Match charting – Page 4

Match Charting Project Rally 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 rally stats can be seen at a variety of leaderboards.

RallyLen – Average rally length. Not everyone counts shots exactly the same way, so I try to follow the closest thing there is to a consensus. The serve counts as a shot, but errors do not. Thus, a double fault is 0 shots, and an ace or unreturned serve is 1. A rally with a serve, four additional shots, and an error on an attempted sixth shot counts as 5.
RLen-Serve – Average rally length on service points.
RLen-Return – Average rally length on return points.
1-3 W% – Winning percentage on points between one and three shots, inclusive. On the match-specific pages for each charted match, you can see winning percentages broken down by server. Click on “Point outcomes by rally length.”
4-6 W% – Winning percentage on points between four and six shots, inclusive.
7-9 W% – Winning percentage on points between seven and nine shots, inclusive.
10+ W% – Winning percentage on points of ten shots or more.
FH/GS – Forehands per groundstroke. This stat counts all baseline shots from the forehand side (including slices, lobs, and dropshots), and divides by all baseline shots, to give an idea of how much each player is favoring the forehand side (or, perhaps, is pushed to one side by his or her opponent’s tactics).
BH Slice% – Backhand slice percentage. Of backhand-side groundstrokes (topspin, slices, dropshots, lobs), the percentage that are slices, including dropshots.
FHP/Match – Forehand Potency per match. FHP and BHP (Backhand Potency) are stats I invented to measure the effectiveness of particular groundstrokes. It adds, roughly, one point for a winner and one half point for the shot before a winner, and subtracts one point for an unforced error. On a per-match basis, the stat is influenced by the length of the match and the number of shots hit. Because each point can be counted 1.5 times in FHP (one for a forehand winner, one-half for a forehand that set it up), divide by 1.5 for a number of points that the forehand contributed to the match, above or below average. For instance, a FHP of +6 suggests that the player won 4 more points than he or she would have with a neutral forehand.
FHP/100 – Forehand potency per 100 forehands. The rate-stat version of FHP allows us to compare stats from different match lengths.
BHP/Match – Backhand Potency per match. Same as FHP, but for topspin backhands. I’ve occasionally calculated backhand-slice potency as well, but slices are not included in BHP itself.
BHP/100 – Backhand potency per 100 backhands. The rate-stat version of BHP.

Match Charting Project Return Stats: Glossary

At the moment, the following return stats can be seen at a variety of leaderboards.

RiP% – Return in play percentage. The percent of return points in which this player got the serve back in play.
RiP W% – Return in play winning percentage. Of points in which the returner got the serve back in play, the percentage that the returner won.
RetWnr% – Return winner percentage. The percentage of return points in which the return was a winner (or induced a forced error).
Wnr FH% – Return winner forehand percentage. Of return winners, the percentage that were forehands (topspin, chip/slice, or dropshot).
RDI – Return Depth Index, a stat recently introduced at Hidden Game of Tennis. The Match Charting Project records the depth of each return, coding each as a “7” (landing in the service box), an “8” (in back half of the court, but closer to the service line than the baseline), or a “9” (in the backmost quarter of the court). In the original formulation, RDI weights those depths 1, 2, and 4, respectively, and then calculates the average. I’ve tweaked it a bit to reflect the effectiveness of various return depths. For men, the weights are 1, 2, and 3.5, and for women, the weights are 1, 2, and 3.7.
Slice% – Slice/chip percentage. Of returns put in play, the percent that are slices or chips, including dropshots.

The return stats leaderboards also show most of these stats for first-serve returns only, and for second-serve returns only.

Match Charting Project Serve Stats: Glossary

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

At the moment, the following serve stats can be seen at a variety of leaderboards.

