combined to log nearly every grand slam final back to 1980, most ATP Masters finals back to 1990, and an increasing number of grand slam semi-finals and WTA Premier title matches. More recently, we’ve covered every tour-level final in 2018 and 2019, every head-to-head meeting between members of the big four, and nearly every final contested by any of the big four.

5,000 is a lot

The breadth of the available data goes beyond those high-profile matches. We have at least one charted match for nearly 1,100 different players, at least 10 matches for 268 players, 20 or more for 117 players, 50-plus for 33 players, and over 100 matches for 11 different players. It’s increasingly possible to use MCP data to track the evolution of individual players, something I assumed would always fall outside the scope of the project. And unlike many sports analytics initiatives, this one is gender balanced. Women’s matches make up 47% of the total, despite the fact that vintage women’s matches are considerably harder to track down. (To say nothing of more recent difficulties with WTA streaming.)

It’s fitting that the 5,000th match was logged by a new contributor, because the first several weeks of 2019 have been one of the best periods in the project’s history both for the number of charters and the volume of matches logged. We’ve already charted more than 150 matches from the 2019 season alone, including 79 from the Australian Open. Spearheading that effort has been another new charter, tsitsi, who has contributed more than 100 matches since joining up about a month ago.

Thanks are in order for everyone who has contributed to the project. About 100 people have charted matches, and some of them have been truly prolific. Edo has logged 661 matches, including many of the grand slam finals and semi-finals. In addition to Edo and tsitsi, eight more charters have been responsible for at least 50 matches apiece: Isaac, Lowell, ChapelHeel66, Edged, Palaver, Salvo, 1HandBH, and DebLDecker.

The next 5,000

I hope you’ll join us. Here’s my “quick start” guide to charting, along with 11 reasons to give it a go. Tennis is a complicated sport, so there’s a bit of a learning curve, but I think it’s worth the investment.

Even if you’re still on the fence about charting yourself, I encourage all fans to take greater advantage of the data on offer. A single chart, like this one of the Australian Open men’s final, contains thousands of data points describing various aspects of the match. What I find most illuminating is to compare those single-match numbers with tour, surface, and player averages. For most of the stats on each page, you can move your cursor over the number and see all of those averages. You can also find the player-specific averages on pages like this one, for Petra Kvitova. Researchers can dig into a significant chunk of the raw data, here.

My goal with Tennis Abstract, the blog, and the Match Charting Project has always been to get smarter about tennis–to better understand what’s really happening on court, and never to take the conventional wisdom at face value. I’d say we’re making progress.

Picking Favorites With Better Davis Cup Rankings

Yesterday, the ITF announced the seedings for the first new-look Davis Cup Finals, to be held in Madrid this November. The 18-country field was completed by the 12 home-and-way ties contested last weekend. Those 12 winners will join France, Croatia, Spain, and USA (last year’s semi-finalists) along with the two wild cards, recent champions Argentina and Great Britain.

The six nations who skipped the qualifying round will make up five of the top six seeds. (Spain is 7th, while Belgium, who had to qualify, is 4th.) The preliminary round of the November event will feature six round-robin groups of three, each consisting of one top-six seed, a second country ranked 7-12, and a third ranked 13-18. Seeding really matters, as a top position (deserved or not!) guarantees that a side will avoid dangerous opponents like last year’s finalists France and Croatia. Even the difference between 12 and 13 could prove decisive, as a 7-through-12 spot ensures that a nation will steer clear of the always-strong Spaniards, who are seeded 7th.

The seeds are based on the Davis Cup’s ranking system, which relies entirely on previous Davis Cup results. While the formula is long-winded, the concept is simple: A country gets more points for advancing further each season, and recent years are worth the most. The last four years of competition are taken into consideration. It’s not how I would do it, but the results aren’t bad. Four or five of the top six seeds will field strong sides, and one of the exceptions–Great Britain–would have done so had Andy Murray’s hip cooperated. Spain is obviously misranked, but given the limitations of the Davis Cup ranking system, it’s understandable, as the 2011 champions spent 2015 and 2016 languishing outside the World Group.

We can do better

The Davis Cup rankings have several flaws. First, they rely heavily on a lot of old results. If we’re interested in how teams will compete in November, it doesn’t matter how well a side fared three or four years ago, especially if some of their best players are no longer in the mix. Second, they don’t reflect the change in format. Until last year, doubles represented one rubber in a best-of-five-match tie. A good doubles pair helped, but it wasn’t particularly necessary. Now, there are only two singles matches alongside the doubles rubber. The quality of a nation’s doubles team is more important than it used to be.

Let’s see what happens to the rankings when we generate a more forward-looking rating system. Using singles and doubles Elo, I’m going to make a few assumptions:

  • Each country’s top two singles players have a 75% chance of participating (due to the possibility of injury, fatigue, or indifference), and if either one doesn’t take part, the country’s third-best player will replace him.
  • Same idea for doubles, but the top two doubles players have an 85% chance of showing up, to be replaced by the third-best doubles player if necessary.
  • The three matches are equally important. (This isn’t technically true–the third match is likely to be necessary less than half the time, though when it does decide the tie, it is twice as important as the other two matches.)
  • Andy Murray won’t play.

