Tactics Archives - Page 2 of 4

Podcast Episode 83: Is the Practice Court Broken?

Episode 83 of the Tennis Abstract Podcast features co-host Carl Bialik, of the Thirty Love podcast, and guest Jeff McFarland of Hidden Game of Tennis. This week we dip our collective toe into a debate in the tennis coaching world.

With rallies short and aggressive, should players be using practice time differently? What types of skills can still be improved, once a player has reached the top? What tactics can a coach teach their charges, and which ones are too deeply ingrained in the physical nature of hitting the shots? The line between technique and tactics may not be a clear-cut as we think.

Is a 3- or 4-shot rally qualitatively different from a 5- or more-shot rally? How would you teach Madison Keys to retain the positives of her aggressive style while dialing back the aggression a bit? We offer more questions than answers, which seems appropriate for a topic that is far from settled, and is likely to remain controversial for years to come.

Thanks for listening!

(Note: this week’s episode is about 67 minutes long; in some browsers the audio player may display a different length. Sorry about that!)

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

Are American Players Screwed Once You Drag Them Into a Rally?

Long after retiring from tennis, Marat Safin remains quotable. The Russian captain at the ATP Cup had this to say to his charge, Karen Khachanov, during a match against Taylor Fritz:

🤣🤣🤣
Safin to Khachanov on typical US player's approach to the game:

They employ shock-and-awe tactics and want to scare you with one-two shots. Once you drag them into the rally they are screwed.
— Oleg S. (@AnnaK_4ever) January 5, 2020

This isn’t exactly testable. I don’t know you’d quantify “shock-and-awe,” or how to identify–let alone measure–attempts to scare one’s opponent. Or screwed-ness, for that matter. But if we take “screwed” to mean the same as “not very likely to win,” we’ve got something we can check.

Many fans would agree with the general claim that American men tend to have big serves, aggressive game styles, and not a whole lot of subtlety. Certainly John Isner fits that mold, and Sam Querrey doesn’t deviate much from it. While Fritz is a big hitter who racks up his share of aces and second-shot putaways, his style isn’t so one-dimensional.

Taylor Fritz: not screwed

Using data from the Match Charting Project, I calculated some rally-length stats for the 70 men with at least 20 charted matches in the last decade. That includes five Americans (Fritz, Isner, Querrey, Steve Johnson, and Jack Sock) and most of the other guys we think of as ATP tour regulars.

Safin’s implied definition is that rallies of four shots or fewer are “shock-and-awe” territory, points that are won or lost within either player’s first two shots. Longer rallies are, supposedly, the points where the Americans lose the edge.

That is certainly the case for Isner. He wins only 40% of points when the rally reaches a fifth shot, by far the worst of these tour regulars. Compared to Isner, even Nick Kyrgios (44%) and Ivo Karlovic (45%) look respectable. The range of winning percentages extends as high as 56%, the mark held by Nikoloz Basilashvili. Rafael Nadal is, unsurprisingly, right behind him in second place at 54%, a whisker ahead of Novak Djokovic.

Fritz, at 50.2%, ranks 28th out of 70, roughly equal to the likes of Gael Monfils, Roberto Bautista Agut, and Dominic Thiem. Best of all–if you’re a contrarian like me, anyway–is that Fritz is almost 20 places higher on the list than Khachanov, who wins 48.5% of points that last five shots or more.

More data

Here are 20 of the 70 players, including some from the top and bottom of the list, along with all the Americans and some other characters of interest. I’ve calculated each player’s percentage of points won for 1- or 2-shot rallies (serve and return winners), 3- or 4-shot rallies (serve- and return-plus-one points), and 5- or more-shot rallies. They are ranked by the 5- or more-shot column:

Rank  Player                 1-2 W%  3-4 W%  5+ W%  
1     Nikoloz Basilashvili    43.7%   54.1%  55.8%  
2     Rafael Nadal            52.7%   51.6%  54.3%  
3     Novak Djokovic          51.8%   54.6%  54.0%  
4     Kei Nishikori           45.5%   51.2%  53.9%  
11    Roger Federer           52.9%   54.9%  52.1%  
22    Philipp Kohlschreiber   50.1%   50.1%  50.7%  
28    Taylor Fritz            51.1%   47.2%  50.2%  
30    Jack Sock               49.0%   46.5%  50.2%  
31    Alexander Zverev        52.8%   50.3%  50.0%  
32    Juan Martin del Potro   53.8%   49.1%  50.0%  
34    Andy Murray             54.3%   49.5%  49.4%  
39    Daniil Medvedev         53.9%   50.4%  49.0%  
43    Stefanos Tsitsipas      51.4%   50.5%  48.6%  
44    Karen Khachanov         53.7%   48.1%  48.5%  
48    Steve Johnson           49.2%   48.8%  48.3%  
61    Sam Querrey             53.5%   48.0%  46.2%  
62    Matteo Berrettini       53.6%   49.3%  46.1%  
66    Ivo Karlovic            51.8%   43.9%  44.9%  
68    Nick Kyrgios            54.6%   47.4%  44.2%  
70    John Isner              52.3%   48.3%  40.2%

Fritz is one of the few players who win more than half of the shortest rallies and more than half of the longest ones. The first category can be the result of a strong serve, as is probably the case with Fritz, and is definitely the case with Isner. But you don’t have to have a big serve to win more than half of the 1- or 2-shot points. Nadal and Djokovic do well in that category (like they do in virtually all categories) in large part because they negate the advantage of their opponents’ serves.

Shifting focus from the Americans for a moment, you might be surprised by the players with positive winning percentages in all three categories. Nadal, Djokovic, and Roger Federer all make the cut, each with plenty of room to spare. The remaining two are the unexpected ones. Philipp Kohlschreiber is just barely better than neutral in both classes of short points, and a bit better than that (50.7%) on long ones. And Alexander Zverev qualifies by the skin of his teeth, winning very slightly more than half of his long rallies. (Yes, that 50.0% is rounded down, not up.) Match Charting Project data is far from complete, so it’s possible that with a different sample, one or both of the Germans would fall below the 50% mark, but the numbers for both are based on sizable datasets.

Back to Fritz, Isner, and company. Safin may be right that the Americans want to scare you with a couple of big shots. Isner has certainly intimidated his share of opponents with the serve alone. Yet Fritz, the player who prompted the comment, is more well-rounded than the Russian captain gave him credit for. Khachanov won the match on Sunday, and at least at this stage in their careers, the Russian is the better player. But not on longer rallies. Based on our broader look at the data, it’s Khachanov who should try to avoid getting dragged into long exchanges, not Fritz.

WTA Decisions From the Backhand Corner

Earlier this week I presented a lot of data about what happens when men face a makeable ball hit to their backhand corner. That post was itself a follow-up on a previous look at what happened when players of both genders attempted down-the-line backhands. You don’t need to read those two articles to know what’s going on in this one, but if you’re interested in the topic, you’ll probably find them worthwhile.

Decision-making in the backhand corner is one of the biggest differences between pro men and women. Let me illustrate in the nerdiest way possible, with bug reports from the code I wrote to assemble these numbers. My first stab at the code to aggregate player-by-player numbers for men failed because some men never hit a topspin backhand from the backhand corner. At least, not in any match recorded by the Match Charting Project. The offending player who generated those divide-by-zero errors was Sam Groth. In his handful of charted matches, he relied entirely on the slice, at least in those rare cases where rallies extended beyond the return of serve.