Unret% – Unreturnable percentage. The percentage of a player’s serves that don’t come back, whether an ace, a service winner, or a return error.
<=3 W% – The percentage of points won by the server either on the serve (unreturnables) or on the third shot of the rally: the “plus one” shot.
RiP W% – Return in play winning percentage. Of points in which the return comes back, the percentage won by the server.
SvImpact – Serve Impact. A stat I invented to measure how much the serve influences points won even when the return comes back. The formula used here reflects the average men’s player in the 2010s: unreturned serves, plus 50% of first-serve points won on the server’s second shot, plus 40% of first-serve points won on the server’s third shot, plus 20% of first-serve points won on the server’s fourth shot, all divided by the number of serve points. It is possible to revise the formula for individual players. SvImpact is not included on women’s pages because, on average, the serve has no influence on winner/induced forced error rates for later shots, so it is equivalent to Unret%.
1st: SvImpact – Serve Impact on first serves only. Similar to the above, but excluding unreturnable second serves from the numerator and all second serves from the denominator.
(1st or 2nd) D Wide% – Deuce-court wide serve percentage. Of deuce-court serves that landed in, the percentage that were hit wide. The Match Charting Project divides serves into three categories: wide, middle/body, and T. Rather than listing three percentages for every type of serve, I’m highlighting the percentage of wide deliveries for several classes of serves.
(1st or 2nd) A Wide% – Ad-court wide serve percentage.
(1st or 2nd) BP Wide% – Break-point wide serve percentage. I include only break-point serves in the ad court, because a substantial majority of break points take place in the ad court. By omitting deuce-court break points, we can more directly measure whether a player changes serve-direction tactics facing the pressure of a break point.

Anatomy of Alex de Minaur’s Serving Masterclass

The ATP Atlanta event is typically packed with big servers. John Isner won five titles in six years between 2013 and 2018, during which time the only man to stop him was Nick Kyrgios–in two tiebreaks, naturally. The last champion before Isner took over was Andy Roddick. It’s a fast hard court and the weather is often scorching, so the tournament tends to be a week-long ace festival.

The 2019 titlist posted another wave of eye-popping service numbers, winning four matches without facing a single break point, and winning more than 90% of his first serve points in each match. Those positively Isnerian numbers didn’t belong to the big man himself, nor were they posted by heir apparent Reilly Opelka. The serve king in Atlanta this year was the “six-feet tall” (sure, buddy) Australian grinder, Alex de Minaur.

Unlike many of his peers, de Minaur doesn’t make his money with a big serve. In the last 52 weeks, both Isner and Opelka have hit aces on one-quarter of their serve points. The Aussie’s 52-week rate is a mere 4.5%. He posted a tour-level career best of 14.8% against Taylor Fritz in the Atlanta final (excluding a Bernard Tomic retirement), but failed to reach double digits in second round against Bradley Klahn, or in the semi-final against Opelka. Last week, de Minaur proved that there are a lot of ways to win serve points without necessarily piling up the aces.

Strike one

The easiest non-ace route to victory is the unreturned serve. Players don’t have the same level of control over the rate of unreturned serves that they do with aces. But many great serves are reachable–if not effectively returnable–so they don’t go down in the ace column. The unreturned-but-not-ace category was de Minaur’s bread and butter in Atlanta.

According to the point-by-point log of the final in the Match Charting Project dataset, Fritz put only 57% of the Aussie’s serves back in play. Across over 1,300 MCP-charted hard court matches from the 2010s, the ATP tour average is 70% returned serves, and de Minaur’s opponents have traditionally done even better than that. De Minaur’s unreturned-serve rate of 43% is exceptionally good, ranking in the 90th percentile of service performances. He was even better against Opelka. Only 5 of his 93 service points went for aces, but 38 more didn’t come back. That’s an unreturned-serve rate of 46%, a 94th-percentile-level showing.

Strike two

De Minaur was even better when the serve wasn’t quite as good. Coaches and commentators like to talk about the “plus one” tactic: Hit a strong serve and get in position to make an aggressive play on whatever comes back. This is where the Aussie truly excelled in the title match.

