On the heels of the announcement that Boris Becker will coach Novak Djokovic, today we learned that Stefan Edberg will be part of Roger Federer‘s team for the first ten weeks of the season. There will be more men’s Grand Slam champions in Australian Open coaching boxes than in the singles draw.
We’ve probably wrenched all possible commentary out of the head-to-head matchups of today’s slate of top players, so why not turn to their coaches instead? Steve Tignor got us started:
Big 3 coaches' head to heads: Edberg > Lendl 14-13; Lendl > Becker 11-10; Becker > Edberg 25-10. Last one's a surprise to me.
Becker, Edberg, and Lendl figure most prominently in these matchups, while Chang, Ivanisevic, and Sergi Bruguera also played plenty of matches against their fellow coaches.
Novak’s new coach barely edges out Andy Murray‘s coach as the king of his generation of advisors. His 66-38 record against these 14 colleagues is slightly better than Lendl’s 47-28. In eight of ten head-to-heads, Becker came out even or better. But one of those, as Tignor pointed out, is the matchup against Lendl, which the Czech leads 11-10. If coaches can possibly accomplish such a thing, this pair might make Djokovic-Murray matches a little more interesting.
The other unfavorable head-to-head of Becker’s is my favorite quirky stat of the lot. Twice in April 1993, when Becker was ranked fourth in the world, Franco Davin defeated him. That’s a little better record for Davin than Juan Martin del Potro‘s 3-11 record against Djokovic.
I’m excited to share with you a couple of new features I’ve been working on for TennisAbstract.com.
First is an interactive ranking map:
The above map shows the geographic concentration of teenagers in the WTA top 1000. Click through to the full-size map, and you can mouse over any country to find out how many players they have in that category.
More importantly, you can customize the map in a variety of ways. Choose from either the ATP or WTA rankings, decide how deep you’d like to go in the rankings, and if you’d like, limit the age range. It’s a great way to see which countries are most dominant on each tour, and it’s also an opportunity to visually investigate which nations are likely to hold that power in the near future.
Next is an interactive ranking history chart:
This chart shows ranking points for the big four over the past three years. Again, if you click through to the full-size map, you’ll get more features: mouse over any line to see the date and the player’s ranking points at the date.
Like the map, the ranking chart is fully interactive. You can select anywhere from one to four players–for now, only in the ATP top 100–choose a timeframe, and select either ranking or ranking points.
Everybody loves a big comeback, but some of the best come-from-behind wins on the ATP tour this year were such unheralded matchups that they’ve already fallen out of the spotlight. While everyone else ranks Nadal–Djokovic matches in their year-end lists, let’s look at the five matches in which the winner had to climb out of the biggest hole.
To do this, I ranked every match this season by Comeback Factor (CF), a stat that identifies the lowest ebb in the match for the eventual winner. If a player breaks serve to open the match and sails to victory, his chance of winning never falls below 50%. But if he goes down a set and a break, his odds fall much lower. If the latter player comes back to win, his CF is much higher.
Lorenzi went up a double break in the final set by winning the first four games on the trot. Simon held twice to force the Italian to serve for it at 5-2. Lorenzi went up 40-15 in that service game, earning two match points, before losing four points in a row and dropping serve. At 5-4, Simon broke him to 15, then broke again to love to seal the final set, 7-5.
At 5-2 40-15 in the 3rd set, Lorenzi’s chance of winning was about 99.8%, the highest recorded in a match this year by a player who didn’t end up winning.
Dodig fought back from nearly the same hole that Simon found himself in, but did so in the second set instead of the third. Ward won the first set in a tight tiebreak, then earned an early break in the second. He held on until he served at 5-3, when he reached 40-15. Dodig won the next four points to erase the break, improving his probability of winning from 0.5% to 21.1%.
Amazingly, the scenario repeated itself in the third set after Dodig won the second in a tiebreak. Ward went up a break and served for the match again at 5-4, but failed to generate another match point. The Croatian won a pair of points from 30-30 in that game, then sealed the match in yet another tiebreak.
http://www.youtube.com/watch?v=xvCuh0YvRow
Dodig wasn’t so lucky a couple of months later, when he nearly upset Juan Martin del Potro in Montreal. In this year’s 7th-biggest comeback, Delpo came back from a double-break hole in the third set to deny Dodig a place in the third round.
