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.

Picking Favorites With Better Davis Cup Rankings

Yesterday, the ITF announced the seedings for the first new-look Davis Cup Finals, to be held in Madrid this November. The 18-country field was completed by the 12 home-and-way ties contested last weekend. Those 12 winners will join France, Croatia, Spain, and USA (last year’s semi-finalists) along with the two wild cards, recent champions Argentina and Great Britain.

The six nations who skipped the qualifying round will make up five of the top six seeds. (Spain is 7th, while Belgium, who had to qualify, is 4th.) The preliminary round of the November event will feature six round-robin groups of three, each consisting of one top-six seed, a second country ranked 7-12, and a third ranked 13-18. Seeding really matters, as a top position (deserved or not!) guarantees that a side will avoid dangerous opponents like last year’s finalists France and Croatia. Even the difference between 12 and 13 could prove decisive, as a 7-through-12 spot ensures that a nation will steer clear of the always-strong Spaniards, who are seeded 7th.

The seeds are based on the Davis Cup’s ranking system, which relies entirely on previous Davis Cup results. While the formula is long-winded, the concept is simple: A country gets more points for advancing further each season, and recent years are worth the most. The last four years of competition are taken into consideration. It’s not how I would do it, but the results aren’t bad. Four or five of the top six seeds will field strong sides, and one of the exceptions–Great Britain–would have done so had Andy Murray’s hip cooperated. Spain is obviously misranked, but given the limitations of the Davis Cup ranking system, it’s understandable, as the 2011 champions spent 2015 and 2016 languishing outside the World Group.

We can do better

The Davis Cup rankings have several flaws. First, they rely heavily on a lot of old results. If we’re interested in how teams will compete in November, it doesn’t matter how well a side fared three or four years ago, especially if some of their best players are no longer in the mix. Second, they don’t reflect the change in format. Until last year, doubles represented one rubber in a best-of-five-match tie. A good doubles pair helped, but it wasn’t particularly necessary. Now, there are only two singles matches alongside the doubles rubber. The quality of a nation’s doubles team is more important than it used to be.

Let’s see what happens to the rankings when we generate a more forward-looking rating system. Using singles and doubles Elo, I’m going to make a few assumptions:

  • Each country’s top two singles players have a 75% chance of participating (due to the possibility of injury, fatigue, or indifference), and if either one doesn’t take part, the country’s third-best player will replace him.
  • Same idea for doubles, but the top two doubles players have an 85% chance of showing up, to be replaced by the third-best doubles player if necessary.
  • The three matches are equally important. (This isn’t technically true–the third match is likely to be necessary less than half the time, though when it does decide the tie, it is twice as important as the other two matches.)
  • Andy Murray won’t play.

Those assumptions allow us to combine the singles and doubles Elo ratings of the best players of each nation. The result is a weighted rating for each side, one that has a lot of bones to pick with the official Davis Cup rankings.

Forward-looking rankings

The following table shows the 18 countries at the Davis Cup finals along with the 12 losing qualifiers. For each team, I’ve listed their Davis Cup ranking, and their finals seed (if applicable). To demonstrate my results, I’ve shown each nation’s weighted Elo rank and rating and their hard-court Elo rank and rating. The table is sorted by hard-court Elo:

Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
ESP            7     7         1  1936          1  1891  
CRO            2     2         2  1898          2  1849  
FRA            1     1         3  1880          3  1845  
USA            6     6         4  1876          4  1835  
RUS           21    17         7  1855          5  1827  
AUS            9     9         5  1857          6  1820  
SRB            8     8         8  1849          7  1808  
GER           11    11         6  1855          8  1799  
AUT           16              10  1800          9  1766  
ARG            3     3         9  1803         10  1755  
                                                         
Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
GBR            5     5        11  1796         11  1750  
SUI           24              14  1763         12  1749  
ITA           10    10        12  1780         13  1745  
CAN           14    13        13  1777         14  1744  
JPN           17    14        15  1735         15  1719  
BEL            4     4        17  1688         16  1673  
CZE           13              16  1712         17  1661  
NED           19    16        18  1685         18  1643  
BRA           28              20  1659         19  1638  
IND           20              21  1652         20  1621  
                                                         
Country  DC Rank  Seed  Elo Rank   Elo  sElo Rank  sElo  
SVK           29              22  1645         21  1617  
CHI           22    18        19  1682         22  1609  
KAZ           12    12        26  1582         23  1574  
COL           18    15        24  1597         24  1551  
SWE           15              27  1570         25  1542  
BIH           27              28  1552         26  1540  
POR           26              23  1610         27  1535  
HUN           23              25  1583         28  1533  
UZB           25              29  1491         29  1489  
CHN           30              30  1468         30  1465

Spain is the comfortable favorite, regardless of whether we look at overall Elo or hard-court Elo. When the draw is conducted, we’ll see which top-six seed is unlucky enough to end up with the Spaniards in their group, and whether the hosts will remain the favorite.

