Podcast Episode 76: US Open Recap

Episode 76 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, reviews the memorable US Open men’s final, featuring a resurgent Daniil Medvedev and a resilient Rafael Nadal, both of whom emptied their tactical toolboxes in Sunday’s five-hour marathon. We cover Nadal’s path to the all-time grand slam lead, whether Medvedev can become the tallest #1 of all time, and whether fellow first-time semi-finalist Matteo Berrettini is in the same league as the Russian.

On the women’s final, we consider whether Serena Williams offered a fair assessment of her own game, and just how high champion Bianca Andreescu can climb. We also touch on Taylor Townsend’s strategic flexibility and Naomi Osaka’s step backward.

Thanks for listening!

(Note: this week’s episode is about 70 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.

Monkeying Around With Rafael Nadal’s 19 Grand Slams

The gap is closing. With his marathon victory last night in the US Open final over Daniil Medvedev, Rafael Nadal is up to 19 career major titles, second only to Roger Federer, who holds 20. Lurking in third place is Novak Djokovic, with 16, who was favored at Flushing Meadows this year, but retired due to injury in the fourth round.

Just two weeks ago, Djokovic seemed to be the biggest threat to Federer’s place atop the leaderboard. Now, with Nadal only one back and Djokovic dealing with another round of physical problems, Rafa has the momentum. Federer, now 38 years old, appears increasingly unlikely to pad his own total.

In an attempt to foresee the future of the grand slam leaderboard, I built a straightforward algorithm last month to predict future major titles. In the spirit of baseball’s “Marcel” projection system, it aims to be so simple that a monkey could do it. It uses the bare minimum of inputs: final-four performance at the last two years’ worth of slams, and age. It trades some optimization in favor of simplicity and ease of understanding. The result is pretty darn good. You can review the algorithm itself and look at how it would have performed in the past in my earlier article here.

Solve for RN = 19 + x

Before the US Open, the algorithm seemed tailor-made to aggravate as many fanbases as possible. It predicted that, over the next five years, Djokovic would win four more majors, Nadal two more, and Federer none, leaving the big three in a tie.

One more slam in the books, and the numbers have changed. Here is the revised forecast, reflecting both Nadal’s 19th slam and his rosier outlook after adding another title to his list of recent results:

Player          Slams  Forecast  Total  
Rafael Nadal       19       3.5   22.5  
Roger Federer      20       0.3   20.3  
Novak Djokovic     16       3.5   19.5

Rafa is in line to improve his total by at least three slams. By the time he’s done, perhaps he will have left Djokovic and Federer in the dust, and we’ll be speculating about whether he’ll catch Serena Williamsor Margaret Court.

More forecasts

My basic algorithm allows us to generate future slam forecasts for any player with at least one major semi-final in the last two years. Keep in mind that I’m not forecasting career slam totals–I’m looking ahead to only the next five years. For the big three, I’m assuming we don’t need to worry about 2025 and beyond.

We have current projections for 18 players:

Player                 Forecast  
Novak Djokovic              3.5  
Rafael Nadal                3.5  
Daniil Medvedev             0.8  
Dominic Thiem               0.7  
Stefanos Tsitsipas          0.6  
Matteo Berrettini           0.5  
Hyeon Chung                 0.4  
Lucas Pouille               0.3  
Kyle Edmund                 0.3  
Roger Federer               0.3  
Grigor Dimitrov             0.1  
Marco Cecchinato            0.1  
Marin Cilic                 0.0  
Juan Martin del Potro       0.0  
Roberto Bautista Agut       0.0  
Kevin Anderson              0.0  
Kei Nishikori               0.0  
John Isner                  0.0

Most of these guys have only a single recent semi-final to their name, and the only thing to separate them is their age. It seems logical to be more optimistic about the future slam performance of Stefanos Tsitsipas (age 21) than that of Roberto Bautista Agut (age 31), even though the algorithm sees their results so far–one semi-final appearance in the last 12 months–as identical.

Five years means 20 slams, and you might notice that the above table doesn’t get close to accounting for all of them. The projections add up to 10.8 majors, leaving plenty of room for players who haven’t even qualified for the list–Alexander Zverev and Felix Auger-Aliassime come to mind. At the 2024 US Open, we’re sure to look back at our late-2019 prognostications and laugh.