Compare with the bug that slowed me down in preparing this post. The problematic player this time was Evgeniya Rodina. In nine charted matches, she has yet to hit a forehand from the backhand corner. If your backhand is the better shot, why would you run around it? Of the nearly 200 players with five charted matches from the 2010s, Rodina is the only one with zero forehands. But she isn’t really an outlier. 23 other women hit fewer than 10 forehands in all of their charted matches, including Timea Bacsinszky, who opted for the forehand only four times in 32 matches.

Faced with a makeable ball in the backhand corner, men and women both hit a non-slice groundstroke about four-fifths of the time. But of those topspin and flat strokes, women stick with the backhand 94% of the time, compared to 82% for men.

A few WTA players seek out opportunities to run around their backhands, including Sam Stosur and Polona Hercog, both of whom hit the forehand 20% of the time they are pushed into the backhand corner. Ashleigh Barty also displays more Federer-like tactics than most of her peers, using the forehand 13% of the time. Yet most of the women with powerful forehands, like Serena Williams, have equal or better backhands, making it counter-productive to run around the shot. Serena hits a forehand only 1% of the time her opponent sends a makeable ball into her backhand corner.

Directional decisions

Backhand or forehand, let’s start by looking at which specific shot that players chose. The Match Charting Project contains shot-by-shot logs of about 2,900 women’s matches from the 2010s, including 365,000 makeable balls hit to one player’s backhand corner. (“Makeable” is defined as a ball that either came back or resulted in an unforced error.)

Here is the frequency with which players hit backhand and forehands in different directions from their backhand corner. I’ve included the ATP numbers for comparison:

BH Direction               WTA Freq  ATP Freq  
Down the line                 17.4%     17.4%  
Down the middle               35.2%     29.5%  
Cross-court                   47.3%     52.9%  
                                               
FH Direction               WTA Freq  ATP Freq  
Down the line (inside-in)     35.2%     35.1%  
Down the middle               16.2%     12.8%  
Cross-court (inside-out)      48.4%     51.8%

Once a forehand or backhand is chosen, there isn’t much difference between men and women. Women go up the middle a bit more often, which may partly be a function of using the topspin or flat backhand in defensive positions slightly more than men do. I’ve also observed that today’s top women are more likely to hit an aggressive shot down the middle than men are. The level of aggression and risk may be similar to that of a bullet aimed at a corner, but when we classify by direction, it looks a bit more conservative. That’s just a theory, however, so we’ll have to test that another day.

Point probability

Things get more interesting when we look at how these choices affect the likelihood of winning the point. On average, a woman faced with a makeable ball in her backhand corner has a 47.2% chance of winning the point. (For men, it’s 47.7%.) The serve has some effect on the potency those shots toward the backhand corner. If the makeable ball was a service return–presumably weaker than the average groundstroke–the probability of winning the point is 48.2%. If the makeable ball is one shot later, an often-aggressive “serve-plus-one” shot, the chances of fighting back and winning the point are only 46.3%. It’s not a huge difference, but it is a reminder that the context of any given shot can affect these probabilities.

The various decisions available to players each have their own effect on the probability of winning the point, at least on average. If a woman chooses to hit a down-the-line backhand, her likelihood of winning the point increases to 53.0%. If she makes that shot, her odds rise to 68.4%.

The following table shows those probabilities for every decision. The first column of percentages, “Post-Shot,” indicates the likelihood of winning after making the decision–the 53.0% I just mentioned. The second column, “In-Play,” is the chance of winning if she makes that shot, like 68.4% for the down-the-line backhand.

Shot      Direction  Post-Shot  In-Play  
Backhand  (all)          48.5%    55.2%  
Backhand  DTL            53.0%    68.4%  
Backhand  Middle         44.6%    48.8%  
Backhand  XC             49.9%    55.8%  
                                         
Forehand  (all)          56.3%    56.1%  
Forehand  DTL (I-I)      61.4%    73.7%  
Forehand  Middle         45.7%    50.3%  
Forehand  XC (I-O)       56.2%    64.4%

The down-the-line shots are risky, so the gap between the two probabilities is a big one. There is little difference between Post-Shot and In-Play for down-the-middle shots, because they almost always go in. For the forehand probabilities, keep in mind that they are skewed by the selection of players who choose to use their forehands more often. Your mileage may vary, especially if you play like Rodina does.

Cautious recommendations

Looking at this table, you might wonder why a player would ever make certain shot selections. The likelihood of winning the point before choosing a wing or direction is 47.2%, so why go with a backhand down the middle (44.6%) when you could hit an inside-in forehand (61.4%)? It’s not the risk of missing, because that’s baked into the numbers.

One obvious reason is that it isn’t always possible to hit the most rewarding shot. Even the most aggressive men run around only about one-quarter of their backhands, suggesting that it would be impractical to hit a forehand on the remaining three-quarters of opportunities. That wipes out half of the choices I’ve listed. And even a backhand wizard such as Simona Halep can’t hit lasers down the line at will. The probabilities reflect what happened when players thought the shot was the best option available to them. Even though were occasionally wrong, this is very, very far from a randomized controlled trial in which a scientist told players to hit a down-the-line backhand no matter what the nature of the incoming shot.

Another complication is one that I’ve already mentioned: The success rates for rarer shots, like inside-in forehands, reflect how things turned out for players who chose to hit them. That is, for players who consider them to be weapons. It might be amusing to watch Monica Niculescu hit inside-out topspin forehands at every opportunity, but it almost certainly wouldn’t improve her chances of winning. You only get those rosy forehand numbers if you can hit a forehand like Stosur does.

That said, the table does drive home the point that conservative shot selection has an effect on the probability of winning points. Some women are happy sending backhand after backhand up the middle of the court, and sometimes that’s all you can do. But when more options are available, the riskier choices can be more rewarding.

Player probabilities

Let’s wrap up for today by taking a player-by-player look at these numbers. We established that the average player has a 47.2% chance of winning the point when a makeable shot is arcing toward her backhand corner. Even though Tsvetana Pironkova’s number is also 47.2%, no player is average. Here are the top 14 players–minimum ten charted matches, ranked by the probability of winning a point from that position. I’ve also included the frequency with which they hit non-slice backhands:

Player                     Post-Shot  BH Freq  
Kim Clijsters                  53.4%    77.6%  
Na Li                          53.2%    87.5%  
Camila Giorgi                  52.9%    93.8%  
Patricia Maria Tig             52.1%    66.1%  
Simona Halep                   52.1%    83.6%  
Belinda Bencic                 51.5%    91.7%  
Dominika Cibulkova             51.3%    70.1%  
Veronika Kudermetova           50.9%    73.9%  
Jessica Pegula                 50.7%    73.7%  
Su-Wei Hsieh                   50.6%    81.8%  
Dayana Yastremska              50.6%    87.6%  
Anna Karolina Schmiedlova      50.3%    87.4%  
Serena Williams                49.9%    89.2%  
Sara Errani                    49.8%    70.0%

These numbers are from the 2010s only, so they don’t encompass the entire careers of the top two players on the list, Kim Clijsters and Li Na. It is particularly impressive that they make the cut, because their charted matches are not a random sample–they heavily tilt toward high-profile clashes against top opponents. The remainder of the list is a mixed bag of elites and journeywomen, backhand bashers and crafty strategists.

