We also consider the decisions that led to a best-ever title for Madison Keys, a player who thrives with high-risk, powerful shotmaking, but dialed things back a bit in Cincinnati. We wrap up with an overview of my new Match Charting Project-derived stat leaderboards and a first-hand glimpse of the new WTA event this week in the Bronx.

Thanks for listening!

*(Note: this week’s episode is about 63 minutes long; in some browsers the audio player may display a different length. Sorry about that!)*

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]]>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.

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.

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.

]]>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.

We continue on the subject of ATP tactics, looking at the importance of point-finishing skills, the declining role of surface speed, and whether the current crop of young players is less surface sensitive than their predecessors.

We also celebrate the return of Bianca Andreescu to the winner’s circle, mull over how Serena Williams will fare at the US Open, and ask whether our current approach to forecasting tournament results is giving the 23-time slam champion her due.

Thanks for listening!

*(Note: this week’s episode is about 60 minutes long; in some browsers the audio player may display a different length. Sorry about that!)*

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

]]>Here’s the nutty thing: It was Bouzkova’s 62nd match of the 2019 season, her 61st against someone with a WTA ranking. She got the win against the highest-ranked foe–Halep–but just last week, she lost to 636th-ranked CoCo Vandeweghe, her *lowest*-ranked opponent of the year. Yeah, the caveats keep coming: Vandeweghe is coming back from injury and is surely better than a ranking outside the top 600, and the ITF Transition Tour hijinks mean that the ranking system didn’t work as usual in 2019. Some players who would normally have a very low ranking, like the Kazakh wild card who Bouzkova crushed a couple of weeks ago, don’t count.

Still. 61 matches, with a win against the highest-ranked player and a loss against the lowest.

That sent me to my database, which had plenty more surprises in store. Going back less than a decade, to 2010, I found *127* players who recorded the same oddball combination of feats in a single season, minimum 30 matches. (To be consistent with the Halep result, I included retirements if at least one set was completed.) While many of the players won’t be of wide interest–last year, one of the exemplars was Mira Antonitsch, who didn’t play anyone ranked in the top 400–63 of the 127 player-seasons involved beating a top-100 opponent, 44 included the defeat of someone in the top 50, and 25 were highlighted by a top-ten upset.

Three of them included Halep as the top-ten scalp! That makes Bouzkova the fourth player to beat Halep, not face anyone higher ranked, and also lose to her lowest-ranked opponent of the season. (Through eight months, anyway.) Halep shouldn’t feel too bad, though, as Angelique Kerber has been the extreme-ranked loser in *five* such cases, four of them in 2017. Ouch.

Here are the 25 player-seasons between 2010 and 2018 in which a WTAer beat her highest-ranked opponent and lost to her lowest:

Year Player High-Ranked Rk Low-Ranked Rk 2017 Kasatkina Kerber 1 Kanepi 418 2018 Hsieh Halep 1 Gasparyan 410 2010 Jankovic Serena 1 Diyas 268 2010 Clijsters Wozniacki 1 G-Vidagany 258 * 2014 Cornet Serena 1 Townsend 205 2010 Yakimova Jankovic 2 Dellacqua 980 2017 Bouchard Kerber 2 Duval 896 * 2017 Vesnina Kerber 2 Azarenka 683 2016 Bencic Kerber 2 Boserup 225 2014 Rybarikova Halep 2 Eguchi 183 2017 Mladenovic Kerber 2 Andreescu 167 * 2018 Goerges Wozniacki 3 Serena 451 2014 Tomljanovic Radwanska 3 A Bogdan 308 2015 Mladenovic Halep 3 Savchuk 262 2017 Kerber Pliskova 4 Stephens 934 2014 Pavlyu'ova Radwanska 4 Wozniak 241 2017 Dodin Cibulkova 5 Rybarikova 453 2017 Bellis Radwanska 6 Azarenka 683 2018 Buyukakcay Ostapenko 6 Di Sarra 555 2017 Sakkari Wozniacki 6 Potapova 454 2015 L Davis Bouchard 7 E Bogdan 527 2015 Ostapenko S-Navarro 9 Dushevina 1100 * 2016 KC Chang Vinci 10 S Murray 862 2018 Pera Konta 10 Hlavackova 825 2018 Danilovic Goerges 10 Pegula 620

** also faced one unranked player*

A quick glance is all it takes to establish that Vandeweghe isn’t the first lowest-ranked player to inspire a “yeah, but” reaction. The list of purportedly weak opponents is very strong for one made up of players with an average ranking outside of the top 500. We have stars such as Victoria Azarenka (twice) and Serena as well as a helping of prospects such as Bianca Andreescu and Victoria Duval.