Fognini never had the double break that led to such disaster for Lorenzi and Ward, but he did have something neither of those men did: a triple match point. At 3-3 in the deciding set, Fognini broke the Russian then consolidated, leading to a chance to serve for the match at 5-4. After winning his first three points for a 40-0 advantage, his win probability climbed as high as 99.1%.
It wouldn’t go any higher. Youzhny won 12 of the next 13 points, breaking the Italian, holding his own serve to love, then earning two match points of his own on the Fognini serve before Fabio gathered himself sufficiently to force a tiebreak. Fognini kept up his streakiness to the end, claiming a minibreak to open the tiebreak, dropping five points in a row, and fighting back to 5-5 before finally losing the match.
Monfils won the first two sets, which you would think put Robredo at enough of a disadvantage. But the Spaniard’s lowest ebb didn’t come until the fourth set. He lost serve in the seventh game, and after fighting off a match point at 3-5, he needed to break serve just to stay alive.
The Frenchman went up 40-15, earning two more match points and a win probability of 98.9%. Robredo won four straight points to get back on serve, easily held, and even challenged Monfils’s own serve (to 0-30) before landing in a tiebreak. He won that breaker and, compared to the fourth set, won the fifth with ease.
After Robredo beat Monfils, he faced Almagro in the 4th round and Ferrer in the quarters. Conicidentally, those are the two men who, at the Australian Open, gave 2013 its fifth-biggest comeback.
As in Robredo did in his comeback, Ferrer dropped the first two sets. Unlike his countryman, he found himself in the most danger in the third set. Almagro broke in the seventh game of the third set and reached 5-4, an opportunity to serve for the match. But here, history (or something) got in the way. Almagro reached his highest chance of winning, 98.7%, at 15-0, before Ferrer fought his way to 15-40, Almagro got back to deuce, but Ferrer won the game.
Almagro earned more chances to serve for the match, but his odds of winning would never again be so high. After breaking in yet another seventh game, Nico served for it at 5-4 and again at 6-5. At 6-5, he reached 15-0 and a win probability of 97.4%, but from that point on, it was all Ferrer.
Since I announced the Match Charting Project last week, the response has been tremendous. More than one thousand of you read the post, more than one hundred people downloaded the match charting spreadsheet, and several people have already charted matches, helping build what is already a very useful resource.
I’ve added functionality to note serve-and-volley points, using the plus sign (“+”) after the serve notation. (I’ve added a bit more detail in the instructions sheet to help explain it.) It’s optional, but it would be very useful information to have, and if you want to track serve-and-volley attempts this way, you’ll need the newest version of the spreadsheet. Download it by clicking on the link.
Match charting tutorial
To give you an idea of what match charting is all about, I recorded my screen while charting the first few games of a match. While it’s not the most captivating entertainment, it demonstrates how I set up my screen, and it may help you make sense out of the notation system we’re using.
Tracking
I maintain two versions of the list of charted matches–by date, or by player. If you’d like to chart a match that isn’t on those lists and is more than a couple of weeks old, you can be almost certain that no one else is working on it. But if you’d like to do a current match, or you just want to make sure, email me to check before you begin. Once you’ve completed your first match, I’ll invite you to a Google doc where charters “claim” matches to avoid duplication.
Charting tools
Here are some tips and tricks that might help you chart a little more effectively.
I find it more convenient to watch video files that are stored on my hard drive–that way, I can work without an internet connection, or survive a weak wireless connection. You can download YouTube videos using KeepVid, and you can download videos from many other sites with Jaksta.
Once you’ve downloaded a video file, I highly recommend using mplayer to view them. The killer feature here is that it allows you to speed up or slow down playback. When you’re starting out, you might want to go as slow as 50% or 60%. As you get better, you can speed up. Another great mplayer feature for charting purposes is the ability to skip forward or backward ten seconds or one minute. It’s a very effective way to rewind and watch a point again, if you missed it. You can also quickly skip through changeovers, or even through long delays between points, if you’re charting that sort of player.
Finally, if you’re watching videos in fullscreen, you might want to try the 4t Tray Minimizer. It allows you to pin any program on top, so for instance, if you want to watch TennisTV in fullscreen but keep the spreadsheet on top, it makes that possible.
If you have any questions or suggestions, please email me or leave them in the comments. Thanks for all your interest so far!
Another year, another new set of tiebreak masters.
Despite the conventional wisdom, very few players demonstrate any kind of consistent tiebreak skill over and above their regular, non-tiebreak tennis playing ability. In other words, while someone like Novak Djokovic is bound to win well over half of the tiebreaks he plays–after all, he’s better than almost everyone he faces–there’s no secret sauce that allows him to win any more than his usual skill level would suggest.