Next are the players with the best chances of winning the point after hitting a forehand from the backhand corner. I’ve drawn the line at 100 charted forehands, a minimum that limits our pool to about 50 players:

Player                Post-Shot  FH Freq  
Maria Sharapova           69.0%     4.1%  
Dominika Cibulkova        65.1%    10.5%  
Ana Ivanovic              64.7%    11.1%  
Yafan Wang                64.4%     8.8%  
Rebecca Peterson          63.4%    15.2%  
Simona Halep              63.1%     6.8%  
Carla Suarez Navarro      63.0%     7.7%  
Andrea Petkovic           62.3%     5.3%  
Christina McHale          61.9%    15.2%  
Anastasija Sevastova      61.3%     4.2%  
Petra Kvitova             60.8%     4.6%  
Caroline Garcia           60.7%     7.5%  
Misaki Doi                60.5%    17.0%  
Madison Keys              59.3%     9.3%  
Elina Svitolina           59.1%     3.9%

Maria Sharapova is the Gilles Simon of the WTA. (Now there’s a sentence I never thought I’d write!) Both players usually opt for the backhand, but are extremely effective when they go for the forehand. Kudos to Sharapova for her well-judged attacks, though it could be that she’s leaving some points on the table by not running around her backhand more often.

Next

As I wrote on Thursday, we’re still just scratching the surface of what can be done with Match Charting Project data to analyze tactics such as this one. A particular area of interest is to break down backhand-corner opportunities (or chances anywhere on the court) even further. The average point probability of 47.2% surely does not hold if we look at makeable balls that started life as, say, inside-out forehands. If some players are facing more tough chances, we should view those numbers differently.

If you’ve gotten this far, you must be interested. The Match Charting Project has accumulated shot-by-shot logs of nearly 7,000 matches. It’s a huge number, but we could always use more. Many up and coming players have only a few matches charted, and many interesting matches of the past (like most of those played by Li and Clijsters!) remain unlogged. You can help, and if you like watching and analyzing tennis, you should.

Weighing Options From the Backhand Corner

A few weeks ago, I offered a “first look” at the down-the-line backhand. I offered a stack of Match Charting Project-based stats showing how often players opted to play that shot, what happened when they did, how lefties differ from righties, and which players stood out thanks to the frequency or success of their down-the-line strikes.

Like Richard Gasquet returning a serve, we need to take a step back before we can move forward. Rather than continuing to focus solely on the down-the-line backhand, let’s expand our view to all shots played from the backhand corner. The DTL backhand is only one choice among many. A player in position to go down the line has the option of a cross-court shot or a more conservative reply up the middle. She also might run around the backhand entirely, taking aim with a forehand up the line (“inside-in”), down the middle, or cross-court (“inside-out”).

Every shot is a choice, and one of the roles of analytics is to analyze the pros and cons of decisions players make. Ideally, we would even be able to identify cases in which pros make poor choices and recommend better ones. We’re still many steps away from that, at least in any kind of systematic way. But thanks to the thousands of matches with shot-by-shot data logged by the Match Charting Project, we have plenty of raw material to help us get closer.

The first choice

In 2,700 charted men’s matches from the last decade (happy new year!), I isolated about 450,000 situations in which one player had a makeable ball in his backhand corner, excluding service returns. The definition of “makeable” is inherently a bit messy. For today’s purposes, a makeable ball is one that the player managed to return or one that turned into an unforced error. With ball-tracking data, we could be more precise, but for now we need to accept this level of imprecision.

Of the 450,000 makeable backhand-corner balls, players hit (non-slice) backhands 63.7% of the time and (non-slice) forehands 14.3% of the time. The remaining 22% were divvied up among slices, dropshots, and lobs, and we’ll set those aside for another day.

Here’s how 2010s men chose to aim their backhands from the backhand corner:

Down the line: 17.4%
Down the middle: 29.5%
Cross-court: 52.9%

And their forehands from the same position:

Down the line (inside-in): 35.1%
Down the middle: 12.8%
Cross-court (inside-out): 51.8%

The inside-in percentage is a bit surprising at first, though we need to keep in mind that it’s 35% of a relatively small number, accounting for only 5% of total shots from the backhand corner. Less surprising is the much higher frequency of shots going cross-court. Not only is that a safer, higher-percentage play, it directs the ball to the opponent’s backhand (unless he’s a lefty), which is typically his weaker side.

Point probability

Shot selection is only a means to an end. More important than deploying textbook-perfect strategy is winning the point, and that’s where we’ll turn next.

The average ATPer has a 47.7% chance of winning the point when faced with a makeable ball in his backhand corner. Of course, any particular opportunity could be much better or worse than that. But again, without camera-based ball-tracking data, we can’t make more accurate estimates for specific chances. We can get some clues as to the range of probabilities by looking at how they vary at different stages of the rally. When a player has an opportunity for a “serve-plus-one” shot in the backhand corner–the third shot of the rally–his chances of winning the point are higher, at 51.1%. On the fourth shot of the rally, when pros are often still recovering from the disadvantage of returning, the chances of winning the point from that position are 45.4%. Context matters, in large part because context offers hints as to whether certain shots are better or worse than average.

So far, we have an idea of the probability of winning the point before making a choice. There are two ways of looking at the probability after choosing and hitting a shot: the odds of winning the point after hitting the shot, and the odds of winning the point after making the shot. The second number is obviously going to be better, because we simply filter out the errors. By excluding what could go wrong, it doesn’t give us the whole picture, but it does provide some useful information, showing which shots have the capacity to put opponents in the worst positions.

Here are the point probabilities for each of the shots we’re considering. For each choice, I’ve shown the probability of winning the point after hitting the shot (“Post-Shot”) and after making the shot (“In-Play”).

Shot      Direction  Post-Shot  In-Play  
Backhand  (all)          48.2%    54.2%  
Backhand  DTL            51.4%    64.6%  
Backhand  Middle         44.2%    48.2%  
Backhand  XC             49.5%    54.6%  
                                         
Forehand  (all)          55.1%    63.0%  
Forehand  DTL (I-I)      58.5%    69.0%  
Forehand  Middle         47.3%    52.0%  
Forehand  XC (I-O)       54.9%    61.9%

Forehands tend to do more to improve point-winning probability than backhands, though the down-the-middle forehand is less effective than a backhand to either corner. Again, this is context talking: A player who runs around a backhand just to hit a conservative forehand may have misjudged the angle or spin of the ball and felt forced to make a more defensive play. Still, it’s a relatively common tactic on slower clay courts (on clay, it is almost twice as common than tour average), and it may be used too often.

The most dramatic differences between the two probabilities are on the down-the-line shots. Both forehand and backhand are aggressive, high-risk shots, something reflected in the winner and unforced error rates for each. 9% of all shots from the backhand corner are winners, and another 11% are unforced errors. Of down-the-line shots, 23% are winners and 19% are unforced errors. While the choice to go down the line isn’t superior to other options, both the forehand and backhand are devastating shots when they work.

Player by player

Let’s tentatively measure “effectiveness” in terms of increasing point probability. Setting aside the complexity of context, which won’t be the same for every player, the most effective pro is the one who makes the most of a certain class of opportunities.

Here are the 10 best active players (of those with at least 20 charted matches) who do the most when faced with a makeable ball in their own backhand corner. Keep in mind that the average player has a 47.7% chance of winning the point from that position:

Player                Post-Shot  
Rafael Nadal              52.9%  
Diego Schwartzman         52.4%  
Novak Djokovic            52.3%  
Nikoloz Basilashvili      51.9%  
Andrey Rublev             51.8%  
Kei Nishikori             51.5%  
Gilles Simon              51.2%  
Pablo Cuevas              50.9%  
Alex De Minaur            50.0%  
Pablo Carreno Busta       49.6%

The Match Charting Project data might understate just how effective Rafael Nadal, Novak Djokovic, and Kei Nishikori are from their backhand corner, since a disproportionate number of their charted matches are against other top players. In any case, it is no surprise to see them here, along with such backhand warriors as Diego Schwartzman and Gilles Simon.