Federer will keep his spot at the top of the game’s most important leaderboard for at least four more months. Djokovic will probably be the top pick in Melbourne, so Roger could well enjoy nine more months as the only 20-slam man. But you won’t need an algorithm–even a simple one–to identify the favorite at Roland Garros next year. Organized men’s tennis lasted over a century without a 20-time major champion. In less than a year, we could have two.

Are You There, Margaret? It’s Me, Serena

As I write this, Serena Williams is two matches away from winning her 24th grand slam. She’s been stuck on 23 since early 2017, which must be frustrating, since the all-time record is 24. Serena already holds the open-era record (for titles since 1968), one ahead of Steffi Graf’s 22. But Margaret Court is the leader across all eras, with 24 major championships between 1960 and 1973.

Williams is, of course, one of the greatest players of all time. Maybe the greatest. Court is also in the conversation, along with other luminaries such as Graf, Martina Navratilova, and Chris Evert. Cross-era comparisons in tennis are extremely difficult, because nearly everything about the game has changed. Serena’s technique, training, equipment, and tour schedule–not to mention wealth and celebrity status!–would all be extremely foreign to a 1960s or 70s superstar such as Court.

The challenge of cross-era comparisons hasn’t stopped fans from expressing opinions about where Williams should stand on the all-time leaderboard. Regardless of whose trophy cabinet numbers 23 or 24, Serena supporters tend to rely on three main arguments:

  1. The level of competition is way higher now than it was back then.
  2. Court won the Australian Open 11 times, back when it was the weakest of the four majors.
  3. Court is an obnoxious blowhard whose opinions are unacceptable.

Number one is probably true, but if we’re going to attempt cross-era comparisons, I think the only valid way to do so is to treat all eras as equal. We’ll never know how Williams would have fared with a wooden racket, or how Court’s body would’ve responded to today’s more physical game. You can make a logical case that today’s players are simply better than those of a couple generations ago, who were better than the ones before them, and so on. But the very idea of a “greatest of all time” implies something different than the “greatest of all time measured by today’s standards,” so we’re going to treat all eras as equal.

Number three is also popular, but my database isn’t able to shine much light on that line of argument.

That leaves number two, the relative weakness of the Australian Open.

Aussie ease

Court won the Australian Open 11 times, more than any other woman has claimed a single major title. In itself, that’s not a negative. Nobody counts it against Rafael Nadal that he’s won the French Open 12 times. But in the amateur era–and for some years after tennis went fully professional–the Australian Open wasn’t a mandatory stop for the best players in the world. It was a long trip, and it hadn’t yet gained the prestige that it holds today.

Thus, it’s fair to conclude that Court’s 1963 Wimbledon title was a more noteworthy accomplishment than her trophy 1963 Australian Championships. Most of us would agree that we should discount those Australian Opens. But by how much?

Difficulty-adjusted slam titles

In the past, I’ve compared men’s greatest-of-all-time candidates by major titles, adjusted for the level of competition. In the modern game, the field is almost exactly the same from one major to the next, but the draw can make one tournament considerably more difficult to win than another. The same technique allows us to compare draw difficulty and field quality for tournaments from the 1970s when both varied. For instance, the difficulty of Court’s path to the 1973 US Open title rated as average, in line with many of Williams’s title paths. But her 1973 Australian crown was only two-thirds as difficult–one of the easiest paths to a major title in the open era.

It’s no accident that I’m using Court’s last few major titles as examples. By analyzing performances from the 1970s, we’re pushing up against the edge of the weakness of historical tennis data. It’s well-nigh impossible to estimate the exact difficulty level of most of Court’s titles, because so little data is available from the amateur era. Instead, we’ll need to approximate using the limited information we have.