Nowhere is this more evident than in this year’s top tiebreak performers. I calculated the likelihood of each player winning every tiebreak they played this year, given their typical rates of serve and return points won, giving us a ranked list of those players who most exceeded and most underperformed expectations. At the top of the list, names like Roberto Bautista Agut, Dmitry Tursunov, Marin Cilic, and Leonardo Mayer.
Maybe Bautista Agut is a clutch monster just waiting for recognition, but it’s more likely he just had a few bounces go his way. Cilic is an excellent example: While he won 54% more tiebreaks than expected this year, 2013 was only the second season of the last six in which the Croat exceeded expectations in tiebreaks. Whether tiebreak performance is clutch skill or simply luck, the numbers show that it isn’t persistent.
However, as I’ve noted before, a very few players do consistently outperform tiebreak expectations. They tend to be players who find themselves in tiebreaks often, and their success may be because they manage to maintain their serve at its usual level.
John Isner and Roger Federer are the usual suspects. Isner won 20% more tiebreaks this year than expected, in line with his numbers in 2011 and 2012. (In 2009 and 2010, he was even better.) Federer beat expectations by 10%, avoiding his first neutral-or-worse season since 2003 by winning a pair of breakers against tough opponents at the Tour Finals in London.
With another year’s worth of data in the books, we can safely add one more active player to this elite group. Rafael Nadal was fifth overall this year, winning 23% more tiebreaks than expected. Nadal hovered around the neutral level until 2008, winning almost exactly as many breakers as his overall skill level would suggest. But since then, he has had only good tiebreak seasons. No other player besides Isner and Federer has posted more than four better-than-expected tiebreak seasons in the last six.
For the rest of the ATP, it’s best to look at these numbers as indexes of luck. The men at the top will probably have to win more non-tiebreak sets next year to maintain their ranking, while the guys at the bottom can expect a modest boost with just a little less bad luck. That is, unless they play too many tiebreaks against John Isner.
—
The complete list of 2013 tiebreak performance is below. ‘TBOE’ is “Tiebreaks Over Expectations,” the difference between the number of tiebreaks my algorithm expects a player to win and the number he actually won. ‘TBOR’ is a rate version of the same stat, calculated by dividing TBOE by the total number of tiebreaks played. TBOE rewards players like Isner who play lots of tiebreaks and play them well, while TBOR identifies those who have been particularly lucky in whatever number of tiebreaks they contested.
Player TB TBWon TBExp TBOE TBOR
Roberto Bautista Agut 21 16 10.3 5.7 27.0%
Dmitry Tursunov 21 16 10.4 5.6 26.8%
Marin Cilic 15 11 8.2 2.8 18.7%
Leonardo Mayer 15 9 6.8 2.2 14.9%
Rafael Nadal 25 18 14.6 3.4 13.6%
Gilles Simon 25 16 12.7 3.3 13.0%
Ivo Karlovic 29 18 14.8 3.2 11.1%
John Isner 53 36 30.1 5.9 11.1%
Andy Murray 23 16 13.5 2.5 11.0%
Fabio Fognini 23 14 11.7 2.3 10.0%
Juan Martin Del Potro 33 21 17.7 3.3 10.0%
Benoit Paire 29 17 14.3 2.7 9.3%
Philipp Kohlschreiber 33 19 15.9 3.1 9.3%
Jerzy Janowicz 26 15 12.9 2.1 8.2%
Jarkko Nieminen 27 14 11.9 2.1 7.9%
Bernard Tomic 30 16 13.7 2.3 7.6%
Julien Benneteau 24 14 12.4 1.6 6.9%
Alexandr Dolgopolov 21 11 9.6 1.4 6.8%
Ernests Gulbis 23 13 11.5 1.5 6.4%
Tommy Haas 26 16 14.4 1.6 6.3%
Jeremy Chardy 21 12 10.7 1.3 6.0%
Roger Federer 25 15 13.6 1.4 5.4%
Grega Zemlja 19 10 9.0 1.0 5.3%
Feliciano Lopez 24 14 12.9 1.1 4.4%
Jo Wilfried Tsonga 30 17 15.8 1.2 4.2%
Ryan Harrison 15 7 6.4 0.6 4.1%
Tommy Robredo 24 14 13.1 0.9 3.8%
Novak Djokovic 28 19 17.9 1.1 3.8%
Lleyton Hewitt 16 9 8.4 0.6 3.5%