In addition to the 43% of unreturned serves against Fritz, another 26% of his service points fell into the “plus one” category: second-strike shots that his opponent couldn’t handle. Tour average is 15%, and again, de Minaur hasn’t always been this good. His average over 15 charted hard-court matches in 2018 was only 12.6%. His 26% rate on Sunday ranks in the 98th percentile among charted hard-court matches. Of the 67 single-match performances on record that were better than 26%, 15 were recorded by Roger Federer. Most players never have such a good day in the plus-one category.

Strike three

Even the best servers have to deal with the occasional long rally. In our sample of charted hard-court matches, 40% of points see the returner survive the plus-one shot and put the ball back in play. From that point, the rally is more balanced, and returners win a bit more than half of points. (That’s partly because 4-shot rallies are more common than 5-shot rallies, and so on, and because a 4-shot rally, by definition, is won by the returner. Put another way, once you exclude 3-or-fewer-shot rallies, you bias the sample toward the returner; if you excluded 4-or-fewer-shot rallies, you would bias the sample toward the server, because 5-shot rallies make up a disproportionate amount of the remaining points.)

Serving like de Minaur did, he didn’t see nearly so many “long” rallies. 22% of his service points against Fritz, and 29% against Opelka, reached four shots. Facing the typical one-dimensional big server, this is the returner’s chance to even the score. But de Minaur is known more for his ground game than his service. In the final, he won 58% of these points, good enough for the 83rd percentile in our sample.

De Minaur’s performance on longer rallies didn’t really move the needle on Sunday, mostly because he so effectively prevented points from lasting that long. But the fact that he won more than half of the extended exchanges is a reminder that a great serving performance depends on more than just the serve. On a good day, even a six-footer can post numbers that leave Isner and Opelka in the dust. It isn’t always about the aces.

Another Match Charting Project Milestone: 6,000 Matches!

Italian translation at settesei.it

It has been a very productive year for the Match Charting Project. The MCP is a collaborative effort to record every shot of professional tennis matches. Since 2013, over one hundred volunteers have charted matches, ranging from 1970’s grand slam finals to marathon Big Four clashes to obscure challenger and ITF matches. The resulting dataset is unique, and represents a rare, public resource in a sport where the best stuff is usually locked up by tournaments, federations, and the companies that collect data for them.

The 6,000th match–a Bucharest quarter-final between Kristyna Pliskova and Patricia Maria Tig, one of over two hundred charted by Zindaras–comes less than six months after the 5,000th. In that time, we’ve logged a huge number of French Open and Wimbledon matches, in addition to every tour-level final. As part of an ongoing sub-project, we’ve also boosted our coverage of vintage major semi-finals, adding many men’s semis from the late 80’s and early 90’s, plus all the vintage women’s semi-finals from the French Open and Wimbledon for which we’ve found video.

(Maybe you could help us find some of the video we’re looking for?)

The resulting data–much of which is easily accessible to researchers on GitHub–has made it possible to research subjects on a scale never before possible. For example, early in the Wimbledon fortnight, I delved into how net play has changed, using shot-by-shot stats from the 1970s to the present at the All England club. And after Simona Halep’s pristine performance in the final, John Burn-Murdoch used MCP data to contextualize her stunningly low unforced error rate.

More casual fans are also steadily discovering the wealth of available MCP data. For all of these thousands of matches, you can look up thousands of match-specific stats, with easy comparisons to player averages. If you’ve never explored these pages, I highly recommend that you do so. One recently-added match that shows off the depth of the MCP stats is the 1987 Australian Open semi-final between Ivan Lendl and Pat Cash. You can also explore aggregate stats for particular players, on pages like this one, for Sloane Stephens.

If you find any of this interesting, now’s a great time to pitch in. Charting matches isn’t rocket science: it requires only attention to detail and a basic knowledge of tennis. Also helpful are a love of the sport and an interest in minutiae–two qualities shared by many of the most prolific contributors. I’ve written more about the benefits of charting here, and when you’re ready to get started, here’s the official Quick Start Guide.

