Forecasting the 2016 ATP World Tour Finals

Andy Murray is the #1 seed this week in London, but as I wrote for The Economist, Novak Djokovic likely remains the best player in the world. According to my Elo ratings, he would have a 63% chance of winning a head-to-head match between the two. And with the added benefit of an easier round-robin draw, the math heavily favors Djokovic to win the tournament.

Here are the results of a Monte Carlo simulation of the draw:

Player        SF      F      W  
Djokovic   95.3%  73.9%  54.6%  
Murray     86.3%  58.3%  29.7%  
Nishikori  60.4%  24.9%   7.8%  
Raonic     50.9%  16.3%   3.3%  
Wawrinka   29.4%   7.8%   1.6%  
Monfils    33.2%   8.7%   1.4%  
Cilic      23.9%   5.8%   1.1%  
Thiem      20.7%   4.1%   0.5%

I don’t think I’ve ever seen a player favored so heavily to progress out of the group stage. Murray’s 86% chance of doing so is quite high in itself; Novak’s 95% is otherworldly. His head-to-heads against the other players in his group are backed up by major differences in Elo points–Dominic Thiem is a lowly 15th on the Elo list, given only a 7.4% chance of beating the Serb.

If Milos Raonic is unable to compete, Djokovic’s chances climb even higher. Here are the probabilities if David Goffin takes Raonic’s place in the bracket:

Player        SF      F      W  
Djokovic   96.8%  75.2%  55.4%  
Murray     86.2%  60.7%  30.6%  
Nishikori  60.7%  26.3%   8.1%  
Monfils    47.7%  12.4%   1.8%  
Wawrinka   29.3%   8.5%   1.7%  
Cilic      23.8%   6.2%   1.1%  
Thiem      29.5%   5.8%   0.7%  
Goffin     26.0%   4.9%   0.5%

The luck of the draw was on Novak’s side. I ran another simulation with Djokovic and Murray swapping groups. Here, Djokovic is still heavily favored to win the tournament, but Murray’s semifinal chances get a sizable boost:

Player        SF      F      W  
Djokovic   92.8%  75.1%  54.9%  
Murray     90.9%  58.1%  29.8%  
Nishikori  58.4%  26.9%   7.5%  
Raonic     52.3%  14.3%   3.3%  
Wawrinka   26.9%   8.4%   1.6%  
Monfils    35.3%   7.5%   1.4%  
Cilic      21.9%   6.2%   1.0%  
Thiem      21.6%   3.4%   0.5%

Elo rates Djokovic so highly that he is favored no matter what the draw. But the draw certainly helped.

Doubles!

I’ve finally put together a sufficient doubles dataset to generate Elo ratings and tournament forecasts for ATP doubles. While I’m not quite ready to go into detail, I can say that, by using the Elo algorithm and rating players individually, the resulting forecasts outperform the ATP rankings about as much as singles Elo ratings do.

Here is the forecast for the doubles event at the World Tour Finals:

Team               SF      F      W  
Herbert/Mahut   76.4%  49.5%  32.1%  
Bryan/Bryan     68.7%  36.8%  19.9%  
Kontinen/Peers  55.7%  29.1%  13.8%  
Dodig/Melo      58.4%  28.1%  13.2%  
Murray/Soares   48.3%  20.8%   8.6%  
Lopez/Lopez     37.7%  16.4%   6.2%  
Klaasen/Ram     30.2%  11.9%   4.0%  
Huey/Mirnyi     24.6%   7.3%   2.2%

This distribution is more like what round-robin forecasts usually look like, without a massive gap between the top of the field and the rest. Pierre-Hugues Herbert and Nicolas Mahut are the top rated team, followed closely by Bob Bryan and Mike Bryan. Max Mirnyi was, at his peak, one of the highest Elo-rated doubles players, but his pairing with Treat Huey is the weakest of the bunch.

The men’s doubles bracket has some legendary names, along with some players–like Herbert and Henri Kontinen–who may develop into all-time greats, but it has no competitors who loom over the rest of the field like Murray and Djokovic do in singles.