Daniel Brands 19 10 9.4 0.6 3.4%
Fernando Verdasco 24 14 13.5 0.5 1.9%
David Ferrer 21 12 11.8 0.2 1.0%
Kei Nishikori 16 9 8.9 0.1 0.9%
Martin Klizan 15 7 6.9 0.1 0.9%
Kevin Anderson 35 19 19.1 -0.1 -0.2%
Marinko Matosevic 16 9 9.1 -0.1 -0.4%
Mikhail Youzhny 23 11 11.4 -0.4 -1.8%
Milos Raonic 36 19 19.7 -0.7 -1.9%
Sam Querrey 31 15 15.6 -0.6 -2.1%
Stanislas Wawrinka 32 17 17.7 -0.7 -2.3%
Florian Mayer 18 8 8.4 -0.4 -2.4%
Gael Monfils 27 13 13.7 -0.7 -2.5%
Igor Sijsling 19 9 9.5 -0.5 -2.6%
Andreas Seppi 19 9 9.5 -0.5 -2.8%
Denis Istomin 24 11 11.8 -0.8 -3.2%
Richard Gasquet 29 15 16.0 -1.0 -3.4%
Daniel Gimeno Traver 18 7 7.6 -0.6 -3.5%
Vasek Pospisil 24 11 11.9 -0.9 -3.6%
Tomas Berdych 34 17 18.6 -1.6 -4.7%
Victor Hanescu 24 10 11.2 -1.2 -5.2%
Ivan Dodig 27 12 13.5 -1.5 -5.7%
Robin Haase 24 10 11.4 -1.4 -5.9%
Albert Ramos 16 7 7.9 -0.9 -5.9%
Benjamin Becker 18 7 8.1 -1.1 -5.9%
Horacio Zeballos 20 7 8.2 -1.2 -6.2%
Jurgen Melzer 19 8 9.4 -1.4 -7.4%
Nicolas Almagro 34 17 19.5 -2.5 -7.5%
Lukas Rosol 15 6 7.3 -1.3 -8.9%
Evgeny Donskoy 17 6 7.7 -1.7 -10.2%
Alejandro Falla 15 6 7.6 -1.6 -10.9%
Grigor Dimitrov 22 9 11.5 -2.5 -11.4%
Marcos Baghdatis 20 6 9.5 -3.5 -17.4%
Carlos Berlocq 18 7 10.2 -3.2 -17.5%
Juan Monaco 15 5 7.7 -2.7 -18.3%
Janko Tipsarevic 19 5 8.7 -3.7 -19.5%
Edouard Roger Vasselin 19 4 8.2 -4.2 -22.3%
Since the US Open, I’ve been developing a system to chart matches. With a bit of practice, anyone can use this system to note the type and direction of every shot in a match–serve direction, return direction and depth, shot patterns, error types, error directions, and more. A single charted match generates an enormous amount of data.
The more matches, the more players, the more surfaces, the better. Want to join the fun?
I hope you do, and the off-season is a great time to start. It will take you a couple of matches to get comfortable with the system, so charting recorded matches, with the ability to rewind and watch points multiple times, is the best way to get started. There are hundreds, if not thousands, on YouTube, with plenty more available through other sources such as ESPN3 and TennisTV.
I’ve created an interactive spreadsheet to make the process as easy as possible. Download it here. The fields highlighted in yellow are yours. The first several rows are for general information about the match. As you chart each point, the spreadsheet will automatically update the score and create an additional row for the next point.
Once you download and open the spreadsheet, click over to the “Instructions” tab. There, you’ll find detailed instructions on the process. It will take some time to understand all the details of how the system works, and then it will take you a match or two to get the hang of entering all that data. Pretty soon, you’ll find that you’re comfortably charting points in real time.
In the next week or two, I’ll try to put together some additional training material. However, if you’d like to get started right away, there’s nothing stopping you. Once you finish charting a match, send the completed spreadsheet back to me (my email address is in the spreadsheet), and I’ll run it through my program to generate detailed stats for that match.
In addition to the interactive spreadsheet itself, you may find it helpful to see a couple of completed charted matches, perhaps following along while watching the matches:
John Isner vs Rafael Nadal Cincinnati final: completed spreadsheet | YouTube video
(lefties are almost as tricky to chart as they are to play–I recommend charting a few righty-righty matches before trying to do one with a left-hander)
(sorry, those two Youtube videos have been removed due to copyright claims. You can still download the completed spreadsheets. At some point, I’ll try to find charted matches with Youtube videos that are unlikely to be taken down, and post those here instead.)