While tennis remains a bit of an analytical backwater, we continue to make progress. We’re completely independent of the tours and federations spending untold sums on consultants that seem to always produce shiny new websites with little new data. This site isn’t shiny (to put it mildly!) but we’re unencumbered by the politics, conflicting priorities, and sheer inertia that hold back so much of the sport. This Match Charting Project milestone is just the latest reminder that the most interesting work often happens far from the biggest platforms.

Will a Back-To-Normal Federer Backhand Be Good Enough?

Italian translation at settesei.it

After Roger Federer’s 2017 triumph over Rafael Nadal at the Australian Open, I credited his narrow victory to his backhand. He came back from the injury that sidelined him for the second half of 2016 having strengthened that wing, ready with the tactics necessary to use it against his long-time rival. Since that time, he has beaten Nadal in five out of six meetings, suggesting that the new-and-improved weapon has remained a part of his game.

The Swiss is riding high after defeating Rafa once again in the Wimbledon semi-finals on Friday. But unlike in Melbourne two-and-a-half years ago, the backhand wasn’t responsible for the victory. In the Australian Open final, Federer’s stylish one-hander earned him 11 more points than in a typical contest, enough to flip the result in his favor. On Friday, Nadal had little reason to fear a Federer backhand that was only a single point better than average. The Swiss owes his semi-final result to some stellar play, but not from his backhand.

BHP redux

I’m deriving these numbers from a stat called Backhand Potency (BHP), which uses Match Charting Project shot-by-shot data to isolate the effect of each one of a player’s shots. The formula is straightforward:

[A]dd 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. Divide by the total number of backhands, multiply by 100, and the result is net effect of each player’s backhand.

The average player hits about 100 backhands per match, so the final step of multiplying by 100 gives us an approximate per-match figure. BHP hands out up to 1.5 “points” per tennis point, since credit is given for both a winning shot and the shot that set it up. Thus, to translate BHP (or any other potency metric, like Forehand Potency, FHP) to points, multiply by two-thirds. In the 2017 Australian Open final, Federer’s backhand was worth +17 BHP, equal to about 11 points.

On Friday, Roger’s backhand was worth only +1 BHP. The best thing we can say about that is that it didn’t hold him back–the sort of comment we might have made as he racked up wins for the first 15 years of his career.

The semi-final performance wasn’t an outlier. In a year-to-year comparison based on the available (admittedly incomplete) MCP data, the 2019 backhand looks an awful lot like the pre-injury backhand:

Year(s)     BHP  
1998-2011  +0.1  
2012       +0.4  
2013       -1.8  
2014       -1.1  
2015       +1.3  
2016       -0.3  
2017       +3.5  
2018       +1.3  
2019       +0.8

There are still good days, like Fed’s whopping +16 BHP against Kei Nishikori in this week’s quarter-finals. But when we tally up all the noise of good and bad days, effective and ineffective opponents, and fast and slow conditions, the net result is that the backhand just doesn’t rack up points the way it did two years ago.

The backhand versus Novak

Federer’s opponent in today’s final, Novak Djokovic, is known for his own rock-solid groundstrokes. Like Nadal did for many years, Djokovic is able to expose the weaker side of Federer’s baseline game. The Serbian has won the last five head-to-head meetings, and nine of the last eleven. In most of those, he reduces Roger’s backhand to a net negative:

Year  Tournament        Result  BHP/100  
2018  Paris             L         -11.0  
2018  Cincinnati        L         -11.0  
2016  Australian Open   L         -12.6  
2015  Tour Finals (F)   L          -4.8  
2015  Tour Finals (RR)  W          +0.7  
2015  US Open           L          +0.8  
2015  Cincinnati        W          -2.2  
2015  Wimbledon         L         -13.4  
2015  Rome              L         -12.2  
2015  Indian Wells      L          -5.0  
2015  Dubai             W          -5.9  
…                                        
2014  Wimbledon         L          -3.1  
2012  Wimbledon         W          +9.6

Out of 438 charted matches, Federer’s BHP was below -10 only 27 times. On nine of those occasions–and two of the five since Fed’s 2017 comeback–the opponent was Djokovic. Incidentally, Novak would do well to study how Borna Coric dismantles the Federer backhand, as Fed suffered his two worst post-injury performances (-20 at 2018 Shanghai, and -19 at 2019 Rome) against the young Croatian.