How To Keep Round Robin Matches Interesting, Part Two

Earlier this week, I published a deep dive into the possible outcomes of four-player round robin groups and offered an ideal schedule that would minimize the likelihood of dead rubbers on the final day. I’ve since heard from a few readers who pointed out two things:

  1. You might do better if you determined the schedule for day two after getting the results of the first two matches.
  2. Major tournaments such as the ATP and WTA Tour Finals already do this, pairing the winners of the first two matches and the losers of the first two matches on day two.

This is an appealing idea. You’re guaranteed to end the second day with one undefeated (2-0) player, two competitors at 1-1, and the last at 0-2. The two participants at 1-1 have everything to play for, and depending on day three’s schedule and tiebreak factors, the 0-2 player could still be in the running as well.

Best of all, you avoid the nightmare scenario of two undefeated players and two eliminated players, in which the final two matches are nearly meaningless.

However, this “contingent schedule” approach isn’t perfect.

Surprise, surprise

We learned in my last post that, if we set the entire schedule before play begins, the likelihood of a dead rubber on the final day is 17%, and if we choose the optimal schedule, leaving #4 vs #1 and #3 vs #2 for the final day, we can drop those chances as low as 10.7%.

(These were based on a range of player skill levels equivalent to 200 points on the Elo scale. The bigger the range of player skills–for instance, the ATP finals is likely to have a group with a range well over 300–the more dramatic the differences in these numbers.)

In addition, we discovered that “dead/seed” matches–those in which one player is already eliminated and the other can only affect their semifinal seeding–are even more common. When the schedule is chosen in advance, the probability of a dead rubber or a “dead/seed” match is always near 40%.

If the day two schedule is determined by day one outcomes, the overall likelihood of these “mostly meaningless” (dead or “dead/seed”) matches drops to about 30%. That’s a major step in the right direction.

Yet there is a drawback: The chances of a dead rubber increase! With the contingent day two schedule, there is a roughly 20% chance of a completely meaningless match on day three.

Our intuition should bear this out. After day two, we are guaranteed one 2-0 player and one 0-2 player. It is somewhat likely that these two have faced each other already, but there still remains a reasonable chance they will play on day three. If they do, the 0-2 player is already eliminated–there will be two 2-1 players at the end of day three. The 2-0 player has clinched a place in the semifinals, so the most that could be at stake is a semifinal seeding.

In other words, if the “winner versus winner” schedule results in a 2-0 vs 0-2 matchup on day three, the odds are that it’s meaningless. And this schedule often does just that.

The ideal contingent schedule

If the goal is to avoid dead rubbers at all costs, the contingent schedule is not for you. You can do a better job by properly arranging the schedule in advance. However, a reasonable person might prefer the contingent schedule because it completely avoids the risk of the low-probability “nightmare scenario” that I described above, of two mostly meaningless day three matches.

Within the contingent schedule, there’s still room for optimization. If the day one slate consists of matches setting #1 against #3 and #2 against #4 (sorted by ranking), the probability of a meaningless match on day three is about average. If day one features #1 vs #2 and #3 vs #4, the odds are even higher: about a 21% chance of a dead rubber and another 11% chance of a “dead/seed” match.

That leaves us with the optimal day one schedule of #1 vs #4 and #2 vs #3. It lowers the probability of a dead rubber to 19% and the chances of a “dead/seed” match to 9.7%. Neither number represents a big difference, but given all the eyes on every match at major year-end events, it seems foolish not to make a small change in order to maximize the probability that both day three matches will matter.

How To Keep Round Robin Matches Interesting

Round robins–such as the formats used by the ATP and WTA Tour Finals–have a lot going for them. Fans are guaranteed at least three matches for every player, and competitors can recover from one (or even two) bad outings. Best of all, when compared to a knockout-style draw, it’s twice as much tennis.