The biggest mismatch between the Davis Cup rankings and my Elo-based approach is in our assessment of the Russian squad. Daniil Medvedev is up to sixth in my singles Elo ratings, with Karen Khachanov at 10th. Those ratings might be a little aggressive, but as it stands, Russia is the only player with two top-ten Elo singles players. Spain is close, with Rafael Nadal ranked 2nd and Roberto Bautista Agut 11th, and the hosts have the additional advantage of a deep reservoir of doubles talent from which to choose.

In the opposite direction, my rankings do not forecast good things for the Belgians. David Goffin has fallen out of the Elo top 20, and there are no superstar doubles players to pick up the slack. In a just world, Spain and Belgium will land in the same round-robin group–preferably one without the Russians as well.

Madrid or Maldives

The results I’ve shown assume that every top singles player has the same chance of participating. That’s certainly not the case, with high-profile stars like Alexander Zverev telling the press that they’ll be spending the week on holiday in the Maldives. Some teams are heavily dependent on one singles player who could make or break their chances with a decision or an injury.

As it stands, Germany is 8th in the surface-weighted Elo. If we take Zverev entirely out of the mix, they drop to a tie for 14th with Japan. It’s something the German side would prefer to avoid, but it’s not catastrophic, partly because the Germans were never among the favorites, and partly because Zverev could play only one singles rubber per tie and the doubles replacements are competent.

Even more reliant on a single player is the Serbian side, which qualified last weekend without the help of their most dangerous threat, Novak Djokovic. With Djokovic, the Serbs rank 7th–a case where my surface Elo ratings almost agree with the official rankings. But without the 15-time major winner, the Serbs fall down to a tie with Belgium in 16th place. While the Serbs are unlikely to take home the trophy regardless, Novak would make a huge difference.

The draw will take place next Thursday. We’ll check back then to see which sides have the best forecasts, nine months out from the showdown in Madrid.

Podcast Episode 47: Davis Cup and Another Week of Tennis Shenanigans

Episode 47 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, takes a look at all things Davis Cup, including the early exit of a Federer-less Swiss team, the unexpected La Liga sponsorship, and the shrinking opportunities for players at ITF events despite all the money that Davis Cup is apparently worth.

We also plow through a list of miscellaneous topics, including a second title for 18-year-old Dayana Yastremska, a career-best final for Donna Vekic, the demise of the Connecticut Open, the persistence of the serve clock, the career slam of Herbert-Mahut, and some new mixed doubles stats.

Thanks for listening!

(Note: this week’s episode is about 65 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.

Unmixing the Gender Gap in Mixed Doubles

Doubles has long been a sort of final frontier in tennis analytics. Double is interesting, at least in part, for the same reason that all team sports are compelling–contributions can come from either player, or a combination of the two. From an analytics perspective, that poses a challenge: Can we isolate what each player brings to the court? I’ve tried to do so with my doubles Elo ratings, but that method relies on players changing partners. It’s not possible to identify how much each half contributed simply by looking at match results.

The problem, as usual, is limited data availability. To know how much value to assign to each player, we need to know what he or she did, even at the basic level of aces, double faults, winners, and errors. The tours report matchstats for many doubles contests, but do not separate the players. Knowing that the Bryan brothers hit 12 aces doesn’t tell us anything about Bob or Mike. The grand slam websites have been better, often providing sequential point-by-point data for some matches, but the same problem persists: They don’t differentiate between players.

That is, until now! The Australian Open website specified the server for each point of every doubles match. (It doesn’t identify the returner on each point, but … baby steps.) That opens up whole new vistas for analytics to separate the contributions of each player.

There’s no I in mixed

A natural place to start is mixed doubles, an event that, due to lack of data, has been almost entirely ignored by analysts. Yet mixed doubles is one of things that everyone seems to have at least a moderate interest in, either because it’s a popular amateur pastime, or because gender differences in sport are inherently fascinating. Due to the variety of skillsets on court at all times, mixed doubles presents tactical puzzles that are different from those posed by same-gender matches.