It is probably too much to ask for Federer to figure out how to beat Djokovic at his own game. The best he can do is minimize the damages by serving big and executing on the forehand. The Swiss has a career average +9 Forehand Potency (FHP), but falls to only +4 FHP against Novak. In last year’s Cincinnati final, Djokovic reduced his opponent to an embarrassing -13 FHP, the worst of his career. It wasn’t a fluke: four of Fed’s five worst single-match FHP numbers have come against the Serb.

If Federer is to win a ninth Wimbledon title, he’ll need to rack up points on at least one wing–either his typical forehand, or the backhand in the way he did against Djokovic in the 2012 semi-final. Whichever one does the damage, he’ll also need the other one to remain steady. His forehand was plenty effective in the semi-final against Nadal, worth +12 FHP in that match. Against a player like Novak who defends even better on a fast surface, Federer will need to somehow tally similar results. It’s a lot to ask, and one thing is certain: No one would be able to complain that his 21st major title came cheaply.

Visualizing Trends in Net Play Across Five Decades of Grass Court Tennis

Earlier this week, I wrote about one aspect of the long-term decline in net play: the widespread belief that approaching the net is more difficult now because fewer players have a weaker side. I presented evidence indicating that most players still have a weaker side, which suggests that all groundstrokes–on both strong and weak sides–have gotten stronger, making net play a riskier proposition.

If that is true, it is reasonable to assume that passing shot winners are more frequent (relative to the number of net approaches), and perhaps that volleys are more aggressive, resulting in more first-volley winners and first-volley errors. More powerful and precise strokes should, on balance, make net points shorter than they used to be.

We can begin to test these theories using the extensive shot-by-shot records assembled by the Match Charting Project (MCP). MCP data includes every men’s Wimbledon final and semi-final back to 1990, as well as many elite-level grass court matches from the 1970s and 80s. For the purposes of today’s study, I will use only Wimbledon semi-finals and finals, plus a handful of other grass court matches from 1970-89 to complement the sparser Wimbledon data. This way, we know we’re comparing the elites of various generations to one another.

Contemporary net approaches

Let’s start by looking at what happens in a 2010s Wimbledon’s men’s final or semi-final when a player approaches the net. I’m excluding serve-and-volley points, and will do so throughout. I’m also excluding approach shot winners, which are often little more than gestures in the direction of the net following a big shot. (Even when they’re not, it can be difficult for charters to distinguish between approach and non-approach winners.) Thus, we’re looking at about 1,250 net approaches in which the other player got his racket on the ball.

The ball came back almost 73% of the time, and on slightly more than half the points, the approaching player put his first volley (or smash, or whatever shot he needed to hit) in play. 19% of the points saw a second passing shot attempt put in play, and nearly 12% had a second net shot keep the point going. About 1 in 30 approach-shot points continued even longer, forcing the the netman to contend with a third pass attempt.

The following visualization is a Sankey diagram showing how these net points developed. “App” stands for approach, “Unret” for “unreturned,” “Pass1” for “first passing shot,” “V1” for “first volley,” and so on. Mouse over any region of the diagram for a brief summary of what it represents.

There’s a lot of information in the graphic, and it may not be entirely intuitive, especially hindered by my clunky design. Each region is sized based on what fraction of points developed in a certain way. As the regions move toward the right side of the diagram, they as classified by whether the approaching player won the point. As we can see, in the 2010s sample, these approach shots resulted in points won about 69% of the time.

The golden era

To compare eras, we need more than just one decade’s worth of data. I separated the approach shots by decade (grouping together the 70s and 80s), and the most distinctive era turned out to be the 1990s, when Pete Sampras ruled the roost and many of his challengers were equally aggressive.