What I love about this project is that we don’t need thousands of matches for it all to be worthwhile. (Though I won’t complain when we accumulate thousands of matches!) Every charted match we can add to the database contributes to our understanding of those two players and professional tennis as a whole.
I sincerely hope you’ll contribute.
Update: I’ve posted a few updates, tips, and tools here.
You’ve probably heard the stats by now. Novak Djokovic ended the 2013 season on a 24-match winning streak. 13 of his last 20 matches–all wins, of course–came against fellow members of the top ten.
Carl Bialik argues that Djokovic’s latest exploits, taken as part of his career as a whole, force us to consider him as an all-time great, in line with the seven other players who have won six to eight Grand Slam titles. It’s a convincing case. While Novak remains well behind Roger Federer and Rafael Nadal in most of the usual GOAT-debate categories, those are some awfully high standards to meet. In any other era, he wouldn’t be burdened with such impossible comparisons.
Djokovic’s season-ending streak is notable in itself. Since 1983, only three other players have won 13 or more consecutive matches against members of the top ten: Federer (24, starting at the end of 2003, among other streaks), John McEnroe (15, in early 1984), and Nadal (13, from 2012 Monte Carlo to 2013 Monte Carlo).
Djokovic’s status among the all-time greats gets a boost when you realize that this isn’t his first such streak. Coinciding with the streak, Novak won 13 consecutive contests against top-ten players in the first five months of 2011.
What makes his most recent run all the more impressive is that he has done it with so few pauses for breath. 11 of his last 14 matches were against top tenners, as were 13 of his last 19. By contrast, Nadal’s otherwise comparable top-ten winning streak spread over 50 matches.
In fact, the tail end of Novak’s 2013 season was one of the most challenging on record. Since 1983, only seven men played more than half of a 20-match span against top-tenners. Federer, Nadal, and Djokovic head the list, as usual; the others are McEnroe, Andre Agassi, Pete Sampras, and Nikolay Davydenko. Aside from Djokovic this fall, Agassi is the only one of the seven who has played 13 or more top-ten opponents in a 20-match span. And unlike Novak, Agassi wasn’t perfect. In his demanding stretch at the end of 1994, he lost to Goran Ivanisevic in Stockholm and to Sampras in the semis of the Tour Finals.
The nature of Djokovic’s season-ending winning streak emphasizes his stature among the sport’s greats. In an era when a handful of contenders so thoroughly dominate the rest of the field, that small group of players is constantly facing one another. While I don’t envy anyone playing the likes of Ivanisevic indoors, even that fearsome thought pales next to the Nadal-led gauntlet that Novak has spent the last three months navigating.
More than any other shot in tennis, the drop shot can make the player who hits it look either brilliant or idiotic. The line separating the two is rarely so fine.
When we combine the brilliance and the idiocy, how does the drop shot measure up? How much does a player gain or lose with frequent use of the dropper?
In the final match of last week’s Challenger Tour Finals between Alejandro Gonzalez and Filippo Volandri, Volandri hit a whopping 23 drop shots–almost one per game (click the “Shot Types” links). Volandri is a seasoned pro with an excellent sense of clay court tactics, so he avoided the clunkiest drop shot misses–only three of the 23 were errors. Yet despite facing an opponent who prefers to camp out well behind the baseline, the Italian won only 11 of the 23 points. Almost half the time the drop shot landed in the court, Gonzalez chased it down, got a return in play, and went on to win the point.
With my shot-by-shot analyses of five matches from last week’s event in Sao Paulo, we can take a somewhat broader look at drop shot tactics and their results. While this subset may not be representative of all clay-court tennis (for one thing, the altitude makes it a bit easier to chase down a dropper), the aggregate numbers raise some questions about the wisdom of the drop-shot tactic.
As a whole, the six players who took part in these five matches hit 95 drop shots. 16 (16.8%) of them were unforced errors, compared to an overall rate of about 1 unforced error per 10 rallying shots. 29 (30.5%) were outright winners, while another five induced forced errors, immediately ending the point. That leaves 45 points (47.4%) in which the opponent got the ball back in play. Of those, the dropshotter won only 19 (42.2%).