Let’s start with the basics. There are only 32 teams in a grand slam mixed doubles event, so it’s possible to extend the dataset even further by manually recording which players returned from which sides. (Thanks to Jeff M for a big assist with this.) Thus, for over 3,000 points, we have the gender of the server and the returner. The following table shows several aggregates: Overall mixed doubles averages, typical performance for male and female servers, and rates for male and female returners, including serve points won, first-serve-in rates, and average first serve speed:

Subset           Hold%    SPW  First In  Avg 1st  
Average          76.0%  63.3%     66.2%    103.1  
Men serving      78.6%  65.1%     65.0%    110.2  
Women serving    72.4%  61.3%     67.6%     94.9  
Men returning        -  60.4%     64.6%    103.5  
Women returning      -  65.9%     67.6%    102.8

I was a bit surprised by how narrow the gap is between men and women serving. In men’s doubles at the Australian Open, servers won 67.8% of points, and in women’s doubles, servers won 58.5%. The pool of players is very similar, but in the mixed event, men won fewer serve points and women won more.

Perhaps there is more insight to be gained by looking at more specific matchups:

Server  Returner    SPW  First In  Avg 1st  
Male      Male    61.7%     63.5%    111.0  
Male      Female  68.1%     66.3%    109.5  
Female    Male    58.9%     66.0%     94.6  
Female    Female  63.3%     69.0%     95.1

Tactics appear to change a bit depending on the gender of the returner. Both men and women land more first serves when facing a female returner. However, first serve speed doesn’t vary much. This suggests that David Marrero–who got himself in hot water by possibly fixing a 2016 Australian Open mixed match and then making some questionable comments about inter-gender competition afterward–is unusual in his reluctance to hit hard against female opponents.

Interestingly, the averages from same-gender doubles matches pop up in this table. When men serve to women in mixed doubles, they win 68.1% of points, almost exactly the same rate of serve points won in men’s doubles. When women serve to men, they take 58.9% of points, just a bit higher than the usual rate in women’s doubles. This suggests that while the server-returner matchup is important, the gender of the net player is a key factor as well.

Beware of Melichar

Individual player results against each gender will tell us more, but a single tournament worth of no-ad, third-set super-tiebreak matches doesn’t give us a lot of data on many players. Many members of first-round losing teams served only 20-25 points each. Of the finalists, John Patrick Smith had the biggest gender gap, winning 54.9% of service points against men and 74.4% against women, and his opponent Barbora Krejcikova was similar, winning 59.6% against men and 73.0% against women. Their partners, Astra Sharma and Rajeev Ram, both had narrower gaps of just a few percentage points.

Over the course of the entire event, Sharma was the best server of the four, winning 69.7% of total service points compared to Ram’s 69.0%. But neither came close to semi-finalist Nicole Melichar, who won a whopping 78.4%, narrowly besting her partner, Bruno Soares, who won 77.7%. The Melichar/Soares duo appears to be particularly effective as a unit: Melichar won only 72.6% of service points in her three women’s doubles matches, and Soares won only 70.2% in his men’s doubles quarter-final run alongside Jamie Murray.

The first step toward analyzing any sporting event is simply understanding what’s going on. In the case of mixed doubles, a big part of that is getting a sense of the gender gap on both serve and return. There’s still a painful dearth of data–we now have a mere 31 matches with servers and returners identified for each point–but the next time you watch a mixed doubles match, you’ll be that much smarter about what to expect and what sorts of performances are worthy of further study.

Another Slam, Another Pointless Serve Clock

Italian translation at settesei.it

The 25-second serve clock has quickly become a regular feature on the ATP and WTA tours. After a few trials, it made a debut in the run-up to last year’s US Open, and has become broadly accepted since. The US Open and Australian Open both used the countdown timer, and the WTA will employ the devices at 2019 Premier events, with an eye toward the full slate of tournaments in 2020.

As I understand it, the goal of the serve clock is twofold: First, to keep matches shorter by holding players to a standard time limit between points; and second, to enforce that time limit fairly. Tennis and broadcasting execs are always looking for ways to make matches shorter (or, at least, more predictable in length), so the first goal fits in with broader aims. The second is more specific. Many of the players best known for using a long time between points are big stars, and umpires were thought to be reluctant to penalize them. In theory, a standardized serve clock should make enforcement more transparent and ensure fairness.

