I introduced the Match Charting Project to the world in November 2013. I had no idea whether it would ever draw the interest of anyone outside of a tiny group of tennis-obsessed friends. I was okay with that. The project was designed to be valuable even if we only ended up charting a few matches.
Eight and a half years later, that group of tennis obsessives has grown. Over 140 of you have learned my detailed charting notation and worked through an entire match. A dozen of you have logged every shot of 100 matches or more. One indefatigable contributor, Edo, is now over 1,300 matches. Another super-charter, Zindaras, is also closing in on four digits.
Yesterday, another all-star contributor, Ludo, sent in charts of two John Isner matches. They were the 9,999th and 10,000th charts for the project. We’ve logged six million shots, over one and a half million points, and yes, ten thousand matches. The 10,001st chart appeared in my inbox as I was writing this post.
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Ten thousand matches is hardly a complete record of professional tennis, but it gives us a remarkably detailed view of both the men’s and women’s game.
In the last few years, we’ve come close to finishing many ongoing sub-projects. We have a chart for every grand slam final back to 1980, and many from the 1970s. We have most grand slam semi-finals back to the mid-1980s. We have nearly every Masters 1000 final back to 1990, when the Masters series began, and we have most Masters semi-finals in that span as well.
We’ve charted every match between members of the Big Four. We have almost every tour-level final ever played by the Big Four. We’ve collected charts of the major WTA events back to the 1990s, and we’ve racked up dozens of Evert-Navratilova matches before that. More recently, we’ve charted every single tour-level final (and a fair number of challengers and ITFs) from the last several years.
We have a shot-by-shot log of nearly every point Simona Halep has played back to the birth of the project, and near-complete sets of Aryna Sabalenka and Bianca Andreescu matches. We have charts of 543 Roger Federer matches, and over 400 each for Novak Djokovic and Rafael Nadal. We’ve charted at least ten matches for most players who have reached the men’s or women’s top 50 in recent memory. We have one hundred matches or more for 34 different players, and we’ll add Iga Swiatek and Diego Schwartzman to that list any day now.
You can always check in on the state of the project, along with a full list of charted matches, here. Raw data from more than half of the charts is available for research purposes and updated monthly, here.
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The best part about this project, to me, is that there is value in every single contribution. It may feel like a drop in the ocean to submit, say, the 544th Federer chart, but even that gives us a fuller historical record of Fed’s career and a more detailed statistical summary of his performance that season–not to mention more data on his opponent. If you’re more interested in breaking new ground, there are always interesting young players who are barely represented in the dataset.
Most of all, it’s fun–at least for the right kind of person. Maybe you think it’s bonkers that some of us enjoy typing alphanumerical codes into a spreadsheet as we watch tennis. Put it that way and … well, I get it. But once you get the hang of charting, it forces you pay more attention and learn more about this endlessly complex sport. While watching tennis. Which I suspect you enjoy quite a bit.
Learn how to get started here. I hope you’ll contribute as we march on to the next 10,000 matches.
Bill, who dug into Match Charting Project data for his piece, explains how Djokovic tactically differs from the competition, how his game has changed over the years, and whether the nature of his game makes it tough to fully appreciate. I also encourage him to speculate about whether Novak will reach 20 slams, and if that would make him the greatest of all time.
Also on the agenda: whether tour-wide parity is better than dominance, how ESPN (and tennis media in general) could cover the sport differently, and why there are so few people who love both tennis and college football.
Thanks for listening!
(Note: this week’s episode is about 48 minutes long; in some browsers the audio player may display a different length. Sorry about that!
In case you haven’t heard, I’m now doing a short (~4 minutes) daily podcast called Expected Points. Here’s today’s episode.
The TAP book club will reconvene in a few weeks with our next selection, John Updike’s 1968 novel, Couples. Read along with us, share your thoughts, and suggest topics/questions/comments for our discussion in a future episode.
An underhand serve functions in two ways–one short term, one long term. The short-term goal is to win a single point. Your opponent is standing way back, and the service equivalent of a drop shot could go for a 50 mile-per-hour ace. The long-term goal is to give your opponent something to worry about, perhaps distracting him or changing his return position for games, or sets to come. It’s not about winning a single point, but about slightly improving your odds in many future points.
In his second-round match yesterday against Ugo Humbert at the Australian Open, Nick Kyrgios opted for both. He unleashed the underhander twice, once at 40-love in his second service game, and again at 5-5, 40-30 in the fourth set.