This list is limited to the tour regulars with at least 20 matches charted. One more name to watch out for is Thomas Fabbiano, with only 12 matches logged so far. In that limited sample, his point probability from the backhand corner is a whopping 59.2%. He isn’t quite that much of an outlier in reality, since his charted matches include contests against Ivo Karlovic, Reilly Opelka, and Sam Querrey, opponents whose ground games leave a bit to be desired. But his overall figure is so far off the charts that, even adjusting downward by a hefty margin, he appears to be one of the more dangerous players on tour from that position.

Forehands and backhands

Let’s wrap up by looking at something a bit more specific. For backhands and forehands (without separating by direction), which players are most effective after hitting that shot from the backhand corner? We’re continuing to define effectiveness as winning as many points as possible after hitting the shot. I’ll also show how often each of the players opts for their effective shot, giving us a glimpse at tactical decisions, not just tactical success.

Here are the best backhands from the backhand corner. It was supposed to be a top ten list, but I think you’ll understand why I struggled to cut it off before listing the top 16 players, roughly one-fifth of the 75 players with at least 20 charted matches:

Player                 Post-shot  BH Freq  
Diego Schwartzman          52.8%    74.0%  
Rafael Nadal               52.7%    64.7%  
Novak Djokovic             52.7%    76.1%  
Kei Nishikori              51.7%    74.0%  
Gilles Simon               51.4%    88.0%  
Andrey Rublev              51.1%    67.1%  
Pablo Carreno Busta        51.1%    75.3%  
Nikoloz Basilashvili       51.0%    75.0%  
Alexander Zverev           50.8%    75.1%  
Alex de Minaur             50.6%    74.8%  
Daniil Medvedev            50.6%    87.2%  
Juan Martin del Potro      50.3%    49.1%  
Pablo Cuevas               50.2%    60.6%  
Andy Murray                50.1%    65.0%  
Richard Gasquet            49.9%    75.8%  
Stan Wawrinka              49.8%    63.4%

The “BH Freq” column–for backhand frequency–really demonstrates the range of tactics used by different players. Gilles Simon and Daniil Medvedev opt for the topspin backhand almost every time, rarely slicing or running around the shot. At the opposite extreme, Juan Martin del Potro hits a topspin backhand less the half the time from that position. Perhaps because of his selectiveness–dealing with awkward positions by slicing–he is effective when he makes that choice.

Now the best forehands from the backhand corner:

Player                 Post-shot  FH Freq  
Gilles Simon               63.1%     6.7%  
Rafael Nadal               61.9%    16.6%  
Benoit Paire               61.9%     1.5%  
Kei Nishikori              61.2%    10.4%  
Andrey Rublev              61.0%    20.1%  
Casper Ruud                60.8%    27.1%  
Marton Fucsovics           60.5%    16.3%  
Novak Djokovic             60.0%     9.7%  
Daniil Medvedev            59.8%     3.3%  
Pablo Cuevas               58.9%    20.9%  
Sam Querrey                58.2%    15.6%  
Felix Auger Aliassime      57.7%    16.0%

This list is more of a mixed bag, in part because there are so many fewer forehands from the backhand corner. Benoit Paire’s numbers are based on a mere 21 shots. I wouldn’t take his effectiveness seriously at all, but it’s always entertaining to see evidence of his uniqueness. At the opposite end of the spectrum is Casper Ruud, who runs around his backhand more than anyone else in the charting dataset except for Jack Sock and Joao Sousa. (Neither one of which is particularly effective, though presumably they do better by avoiding their backhands than they would by hitting it.)

One name you might have expected to see on the last list is Roger Federer. He’s around the 80th percentile in the forehand category, winning 56.9% of points when hitting a forehand from the backhand corner. He’s good, but not off the charts in this category. Like Nadal and Djokovic, he might look better if these numbers were adjusted for opponent, because so many of his charted matches are against fellow elites.

Next

There’s clearly a lot more to do here, including looking at probabilities for direction-specific shots, isolating the effect of certain opponents, and trying to control for more of the factors that aren’t explicitly present in the data. Not to mention extending the same framework to other shots from other positions on court. Stay tuned.

A First Look at the Down-the-Line Backhand

When executed correctly and deployed at the right moment, the down-the-line backhand is one of the most devastating shots in tennis. How valuable is it, and which players use it the most effectively? These are surprisingly complicated questions, and I don’t yet have solid answers. But the preliminary work, of determining the frequency with which players use the down-the-line backhand, as well as their success rate when they do, is illuminating in itself.

The Match Charting Project offers a lot of data on tactics like this. MCP charts record the type and direction of every shot. In a rally between right-handers, a down-the-line (DTL) backhand is simple to identify: a backhand from the backhand corner to the opponent’s forehand corner. (Or in MCP parlance, 3b1.) From the 2010s alone, the MCP has logged close to 100,000 DTL backhands, roughly evenly split by gender.

MCP charts also give us an idea of the bigger picture. We can identify opportunities to hit DTL backhands, in which a player might choose instead to hit a backhand in a different direction, or even to use a different shot entirely. A player who hits a lot of slices, or runs around the backhand to hit forehands, might hit a very high percentage of their backhands down the line, but those DTL backhands wouldn’t make up a high proportion of their total chances in that corner.

DTL opportunities

Let’s start with a look at those opportunities. The following table shows three rates for each tour, covering charted matches, 2010-present. The first rate is the percentage of backhand-corner opportunities that resulted in backhands. (For today’s purposes, I’m excluding slice backhands. DTL slices can be devilish, but they are an entirely different weapon. I’ve also excluded service returns, which present their own complications.) The second is the percentage of opportunities that results in down-the-line backhands, and the third is a combination of those two, the percentage of backhands from the backhand corner that were hit down the line.

Tour  BH/Opps  DTL BH/Opps  DTL/All BH  
ATP     63.7%        11.1%       17.4%  
WTA     73.6%        12.8%       17.4%

Women are much more likely than men to hit a non-slice backhand from their backhand corner. There are two reasons for that. First: men, on average, hit more slices, largely because a small number of men hit a lot of slices. Second, men are somewhat more likely to run around the backhand and hit a forehand from that corner. Because men hit fewer backhands in total, men also hit fewer DTL backhands as a percentage of all shots from that corner.

However, once they choose to hit a backhand, men and women go down the line at exactly the same rate, 17.4%, or roughly once per six backhands.

DTL results

Let’s look at the results of those DTL backhands. Here’s another table with aggregate ATP and WTA numbers, showing the percentage of DTL backhands that go for winners (including shots that induce forced errors), the percentage that are unforced errors, and the percentage that lead to the most important thing–ultimately winning the point:

Tour  Winner%   UFE%  Points Won%  
ATP     22.1%  18.1%        51.6%  
WTA     26.0%  22.2%        52.4%

Both men and women have a “positive” ratio of winners to unforced errors. But women hit a lot more of both. (As we’ll see, some women have eye-poppingly aggressive numbers.) And both genders, on average, end points more frequently with DTL backhands than they do with other shots, whether we look at all shots, or all backhands. The average non-slice backhand–counting those from every position, hit in every direction–goes for approximately 10% winners and 10% unforced errors.