The success of the second goal is difficult to assess. In one regard, it seems to be working, because we haven’t heard many players complaining about the system. Progress toward the first goal is much easier to judge, and I’ve done so three times: Once after the 2018 Rogers Cup, once after the joint event in Cincinnati, and a third time following the US Open. Each time, the conclusion was clear: The serve clock did not speed up play, and in many cases, it coincided with slower matches.

Count down under

The simplest way to measure the speed of a tennis match is to use the official match time and number of points played, then calculate the number of seconds per point. It’s a crude technique, since the official match time includes time spent playing, pauses between points, changeovers, heat breaks, medical time outs, challenges, and short rain delays. It’s imperfect. But the time spent on changeovers and the like is usually fairly consistent, making comparisons possible.

Here is the average seconds per point for men and women at the 2018 and 2019 Australian Open, reflecting the pace of play both before and after the introduction of the serve clock:

Year  Men Sec/Pt  Women Sec/Pt  
2018        40.2          40.4  
2019        41.0          40.3

This doesn’t exactly constitute a ringing endorsement of the serve clock. On average, matches were a bit slower in 2019 than in 2018. On the other hand, it’s a better result than the 2018 US Open, which was about 2.5 seconds slower than the 2017 pre-serve clock edition.

More precision, still rather slow

As I said, this is a crude way of measuring match speed. For most tournaments, it’s the best we can do without access to proprietary data that the ATP and WTA (presumably) possess. But at the majors, more detailed information is available. At the US Open, and at the Australian Open until 2017, that was the IBM “Slamtracker” data. The Australian Open no longer works with IBM, but it displays similar point-by-point data on its website.

Armed with better data, we can offer more precise estimates of how often players have exceeded the 25-second limit, both before and after the introduction of the serve clock. (Before the timer, the official limit at slams was 20 seconds, but I don’t think that a single time violation was assessed before at least 25 seconds–or more–had elapsed.) After the US Open last year, I found the number of times that players exceeded 25 seconds increased dramatically, as did the frequency that they went over 30 seconds. If you’re interested, went into more methodological detail in that article.

Again, the Australian Open fares better than its American counterpart, but that doesn’t exactly mean the clock is working, just that it isn’t dramatically slowing things down. Here are some figures from the 2017 and 2019 Australian Opens (I didn’t collect the relevant data last year), showing how often players violated the time limit both before and after the introduction of the timer:

Time Between   2017   2019  Change (%)  
under 20s     77.6%  75.9%       -2.2%  
under 25s     91.6%  91.8%        0.2%  
over 25s       8.4%   8.2%       -1.7%  
over 30s       2.8%   2.1%      -25.2%

The last row of this table is the first point I’ve seen that indicates the serve clock is working. Players are exceeding 30 seconds between points far less often than they did two years ago. On the other hand, there’s almost no difference in how often they cross the 25-second mark. And another negative: The “improved” figure of 2.1% of points over 30 seconds is considerably worse than the same rate in New York last year, which was a mere 0.8%. The clock has eliminated some of the most egregious offenses in Melbourne, but a lot more remain.

Carpenters, not tools

The main problem continues to be the way the serve clock is used. The countdown begins when the score is called, and umpires generally wait until crowd noise has subsided before making their announcement. Thus, after exciting shots or long rallies–the very points after which players have historically taken a long time to serve–the time limit is effectively extended. There’s simply no reason for this. Start the timer when the point is over, and if the crowd is still going wild 20 or 25 seconds later, make the appropriate adjustments. But many servers are already playing “to” the serve clock, using all the time they are allotted. The longer the umpire waits to start the clock, the longer all of us must wait until play resumes.

My primary complaint with delayed clock-starting, though, is a different one. Yes, I’d like matches to move along faster. But as with just about every line in the rulebook, the time limit ends up being extended for stars more than it is for journeymen. On a stadium court like Rod Laver Arena, a modest ovation follows nearly every point played, especially those won by a big name like Federer, Nadal, or Serena. Out on Court 20, Johanna Larsson can play a bruising rally and earn nothing more than a polite golf clap. The more anonymous the player, the less recovery time. After a couple of matches, that adds up. A rule designed to increase fairness and transparency shouldn’t work against unknowns, but in this case, at majors, it appears to do just that.

Eventually, I may stop writing about the serve clock. But as long as the tours are pushing an innovation that fails to meet its stated goals, I’ll keep auditing the results. Given a few more years, maybe they’ll get it right.