The first dropper was on as meaningless a point as he could ask for. Kyrgios’s probability of winning a service game from 40-love is about 99.6% (really!), so the risk of losing the game after throwing away a point is essentially nil. He won the point with a backhand winner on his next shot, but the object of the exercise–assuming there was a tactical one, and I’ll give Nick the benefit of the doubt here–was more long-term oriented.
He delivered the second underarm serve on a much higher-pressure point. Kyrgios is still heavily favored to hold serve from 40-30, but he could be forgiven for feeling some nerves and wishing for a free point. This time he netted the underhand attempt and ended up winning the point after a (conventional) second serve.
A drop of data
When the underhand serve first started to go mainstream a couple of years ago, I updated the Match Charting Project spreadsheet to allow us to track these attempts. Counting the Kyrgios-Humbert match, we’ve now gathered the results of 35 drop-serve attempts across 20 different men’s matches. (We’ve recorded many women’s underhand serves as well, but most of those belong to Sara Errani, who has a different set of goals when she goes that route.)
35 points is awfully far from big data, but it is enough to get a taste of how a handful of players are deploying this unorthodox weapon.
The most common point score for an underarm serve is 40-love. Of the 35 attempts, 40-love accounts for 12 of them. Another 4 occured at 40-15, plus two more at 30-love, so roughly half of the recorded drop serves came with a service game more or less secured. A few of the remaining points were also relatively unimportant ones, like Daniil Medvedev’s underhander at love-40 toward the end of a 2019 US Open match against Hugo Dellien, and Alexander Bublik’s back-to-back tries at 0-5 and 1-5 in a tiebreak against John Isner.
Bublik is the major source of unimportant-point underarm serving. He’s responsible for 19 of the recorded points, 16 of which were at 40-love, 40-15, 30-love, or those two tiebreak points I just mentioned.
Inferring tactics
Since so many underarm serves are deployed at low-pressure moments, it’s tempting to conclude that players are thinking long term.
On the other hand, our handful of recorded underhand deliveries–even the ones on 40-love points–don’t skew toward the beginning of matches. We have two charted matches in which Robin Haase tried an underhander: a 2019 Budapest tilt against Borna Coric in which he made his first attempt in the third game, and a 2020 Davis Cup rubber when he waited until the 32nd game of the match.
Poster boy Bublik is inconsistent on this as well. Twice he has brought out the underhander in his second service game–once in the Newport match versus Isner, and another time the same summer in Washington against Bradley Klahn. Yet at the US Open against Thomas Fabbiano the same year, he didn’t unleash the secret weapon until 40-love in the 32nd game of the match.
I’ll admit, it might be foolish to try to detect the grand plan underlying the behavior of Alexander Bublik.
But it works!
Yeah, our 35 points make up tiny sample, but… the server won 27 of these 35 points! That’s 77%, and it includes underarm first-serve attempts that missed. When players had to hit a conventional second serve, they still won 7 of 10 points–a rate of second serve points won that any player would happily accept.
These numbers–cautiously as we must treat them–suggest that the underarm serve trend has plenty of room to run. The rare players who dare to risk ridicule are still only using the drop serve less than twice per match, and of course the vast majority of men on tour are never hitting them at all. The more common the underhand delivery becomes, the less effective it will be, but there’s a lot of space between the current drop-serve win percentage of 77% and the typical player’s success rate on serve. Tour average is around 65%, and only the most dominant servers exceed 70%.
As Bublik and friends have discovered, there’s little risk in mixing things up. Strong servers like him and Kyrgios have plenty of low-leverage opportunities to remind their opponents that surprises could be in store later in the match, when the stakes are raised. Our very early indicators suggest that where Kyrgios has gone, the rest of the tour could profitably follow.
Aryna Sabalenka has won 3 titles and 14 matches in a row. Let’s dig into the data and see if we can identify any improvements that would account for her success.
For the Match Charting Project, I’ve logged every shot of each of the Belarussian’s tour-level matches. (There are a few exceptions where I haven’t found video.) We’ll look at hard-court matches only today. With that constraint, we have 140 Sabalenka matches, dating back to early 2017 (including the current streak), and another 1,121 women’s tour-level contests over the same time period for reference.
Big serving?
Aryna always brings a powerful serve, but it remains a work in progress, at least tactically. The key metric for pure serve dominance is unreturned serves–quite simply, serves that don’t come back. While some are aces, they don’t have to be, and the distinction doesn’t really matter.