Those assumptions allow us to combine the singles and doubles Elo ratings of the best players of each nation. The result is a weighted rating for each side, one that has a lot of bones to pick with the official Davis Cup rankings.

Forward-looking rankings

The following table shows the 18 countries at the Davis Cup finals along with the 12 losing qualifiers. For each team, I’ve listed their Davis Cup ranking, and their finals seed (if applicable). To demonstrate my results, I’ve shown each nation’s weighted Elo rank and rating and their hard-court Elo rank and rating. The table is sorted by hard-court Elo:

Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
ESP            7     7         1  1936          1  1891  
CRO            2     2         2  1898          2  1849  
FRA            1     1         3  1880          3  1845  
USA            6     6         4  1876          4  1835  
RUS           21    17         7  1855          5  1827  
AUS            9     9         5  1857          6  1820  
SRB            8     8         8  1849          7  1808  
GER           11    11         6  1855          8  1799  
AUT           16              10  1800          9  1766  
ARG            3     3         9  1803         10  1755  
                                                         
Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
GBR            5     5        11  1796         11  1750  
SUI           24              14  1763         12  1749  
ITA           10    10        12  1780         13  1745  
CAN           14    13        13  1777         14  1744  
JPN           17    14        15  1735         15  1719  
BEL            4     4        17  1688         16  1673  
CZE           13              16  1712         17  1661  
NED           19    16        18  1685         18  1643  
BRA           28              20  1659         19  1638  
IND           20              21  1652         20  1621  
                                                         
Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
SVK           29              22  1645         21  1617  
CHI           22    18        19  1682         22  1609  
KAZ           12    12        26  1582         23  1574  
COL           18    15        24  1597         24  1551  
SWE           15              27  1570         25  1542  
BIH           27              28  1552         26  1540  
POR           26              23  1610         27  1535  
HUN           23              25  1583         28  1533  
UZB           25              29  1491         29  1489  
CHN           30              30  1468         30  1465

Spain is the comfortable favorite, regardless of whether we look at overall Elo or hard-court Elo. When the draw is conducted, we’ll see which top-six seed is unlucky enough to end up with the Spaniards in their group, and whether the hosts will remain the favorite.

The biggest mismatch between the Davis Cup rankings and my Elo-based approach is in our assessment of the Russian squad. Daniil Medvedev is up to sixth in my singles Elo ratings, with Karen Khachanov at 10th. Those ratings might be a little aggressive, but as it stands, Russia is the only player with two top-ten Elo singles players. Spain is close, with Rafael Nadal ranked 2nd and Roberto Bautista Agut 11th, and the hosts have the additional advantage of a deep reservoir of doubles talent from which to choose.

In the opposite direction, my rankings do not forecast good things for the Belgians. David Goffin has fallen out of the Elo top 20, and there are no superstar doubles players to pick up the slack. In a just world, Spain and Belgium will land in the same round-robin group–preferably one without the Russians as well.

Madrid or Maldives

The results I’ve shown assume that every top singles player has the same chance of participating. That’s certainly not the case, with high-profile stars like Alexander Zverev telling the press that they’ll be spending the week on holiday in the Maldives. Some teams are heavily dependent on one singles player who could make or break their chances with a decision or an injury.

As it stands, Germany is 8th in the surface-weighted Elo. If we take Zverev entirely out of the mix, they drop to a tie for 14th with Japan. It’s something the German side would prefer to avoid, but it’s not catastrophic, partly because the Germans were never among the favorites, and partly because Zverev could play only one singles rubber per tie and the doubles replacements are competent.

Even more reliant on a single player is the Serbian side, which qualified last weekend without the help of their most dangerous threat, Novak Djokovic. With Djokovic, the Serbs rank 7th–a case where my surface Elo ratings almost agree with the official rankings. But without the 15-time major winner, the Serbs fall down to a tie with Belgium in 16th place. While the Serbs are unlikely to take home the trophy regardless, Novak would make a huge difference.

The draw will take place next Thursday. We’ll check back then to see which sides have the best forecasts, nine months out from the showdown in Madrid.

Podcast Episode 47: Davis Cup and Another Week of Tennis Shenanigans

Episode 47 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, takes a look at all things Davis Cup, including the early exit of a Federer-less Swiss team, the unexpected La Liga sponsorship, and the shrinking opportunities for players at ITF events despite all the money that Davis Cup is apparently worth.

We also plow through a list of miscellaneous topics, including a second title for 18-year-old Dayana Yastremska, a career-best final for Donna Vekic, the demise of the Connecticut Open, the persistence of the serve clock, the career slam of Herbert-Mahut, and some new mixed doubles stats.

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!)

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