Novak Djokovic and the Narrowing Slam Race

Italian translation at settesei.it

It doesn’t take a statistician, or even a spreadsheet, to recognize that the 2019 Australian Open wasn’t Novak Djokovic’s most difficult path to a major title. We can debate whether the straight-set win over Rafael Nadal in the final was due to Djokovic’s utter dominance or a subpar performance from (a possibly still recovering) Rafa. But there’s more to a grand slam title than the final, and the only top-18 opponent Novak faced in the first six rounds was Kei Nishikori, who retired after 52 minutes.

On the traditional grand slam leaderboard, quality of competition doesn’t matter. Roger Federer has 20, Nadal has 17, and now Djokovic has 15. As I’ve written before, the race is closer than that, since Nadal’s and Djokovic’s opponents have, on average, been stronger than Federer’s. My metric for “adjusted slams” estimates the likelihood that a typical major titlist would defeat the specific seven opponents that a player faced, based on their surface-weighted Elo at the time of the match. (I’ve also used this approach for Masters titles.) The explanation is a mouthful, but the underlying idea is simple: Some majors represent greater achievements than others, both because some eras offer stiffer competition and because some draws are particularly daunting.

A slam title against an average level of competition is worth exactly 1. Tougher paths are worth more than 1, and easier draws are worth less. Here is the current leaderboard, with each player’s raw tally, average difficulty rating of their titles, and adjusted total:

Player          Slams  Avg Diff  Adj Slams  
Roger Federer      20      0.88       17.7  
Rafael Nadal       17      1.01       17.1  
Novak Djokovic     15      1.11       16.6

(The numbers in this post do not all precisely agree with those I’ve published in the past, because I’ve improved the accuracy of my Elo-based rating system. All three of the players have seen their adjusted slam totals decrease, because the improved Elo algorithm eliminates some of the Elo “inflation” that overvalued recent achievements.)

These three guys have often had to go through each other, but Djokovic has had the toughest paths of all. The average difficulty of his first 12 majors was 1.2, higher than all but three of Rafa’s titles, one of Roger’s, and two of those won by Pete Sampras. Only recently has he been able to boost his total without quite so much of a challenge. His Australian Open title was worth 0.84 majors, only the fourth of his titles against a below-average set of opponents. It was, however, tougher than Wimbledon or the US Open last year, which were worth 0.77 and 0.65, respectively.

It’s unlikely, of course, that the current leaderboard–adjusted or otherwise–will be the final reckoning among these three men. But on the adjusted list, they will probably remain tightly packed. Because the rest of the pack has weakened, with Andy Murray and Stan Wawrinka no longer regular features of the second week, major titles aren’t what they used to be. Early in the decade, it wasn’t uncommon for a player to beat multiple members of the big four en route to a title and add at least 1.2 to his adjusted tally.

In 2018, slam difficulty was barely half of that recent peak level:

Year    Avg Diff  
2002        0.73  
2003        0.65  
2004        0.82  
2005        0.95  
2006        0.77  
2007        0.93  
2008        1.05  
2009        1.00  
2010        0.95  
2011        1.19  
2012        1.23  
2013        1.22  
2014        1.28  
2015        1.12  
2016        1.27  
2017        0.91  
2018        0.69

This could all change, especially if Djokovic wins a Roland Garros title by upsetting Nadal. (Nothing generates high competition-adjusted numbers like beating Nadal on clay.) But it’s more likely that these three men will have to keep incrementing their totals by 0.6s and 0.7s. While that could be enough to put Rafa or Novak on top by the end of the 2019, it won’t give anyone a commanding lead. It’s a good thing that there’s a lot more to the GOAT debate than slam totals, because slam totals–when properly adjusted for the difficulty of achieving them–make it awfully hard to pick a winner.

Australian Open Coverage at The Economist

I wrote three pieces for the Economist’s Game Theory blog in the last week. The most recent was on Novak Djokovic, who has been dominant on hard courts, but whose few hiccups have come mostly against young players:

Mr Medvedev [followed] a path blazed by Mr Tsitsipas. The Greek prospect allowed Mr Djokovic to hit backhands at a typical 46% clip. But by hitting harder, riskier shots to that side of his opponent, he took Mr Djokovic’s down-the-line weapon out of the game. Mr Djokovic typically sends about one-seventh of his backhands up the line, but against Mr Tsitsipas last summer, that number was cut in half, and Mr Djokovic failed to record a single winner in that direction. In the Melbourne final, Mr Nadal allowed the world’s top-ranked player far more freedom: Mr Djokovic hit one in five of his backhands down the line, and a quarter of those shots ended the point in his favour. Only once has Mr Nadal held his rival’s down-the-line rate below 10%: the 2013 US Open final, the last time the Spaniard got the better of one of their hard-court duels.