This first graph has a lot going on, but as I’ll use the same basic template for several more figures, it’s worth taking a moment to understand what we’re looking at. The two dotted lines show tour average rates of unreturned serves (the lower average is for all players; the higher one is for match winners), the thin jagged line shows Sabalenka’s rate of unreturned serves for each individual match, and the thicker red line shows her five-match rolling average.
Her five-match rolling average has been above 30% for the entire win streak. It’s not an unprecedented level for her, though–she sustained similarly high levels at various points over the last three years. (We should also be a bit cautious ascribing serve effectiveness to a player when the Ostrava, Linz, and Abu Dhabi courts might have been faster than average.) Consistently powerful serving has certainly helped Sabalenka’s cause, but it probably isn’t the whole story.
We might gain from breaking down Aryna’s serve effectiveness into first and second serves. First, let’s look at something else:
Serve plus one
There are two ways we could look at “serve plus one” effectiveness, and we’ll do both. First, let’s count Sabalenka’s opportunities to hit a second shot behind her serve, and see what percentage she puts away. (As with aces and other unreturned serves, the “winner” concept is a distraction: I’m counting second-shot winners together with shots that force errors. If you end the point, it doesn’t matter much whether your opponent touches the ball.)
The second figure shows us that, on hard courts, when women are faced with a second shot behind their serve, they finish the point about 20% of the time. Sabalenka’s career average is 28%. She far exceeded that over a string of four matches to finish Ostrava and start Linz, maxing out at 42% against Jennifer Brady in the Ostrava semi-final. Since then, her rate returned to roughly her (impressive) career average.
This measure is something of a “key to the match” for Sabalenka. When she converts at least 30% of second-shot opportunities behind her serve, she wins 91% of her matches. When she doesn’t, she wins 62%. Of course, 62% is nothing to be ashamed of, and the dip visible in early 2020 coincides with her Doha title, the one time in her career that the five-match rolling average fell below 20%.
Serve plus serve plus one
These first two measures are related, of course. A big server should post good numbers in both. But a great “pure” serving day might mean a worse-looking serve-plus-one day, because fewer weak returns are coming back at all. The reverse holds as well: A strong server might not hit as many unreturned serves as usual because her opponent is managing to just barely put them back in play–easy sitters for second shots.
To identify the combined benefits of good serving and efficient serve-plus-one’ing, we simply count how often Sabalenka wins service points in two shots or less.
We’ve already seen the two components of this, so there are no surprises here. The typical player wins about 40% of her service points this way, and Aryna has historically averaged 46% on hard courts. This number looks as good for her recent winning streak as we’d expect. But as with the previous graph, it suggests weakness during her 2020 Doha title, so the predictive power here is limited.
First and second serves
The combined metric of unreturned serves plus second-shot putaways gives us a good snapshot of when the offensive game is working. Let’s break down the previous graph into first- and second-serve specific numbers:
These track the overall numbers. Aryna has generally been good lately on both first and second serves, but with neither one has she been more successful or consistent than in previous hot streaks. Second serves are particularly hard to rate because the per-match sample size is so small–fewer than 30 second serve points per player per match, and some of those end up as double faults.
Before moving on to the return game, let’s look at one more indicator of service-point success:
Longer points on serve
As I said at the outset, Sabalenka has always been a good server. While her current momentum might owe a bit to fewer mental lapses on serve, it would be logical to look elsewhere for an explanation, simply because there was more room to improve in other areas.
We’ve seen how her serve and second shot rate. What about serve points that go deeper? This metric considers all points where the returner’s second shot comes back, and then counts how often the server goes on to win the point.
The average hard-court WTA match winner claims almost exactly half of her service points when the rally reaches five shots. Over her career, Sabalenka has won 48%, worse than the typical match winner but better than the overall tour average.
Aryna has done better lately. To cherry-pick a starting point, she has won 51% of these points in her last 24 matches, dating back to the Doha second round. Her average over the first five matches in Abu Dhabi was 55%, the best she has managed since her breakout run in late 2018, when she pushed Naomi Osaka to three sets at the US Open and hoisted the Wuhan trophy a few weeks later.
Return winners
We’ll walk through the dimensions of her return performance in a similar manner, starting with return winners (and point-ending non-winners), then on to “return-plus-one” putaways, followed by the combination of the two.
First, return winners. I use the number of point-ending return winners divided by in-play serves–that is, excluding double faults.