Consider this as today’s reminder of the limitations of the WTA computer rankings. They tell us who has won a lot of matches in the last 52 weeks, not necessarily who is playing well right now. These cases include many of the most extreme mismatches between official ranking and on-the-day ability. I don’t think it says anything meaningful about a player to show up on this list–though Kerber’s many appearances (as both player and scalp!) are a good summary of her disappointing 2017 campaign.

Bouzkova will remain on the list for at least a couple more days: Serena is currently ranked 10th and both of the other semi-finalists are ranked lower, so Halep will remain her “toughest” opponent. Despite the Czech’s breakout week, it would be understandable if she found herself overawed to face a 23-time slam champion across the net. But one thing is certain: Bouzkova couldn’t care less about the number next to the name.

]]>Forecasting tennis is hard, and that’s just if you’re trying to pick the results of tomorrow’s matches. Players improve and regress seemingly at random, making it difficult to predict what the ranking table will look like only a few months from now. Fans love to speculate about which of the big three will, in the end, win the most slams, but there are an awful lot of unknowns to contend with.

One can imagine *some* way to construct a crystal ball to get these numbers in a rigorous way. Consider each player’s age, his likely career length, his chances of injury, his recent performance at each of the four slams, his current ranking, the quality of the field on each surface, and probably more, and maybe you could come up with some plausible numbers. Or… what if we skip most of that, and build the simplest model possible?

**Enter the monkey**

Baseball statheads are familiar with the Marcel projection system, named after a fictional monkey because it “uses as little intelligence as possible.” Just three years of results and an age adjustment. It isn’t perfect, and there are plenty of “obvious” improvements that it leaves on the table. But as in tennis, baseball stats are noisy. For most purposes, a “basic” forecasting system is as good as a complicated one, and over the years, Marcel has outperformed a lot of models that are considerably more complex.

Let’s apply primate logic to slam predictions. First, I’m going to slightly re-cast the question to something a bit more straightforward. Instead of forecasting “career” slam results, we’re going to focus on major titles over the next five years. (That should cover the big three, anyway.) And in keeping with Marcel, we’ll use just a few inputs: slam semi-finals, finals, and titles for the last three years, plus age. Actually, we’re going to lop off a bit of the monkey’s brain right away, because slam results from three years ago aren’t that predictive. So our list of inputs is even shorter: *two* years of slam semi-finals, finals, and titles, plus age.

The resulting model is pretty good! For players who have reached a major semi-final in any of the last eight slams, it predicts 40% of the variation in next-five-years slam titles. Without building the hyper-complex, optimal model, we don’t know exactly how good that is, but for a forecast that extends so far into the future, capturing almost half of the player-to-player variation in slam results sounds good to me. Think of all the things we don’t know about the slams in 2022, let alone 2024: who is still playing, who gets hurt, who has improved enough to contend, which prospects have come out of nowhere, and so on. Point being, the *best* model is going to miss a lot, so we shouldn’t set our standards too high.

**Follow the monkey**

The two-years-plus-age algorithm is so simple that you can literally do it on the back of an envelope. For any player, count his semi-final appearances (won or lost), final appearances (won or lost), and titles at the last four slams, then do the same for the previous four. Then note his age at the start of the next major. Start with zero points, then follow along:

- add 15 points for each semi-final appearance in the last four slams
- add 30 points for each final appearance in the last four slams
- add 90 points for each title in the last four slams
- add 6 points for each semi-final appearance in the previous four slams
- add 12 points for each final appearance in the previous four slams
- add 36 points for each title in the previous four slams
- if the player is older than 27, subtract 8 points for each year he is older than 27
- if the player is younger than 27, add 8 points for each year he is younger than 27
- divide the sum by 100

That’s it! Let’s try Djokovic. In the last four majors, he’s won three titles and made one more semi-final. In the four before that, he won one title. He’ll enter the US Open at 32 years of age. Here goes:

- +60 (15 points for each of his four semi-finals in the last four slams)
- +90 (30 points for each of his three finals in the last four slams)
- +270 (90 points for each of his three titles in the last four slams)
- +6 (6 points for his 2017 Wimbledon semi-final)
- +12 (12 points for his 2017 Wimbledon final)
- +36 (36 points for his 2017 Wimbledon title)
- -40 (Novak is 32, so we subtract 8 points for each of the 5 years he is older than 27)

Add it all up, and you get 434. Divide by 100, and we’re predicting 4.34 more slams for Novak.