Taken together, the results aren’t bad. The player who hit the drop shot won 53 (55.8%) of the points, and 67.1% of the points when the drop shot landed in play.
There is a noticeable difference, however, in the success rates of the frequent dropshotters (Voladri and Nedovyesov) compared to those of the other four players, who averaged fewer than four drop shots per match. While the players of what I’ll call the “infrequent group”–Gabashvili, Gonzalez, Guilherme Clezar, and Jesse Huta Galung–may not be as practiced in the art, it is likely that they chose their moments much more carefully, hitting drop shots when the tactic was obvious.
The infrequent group hit 22 drop shots, missing only two. Not only did nine go for winners, but the overall results were positive as well, as they won 14 (63.6%) of those points.
Remove the infrequent group from the overall numbers, and the aggressive dropshotters won a mere 53.4% of points in which they used the tactic.
53.4% isn’t awful–if you win 53.4% of the points in a match, you almost always win. However, the type of point in which the drop shot makes sense isn’t an average point. Usually the dropshotter has better court position than his opponent, who may be off-balance or far behind the baseline. This isn’t always the case, especially when the dropshotter is simply trying to end the point, or when his brain stops working. But in the majority of cases, the dropshotter has such an advantage in court position, it seems likely that a more common tactic–such as an aggressive groundstroke, perhaps followed by a net approach–would do better.
Another consideration goes beyond the outcome of a specific point. A player who fails to run down a drop shot will probably remember that lost point for a game or two and play a little closer to the baseline, maybe making himself less comfortable in the process. It’s possible that the long-term effect gives an advantage to the player who regularly uses the tactic.
But somewhere between Gonzalez’s four drop shots on Sunday and Volandri’s 23, the marginal advantage of each additional dropper must wear off. I find it hard to imagine that one drop shot per game has any more of a long-term strategic effect than one drop shot per three games. If that’s true, Volandri hit 13 or 14 more drop shots than required. Thus, in about 8% of Sunday’s 162 points, he took an advantageous court position and wasted it on an even-odds shot.
More evidence will surely give us a fuller picture of drop-shot tactics on clay courts. We may be able to determine whether there is a post-dropper “hangover effect” and if so, how many drop shots are required to reap the benefits. Until then, it’s worth considering whether drop shots are worth the risk, especially when there may be such a high-percentage alternative.
Few debates get tennis fans as riled up as the general slowing–or homogenization–of surface speeds. While indoor tennis (to take a recent example) is a different animal than it was fifteen or twenty years ago, it’s tough to separate the effect of the court itself from the other changes in the game that have taken place in that time.
Further, the “court effect” itself is multi-dimensional. The surface makes a big difference, as grass will almost always play quicker than a hard court, which will usually play faster than clay. But as we’ve seen with the persistence of Sao Paulo as one of the fastest-playing events on tour, altitude is a major factor, as is weather, which can slow down a normally speedy tournament, as was the case with Hurricane Irene at the 2011 US Open. The choice of balls can influence the speed of play as well.
With all of these factors in play, what we often refer to as “surface speed” is really “court speed” or even “playing environment.” It’s not just the surface. That said, I’ll continue to use the terms interchangeably.
Because of there is only limited data available, if we want to quantify surface differences, we must use a proxy for court speed. What has worked in the past is ace rate–adjusted for the server and returner in each match. On a fast court–a surface that doesn’t grip the ball; or one like grass with a low, less predictable bounce; or at a high altitude; or in particularly hot weather–a player who normally hits 5% of his service points for aces might see that number increase to 8%. (Returners influence ace rate as well. A field with Andy Murray will allow fewer aces than a field with Juan Martin del Potro, so I’ve controlled for that as well.)
Aggregate these server- and returner-adjusted ace rates, and at the very least, we have an approximation of which courts on tour are most ace-friendly. Since most of the characteristics of an ace-friendly court overlap with what we consider to be a fast court, we can use that number as an marker for surface speed.
2013 Court Speed Numbers
For the second year in a row, the high-altitude clay of Sao Paulo was the fastest-playing surface on tour. The altitude also appears to play a role in making Gstaad quicker than the typical clay.
As for the slowing of indoor courts, the evidence is inconclusive. The O2 Arena, site of the World Tour Finals, rated as slower than average in 2011 and 2012, on a level with some of the slowest hard courts on tour. This year, it came out above average, and a three-year weighted average puts the O2 at the exact middle of the ATP court-speed range.