Veronika Kudermetova had a rough day last Wednesday, so Sabalenka’s current five-match rolling average is as high as it’s been since early 2018. Apart from that last-minute burst of return dominance, her recent return winner rates look a bit like the serve stats: consistently solid, if not spectacular.
Return plus one
How about when the serve return doesn’t finish the job? This “return plus one” metric counts opportunities when the server puts her second shot in play and measures how often the returner hits a winner or forces an error with her own second shot. The sample sizes are a getting a bit small here (each player has 43 such opportunities in an average hard-court match), so the per-match rates are rather spiky:
The small single-match samples, combined with the relationship between return-plus-one and return winners–almost interchangeable ways to respond successfully to a mediocre serve–render conclusions a bit tough to come by. Sabalenka was average by this measure in Ostrava, great in Linz, and all over the place in Abu Dhabi.
Short return points won
Will things be clearer when we combine both methods of quickly winning a return point?
Aside from a weak return performance against Elena Rybakina in Abu Dhabi, Sabalenka has been comfortably above average in this metric in every match since she faced Victoria Azarenka in the Ostrava final.
Like “serve plus one,” this is a good indicator of overall success for the Belarussian. If we use this metric to split her 140 charted hard-court matches in half, the dividing line is 27.5% of return points won with a return winner or a return-plus-one putaway. Above that mark, she has won 62 matches, or 88.6%. Below it, she has won only 41, or 58.6%. She was above the line in nearly all of her matches in Linz and Abu Dhabi, and she sat at 25% or higher in every round of her 2020 Doha triumph, clearing 30% in three of five matches there.
First and second serve returns
Has she been particularly devastating against first or second serves? Let’s see:
Few women feast on second serves the way Sabalenka does, and she’s been particularly relentless of late. The typical tour player wins about 30% of second-serve return points with a first- or second-shot putaway, and over her last 15 matches, Aryna has won 41% that way. 41% is a respectable total percentage of return points won against many servers, and Sablaenka would be winning that many even if she refused to hit more than two shots per rally.
Granted, Sabalenka doesn’t hit that many fifth or sixth shots. How does she fare when her return points extend that far?
Long return points
You’ll be glad to know that the code for this final* graph didn’t throw any divide-by-zero errors–Aryna has played at least one “long” return point in each of her hard-court matches. This metric tallies up all return points in which the server puts her third shot in play, then calculates how often the returner won the point.
** Yes! It’ll be over soon!
This is another spiky mess, with an average of only 20 points per match. Still, if we’re looking for a category in which Sabalenka is newly excelling–not just thriving as usual–this could be our smoking gun.
Tour average for match winners on this stat is 46.7%. The server has an advantage by definition, because she has just put the ball back in play. The Belarussian’s career mark is 44.4%, only a bit better than the overall average. Yet in her last 15 matches, she has won 48.0% of these long return points, her best 15-match span since early in her career, when she faced a weaker mix of opponents.
I don’t want to overemphasize this: When there are only 20 points of this type per match, an improvement of 3.6 percentage points translates to a gain of less than one point per match. That doesn’t explain the magnitude of Sabalenka’s recent gains. But it does indicate that she is shoring up one of her few weaknesses, and in combination with her solid play on long serve points, it suggests that she no longer needs to rely on a one-two punch, even if her one-two punch is as dizzying as anyone’s.
Don’t make me say consistency
Tennis matches are decided by a handful of points: While Sabalenka has been dominant lately, she lost more points than she won against Coco Gauff in the Ostrava opening round. As such, improvements always look minor when we try to quantify them, if we can quantify them at all.
I’ve pointed out some areas where Sabalenka may be improving, others where a good statistical showing usually coincides with a W, and still others where an excellent performance doesn’t seem to matter much. All of these categories have one thing in common: She is putting up stellar numbers right now.
Remember, in the twelve graphs above (yes, twelve, sheesh), the dotted yellow lines indicate the average performance of match winners. In every single one of the categories, Aryna’s five-match rolling average is above that line. Every single one! In most cases, it has been above the line for some time.
It doesn’t take any statistical savvy to see that if a player is better than the average match winner in every category, she’ll be awfully tough to beat. The rest of the Australian Open field can only cross their fingers that Sabalenka’s current form won’t survive two weeks of quarantine.
From our vantage point almost a half-century later, it’s easy to forget just how big an upset Arthur Ashe scored with his 1975 Wimbledon victory over Jimmy Connors. Connors was the top seed and defending champion, still riding high from a 1974 campaign that ranks among the best ever. Ashe was a few days short of his 32nd birthday, had a reputation of coming up short in finals, and had lost to Connors in their three previous meetings.