My difficulty adjustments rely on Elo ratings, which I have calculated as far back as 1972. (We have fairly complete results back to 1970 or so, but it takes a bit of time to amass a decent sample of match results for each player and for ratings to stabilize.) Let’s look at the relative difficulty of the four grand slams in the first five possible years, 1972-76:

Major            Difficulty  
Australian Open        0.60  
French Open            0.54  
Wimbledon              0.99  
US Open                0.85

The average major title, 1972-present, rates 1.0, with more difficult paths earning higher numbers. The fields weren’t as deep in the 1970s as they are now, so the typical path to a slam title then was lower than 1.0. In this first five-year period, we see that Wimbledon was in line with the historical average, the US Open was a bit easier, and the other two quarters of the grand slam were considerably less challenging. If we follow my suggestion above, to treat all eras as equal–except for the weakness of the Australian draws–we need to normalize these difficulties so that the other three slams average 1.0:

Major            Difficulty  
Australian Open        0.76  
French Open            0.68
Wimbledon              1.25  
US Open                1.07

Extrapolating backwards

We don’t know much about the field quality of the Australian majors in Court’s prime. For lack of a better option, then, we’ll use the 1972-76 average, since that’s as close as we can get. These probably overstate the quality of the Australian draws relative to the other slams, but if we’re inching toward calling Serena the all-time leader at Court’s expense, we should make conservative assumptions, to give us more confidence in our end result.

Here’s what happens to Court’s career totals if we apply the normalized adjustments:

Major            Difficulty  Slams  Adj Slams  
Australian Open        0.76     11        8.3  
French Open            0.68      5        3.4  
Wimbledon              1.25      3        3.7  
US Open                1.07      5        5.4  
Total                           24       20.8

The same process–adjusting each slam for difficulty, and normalizing for era–makes milder tweaks to Williams’s and Graf’s totals. Serena ends up with 23.3, and Graf with 21.9. Neither is enough to give us reason to change how we view those players’ accomplishments. And both are better than Court’s modified tally.

The small herd of GOATs

Remember that this is not an era adjustment. To the contrary, this calculation is based on the simplifying assumption that all eras are equal, except for the fact that for many years, some of the best players didn’t travel to Australia, making that major easier to win than the others.

These numbers also–obviously!–don’t tell us that Court wasn’t one of the best ever. Even if she had skipped her home slam, she still would’ve retired with 13 majors, plus a pile of doubles grand slam trophies and a long list of other career accomplishments. If Australia were less geographically remote, she probably wouldn’t have won those eleven titles–but she may well have won eight.

For all of Court’s accomplishments, she loses her top spot on the sport’s most hallowed list once we account for the weakness of the early Australian Open draws. At the very least, she falls behind Williams and Graf. Remember that my adjustments are conservative ones, so if we collect more data and discover that we should more aggressively discount her 1960’s Australian titles, her resulting total might leave her closer to 18, tied with Evert and Navratilova.

Serena may never equal or beat Court’s 24 titles. But even if she retires with 23, the modern level of competition–which showed up at every major, every year–means that she already deserves her place atop the leaderboard.

Podcast Episode 75: US Open Preview

Episode 75 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, starts by previewing the new Tennis Abstract player pages, which you can expect to see roll out over the next few days. Look forward to tons of new stats, plus new ways of looking at traditional numbers.

Most of the episode is devoted to our US Open preview. We consider much Novak Djokovic’s chances are hurt by the draw, in particular his likelihood of facing Daniil Medvedev again in the quarter-finals. We highlight some notable early-round matches in both the men’s and women’s draws, and talk in further depth about Coco Gauff, Sloane Stephens, Serena Williams, Karolina Muchova, and Su-Wei Hsieh.

Finally, we touch on the lingering Serena/Ramos controversy (the focus of an upcoming Thirty Love episode) and which players were worth watching in last week’s qualifying rounds.

Thanks for listening!

(Note: this week’s episode is about 64 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.

Podcast Episode 74: More Aggression For Medvedev, More Control For Madison Keys

Episode 74 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, resumes last week’s conversation about Daniil Medvedev’s tactical choices, now that he took down Novak Djokovic with big second serving en route to his first Masters title. We also consider whether Medvedev’s generation is more aggressive than the ones that came before, notwithstanding the Russian’s predilection for 30-stroke rallies.

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!)

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

Match Charting Project Tactics Stats: Glossary

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

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

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

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

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 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.

Podcast Episode 73: Rogers Cup Review, on Rafa, Medvedev, Andreescu, Serena, Kenin, and More

Episode 73 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, digs into the unique tactical approach of fast-rising Daniil Medvedev, whose inside-out backhand offers an unusual look to the rest of the tour. Medvedev reached the Montreal final last week but failed to make a dent against Rafael Nadal.

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.