On the other hand, round robins have one major drawback: They can result in meaningless matches. It’s fairly common that, after two matches, a player is guaranteed a spot in the semifinals (sometimes even a specific seed) or eliminated from contention altogether. At a high-profile event such as the Tour Finals, with sky-high ticket prices, do we really want to run the risk of dead rubbers?

I don’t claim to have the answer to that question. However, we can take a closer look at the round robin format to answer several relevant questions. What is the probability that the final day of a four-player group will include at least one dead rubber? What about the final match? And most importantly, before the event begins, can we set the schedule in such a way to minimize the likelihood of dead rubbers?

The range of possibilities

As a first step, let’s determine all of the possible outcomes of the first four matches in a four-player round robin group. For convenience, I’ll refer to the players as A, B, C, and D. The first day features two matches, A vs B and C vs D. The second day is A vs C and B vs D, leaving us with a final day of A vs D and B vs C.

Each match has four possible outcomes: the first player wins in two sets, the first player wins in three, the second wins in two, or the second wins in three. (Sets won are important because they are used as a tiebreaker when, for instance, three players win two matches apiece.) Thus, there are 4 x 4 x 4 x 4 = 256 possible arrangements of the group standings entering the final day of round robin play.

Of those 256 permutations, 32 of them (12.5%) include one dead rubber on the final day. In those cases, the other match is played only to decide semifinal seeding between the players who will advance. Another 32 of the 256 permutations involve one “almost-dead” match, between a player who has been eliminated and a player who is competing only to determine semifinal seeding.

In other words, one out of every four possible outcomes of the first two days results in a day three match that is either entirely or mostly meaningless. Later on, we’ll dig into the probability that these outcomes occur, which depends on the relative skill levels of the four players in the group.

Before we do that, let’s take a little detour to define our terms. Because of the importance of semifinal seeding, some dead rubbers are less dead than others. Further, it is frequently the case that one player in a match still has a shot at the semifinals and the other doesn’t. Altogether, from “live” to “dead,” there are six gradations:

  1. live/live — both players are competing to determine whether they survive
  2. live/seed — one player could advance or not; the other will advance, and is playing to try to earn the #1 group seed
  3. live/dead — one player is trying to survive; the other is eliminated
  4. seed/seed — both players will advance; the winner gets the #1 group seed
  5. seed/dead — one player is in the running for the #1 seed; the other is eliminated
  6. dead/dead — both players are eliminated

All else equal, the higher a match lies on that scale, the more engaging its implications for the tournament. For the remainder of this article, I’ll refer only to the “dead/dead” category as “dead rubbers,” though I will occasionally discuss the likelihood of “dead/seed” matches as well. I’ll assume that the #1 seed is always more desirable than #2 and ignore the fascinating but far-too-complex ramifications of situations in which a player might prefer the #2 spot.

The sixth match

As we’ve seen, there are many sequences of wins and losses that result in a dead rubber on day three. Once the fifth match is played, it is even more likely that the seedings have been determined, making the sixth match meaningless.

After five matches, there are 1,024 possible group standings. (256 permutations after the first four matches, multiplied by the four possible outcomes of the fifth match.) Of those, 145 (14.1%) result in a dead sixth rubber, and another 120 (11.7%) give us a “dead/seed” sixth match.

We haven’t yet determined how likely it is that we’ll arrive at the specific standings that result in dead sixth rubbers. So far, the important point is that dead rubbers on day three aren’t just flukes. In a four-player round robin, they are always a real possibility, and if there is way to minimize their likelihood, we should jump at the chance.

Real scenarios, really dead rubbers

To figure out the likelihood of dead rubbers in practical situations, like the ATP and WTA Tour Finals, I used a hypothetical group of four players with Elo ratings spread over a 200-point range.

Why 200? This year’s Singapore field was very tightly packed, within a little bit more than 100 points, implying that the best player, Angelique Kerber, had about a 65% chance of beating the weakest, Svetlana Kuznetsova. By contrast, the ATP finalists in London are likely to be spread out over a 400-point range, giving the strongest competitor, Novak Djokovic, at least a 90% edge over the weakest.