(For what it’s worth, my Elo algorithm thinks it was a much closer match than the bookies did at the time. It rated Ashe the second-best player in the tournament on grass courts, and gave the underdog a 39% chance of winning.)
Ashe ran away with the first two sets and held on to win in four, 6-1 6-1 5-7 6-4. Perhaps because the two men didn’t get along–apart from striking personality differences, Connors and his manager targeted Ashe with one of many lawsuits–the veteran was uncharacteristically critical of his opponent after the match. Ashe claimed that Connors missed many of his shots into the net (rather than long), a sign of choking.
Connors denied it, of course. It later came out that Jimmy was dealing with a foot problem which probably affected his play that day. In any case, fans and pundits surely had their fun debating whether Connors was a choker. I don’t know of anyone who took the question beyond simple speculation. No amount of statistical analysis can settle whether a player choked, but we can often answer adjacent questions to shed more light on the issue.
Counting errors
A couple of years ago I charted the Wimbledon final for the Match Charting Project, so we have a full count of errors–forced and unforced, serves and rallying shots, net and deep–for the entire match. We also have similar shot-by-shot stats for 25 other Connors matches for comparison. (Unfortunately, 24 of the 25 are chronologically later than the Ashe match, because there’s not much full-match footage from the early 70s.)
Here’s the tally: Excluding serves, Connors committed 13 unforced errors, 10 of them into the net. I recorded the type of error for 65 more forced errors: 32 into the net, 33 other. (Ashe was a netrusher, so many of Jimbo’s mistakes were failed passing shots.) On serve, he missed 29 first deliveries: 16 into the net, 13 otherwise. And his two second serve faults were split between one into the net and one elsewhere.
The unforced error split of 10-to-3 means that 77% of his UFEs were netted. That’s the most extreme of any of his charted matches; on average, his unforced errors were half nets, half others. While suggestive, that’s an awfully small sample from which to draw any conclusions.
Using larger samples that include forced errors and serves, the Wimbledon final doesn’t particularly stand out among other charted Connors matches. 54% of his non-serve errors (forced or unforced) in that match were netted, compared to 52% over the whole sample. 55% of his service faults against Ashe were hit into the net, versus 49% across the 26 matches. Altogether, Connors made 54% of his total errors and faults into the net in the Wimbledon final, compared to 51% in the broader sample.
Does it matter?
You’ve probably heard the tennis coaching conventional wisdom that it’s better to hit long than to hit into the net. Like most tennis shibboleths, this one has been around for a very long time. Ashe had surely heard it, which partly explains why he made the comment he did. Arthur didn’t have a printout with match stats generated by a consulting company with a gargantuan marketing budget, so he probably recalled a few key points and generalized from there.
If error types matter, we’d expect to see at least a mild correlation between results (say, percentage of points won) and error types. Let’s stay focused on the 26 charted Connors matches for today’s purposes. Here’s a version of the Ashe hypothesis, stripped of emotional content:
When Connors hits more errors than usual into the net, it’s a sign that he’s playing below his standard level.
It turns out that this theory is wrong–or, at best, possibly correct if narrowly defined. I considered five main stats as indicators of errors and faults going into the net:
Unforced errors (excluding double faults) into the net as a percentage of total unforced errors
Total rally errors (forced and unforced) into the net as a percentage of total errors
First serve faults into the net as a percentage of total first serve faults
All serve faults into the net as a percentage of all serve faults
All errors and faults into the net as a percentage of all errors and faults
The second (total rally errors) and last (all errors and faults) seem like the most valid of the five, because they give us a decent sample of error types for each match. There is almost exactly zero correlation between the last stat and total points won. And there is a very weak negative correlation (r^2 = 0.05) between the second stat and total points won.
In other words, the Ashe hypothesis might be on to something very minor if our focus in on rally shots. But the correlation is so weak that no human observer would ever notice it, unless they lucked into it by watching a few confirming key moments after being primed by the conventional wisdom.
He didn’t choke like that
I said above that statistical analysis couldn’t settle issues like whether a player choked. We can study what happened, but without machines hooked up to a player’s brain, we can’t tell what was going on inside their heads that might have caused it.
