The Luck of the Tiebreak, 2013 Edition

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%

The Match Charting Project

Tennis needs better stats.  Now you can help.

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 true potential of match charting lies in the bigger picture.  So far, we have nearly 50 matches in the books–mostly from ATP events this fall.  Even with this relatively small subset of matches, I’ve been able to do some interesting research, such as analyzing how quickly Novak Djokovic can neutralize a server’s advantage, and evaluating the wisdom of the drop shot.

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.

(Thanks to Brian Hrebec, there’s also an Android app you can use to chart matches. Find it here.)

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:

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.

Update: At least a dozen people are now charting matches, so in an effort to avoid duplicating our efforts, I’ve started a google doc to track who is working on what. Before you start working on a match, check the doc to make sure no one else is already charting it. And when you start charting a match (and think you’ll finish it!), enter your name/handle and the match in that sheet so that others can focus their efforts elsewhere.

Novak Djokovic’s Amazing, Challenging Season End

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.

The Questionable Wisdom of the Drop Shot

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.

Volandri’s performance in the final wasn’t an anomaly.  In the semifinal against Teymuraz Gabashvili, he attempted 17 drop shots and won only nine of those points.  The other aggressive drop-shotter at the CTF, Oleksandr Nedovyesov, hit 19 drop shots against Gabashvili in a round-robin match.  Even though eight of those 19 drop shots were winners, Nedovyesov lost ten of the ensuing points.

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.

The Speed of Every 2013 Surface

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

Berdych, Djokovic, and Stars in Davis Cup

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% 

Doubles Wins and Davis Cup Results

Today, Tomas Berdych added another chapter to his outstanding Davis Cup doubles career, partnering Radek Stepanek to give his Czech Republic a 2-1 lead in this weekend’s Davis Cup final.

The absence of Janko Tipsarevic meant that the doubles rubber was particularly crucial.  While Novak Djokovic will probably defeat Berdych tomorrow, Stepanek is equally likely to dismiss Dusan Lajovic, giving the Czechs a second consecutive Davis Cup title.

Since the Saturday doubles match is so often a pivotal juncture in a Davis Cup tie, I was curious whether the doubles match was particularly predictive of the end result.  If you’re a believer in momentum, it would seem possible.

However, if a side is to take a 2-1 lead, it’s better to win two singles matches and lose the doubles than to drop one of the singles matches.  Or, to put it another, probably more accurate, way: It’s best to have a squad that dominates the singles.  (Stunning insight, I know.)

There have been 435 World Group ties (including playoffs) since 1981 in which the outcome was undecided after the doubles match.  In 296 of those, the two sides split the singles.  In the other 139, one side swept the first-day singles and the opposing team won the doubles.

Of the first group of 296, the side that won the doubles won 80.4% of ties.  That pales in comparison to the singles-sweeping sample. Of those 139 ties, the side that won both singles and lost the doubles proved triumphant 93.5% of the time.

This shouldn’t be too surprising.  Momentum or no momentum, the third day of a Davis Cup tie is nothing but singles matches.  When the outcome is to be decided by two singles rubbers, would you rather have two great singles players or a pair of momentum-swaying doubles players?

Fortunately for the Czechs, 80% is still awfully good, and it probably understates the likelihood that Stepanek will beat Lajovic tomorrow.   Nice as it would have been to sweep opening-day singles, it helps to have a backup plan when Djokovic is playing for the other side.