The percentage of points won doesn’t look very dramatic, at 51.6% and 52.4% for men and women, respectively. Yet both numbers reverse the usual expectation for backhands. Backhands occur more often in defensive positions, so backhands are slightly more likely to occur in points lost than in points won, so the corresponding numbers for all backhands are below 50%. This is a good example of what makes shot analysis so difficult: Do DTL backhands result in more points won because they are a better tactical decision, or because players hit them more often in response to weak balls? It’s probably a combination of both, but more a reflection of the latter.

A lefty digression

I will get to some player-by-player numbers shortly, but first, let’s look at an interesting comparison between lefties and righties. I’ve long speculated that lefties–because they mostly face right-handed opponents–must learn to play “backwards.” While righties can whack crosscourt forehands at each other, a lefty rarely has the chance to do that. As a result, left-handers spend more time practicing unusual shots, like inside-out groundstrokes and the DTL backhand. That’s my theory, anyway.

Sure enough, left-handed men hit quite a few more DTL backhands than their right-handed peers. Righties go down the line on 16.9% of their backhands from the backhand corner, while lefties do so 21.4% of the time. Rafael Nadal plays a sizable part in this, as he represents a lot of the charted matches of left-handers, and he goes down the line 24.4% of the time, more than almost any other man. (Another lefty, Martin Klizan, is one of the few to be more extreme than Rafa, at 25.2%.) Still, a gap of several percentage points remains even if we exclude Nadal.

But this is hardly a physical law. Women show the opposite trend, in the aggregate. Right-handed women go down the line 17.6% of the time, while lefties do so 15.8% of the time. A few female lefties fit the mold of Nadal and Klizan, including Lucie Safarova (26.3%) and Ekaterina Makarova (26.1%). But in general, it is the most aggressive women–regardless of their dominant hand–who use the DTL backhand the most often. Jelena Ostapenko tops 27%, and Dayana Yastremska forces us to rescale the y-axis with a rate of 33%.

The DTL trade-off

I mentioned above a prime difficulty in evaluating shot selection. The most important measurement of any tactic is whether it results in more points won. If hitting more DTL backhands didn’t improve a player’s rate of points won, why would she do it? But if hitting more DTL backhands does improve her rate of points won, she should look to hit more of them, which means finding opportunities from slightly more challenging positions … which means winning points at a slightly lower rate. Push that logic to its extreme, and a superior tactic will no longer result in many more points won than the inferior tactic it replaces.

This problem, combined with the obvious fact that players have different skills and preferences, means that there’s not a strong relationship between a player’s rate of DTL backhands and their success–measured in points won–when they hit them. There is a very slight negative correlation (for both and women) between the frequency with which a player hits DTL backhands and the number of DTL backhand winners he or she hits, suggesting that there are limited opportunities to swing away and hit a clean winner. For women, there is no relationship, however, between the rate of DTL backhands and the rate of points won.

There is one minor exception to the barrage of non-relationships. For men, there is a weak negative correlation (r^2 = 0.13) between the rate at which the player hits DTL backhands and the rate of points won. That result tracks with the intuition described above, that as a player opts for the tactic more often, his results will decline–not because he plays worse, but because he is opting for the tactic in riskier situations. A player who goes down the line on 10% of his backhands is just picking the low-hanging fruit, while a player who does so 25% of the time is sometimes hitting an awfully low-percentage shot.

DTL by player: ATP

Thus, we might–very cautiously!–conclude a player who is winning a high percentage of points when he hits a DTL backhand should do so even more often. Here are 25 of the most prominent ATPers, sorted by the frequency with which they hit the DTL backhand:

Player                 DTL/BH   Wnr%   UFE%  Pts Won%  
Rafael Nadal            24.5%  12.1%  11.1%     54.7%  
John Isner              22.0%  23.2%  27.3%     38.2%  
Novak Djokovic          21.2%  16.7%  16.1%     54.2%  
Jo Wilfried Tsonga      21.0%  20.6%  27.7%     45.8%  
Denis Shapovalov        20.5%  20.1%  23.5%     49.1%  
Stan Wawrinka           19.1%  28.8%  26.8%     51.4%  
Kei Nishikori           18.8%  27.7%  19.1%     56.7%  
Dominic Thiem           18.4%  28.5%  28.2%     51.6%  
Fabio Fognini           18.3%  20.4%  23.8%     49.3%  
David Goffin            18.2%  23.5%  23.8%     49.5%  
Roger Federer           18.2%  25.5%  21.0%     53.2%  
Grigor Dimitrov         17.7%  27.4%  23.6%     50.5%  
Nick Kyrgios            17.7%  19.5%  23.5%     44.4%  
Andy Murray             16.8%  21.7%  16.5%     54.2%  
Richard Gasquet         16.6%  33.5%  23.1%     55.2%  
Juan Martin del Potro   15.5%  24.6%  15.7%     52.2%  
Alexander Zverev        15.3%  32.5%  19.0%     56.1%  
Gael Monfils            14.3%  25.9%  17.6%     54.7%  
Daniil Medvedev         14.3%  17.0%  16.9%     49.6%  
David Ferrer            14.2%  16.9%  18.1%     48.0%  
Stefanos Tsitsipas      14.1%  24.3%  22.9%     49.3%  
Borna Coric             13.6%  29.3%  24.1%     55.4%  
Kevin Anderson          13.3%  25.3%  24.9%     45.9%  
Roberto Bautista Agut   10.4%  17.3%  20.2%     46.3%  
Diego Schwartzman       10.3%  32.5%  22.3%     55.7%

If nothing else, these numbers show us that there are a lot of different ways to win tennis matches. Nadal hits a lot of backhands down the line, but he rarely ends the point that way. Only a bit further down the list, we find players who end the point with DTL backhands more than twice as often. The bottom of the table is filled with players who don’t win many points going down the line, but they are mixed with Diego Schwartzman and Borna Coric, two men who are very effective on the rare occasions they hit the more difficult shot.

DTL by player: WTA

There is no similar tour-wide correlation for women, but that doesn’t mean that each player’s shot selection is optimal. Here are the same stats for 25 prominent WTAers:

Player                DTL/BH   Wnr%   UFE%  Pts Won%  
Dayana Yastremska      33.7%  27.4%  24.7%     54.8%  
Jelena Ostapenko       27.1%  35.0%  33.6%     51.0%  
Serena Williams        25.2%  28.3%  19.6%     57.4%  
Belinda Bencic         21.6%  28.1%  14.6%     59.1%  
Aryna Sabalenka        21.2%  38.7%  25.5%     57.1%  
Madison Keys           20.4%  27.7%  39.9%     46.7%  
Simona Halep           20.1%  25.3%  21.7%     55.8%  
Venus Williams         19.2%  26.1%  19.7%     49.7%  
Bianca Andreescu       19.2%  22.6%  17.9%     59.7%  
Victoria Azarenka      19.1%  25.9%  16.2%     57.3%  
Karolina Pliskova      18.9%  26.6%  23.1%     51.6%  
Garbine Muguruza       18.1%  28.2%  18.9%     57.5%  
Maria Sharapova        18.0%  27.1%  21.4%     53.2%  
Naomi Osaka            17.9%  28.2%  27.7%     48.6%  
Johanna Konta          16.1%  33.4%  29.9%     53.6%  
Petra Kvitova          15.8%  30.9%  24.0%     54.0%  
Caroline Wozniacki     15.6%  25.5%  15.9%     56.8%  
Sloane Stephens        15.1%  25.9%  26.4%     53.2%  
Kiki Bertens           14.7%  21.6%  21.7%     49.0%  
Monica Niculescu       13.2%  29.7%  14.7%     62.9%  
Angelique Kerber       13.2%  26.7%  18.5%     56.2%  
Ashleigh Barty         13.1%  26.9%  29.0%     50.6%  
Marketa Vondrousova    11.5%  29.8%  18.5%     52.3%  
Carla Suarez Navarro   10.9%  33.1%  25.8%     55.9%  
Elina Svitolina        10.2%  27.6%  20.5%     53.9%

A dramatic example is that of Belinda Bencic, who hits more DTL backhands than almost anyone else on this list and is one of the most successful, in terms of points won, when she does so. It’s tough to avoid the hypothesis that she is squandering some opportunities to deploy this weapon. At the opposite extreme, Ostapenko and Madison Keys are extremely aggressive, hitting almost as many errors as winners, and in the case of Keys, winning considerably fewer than half of those points.