Far more points were opened with a serve-and-volley: almost 81% in the 1990s compared to 7% in this decade. Even with the server claiming the net so early and so often, there were still many more non-serve-and-volley net approaches two decades ago. Then, there were about 85 “other” net approaches per match; this decade, there have been about 27. Thus, it is reasonable to assume that the typical net approach started from a less favorable position. These days, players only approach when the point has developed in a particularly inviting way.

Here is another diagram, this one showing what happened following 1990s net approaches:

It’s striking to see that, back when net play was much more common, with a master such as Sampras dominating our sample, net approaches were less successful than they are today, resulting in a 67% win rate instead of 69%. However, it’s tough to know how today’s players–even a confident aggressor like Roger Federer or a volleying wizard like Rafael Nadal–would fare if they came forward four times as much. Assuming they pick their spots wisely, their success rate would be lower than 69%. The only question is how much lower.

Contrary to my inital hypothesis, passing shots seemed to be higher-risk and higher-reward in the 1990s than in the 2010s. Two decades ago, only 65.5% of initial passing shot attempts were put in play (compared to 72.6% today), though nearly as many of those attempts resulted in winners (21.1% to 21.4%). It was the volleyers who were either more conservative or less powerful in the 1990s. Then, barely half of first volleys ended the point in the netman’s favor; now, the number is closer to 60%. Again, this could be because today’s players pick their spots more carefully, allowing them to hit easier first volleys.

The early days

We’ve seen how net approaches developed in the 1990s and the 2010s. It would be reasonable to assume that the 1980s (with several late ’70s matches thrown in) were like the 1990s, but more so. Instead, the results are more of a mixed bag, with some characteristics that look like the ’90s, and others that are closer to today’s numbers.

Here is the diagram:

In the 1980s, nearly as many passing shot attempts were put in play as they are today, in contrast to the lower rate during the 1990s. First volleys are a similar story. When passing shot attempts came back, approaching players put a volley (or other net shot) back in the court about 70% of the time–similar numbers in the 1980s and 2010s, but a couple percentage points higher than in the 1990s.

What is different is what happened next. In the 1980s, if the approaching player put his first volley back in play, it came back again 53% of the time. That rate is one of the few clear trends over time: It fell to 46% in the 1990s, 45% in the 2000s, and 37% in the 2010s. As a result, the ’80s saw far more second volleys and points that extended even further, compared to more recent eras. The lack of first-volley putaways meant that net approaches only converted into points won about 65% of the time.

A cautious narrative

There is no simple explanation that accounts for all of these numbers, because we are not seeing the direct result of a single factor, like the shift from wooden rackets or to more topspin-friendly string. Technological changes certainly have an impact, but as soon as the balance between approacher and opponent shifts, players adjust their strategy accordingly.

For instance, the rate of points won on net approaches appears to have steadily increased, from 65% in the 1980s to 67% in the 1990s to 69% today. The first rise could be attributed to racket technology, which gave aggressors more power and control. But the second rise came over a time period in which string technology offered more help to defenders. The higher rate of approach points won isn’t because players are better at it, it’s because they picked their spots more carefully.

What we might focus on instead, then, is how much these diagrams look alike, even though they represent vastly different eras. While there isn’t exactly a net-approach-strategy equilibrium that has held through the decades, player decision-making has kept these rates from varying too wildly. If passing shot winners start going up, we’ll probably see even fewer approaches–with the remaining approaches in still more favorable moments–or a continued increase in the percentage of approaches to the backhand side. That’s another clear trend over the last few decades, but it’s a topic for another day.

Rather than succumbing to nostalgia and bemoaning the decline of net play, it’s better to celebrate the adaptability of tennis players at the highest level. While the game a whole has become more defensive, backcourt denizens from Bjorn Borg (94 approaches per charted grass-court match) to Novak Djokovic (21 approaches per match) have reminded us that adjustments work in both directions. With parameters such as technology, surface, and opponent skills constantly changing, we can’t expect winning strategy to remain the same.