**Next-level GOAT trolling**

I promise, I went about this project solely as a disinterested analyst. I just wanted to know how accurate a bare-bones long-term slam forecast could be. My goal was not to make you tear your hair out. But hey, you were probably going to lose your hair anyway.

Here is the number of slams that the model predicts for the big three between the 2019 US Open and 2024 Wimbledon:

- Djokovic: 4.34
- Nadal: 2.22
- Federer: 0.26

You probably don’t need me to do the math for the next step, but you know I can’t *not* do it. Projected career totals:

- Djokovic: 20.34
- Federer: 20.26
- Nadal: 20.22

Or, since we live in a world where you can’t win fractional majors:

- Djokovic: 20
- Federer: 20
- Nadal: 20

Ha.

**Back to the model**

Djokovic’s forecast of 4.34 is quite high, in keeping with a player who has won three of the last four majors. For each year since 1971, I calculated a slam prediction for every player who had made a major semi-final in the previous two years–a total of more than 800 forecasts. Only 14 of those forecasts were higher than 4.34, and several of those belonged to the big three. Here are the top ten:

Year Player Age Predicted Actual 2008 Roger Federer 26 6.38 5 2007 Roger Federer 25 5.86 7 2016 Novak Djokovic 28 5.20 6 * 2005 Roger Federer 23 4.91 11 2011 Rafael Nadal 24 4.89 5 2006 Roger Federer 24 4.86 10 2017 Novak Djokovic 29 4.79 4 * 2012 Novak Djokovic 24 4.68 8 1989 Mats Wilander 24 4.65 0 1988 Ivan Lendl 27 4.56 2

** actual slam counts that could still increase*

All of these predictions are based on data available at the beginning of the named year. So the top row, 2008 Federer, is the forecast for Federer’s 2008-12 title count, based on his 2006-07 performance and his age entering the 2008 Australian. Had the model existed back then, it would have guessed he’d win a half-dozen slams in that time period. He came close, winning five.

There will be plenty of noise at the extreme ends of any model like this. At the beginning of 2005, the algorithm pegged Federer to win “only” five of the next twenty majors. Instead, he won 11. I can’t imagine any data-based system would have been so optimistic as to guess double digits. On the flip side, the 1989 edition of the monkey would’ve been nearly as hopeful for Mats Wilander, who was coming off a three-slam campaign. Sadly for the Swede, a gang of youngsters overtook him and he never made another major final.

Let’s also take a look at the next 10 rosiest forecasts, plus the current guesstimate for Djokovic:

Year Player Age Predicted Actual 2010 Roger Federer 28 4.48 2 1981 Bjorn Borg 24 4.47 1 1996 Pete Sampras 24 4.47 6 1975 Jimmy Connors 22 4.45 2 Curr Novak Djokovic 32 4.34 0 * 1980 Bjorn Borg 23 4.28 3 2013 Novak Djokovic 25 4.24 7 2009 Roger Federer 27 4.20 4 1995 Pete Sampras 23 4.16 7 2009 Rafael Nadal 22 4.12 8 1979 Bjorn Borg 22 4.09 5

Plenty more noise here, with outcomes between 0 and 8 slams. Still, the average result of the 10 other predictions on this list is 4.5 slams, right in line with our forecast for Novak.

**Missing slams…**

The model expects that the big three will win around seven of the next twenty slams. You might reasonably wonder: What about the other thirteen?