As it says on the tin, this is just a first look at the DTL backhand. Evaluating shot selection is hard, and quantifying the effects of shot-level tactics is even harder. But we can’t do it unless we’ve pinned down some of the basics, picking out some useful metrics and doing a first pass for any correlations that might (or probably don’t) exist. While it’s a long process, we’re one baby step closer to some answers.

Match Charting Project Tactics Stats: Glossary

I’m in the process of rolling out more stats based on Match Charting Project data across Tennis Abstract. This is one of several glossaries intended to explain those stats and point interested visitors to further reading.

At the moment, the following tactics-related stats can be seen at a variety of leaderboards.

SnV Freq% – Serve-and-volley frequency. The percentage of service points (excluding aces) on which the server comes in behind the serve. I exclude aces because serve-and-volley attempts are less clear (and thus less consistently charted) if the server realizes immediately that he or she has hit an unreturnable serve. I realize this is a minority opinion and thus an unorthodox way to calculate the stat, but I’m sticking with it.
SnV W% – Serve-and-volley winning percentage. The percentage of (non-ace) serve-and-volley attempts that result in the server winning the point.
Net Freq – Net point frequency. The percentage of total points in which the player comes to net, including serve-and-volley points. I include points in which the player doesn’t hit any net shots (such as an approach shot that leads to a lob winner), but I do not count points ended by a winner that appears to be an approach shot.
Net W% – Net point winning percentage. The percentage of net points won by this player.
FH Wnr% – Forehand winner percentage. The percentage of topspin forehands (excluding forced errors) that result in winners or induced forced errors.
FH DTL Wnr% – Forehand down-the-line winning percentage. The percentage of topspin down-the-line forehands (excluding forced errors) that result in winners or induced forced errors. Here, I define “down-the-line” a bit broadly. The Match Charting Project classifies the direction of every shot in one of three categories. If a forehand is hit from the middle of the court or the player’s forehand corner and hit to the opponent’s backhand corner (or a lefty’s forehand corner), it counts as a down-the-line shot. Thus, some shots that would typically be called “off” forehands end up in this category.
FH IO Wnr% – Forehand inside-out winning percentage. The percentage of topspin inside-out forehands (excluding forced errors) that result in winners or induced forced errors. This one is defined more strictly, only counting forehands hit from the player’s own backhand corner to the opponent’s backhand corner (or a lefty’s forehand corner).
BH Wnr% – Backhand winner percentage. The percentage of topspin backhands (excluding forced errors) that result in winners or induced forced errors.
BH DTL Wnr% – Backhand down-the-line winner percentage. The percentage of topspin down-the-line backhands (excluding forced errors) that result in winners or induced forced errors. As with the forehand down-the-line stat, I define these a bit broadly, catching some “off” backhands as well.
Drop Freq – Dropshot frequency. The percentage of groundstrokes that are dropshots. This excludes dropshots hit at the net and those hit in response to an opponent’s dropshot (re-drops).
Drop Wnr% – Dropshot winner percentage. The percentage of dropshots that result in winners or induced forced errors. Note that this number itself isn’t a verdict on the dropshot tactic, as it doesn’t count extended points that the player who hit the dropshot went on to win.
RallyAgg – Rally Aggression Score. A variation of Aggression Score, a stat invented by MCP contributor Lowell West. At its simplest, any member of this family of aggression metrics is the percentage of shots that end the point–winners, unforced errors, and shots that induce forced errors. RallyAgg excludes serves and is a bit more complex, following the logic that I outlined for Return Aggression by separating winners from unforced errors. For each match, the player’s unforced error rate and winner rate are normalized relative to tour average and expressed in standard deviations above or below the mean. RallyAgg is the average of those two numbers, multiplied by 100 for the sake of readability. The higher the score, the more aggressive the player. Tour average is zero.
ReturnAgg – Return Aggression Score. Another variation of Aggression score, considering only return winners and return errors. As with RallyAgg, winners and errors are separated, and each rate is normalized relative to tour average. ReturnAgg is the average of those two normalized rates, multiplied by 100 for the sake of readability. The higher the number, the more aggressive the returner, and tour average is zero.

Net Play Has Declined, But This Isn’t Why

Italian translation at settesei.it

Wimbledon is here, so it’s time for another cycle of media commentary about the demise of net play, especially the serve-and-volley. The New York Times published a piece by Joel Drucker last week that covered this familiar territory, cataloguing various reasons why the game has changed. Racket and string technology, along with tweaks to the All England Club playing surface, are rightfully on the list.

But the first reason Drucker gives is the rise of the two-handed backhand and, by extension, the threat posed by players with weapons on both sides:

In May 1999, 43 of the top 100 male players in the world hit their backhands with one hand. As of June 2019, there were 15. According to Mark Kovacs, a sports science consultant and tennis coach, “Most players used to have a weaker side, usually the backhand. And the two-handed backhand changed that completely. It doesn’t give you a spot you can hit to.”

I’m more interested in the “weaker side” argument than the fortunes of the one-handed and two-handed backhands. Many players who still use one-handers, such as Stan Wawrinka, would rightly bristle at a claim that their shots are weak. In terms of effectiveness, the contemporary one-handed shot might have more in common with a two-hander of old than the all-slice, only-defensive backhand favored by many pros in the 1970s and 1980s.

Both sides, now

The “weaker side” argument can be slightly rephrased into a research question: For contemporary players, is there a smaller gap between forehand effectiveness and backhand effectiveness than there used to be?

To answer that, we need a working definition of “effectiveness.” Long-time readers may recall a stat of mine called “potency,” as in “backhand potency” (BHP) or “forehand potency” (FHP). It’s a simple stat, using data derived from the shot-by-shot records of the Match Charting Project, calculated as follows:

BHP approximates the number of points whose outcomes were affected by the backhand: add one point for a winner or an opponent’s forced error, subtract one for an unforced error, add a half-point for a backhand that set up a winner or opponent’s error on the following shot, and subtract a half-point for a backhand that set up a winning shot from the opponent.

The same procedure applies to forehand potency and slice potency. The weights–plus one for some shots, plus a half point for others, and so on–are not precise. But the results generally jibe with intuition. Across 3,000 charted ATP matches, an average player’s results from a single match are:

Forehand potency (FHP): +6.5
Backhand potency (BHP): +0.8
Slice potency (SLP): -1.3
Backhand side potency (BSP): -0.5

The first three stats isolate single shots, while the final one combines BHP and SLP into a single “backhand side” metric. All of these exclude net shots, and since forehand slices are so rare, I’ve left those out of today’s discussion as well.