I’ve given our hypothetical best player a rating of 2200, followed by a field of one player at 2130, one at 2060, and one at 2000. Thus, our favorite has a 60% chance of beating the #2 seed, a 69% chance of defeating the #3 seed, and a 76% chance of besting the #4 seed.

For any random arrangement of the schedule, after the first two days of play, this group has a 17% chance of giving us a dead rubber on day three, plus a 23% chance of a “dead/seed” match on day three.

After the fifth match is contested, there is a 16% chance of that the sixth match is meaningless, with an additional 12% chance that the sixth match falls into the “dead/seed” category.

The wider the range of skill levels, the higher the probability of dead rubbers. This is intuitive: The bigger the range between the top and bottom, the more likely that the best player will win their first two matches–and the more likely they will be straight-setters. Similarly, the chances are higher that the weakest player will lose theirs. The higher the probability that players go into day three with 2-0 or 0-2 records, the less likely that day three matches have an impact on the outcome of the group.

How to schedule a round robin group

A 17% chance of a dead rubber on day three is rather sad. But there is a bright spot in my analysis: By rearranging the schedule, you can raise that probability as high as 24.7% … or drop it as low as 10.7%.

Remember that our schedule looks like this:

Day one: A vs B, C vs D

Day two: A vs C, B vs D

Day three: A vs D, B vs C

We get the lowest possible chance of a day three dead rubber if we put the players on the schedule in order from weakest to strongest: A is #4, B is #3, and so on:

Day one: #4 vs #3, #2 vs #1

Day two: #4 vs #2, #3 vs #1

Day three: #4 vs #1, #3 vs #2

There is a small drawback to our optimal arrangement: It increases the odds of a “dead/seed” match. It turns out that you can only optimize so much: No matter what the arrangement of the competitors, the probability of a “dead/dead” or “dead/seed” match on day three stays about the same, between 39.7% and 41.7%. While neither type of match is desirable, we’re stuck with a certain likelihood of one or the other, and it seems safe to assume that a “dead/seed” rubber is better than a totally meaningless one.

Given how much is at stake, I hope that tournament organizers heed this advice and schedule round robin groups in order to minimize the chances of dead rubbers. The math gets a bit hairy, but the conclusions are straightforward and dramatic enough to make it clear that scheduling can make a difference. Over the course of the season, almost every tennis match matters–it would be nice if every match at the Tour Finals did, too.

(I wrote more about this, which you can read here.)

Novak Djokovic and Neutralizing the Second Serve

When Novak Djokovic stands on the other side of the court, you’d better make some first serves.

Djokovic is one of only two players this year to win more than 55% of second-serve-return points (David Ferrer is the other).  When you consider that he also wins more than 35% of first-serve-return points, it’s less clear that the server has much of an advantage.  In fact, when Novak is performing at that level, if his opponent goes through a bad patch and only makes a quarter of his first serves, Djokovic has a better than 50% chance of breaking serve.

Commentators often refer to Djokovic’s return as a weapon, and they’re not joking.  Only six players (including Novak himself and, invariably, John Isner) won as many second-serve points as Novak won second-serve-return points.

What’s most remarkable about his return game is how quickly he neutralizes the second serve, often using tactics that, in the hands of lesser mortals, would be more appropriate for service points.  Unlike other returners, he is somewhat more likely to win a short return point than a long one.  While other players need a few shots to negate the advantage conferred by serving, Djokovic is most effective early in service points.

This graph shows the percentage of second-serve-return points won by Djokovic, by rally length, in four matches I’ve charted (US Open vs Stanislas Wawrinka and Rafael Nadal; Tour Finals vs Wawrinka and Juan Martin del Potro), compared to the the same percentage for other top-ten players (excluding Rafael Nadal) in 19 other matches I’ve charted from the US Open and Tour Finals this year:

novak1

When the return lands in play, Djokovic wins almost 53% of return points, while the pack manages less than 44%.  (All of these matches are between top-ten opponents, so the averages are much lower than season numbers, which are affected by matches against lesser opponents.)  The difference stays about the same when we take out 2- and 3-shot rallies.