So we can’t say that Connors didn’t choke in the 1975 Wimbledon final. But we have seen that his percentage of into-the-net errors wasn’t that unusual for him (except for the small sample of unforced errors), and we’ve recognized that the number of mistakes he made into the net didn’t have much to say about his level of play that day. If Connors choked, then, it didn’t have anything to do with the low trajectory of his missed shots.
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I learned of Ashe’s post-match comment in Raymond Arsenault’s excellent biography, Arthur Ashe: A Life.
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.
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.
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”).
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.
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:
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.
The NextGen Finals are played on an unusual court, in that the surface is marked only for singles matches, leaving out the “tramlines” that define the doubles alleys. Virtually all tennis events includes doubles, as well, so this is rarely an option. The ATP has skipped tramlines at season-ending events before, but at the end of the 2010s, the singles-only court is exclusive to the NextGen Finals.
One might reasonably wonder whether the unique paint job has any effect on play:
I’d be fascinated to know if less balls go ‘wide’ on a court without trams. Does it focus the mind more? 🤔
I discussed this on a recent podcast with Erik Jonsson, and we tentatively concluded that tennis pros (even young ones) with thousands of hours of playing experience shouldn’t be affected by a tweak to the appearance of the court. But why speculate when we can look at some data?
The Match Charting Project, my volunteer-driven effort to log shot-by-shot records of professional tennis matches, notes various details about errors–forced or unforced, and “type”–net, deep, wide, or wide-and-deep. MCP contributors didn’t immediately take to the NextGen Finals–before this week, the 2018 final was the only charted match out of the 6,600 matches in the dataset–but 2019 was different. We now have shot-by-shot stats for 8 of the 15 matches played in Milan last week. (Big thanks to Carrie, who took charge of Alex de Minaur’s entire run to the final.)
Quantifying wide errors
We’re interested in the frequency of wide errors, which isn’t quite as simple as it sounds. I chose to focus only groundstrokes, and I also excluded forced errors–shots on which the player might not have much control of the direction of the ball.
Here are three metrics we could use for the frequency of wide errors:
Wide errors per point
Wide errors per unforced error
Wide errors per “makeable” groundstroke–that is, groundstrokes that were either unforced errors or put in play
Wide errors per point is probably too crude, but it does have the advantage of simplicity. Wide errors per unforced error might have some value, telling us in what direction a player was most aggressive. The last, wide errors per makeable groundstroke, is probably the best representation of what we’re looking for, as it tells us how frequently a player tried to hit a shot and it went wide.
Here are de Minaur’s numbers for his five 2019 NextGen matches, along with his hard-court aggregates from 28 other charted matches in the last two years:
Wide / Pt Wide / UFE Wide / GS
NextGen 2.7% 1.5% 21.7%
ATP Hard 3.0% 1.4% 21.4%
At least for Alex, the tramlines don’t seem to make much of a difference.
Let’s look at the slightly larger group of players. We have eight matches, which means 16 records of one match for a single player, including at least one for each of the eight guys who qualified for Milan. Here are the three wide-error rates for the NextGen Finals matches, along with the same players’ wide-error rates for other charted hard court matches in the last two years:
Wide / Pt Wide / UFE Wide / GS
NextGen 3.2% 1.8% 19.5%
ATP Hard 3.2% 1.8% 23.1%
For our first two metrics, there is absolutely no effect. Tramlines or no tramlines, wide errors mark the end of 3.2% of points, and 1.8% of total unforced errors. (The 3.2% figure is per player, meaning that 6.4% of points were ended with a wide error.)
The third metric, though, is more interesting. On tour, these players make a wide error on 23.1% of their “makeable” groundstrokes. That number dropped by more than one-seventh, to 19.5%, on the tramline-free court in Milan. At the same time, the overall rate of unforced errors (not just wide errors) increased compared to the same players’ efforts on hard courts at other events.
Deep mind
I see two possible explanations for such a substantial drop. First, we don’t have much data, and maybe it’s just a fluke of a small sample. Some of the difference can be traced to Ugo Humbert, who didn’t make a single wide error in his one charted NextGen Finals match. (Humbert’s usual wide-error rates are close to average.) Without a lot more matches played on tramline-free surfaces–not to mention charts of those matches–we won’t be able to draw a firm conclusion.
Second, it could be a real effect stemming from some aspect of the conditions in Milan. The lack of tramlines really might, as Lisa puts it, “focus the mind.”
Compared to other innovations trialed at the NextGen Finals, the singles-only court gets very little press. But unlike, say, the towel rack or the shot clock, it might just have a small effect on play.
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.