The forehand reigns

The numbers above shouldn’t come as a surprise. The average ATP player has a stronger forehand than backhand, regardless of how many hands are on the racket for the latter shot. Novak Djokovic possesses one of the best backhands in the history of sport, but the gap between his FHP and BSP numbers is greater than average: +11.3 per match for the forehand, and +2.5 for the backhand, resulting in a difference of 8.8. Even a backhand master reaps more rewards on his other side.

The Match Charting Project has at least three matches worth of data for 299 different men across several generations, spanning from Vitas Gerulaitis to Jannik Sinner. Only 30 of them–about one in ten–gain more points on their backhand than on their forehands, and for half of that minority, the difference is less than a single point. It’s a diverse group, including Pat Cash, Jimmy Connors, Guillermo Coria, Ernests Gulbis, Daniil Medvedev, and Benoit Paire. This mixed-bag minority doesn’t provide much evidence to settle the question.

Proponents of the “weaker side” argument often point to the arrival of Lleyton Hewitt as a turning point between the net-play-was-feasible era and the approach-at-your-peril era. Others might point to Andre Agassi. As it turns out, both of these figures are surprisingly average.

The Match Charting Project has extensive records on both men. Hewitt’s forehand was worth +10.0 per match, while his backhand and slice combined for +2.9. That’s a difference of 7.1, a bit greater than average, though less than Djokovic’s. Agassi’s FHP was good for +13.0 per match, compared to a BSP of +6.8. That’s a difference of 6.2, even closer to the mean than Hewitt. Ironically, that gap is almost identical to that of Pete Sampras, whose FHP of +6.3 and BSP of -0.1 were equally spaced, even though his groundstrokes were considerably less effective.

Comparing eras

We can’t answer a general question about trends over time simply by calculating shot potencies for individual players, no matter how pivotal. Instead, we need to look at the whole population.

First, a quick note about our data: The Match Charting Project is extremely heavily weighted toward current players. Our sample of 300 players consists of only 40 whose careers were mostly or entirely in the 20th century, and 30 more whose matches mostly took place in the first decade of this century. Thus, the averages mentioned above are skewed toward the 2010s. That said, the 70 “older” players in the sample are the most prominent–the guys who played in major finals and semi-finals, and Masters finals. If there has been a marked trend across decades, those players should help us reveal it.

The earlier players in our sample are, in fact, quite similar to the contemporary ones. I ranked the 299 players by the absolute difference between their FHP and their BSP, with the most balanced player ranked 1, and the least balanced ranked 299. I looked at two subgroups: the 52 oldest players in the sample, most of whose careers were fading out when Hewitt arrived; and the 78 players with the most recent matches in the sample.

Oldest — Average rank: 143, Average (FHP – BSP): 5.7
Most recent — Average rank: 155, Average (FHP – BSP): 6.5

These numbers do not indicate that players used to have a weak side, and now they don’t. They don’t really reflect any trend at all. The difference between forehand effectiveness and backhand side effectiveness has barely changed over several decades.

As further evidence, here is a selection of players who are both well-represented in the Match Charting Project data and noteworthy representatives of their eras. They’re listed in approximate chronological order. Each of the shot-potency numbers is given on a per-match basis, and the final column (“Diff”) is the difference between FHP and BSP–the gap between each player’s forehand and backhand sides.

Player              FHP    BHP   SLP   BSP  Diff  
Bjorn Borg          12.9  11.5  -0.5  11.0   2.0  
Jimmy Connors       6.5    9.1  -0.3   8.9  -2.4  
John McEnroe        2.0   -0.4  -2.1  -2.4   4.4  
Mats Wilander       7.2    6.8  -0.5   6.3   0.9  
Ivan Lendl          10.3   4.0   0.6   4.6   5.7  
Stefan Edberg       1.9    1.8  -1.1   0.7   1.1  
Boris Becker        5.9    2.1  -1.5   0.7   5.2  
Jim Courier         13.3   4.2  -0.3   3.9   9.4  
Michael Stich       2.0    2.0  -3.4  -1.4   3.4  
Michael Chang       9.7    5.0  -0.6   4.4   5.3  
                                                  
Player              FHP    BHP   SLP   BSP  Diff  
Thomas Muster       18.4   2.2  -1.1   1.1  17.3  
Pete Sampras        6.3    0.7  -0.7  -0.1   6.4  
Andre Agassi        13.0   7.2  -0.5   6.8   6.3  
Patrick Rafter      3.5    0.5  -1.6  -1.1   4.6  
Carlos Moya         9.8   -0.9  -1.4  -2.3  12.1  
Lleyton Hewitt      10.0   3.5  -0.6   2.9   7.1  
Guillermo Coria     4.7    6.3  -1.2   5.2  -0.5  
David Nalbandian    8.8    5.6  -1.7   3.9   4.9  
Nikolay Davydenko   7.2    4.4  -1.2   3.2   4.0  
Roger Federer       10.0   0.2  -0.4  -0.3  10.2  
                                                  
Player              FHP    BHP   SLP   BSP  Diff  
Rafael Nadal        15.3   2.6  -1.0   1.6  13.7  
Andy Murray         7.2    2.9  -1.8   1.1   6.1  
Novak Djokovic      11.3   3.4  -0.8   2.5   8.8  
Richard Gasquet     1.9    1.4  -1.4   0.0   1.9  
Stan Wawrinka       6.2    0.5  -1.7  -1.2   7.3  
Kei Nishikori       5.4    3.8  -1.1   2.7   2.8  
Dominic Thiem       9.3   -0.1  -1.6  -1.7  11.0  
Alexander Zverev    3.6    4.2  -1.1   3.0   0.6  
Stefanos Tsitsipas  8.3   -0.9  -2.2  -3.0  11.4  
Daniil Medvedev     1.6    3.3  -1.4   1.9  -0.3

Not weaker, but weak

These numbers cast a lot of doubt on the “weaker side” hypothesis, that it used to be easier to move forward by approaching an opponent’s less dangerous wing.

Instead, what has probably happened is that for the typical player, both sides got stronger. As a result, the weaker side was no longer flimsy enough to make approaching the net a profitable strategy. Even players with weaker-than-average backhands are now able to hit powerful topspin passing shots. This is essentially the racket-and-string-technology argument, and it seems to me to be the most valid.

There’s no question that tennis has drastically changed in the last few decades. But the conventional explanations for those trends don’t always hold up under scrutiny. In this case, while volleys have been reduced to a vestigial part of the singles game, groundstrokes–on both sides–have only gotten better.

Break Point Serve Tendencies on the ATP Tour

Italian translation at settesei.it

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Petra Kvitova’s Current Status: Low Risk, High Reward

Italian translation at settesei.it

For more a decade, Petra Kvitova has been one of the most aggressive women in tennis. She aims for the corners, hits hard, and lets the chips fall where they may. Sometimes the results are ugly, like a 6-4 6-0 loss to Monica Niculescu in the 2016 Luxembourg final, but when it works, the rewards–two Wimbledon titles, for starters–more than make up for it.

She’s currently riding another wave of winners. Her 11-match win streak–which has involved the loss of only a single set–puts her one more victory away from a third major championship. The 28-year-old Czech has gotten this far by persisting with her big-hitting style, but with a twist: In Melbourne, she’s not missing very often. While she’s ending as many points as ever on her own racket, she’s missing less often than many of her more conservative peers.