Valencia and the Paris Masters played about as fast as they have in the past, while Marseille remained near the top of the rankings. If there is evidence for a mass slowing of indoor speeds, it comes from some unlikely sources: Both Moscow and San Jose were among the quickest surfaces on tour in 2010 and 2011, but have been right in the middle of the pack for the last two years.
The table below shows the relative ace rate of every tournament for the last four years, along with a weighted averaged of the last three years. The weighted average is the most useful number here, especially for the smaller 28- and 32-player events. The limited extent of a 31-match tournament can amplify the anomalous performance of one player–as you can see from some of the bigger year-to-year movements. But over the course of three years, individual outliers have less impact.
The “Sf” column is each event’s surface: “C” for clay, “H” for hard, and “G” for grass. The numbers are multipliers, so Sao Paulo’s three-year weighted average of 1.58 means that players at that event hit 58% more aces than they would have on a neutral court. Monte Carlo’s 0.67 means 33% less than neutral.
Event Sf 10 A% 11 A% 12 A% 13 A% 3yr
Sao Paulo C 1.44 1.08 1.58 1.74 1.58
Marseille H 1.09 1.24 1.41 1.26 1.30
Halle G 1.20 1.39 1.26 1.20 1.25
Wimbledon G 1.36 1.18 1.24 1.25 1.24
Shanghai H 0.96 1.05 1.08 1.37 1.22
Montpellier H 1.28 1.40 1.16 1.21
Brisbane H 1.01 1.20 1.08 1.27 1.19
Tokyo H 1.35 0.98 1.17 1.26 1.18
Gstaad C 0.87 1.13 0.90 1.35 1.16
Winston-Salem H 1.20 1.10 1.18 1.16
Chennai H 0.75 0.77 1.21 1.25 1.16
Valencia H 1.02 1.10 1.12 1.19 1.15
Zagreb H 1.09 1.16 1.20 1.11 1.15
Washington H 0.96 0.93 1.34 1.10 1.15
Vienna H 1.42 1.22 1.01 1.19 1.14
Santiago C 1.23 1.21 0.86 1.29 1.13
Sydney H 1.08 1.14 0.94 1.25 1.13
Atlanta H 0.92 0.82 1.06 1.26 1.12
Eastbourne G 1.07 1.13 0.92 1.22 1.11
Queen's Club G 1.07 1.13 1.09 1.12 1.11
Paris H 1.38 0.97 1.16 1.12 1.11
Cincinnati H 1.09 1.02 1.08 1.13 1.10
s-Hertogenbosch G 1.13 1.08 1.03 1.15 1.10
Auckland H 1.01 1.08 1.06 1.12 1.09
Memphis H 1.08 1.12 0.95 1.09 1.05
Stuttgart C 1.09 1.05 1.04 1.06 1.05
Bogota H 1.09 1.05
Rotterdam H 0.88 1.21 0.83 1.12 1.04
Stockholm H 0.93 0.96 1.15 0.99 1.04
Basel H 0.98 1.05 1.16 0.96 1.04
Bangkok H 1.20 1.12 0.73 1.19 1.03
Australian Open H 0.98 1.10 0.92 1.08 1.03
US Open H 1.14 0.93 1.06 1.04 1.03
San Jose H 1.21 1.23 0.96 0.99 1.02
Moscow H 1.28 1.12 1.01 0.99 1.02
Dubai H 1.13 1.07 1.14 0.92 1.02
Doha H 0.88 1.29 0.90 0.98 1.00
Tour Finals H 1.07 0.93 0.87 1.11 1.00
Beijing H 1.01 1.01 1.06 0.94 0.99
Canada H 0.99 1.02 1.04 0.95 0.99
Madrid C 0.76 0.86 1.19 0.89 0.98
Kitzbuhel C 1.12 0.70 1.12 0.98
Metz H 1.14 0.96 1.07 0.90 0.97
Dusseldorf C 0.92 0.96
Munich C 0.77 0.82 0.91 0.97 0.92
St. Petersburg H 1.02 0.84 0.86 0.99 0.92
Acapulco C 0.88 0.89 1.06 0.84 0.92
Delray Beach H 0.98 1.07 0.92 0.85 0.91
Newport G 1.46 0.72 1.04 0.89 0.91
Kuala Lumpur H 0.96 0.97 0.81 0.94 0.90
Miami H 0.91 0.98 0.86 0.89 0.89
Umag C 0.56 0.74 0.67 1.04 0.87
Hamburg C 1.04 0.85 0.75 0.92 0.85
Buenos Aires C 0.84 0.86 0.93 0.74 0.82
Indian Wells H 0.92 0.90 0.86 0.77 0.82
Roland Garros C 0.82 0.86 0.81 0.78 0.81
Barcelona C 0.73 0.65 0.91 0.78 0.80
Casablanca C 0.82 0.91 0.77 0.75 0.79
Estoril C 0.62 0.73 0.79 0.71 0.74
Houston C 0.85 0.71 0.71 0.77 0.74
Bucharest C 0.61 1.08 0.62 0.68 0.73
Rome C 0.78 0.67 0.64 0.81 0.73
Nice C 0.88 0.84 0.79 0.64 0.72
Bastad C 0.93 0.74 0.86 0.58 0.70
Monte Carlo C 0.63 0.60 0.71 0.67 0.67
Tennis fans–especially the more old-fashioned among us–tend to agree on some things that players should always do. Among them: revere Wimbledon, admit to a net touch, and play Davis Cup.