The monkey only considers players with a slam semi-final in the last eight majors, so the forecasts *shouldn’t* add up to 20. There’s a chance that the champions in 2023 and 2024 aren’t yet on our radar, and many young names of interest to pundits these days, like Alexander Zverev, Felix Auger Aliassime, and Daniil Medvedev, haven’t yet reached the final four of a major. Here are the players for whom we *can* make predictions:

Player Predicted Slams Novak Djokovic 4.34 Rafael Nadal 2.22 Dominic Thiem 0.71 Stefanos Tsitsipas 0.63 Hyeon Chung 0.38 Lucas Pouille 0.31 Kyle Edmund 0.30 Roger Federer 0.26 Juan Martin del Potro 0.19 Marco Cecchinato 0.06 ---------------- ---- TOTAL 9.40

*(The five other players with semi-final appearances since the 2017 US Open are forecast to win zero slams.)*

Yeah, I know, Lucas Pouille and Hyeon Chung aren’t really better bets to win a slam than Federer is. But they are (relatively) young, and the model recognizes that many players who reach slam semi-finals early in their careers are able to build on that success.

More to the point, we’re leaving a lot of majors on the table. If the overall forecast is correct, that list of players will win fewer than half of the next 20 slams, leaving at least ten championships to players who have yet to win a major quarter-final.

**…and age**

Remember, I retro-forecasted every five-year period back to 1971-75. Over the 44 five-year spans starting each season between 1971 and 2014, the model typically predicted that the players it knew about–the ones who had reached slam semi-finals in the last two years–would win 13 of the next 20 slams. In fact, those on-the-radar players combined to win an average of 12 majors in the ensuing five-year spans.

Only in the last few years has the total number of *predicted* slams fallen below 10. The culprit is age: Recall that every forecast has an age adjustment, and we subtract 8 points (0.08 slams) for each year a player is older than 27. That’s a 0.4-slam penalty for both Djokovic and Nadal, and it’s 0.8 slams erased from Federer’s future tally. Thus, the model predicts that the big three are fading, and there aren’t many youngsters (like Pouille and Chung) on the list to compensate.

How you interpret these big three forecasts in light of the “missing” slams depends on a couple of factors:

- Has the aging curve for superstars has changed? Is 30 the new 25; 32 the new 27?
- Will the next few generations of players soon be good enough to topple the big three?

There’s plenty of evidence that the aging curve has changed, that we should expect more from 30-somethings these days than we did in the 1980s and 1990s. That would close much of the gap. Let’s say we set the new peak age at 31, four years later than the men’s Open Era average of 27. That would add 0.32 slams to every player’s forecast, possibly adding one more slam to each of the big three’s forecasted total. Overall, it would add a bit more than an additional three slams to the total of the the previous table, putting that number close to the historical average of 13.

Shifting the age adjustment doesn’t disentangle the big three, though, because it affects them all equally. It just means a three-way tie at 21 is a bit more likely than a three-way tie at 20.

The second question is the more important–and less predictable–one. It’s hard enough to know how well a single player will be competing in three, four, or five years. (Or, sometimes, tomorrow.) But even if we could puzzle out *that* problem, we’d be left with the still more difficult task of predicting the level of competition. Entering the 2003 season, the monkey would have opined that the then-current crop of stars–men who made slam semis in 2001 and 2002–would account for a combined 13 majors between 2003 and 2007. That included 2.5 for Lleyton Hewitt, plus one apiece for Thomas Johansson, Albert Costa, Pete Sampras, Marat Safin, David Nalbandian, and Juan Carlos Ferrero. Those seven men won only two. The entire group of 20 players who merited forecasts entering the 2003 Australian Open won only three.

We’ll probably never establish exactly how strong that group was in comparison with other eras. What we know for sure is that none of those men were as good as Federer in 2003-05, and by the end of the five-year span, they’d been shunted aside by Nadal as well. (Only Nalbandian ranked in the 2007 year-end top ten.) The generation of Zverev/Tsitsipas/Auger-Aliassime/etc won’t be as good as peak Big Four, but the course of the next 20 slams will depend a lot more on those players that it will on the (relatively) more predictable career trajectories of Djokovic, Federer, and Nadal.

So we’re left with a stack of known unknowns and error bars wider than a shanked Federer backhand. But based on what we *do* know, the top of the all-time slam leaderboard is going to get even more crowded. At least, that’s what the monkey says.

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.

]]>On court, they look at the spectacular serving performance of sub-six-foot Alex de Minaur, another disappointing loss for Alexander Zverev, and a shock comeback from Cedrik-Marcel Stebe.

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