When we limit our view to points that reach six shots, Novak still has a substantial edge, roughly 48% to 42%.  But in points longer than seven shots, there’s virtually no difference.

Djokovic’s return is so good that if his opponent misses his first serve, the point has turned into a Novak service point.  Opponents are forced to fight their way into their own service points!

This was particularly true in the Djokovic-Nadal US Open final.  (Follow the link, then click the ‘Serve Influence’ tab for a shot-by-shot winning percentage breakdown.)  Nadal won barely half of his second-serve points when Djokovic got his return in play, but once the rally reached five shots (or six, or seven, and so on), Nadal had the edge, winning 60% of points.  From the five-shot mark, Rafa’s advantage only increased.

Of course, Nadal won that match.  It’s not quite so useful to convert return points into service against an opponent whose own return of serve is so effective.  To win today, Novak needs to do more than just attack Rafa’s second serve.  He must either do so even more effectively than he did in New York, or put himself in a better position to win longer return points after the effect of his return has worn off.

Round Robin Shutouts

At this year’s World Tour Finals, we were spared the knottiest sort of round robin tiebreakers.  Each group had a clear winner (Rafael Nadal and Novak Djokovic) who went undefeated, along with another player (David Ferrer and Richard Gasquet) who failed to win a single match.

Since 1987, 33 players have recorded a 3-0 record in Tour Finals round-robin play.  This year is the first time since 2010 (Nadal and Roger Federer) that two players have done so, and before that, we have to go back to 2005 (Federer and Nikolay Davydenko).  It’s not that rare of an event–this year is the 11th time since 1987 that two players have beaten every opponent in their group.

Undefeated players are hardly guaranteed further advances, however.  Those 33 undefeated competitors have a mere 17-16 record in the semifinals, and the 17 men who reached the final won the title only nine times, against nine final-round losses.  (Twice, two undefeated players faced off in the finals–the aforementioned 2010 event along with 1993, when Michael Stich and Pete Sampras contested the title.)

The tiny sample of three round-robin matches pales in predictive value next to the old standby of ATP ranking.  In the last 26 years, the higher-ranked player has won 16 finals.  In the more top-heavy 21st century, the title has gone to the man with the superior ranking 11 of 13 times.  (Advantage: Nadal.)

That said, the gap between the two finalists is traditionally greater than it is expected to be tomorrow.  (If Stanislas Wawrinka upsets Novak Djokovic in the second semifinal, you can disregard this paragraph.  Sorry, Stan, but I’m betting against you.)  Only twice in the round-robin era have the top two players in the ATP rankings met in the concluding match of the Tour Finals–2010 (again) and 2012 (Djokovic d. Federer).

Not a shutout, but shut out

Exactly as many players–33 through 2012–have gone 0-3 in the round robin as the number who did the opposite.  Ferrer and Gasquet find themselves in quality company.

Ferrer is the 7th player ranked in the top three to lose three round robin matches.  In 2001, #1 Gustavo Kuerten was winless, only a year after claiming the championship.  Jim Courier (1993), Juan Carlos Ferrero (2003), and Nadal (2009) went 0-3 from a #2 ranking, while Thomas Muster (1995) and Djokovic (2007) did so while ranked #3.

Ferrer is notable for another dubious achievement: going 0-3 twice.  He previously did so in 2010, so this year, he matches the mark of Michael Chang, the only other man in the round-robin era to post multiple 0-3s, having gone winless in both 1989 and 1992.

His age may work against him, but there is a glimmer of hope for Ferrer.  Four players (including Kuerten, mentioned above) have gone 0-3 at one Tour Finals and won the title at another.  Andre Agassi was winless in 1989, then won the event in 1990.  Stich was 0-3 in 1991, then claimed the title in 1993.  As we’ve seen, Djokovic failed to win a single match in 2007, yet came back to win the tournament in 2008.  (Then did so again last year.)