The top singles players on the two sides of last weekend’s tie between Serbia and the Czech Republic are good examples of what fans like to see. Tomas Berdych has played 12 of 14 Davis Cup ties while a member of the top ten, and in that time, the Czech team has never lost a tie because he didn’t show up. Novak Djokovic hasn’t been quite as reliable, playing singles in 13 of 18 ties since breaking into the top ten, though of the five he didn’t play, Serbia lost only one.
However, plenty of tennis megastars have been even more consistent cogs on their national teams. In the years when Goran Ivanisevic was in the top ten, his Croatian team played ten ties, and Goran was there for all 10. Since 1991, three other players have played at least ten ties while missing only one: Yevgeny Kafelnikov, Lleyton Hewitt, and Michael Stich.
Aside from Berdych and Djokovic, today’s top players are not so reliable. Roger Federer has participated in 14 of 24 ties since he became a top-tenner, and the Swiss side has lost eight of the ten ties he’s missed. Andy Murray has offered his services for only 5 of 12 as a top ten player, and the Brits have lost four of their seven Murray-less weekend.
Even less of a Davis Cup stalwart than Murray, however, is Rafael Nadal. Thanks to a combination of injury, fatigue, and a frequent lack of necessity, Rafa has played singles in only 10 of 25 ties since breaking into the top ten.
The table below compares all players who, since 1991, have been in the top ten while their countries played at least ten Davis Cup ties. It shows their record when participating (“In W-L”), their team’s success rate when they sat out (“Out W-L”), the percentage of ties in which they took part (“In%”), and the percentage of ties in which either they played or their team won anyway (“AllGood%”).
(I only count someone as participating if he contested at least one singles match. In a few cases–such as Serbia’s defeat last year of Sweden, in which Djokovic only played doubles–that blurs the line between wins with and without the player.)
Player In W-L Out W-L In% AllGood%
Goran Ivanisevic 5-5 0-0 100.0% 100.0%
Yevgeny Kafelnikov 13-6 0-1 95.0% 95.0%
Lleyton Hewitt 10-3 0-1 92.9% 92.9%
Michael Stich 8-2 0-1 90.9% 90.9%
Andy Roddick 15-5 0-3 87.0% 87.0%
David Nalbandian 11-2 0-2 86.7% 86.7%
Tomas Berdych 9-3 2-0 85.7% 100.0%
Carlos Moya 8-4 1-1 85.7% 92.9%
Stefan Edberg 8-3 2-0 84.6% 100.0%
Marcelo Rios 5-3 2-0 80.0% 100.0%
Novak Djokovic 10-3 4-1 72.2% 94.4%
Nikolay Davydenko 8-3 4-1 68.8% 93.8%
David Ferrer 7-2 3-2 64.3% 85.7%
Marat Safin 7-0 2-3 58.3% 75.0%
Roger Federer 10-4 2-8 58.3% 66.7%
Boris Becker 5-2 5-3 46.7% 80.0%
Andy Murray 3-2 3-4 41.7% 66.7%
Jim Courier 6-0 6-3 40.0% 80.0%
Rafael Nadal 9-1 10-5 40.0% 80.0%
J M Del Potro 1-3 6-1 36.4% 90.9%
Pete Sampras 8-3 16-6 33.3% 81.8%
Andre Agassi 7-2 14-10 27.3% 69.7%
Michael Chang 2-1 13-3 15.8% 84.2%
