With unforced errors removed, the ratio is close to , suggesting that when the server hits a winner on his second shot, the serve and the winner contributed roughly equally to the outcome of the point. It seems more appropriate to skip opponent unforced errors when measuring the effect of the serve, and the resulting ratio jibes better with my intuition.
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Now for the fun part. For unreturned serves and unforced errors, we might be tempted to give negative credit to the other player. Compared to a base rate of To do that, we take the same subset of 1, or so charted matches, tally the number of unreturned serves and first-serve points that ended with various numbers of shots, and assign credit to the serve based on the multipliers above.
When we divide that number by the actual number of service points won, we find out how much of his service success was due to the serve itself. Bottom line, it appears that just over half of service points won are attributable to the serve itself. As expected, that number is lower on clay and higher on grass. These are averages, and the most interesting players rarely hew to the mean. As far from the middle as those are, they understate the uniqueness of these players.
I hinted above that the same multipliers are not appropriate for everyone; the average player reaps no positive after-effects of his second serve, but Isner certainly does. In other words, to get player-specific results, we need player-specific multipliers. Here are the resulting multipliers for a quartet of players you might find interesting, with plenty of surprises already:. Roger Federer gets more positive after-effects from his first serve than average, more even than Isner does.
The big American is a tricky case, both because so few of his serves come back and because he is so aggressive at all times, meaning that his base winner rate is very high. At the other extreme, Schwartzman and Rafael Nadal get very little follow-on benefit from their serves. Serve plus two, anyone? At the risk of belaboring the point, this table shows just how massive the difference is between the biggest servers and their opposites.
But now, instead of asserting a vague truism—the serve is a big deal—we can begin to understand just how much it influences results, and how much weak-serving players need to compensate just to stay even with their more powerful peers. Compared to the likes of Simona Halep , Timea Bacsinszky , and Caroline Wozniacki , the last three women she upset en route to her maiden title, Ostapenko was practically playing a different game.
This stat requires that we know three things about every point: How many shots were hit, who won it, and how. The typical range of this version AGG is between 0. We only have four Samantha Crawford matches in the database, but early signs suggest she could outpace even those women, as her average is at.
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At the other end of the spectrum, Madison Brengle is at 0. In the Match Charting data, there are single-day performances that rise as high as 0. Against Halep, her AGG was a whopping.
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We have data for every Grand Slam final back to , and most of them before that. It was also the third-highest recorded against Halep out of more than Simona matches in the Match Charting dataset. You get the picture: The French Open final was a serious display of aggression, at least from one side of the court.
That level of ball-bashing was nothing new for the Latvian, either. We have charting data for her last three matches at Roland Garros, along with two matches from Charleston and one from Prague this clay season. Her average across the six was. Because she played less aggressively in her earlier matches on tour, her career average still trails those of Kvitova and Goerges, but not by much—and probably not for long. The Match Charting Project contains at least 15 matches on 62 different players—here is the rally-only aggression score for all of them:. In my post last week, I outlined what the error stats of the future may look like.
A wide range of advanced stats across different sports, from baseball to ice hockey—and increasingly in tennis—follow the same general algorithm:. The first step is, by far, the most complex. Classification depends in large part on available data. In baseball, for example, the earliest fielding metrics of this type had little more to work with than the number of balls in play.
Now, batted balls can be categorized by exact location, launch angle, speed off the bat, and more. The same will be true in tennis, eventually, when Hawkeye data or something similar is publicly available. For now, those of us relying on public datasets still have plenty to work with, particularly the 1.
Please help us improve tennis analytics by contributing to this one-of-a-kind dataset. Click here to find out how to get started. The shot-coding method I adopted for the Match Charting Project makes step one of the algorithm relatively straightforward. That is, instead of asking what happens when a player is in position to hit a specific shot, we should be figuring out what happens when the player is presented with a chance to hit a shot in a certain part of the court.
This is particularly important if we want to get beyond the misleading distinction between forced and unforced errors.
For instance, assuming a matchup between right-handers, here is a cross-court forehand:. Player A, at the top of the diagram, is hitting the shot, presenting player B with a shot opportunity. We might also look at different categories altogether, like shot selection. In any case, the categories above give us a good general idea of how players respond to different opportunities, and how those opportunities differ from each other.
The outcomes are stacked from worst to best. At the top is the percentage of winners W that our player hit in response to the opportunity. Players are able to convert those opportunities into points won only The above chart is based on about , shots: All the baseline opportunities that arose that is, excluding serves, which need to be treated separately in over 1, logged matches between two righties.
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Of course, there are plenty of important variables to further distinguish those shots, beyond simply categorizing by shot type. We can see that in another way in the next column, representing opportunities to hit a fourth shot. This is a confusing instance of selection bias that crops up occasionally in tennis analytics: Because a significant percentage of shots are errors, the player who just placed a shot in the court has a temporary advantage. And I appreciate you getting this far despite your reservations.
Until we drill down to much more specific situations—and maybe even then—these tour averages are no more than curiosities. The Wall Street Journal. The Tennis Drill Book. Champaign, Ill. Oxford University Press. The New York Times. Tennis Australia. International Tennis Federation. Retrieved 2 February Speaking of animals: a dictionary of animal metaphors , page Rafael Nadal , page 13 Retrieved BNP Paribas Open.
ATP World Tour. The Hindu.
http://ipdwew0030atl2.public.registeredsite.com/205794-tracker-my-mobile.php Retrieved 13 September Retrieved 8 December Retrieved 3 May So, essentially, did the match. Nor did Berrettini gain any break points on Nadal. In the first semifinal, fifth-seeded Medvedev held off unseeded Grigor Dimitrov early and pulled away late for a 5 , , win that improved his summer hardcourt record to One of those losses, however, was to Nadal in the final of a tournament at Montreal last month. Nadal appreciated the compliment. I will need it. The fans who booed him so lustily last week seemed to be on his side now. Is arriving to the end. I am Novak is Roger is About Us.
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