If Nadal wins tomorrow, we can add one more name to this list, in his case finally adding the trophy to his collection four years after suffering through a winless week.  His 4-0 record so far this week may be no guarantee of success in the final, but it will hardly count against him.

Match reports: I charted today’s Federer-Nadal semifinal, as well as yesterday’s Federer-del Potro match.  Click the links for exhaustive serve, return, and shot statistics.

Worth a read: Carl Bialik analyzes ATP rematches–pairings like Fed-Delpo that faced off in back-to-back weeks.  As usual, we have to rewrite the rules for Rafa.

David Ferrer and Defiance of the Aging Curve (+Updated WTForecast)

At the end of 2009, aged 27, David Ferrer finished the year with an ATP ranking of 17.  It had been a rough 15 months.  A poor pair of Masters events at the end of 2008 knocked him out of the top five, all the way down to 12.  An indifferent season saw him fall out of the top 20 for a few weeks.  Many players never improve upon their mid-20’s form, so had things gone according to script, Ferrer might still be kicking around the high teens.  His near contemporaries Mikhail Youzhny and Tommy Robredo have followed paths of that nature.

Instead, the Spaniard has only gotten better.  He finished 2010 back in the top ten, at #7.  At the end of 2011 and 2012, he sat at #5.  He’s likely to conclude 2013 at his career-high position of #3.  All this at the age of 31, when many players have shifted focus to their golf games.

This is unprecedented.  Ferrer is only the 12th player of the last 30 years to string together four consecutive year-to-year ranking improvements starting at age 24 or later.  He’s only the second to do so starting at 27, and no one has done it from a more advanced age.  The only other man to match Ferru’s current streak doesn’t really compare: Wayne Arthurs improved his ranking from 1998 to 2002 up to an ’02 year-end position of #52.

Admittedly, this streak is a bit of a sideshow curiosity.  But the underlying issue it reveals is more significant.  Even in an era of 30-something stars on the ATP, tennis is a young man’s game.  At the age when Ferrer began his resurgence, most players are fading, if they’re not already gone.

The exact trajectory of the aging curve depends on the data you choose to examine.  I ran the numbers twice: first with all players in the top 300 since 1983, then limited to players born in 1975 or later.  With the bigger dataset, the apparent peak is at age 23-24.  The average player maintains their level from their age 23 season to their age 24 season, but every year beyond 24 brings with it an increasing decline.  For instance, if we set aside those who disappear from the top 300 entirely, 45% of players improve their ranking in their age 25 season, while 2% maintain it and 53% decline.  At age 26, it’s 38%, 2%, and 60%, while at age 31, it’s 30%, 1%, and 69%.

The following graph shows the percentage of players who improve and decline in the rankings at each age.  While there are still a few guys like Ferrer who post a year-to-year improvement at any age, they are harder to find at each successive age.  Also, keep in mind that the later-career numbers include players returning from injury–Lleyton Hewitt, for example, has improved his ranking each of the last two years.

ferru2

Limiting our view to those players born in 1975 or later, we have a smaller dataset, but one that should better reflect the current state of affairs.  Here, the peak is one year later, at age 24-25.  Despite the Ferrers, Roger Federers, and Radek Stepaneks who seem to be rewriting the rules, it is still the case that only 42% of 26-year-olds improve their rankings from their age 25 season, while 3% maintain and 55% decline.

Another way of looking at the decline is by measuring and then aggregating the magnitude of ranking changes.  In the dataset limited to 1975-and-later births, he average player loses roughly 2.5% of his ranking from his age 25 season to his age 26 season, and almost 19% of his ranking from age 31 to age 32.  Using this metric, here is a graph of two “decline curves”–ranking position lost at each age.  Both the overall dataset and the more limited, recent dataset are shown:

ferru3

While the overall direction hasn’t changed from the 80s to the present, the trend in magnitude is clear. At every age in the decline phase, the curve has flattened out, making it a bit more likely that someone like Ferrer would improve throughout his late 20s.