Thanks to SankeyMATIC for making it easy to create the diagrams.

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.

The Match Charting Project Reaches 5,000 Matches

Italian translation at settesei.it

Now this is a milestone. Last night, The Match Charting Project–my volunteer-driven effort to collect shot-by-shot logs of professional tennis–posted it’s 5,000th match! The magic-numbered chart was of one of last weekend’s Davis Cup Qualifiers matches, between Robin Haase and Lukas Rosol, charted by Zindaras, who just began contributing to the project. Number 5,001 is already up–a log of Sunday’s Hua Hin final between Dayana Yastremska and Ajla Tomljanovic.

MCP charts reveal data that simply isn’t available anywhere else. We track every shot–its type and direction–as well as the direction of every serve and and the depth of every return. All told, we’ve amassed these records for over 770,000 points, and almost 3 million shots. (At time of writing, we’re just over 2,992,000.) The dataset has made possible all kinds of research projects, like my recent Economist post about anti-Novak Djokovic tactics, an attempt to quantify the value of smashes, an evaluation of Kei Nishikori’s unusual return stance, and a look at the evolution of Juan Martin del Potro’s backhand.

When I launched the project in 2013, I never imagined we would amass so much information. My goal then was depth, not breadth. Now we have both. The 100 or so charters who have contributed to the project have combined to log nearly every grand slam final back to 1980, most ATP Masters finals back to 1990, and an increasing number of grand slam semi-finals and WTA Premier title matches. More recently, we’ve covered every tour-level final in 2018 and 2019, every head-to-head meeting between members of the big four, and nearly every final contested by any of the big four.

5,000 is a lot

The breadth of the available data goes beyond those high-profile matches. We have at least one charted match for nearly 1,100 different players, at least 10 matches for 268 players, 20 or more for 117 players, 50-plus for 33 players, and over 100 matches for 11 different players. It’s increasingly possible to use MCP data to track the evolution of individual players, something I assumed would always fall outside the scope of the project. And unlike many sports analytics initiatives, this one is gender balanced. Women’s matches make up 47% of the total, despite the fact that vintage women’s matches are considerably harder to track down. (To say nothing of more recent difficulties with WTA streaming.)

It’s fitting that the 5,000th match was logged by a new contributor, because the first several weeks of 2019 have been one of the best periods in the project’s history both for the number of charters and the volume of matches logged. We’ve already charted more than 150 matches from the 2019 season alone, including 79 from the Australian Open. Spearheading that effort has been another new charter, tsitsi, who has contributed more than 100 matches since joining up about a month ago.

Thanks are in order for everyone who has contributed to the project. About 100 people have charted matches, and some of them have been truly prolific. Edo has logged 661 matches, including many of the grand slam finals and semi-finals. In addition to Edo and tsitsi, eight more charters have been responsible for at least 50 matches apiece: Isaac, Lowell, ChapelHeel66, Edged, Palaver, Salvo, 1HandBH, and DebLDecker.

The next 5,000

I hope you’ll join us. Here’s my “quick start” guide to charting, along with 11 reasons to give it a go. Tennis is a complicated sport, so there’s a bit of a learning curve, but I think it’s worth the investment.

Even if you’re still on the fence about charting yourself, I encourage all fans to take greater advantage of the data on offer. A single chart, like this one of the Australian Open men’s final, contains thousands of data points describing various aspects of the match. What I find most illuminating is to compare those single-match numbers with tour, surface, and player averages. For most of the stats on each page, you can move your cursor over the number and see all of those averages. You can also find the player-specific averages on pages like this one, for Petra Kvitova. Researchers can dig into a significant chunk of the raw data, here.

My goal with Tennis Abstract, the blog, and the Match Charting Project has always been to get smarter about tennis–to better understand what’s really happening on court, and never to take the conventional wisdom at face value. I’d say we’re making progress.