In her last five matches at the Australian Open, from the second round through the semi-finals, 7.9% of her shots (including serves) have resulted in unforced errors. In the 88 Petra matches logged by the Match Charting Project, that’s the stingiest five-match stretch of her career. In charted matches since 2010, the average WTA player hits unforced errors on 8.0% of their shots. So Kvitova, the third-most aggressive player on tour, is somehow making errors at a below-average rate. It’s high-risk, high-reward tennis … without the risk.

And it isn’t because her go-for-broke tactics have changed. In Thursday’s semi-final against Danielle Collins, her aggression score–an aggregate measure of point-ending shots including winners, induced forced errors, and unforced errors–was 30.5%, the third-highest of all of her charted matches since her 2017 return to the tour. Her overall aggression score in Melbourne, 28.2%, is also higher than her career average of 27.1%.

In other words, she’s making fewer errors, and the missing errors are turning into point-ending shots in her favor. The following graph shows five-match rolling averages of winners (and induced forced errors) per shot and unforced errors per shot for all charted matches in Kvitova’s career:

Even with the winner and error rates smoothed out by five-match rolling averages, these are still some noisy trend lines. Still, some stories are quite clear. This month, Kvitova is hitting winners at close to her best-ever rate. Her average since the second round in Melbourne has been 20.3%, as high as anything she’s posted before with the exception of her 2014 Wimbledon title. (I’ve never tried to adjust winner totals for surface; it’s possible that the difference can be explained entirely by the grass.)

And most strikingly, this is as big a gap between winner rate and error rate as she’s achieved since her 2014 Wimbledon title run. In fact, between the second round and semi-finals at that tournament, she averaged 8.1% errors and 20.0% winners. Both of her numbers in Australia this year have been a tiny bit better.

Best of all, the error rate has–for the most part–seen a steady downward trend since 2016. The recent error spike is largely due to her three losses in Singapore last October and a bumpy start to this season in Brisbane. We can’t write those off entirely–perhaps Kvitova will always suffer through weeks when her aim goes awry–but she appears to have put them solidly behind her.

None of this is a guarantee that Petra will continue to avoid errors in Saturday’s final against Naomi Osaka. I could’ve written something about her encouraging error rates before the tour finals in Singapore last fall, and she failed to win a round-robin match there. And Osaka is likely to offer a stiffer challenge than any of Kvitova’s previous six opponents in Melbourne, even if her second serve doesn’t. That said, a stingy Kvitova is a terrifying prospect, one with the potential to end the brief WTA depth era and dominate women’s tennis.

The Oddity of Naomi Osaka’s Soft Second Serves

Italian translation at settesei.it

Naomi Osaka has quickly risen to the top of the women’s game on the back of some big hitting, especially a first serve that is one of the fastest in the game. Through Thursday’s semi-final, Osaka’s average first-serve speed in Melbourne was 105 mph, faster than all but two of the other women who reached the third round. Even those two–Aryna Sabalenka and Camila Giorgi–barely edged her out, each with average speeds of 106.

Shift the view to second serves, and Osaka’s place on the list is reversed. While Sabalenka’s typical second offering last week was 90 mph and Giorgi’s was 94, Osaka’s has been a mere 78 mph, the fourth-slowest of the final 32. That mark puts her just ahead of the likes of Angelique Kerber and Sloane Stephens, both whose average first serves are nearly 10 mph slower.

Osaka’s 27 mph gap is the biggest of anyone in this group. The next closest is Caroline Wozniacki’s 23 mph gap, between her 102 mph first serve and 79 mph second serve–both of which are less extreme than the Japanese player’s. Expressed as a ratio, Osaka’s average second serve is only 74% the speed of her typical first. That’s also the widest gap of any third-rounder in Melbourne; Wozniacki is again second-most extreme at 77%.

The following table shows first and second serve speeds, along with the gap and ratio between those two numbers, for a slightly smaller group: women for whom the Australian Open published at least four matches worth of serve-speed data:

Player          Avg 1st  Avg 2nd   Gap  Ratio  
Osaka             105.5     78.5  27.0   0.74  
Keys              105.2     85.4  19.7   0.81  
SWilliams         103.8     88.6  15.2   0.85  
Barty             102.0     88.2  13.7   0.87  
KaPliskova        101.9     80.5  21.4   0.79  
Collins           101.2     82.2  19.1   0.81  
Kvitova            99.6     91.6   8.0   0.92  
Muguruza           98.1     82.5  15.6   0.84  
Pavlyuchenkova     97.9     84.5  13.4   0.86  
Sharapova          97.9     89.6   8.2   0.92  
Svitolina          97.6     78.2  19.4   0.80  
Stephens           96.1     75.1  21.0   0.78  
Halep              95.3     80.9  14.4   0.85  
Kerber             94.0     78.4  15.7   0.83

Oddly enough, having such a slow second serve doesn’t seem to be causing any problems. In today’s semi-final against Karolina Pliskova, Osaka won 81% of first serve points and only 41% of second serve points, but her typical performance behind her second serve is better than that. And in this match, both women feasted on the other’s weaker serves: Pliskova won only 32% of her own second serves. (Though to be fair, Pliskova had the second-largest gap of the players listed above. She tends to rely more on spin than speed when her first serve misses.)

Across her six matches, Osaka has won 73.3% of her first serve points and 49.7% of her second serve points–a bit better than the average quarter-finalist in the former category, a very small amount worse than her peers in the latter. The ratio of those two numbers–68%–is almost identical to those of Danielle Collins, Petra Kvitova, Anastasia Pavlyuchenkova, and Serena Williams, all of whom have smaller gaps between their first and second serve speeds. Of the eight quarter-finalists, Kvitova has the smallest speed gap of all, yet the end result is the same as Osaka’s, she’s just a few percentage points better on both offerings.

Here are the first- and second-serve points won in Melbourne for the eight quarter-finalists, along with the ratio of those two figures and each player’s serve-speed ratio from the previous table:

QFist           1SPW%  2SPW%  W% Ratio  Speed Ratio  
Kvitova         77.9%  52.8%      0.68         0.92  
Williams        74.7%  50.0%      0.67         0.85  
Osaka           73.3%  49.7%      0.68         0.74  
Collins         72.5%  50.0%      0.69         0.81  
Barty           70.8%  55.7%      0.79         0.87  
Pliskova        70.5%  50.0%      0.71         0.79  
Pavlyuchenkova  67.0%  44.9%      0.67         0.86  
Svitolina       66.5%  48.1%      0.72         0.80

Clearly, there’s more than one way to crack the final eight. With Kvitova, we have a server who racks up cheap points with angles instead of speed, rendering the miles-per-hour comparison a bit irrelevant. Serena’s results are close to Osaka’s, though she gets there with bit more bite on her second serves. And then there’s Svitolina, who doesn’t serve very hard or that effectively but can beat you in other ways.

Knowing all this, should Osaka hit harder second serves? In extreme cases, like today’s 81%/41% performance against Pliskova, the answer is yes–had she simply hit nothing but first serves and succeeded at the same rate, she would’ve piled up a lot of double faults but won more total points. But the margins are usually slimmer, and as we’ve seen, her second-serve performance isn’t bad, it just might offer room for improvement. Every player is different, but faster is usually better.

A thorough analysis of that question may be possible with the available data, but it will have to wait for another day. In the meantime, Saturday’s final will offer us a glimpse of contrasting styles: Osaka’s powerful first offering and soft second ball, against Kvitova’s angles and placement on both serves. Both my forecast and the betting market see the title match as a close one–perhaps Osaka’s second serve will be the shot that makes the difference.