Keep in mind that we’re only measuring those players who remain in the top 300.  Those who retire or fall out of the rankings due to injury aren’t considered, so the actual effect of age–in either dataset–is more severe than these numbers represent.  However, without forcing those guys to play, we can only estimate their aging patterns based on those who do stick around.

Having determined the percentages of players in the current era who improve and maintain their rankings at each age, we can calculate the likelihood that someone would do what Ferrer has done, keeping his ranking moving in the right direction from his age 27 to his age 31 season.  For any individual year, the chances are about 40%, giving us an overall probability of roughly 2.5%, or 1 in 40.  Even limiting our scope to the pool of players in the ATP top 300 at age 27, that seems reasonable–Ferru is, at the very least, a 1-in-40 aberration.

Ferrer’s biggest test yet will be his age-32 season in 2014.  Of players in the current era, 18% of 31-year-olds fall out of the top 300 by the end of their age-32 season.  (In the bigger dataset going back to 1983, 27% disappear.)  Of those who remain, only a quarter improve, and the average ranking change is strongly negative.

Eventually, nature will stop David Ferrer.  Precedents or no precedents, though, he’s a hard man to bet against.  He hasn’t been particularly constrained by nature thus far.

London forecast: After today’s Group B matches, Djokovic is guaranteed a spot in the semis, while Federer’s match on Saturday with del Potro will determine the other semifinalist.  My ratings consider those to be nearly equal on this surface, giving the slight edge to Delpo.  Here is the complete forecast:

Player     3-0  2-1  1-2  0-3        SF      F      W  
Nadal      70%  30%   0%   0%     98.5%  57.9%  33.3%  
Djokovic   73%  27%   0%   0%    100.0%  65.8%  36.3%  
Ferrer      0%   0%  54%  46%     14.7%   5.1%   1.9%  
Del Potro   0%  52%  48%   0%     52.3%  23.3%  10.7%  
Federer     0%  48%  52%   0%     47.7%  20.1%   8.8%  
Berdych     0%  30%  70%   0%     35.8%  11.9%   3.9%  
Wawrinka    0%  46%  54%   0%     51.1%  16.0%   5.2%  
Gasquet     0%   0%  27%  73%      0.0%   0.0%   0.0%

My algorithm doesn’t capture all the complexity of the tiebreak rules, so it’s got Group A a bit wrong right now.  Nadal has locked up a spot in the semis. To clear up any remaining confusion, we’re lucky to have Anna, who lays out the qualification scenarios very clearly for both Group A and Group B.

Today’s matches: I charted both Group B matches today, so there are detailed serve, return, and shot-by-shot stats for each one.  Here is Federer-Gasquet, and here’s Djokovic-del Potro.

Finally, it’s already time to look ahead to Melbourne, as Foot Soldiers of Tennis is monitoring the players on the cusp of direct entry.

Juan Martin del Potro and Return of Serve Gaps (+Updated WTForecast)

While Juan Martin del Potro isn’t known for his return of serve, it isn’t a major hole in his game.  This year, he has won 38.5% of return points, worse than most of the top 10, but better than Stanislas Wawrinka, Jo Wilfried Tsonga, and about 30 other members of the ATP top 50.

Where del Potro underwhelms is more specific.  Despite effectively returning second serves, he’s far worse than average against first serves.  In 2013, his 28.4% of first-serve-return points won ranked him 36th among the top 50, only 0.1% above Milos Raonic and far behind every other member of the top 10.  Yet Delpo is in the top ten when it comes to second-serve-return points.

Even for a big server like del Potro, it’s difficult to reach the top five without an effective return game.  While he breaks serve less often than any other World Tour Finals qualifier this year, he’s within a percentage point of Wawrinka, Tomas Berdych, and Roger Federer, so it’s clear that statistically, the Argentine is far from being a John Isner-style one-trick pony.

What sets him apart, then, is the enormous gap between first- and second-serve-return effectiveness.  To illustrate the difference, I calculated the ratio of second-serve-return points to first-serve-return points for all eight men in London this week, plus Andy Murray.  Delpo is third among all players with 40 or more tour-level matches this year, while the bottom five names on this list are all in the opposite third of ATP regulars.

Player                  v1W%   v2W%  v2/v1  
Juan Martin Del Potro  28.4%  53.4%   1.88  
Tomas Berdych          30.6%  54.6%   1.79  
Richard Gasquet        30.5%  54.2%   1.78  
Stanislas Wawrinka     30.7%  50.3%   1.64  
David Ferrer           34.5%  56.4%   1.63  
Andy Murray            33.7%  54.7%   1.62  
Roger Federer          32.9%  51.6%   1.57  
Novak Djokovic         35.5%  55.4%   1.56  
Rafael Nadal           35.0%  54.6%   1.56

An aspect–or perhaps a cause–of del Potro’s first-serve-return woes is his knack for letting aces sail by him.  In 2013, 10.5% of his opponents’ first serves were aces, more than any other member of the top 50.  Controlling for opponent serve quality (he did play Isner twice this year), he “improves” to third-worst, ahead of Dmitry Tursunov and Feliciano Lopez.  After this adjustment, we discover that Delpo allowed 22% more aces than an average player would have against the same set of opponents.

When aces are removed from the calculation, del Potro still stands out in comparison to other top players, but he is no longer quite so extreme.  His ratio of second-serve-return points won to first-serve-return points won ignoring aces is 1.55, just a bit higher than Berdych’s 1.53, Richard Gasquet‘s 1.52, and David Ferrer‘s 1.51.

If Delpo gets a racquet on the ball, then, he’s not that much less effective against first offerings than his London competitors.  But he doesn’t get his racquet on as many balls, and however we might manipulate the numbers for fun and profit, the Argentine doesn’t have the option to ignore aces.

So, how much does a poor first-serve return matter?  As with Murray’s infamous second serve, it’s tough to say.  In both cases, the weakness doesn’t keep its possessor from winning big matches against the game’s best, but it might be what is preventing him from ascending from the very top of the rankings.

Were del Potro to improve his first-serve return to the level of the next-worst London participant, Gasquet, it would mean a jump this year from 28.3% of first-serve-return points won to 30.5%.  That would bump up his overall return points won to just short of 40%, and improve his break percentage from its current middle-of-the-pack 23.8% to a nearly-top-ten 26.0%, in the neighborhood of Berdych and Federer.

An improvement of that nature would make Delpo a much bigger factor at the very top of the men’s game.  But like Murray’s second serve, it isn’t that easy.  There’s more than one route to the top–del Potro’s game isn’t so unbalanced to keep him from beating the best players in the world, so perhaps he could more easily improve, say, his second serve than his first-serve return.  It’s tough to tell from the sideline or, especially, the statsheet.

In the meantime, if you’re supporting del Potro tomorrow against Novak Djokovic, you might consider becoming one of those boorish fans that cheers every first-serve miss off of Novak’s racquet.  Lots of Djokovic second serves might be Delpo’s best path to victory.

London forecast: With Berdych’s win today, all eight players remain in contention.  A lot hinges on Friday’s match between Wawrinka and Ferrer, while we won’t gain much clarity on Group B until tomorrow.

Player     3-0  2-1  1-2  0-3       SF      F      W  
Nadal      70%  30%   0%   0%    98.4%  57.0%  34.1%  
Djokovic   42%  46%  11%   0%    88.3%  54.9%  30.9%  
Ferrer      0%   0%  54%  46%    14.8%   5.5%   2.0%  
Del Potro  22%  50%  28%   0%    71.6%  36.3%  16.4%  
Federer     0%  30%  51%  20%    29.9%  13.1%   5.9%  
Berdych     0%  30%  70%   0%    36.0%  12.4%   4.0%  
Wawrinka    0%  46%  54%   0%    50.9%  17.4%   5.6%  
Gasquet     0%  10%  44%  45%    10.1%   3.2%   1.0%

For the pre-tournament forecast, click here.

Berdych d. Ferrer: Click here for detailed serve, return, and shot-by-shot stats for today’s evening match.