After the women’s final, I looked at Naomi Osaka’s accomplishments in comparison to other players in history who were so much younger than tour average. She fares very well by that measure:

Few women have achieved as much as Ms Osaka while being so much younger than tour members as a group. The average age of the top 50 is about 27, nearly six years older than the back-to-back major winner. Only four other players since 1985 have won majors while they were at least 5.5 years younger than the mean of their peers: Ms Williams, Martina Hingis, Maria Sharapova, and Jelena Ostapenko, who won the 2017 French Open but failed to maintain her place in the top ten. None of those players matched Ms Osaka’s feat of following her first grand slam championship by winning another at the first opportunity, and only Ms Hingis claimed her second grand slam within a year of her first. It is too much to predict of any young player that she match the career accomplishments of Ms Williams, whose big-serving style Ms Osaka emulates. But even matching the more modest feats of Ms Hingis and Ms Sharapova, who are tied with five slams apiece, would rank her among the all-time greats.

Finally, I covered Karolina Pliskova’s monumental quarter-final comeback against Serena Williams. There are few, if any, precedents for such a momentum shift in the modern era:

Because collecting point-by-point data for tennis matches is a fairly modern practice, we cannot know for sure where this turnaround ranks in the sport’s long history. But among the 2,300-odd women’s contests that have been manually recorded by volunteers for the Match Charting Project, an online repository of tennis data, there is no example of a greater collapse. Most of the project’s sample is composed of high-profile matches from the 21st century, but there are also a handful of grand-slam duels of yore. Tennis’s most notorious choking incident—when Jana Novotna seemingly lost the ability to hit the ball against Steffi Graf in the 1993 Wimbledon final, after serving for game point at 4-1 in the deciding set—looks unremarkable when compared to Ms Williams’ downfall, with a peak win probability of 95.6%.

Go read them all:

Podcast Episode 46: Australian Open Recap

Episode 46 of the Tennis Abstract Podcast, with Carl Bialik of the Thirty Love podcast, focuses on the eye-popping achievements of Australian Open champs Naomi Osaka and Novak Djokovic. With Osaka, we consider how much she has accomplished in a single year, and whether she has distanced herself from the WTA pack. With Djokovic, we wonder how anyone could ever beat him on a hard court, and how high he’ll climb on the all-time grand slam leaderboard.

Thanks for listening!

(Note: this week’s episode is about 66 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.

The Impact of Rafael Nadal’s New Serve

Italian translation at settesei.it

A couple of years ago, the story of the Australian Open was a certain veteran Swiss player’s new backhand. Roger Federer won the tournament, raced back up the rankings, and eventually reclaimed the No. 1 spot. This season has kicked off with another superstar, Rafael Nadal, attempting to shore up his own relative weakness by streamlining his serve.

The early results are extremely positive. Through the semi-final, Nadal’s first serves in Melbourne have averaged 115 mph, compared to 110 mph at the US Open last fall. He hasn’t been broken in five straight matches, dating back to the second round, and has faced only 13 break points in his last 15 sets. True, he hasn’t faced a truly tough test, as the draw has handed him only two seeds, neither in the top ten. But his lopsided results thus far could equally be ascribed to his own dominance. After all, he demolished Stefanos Tsitsipas only a few days after the Greek prospect ousted Federer.

Serve speed numbers are encouraging and lopsided wins are great for the body, but our focus should always be on points, and how many of them he’s winning. By that measure, Rafa’s retooled serve has excelled, helping the Spaniard post some of the best-ever serving numbers of his grand slam career.

In six matches, Nadal has won 80.9% of his first-serve points. (Fellow finalist Novak Djokovic has won 77.5% of his. Both numbers are outstanding, as the hard-court tour average is below 75%, a figure that includes the contributions of much more dominant servers.) At hard and grass court grand slams, Rafa has done better only twice: 83.6% at the 2010 US Open and 81.3% and Wimbledon in 2008. Here are his top-ten first-serve performances through the semi-finals at hard court majors:

Tournament            1st W%  2nd W%            
2010 US Open           83.6%   66.9%            
2008 Wimbledon         81.3%   64.3%            
2019 Australian Open   80.9%   58.0%            
2013 US Open           79.5%   64.7%            
2017 Wimbledon         79.4%   58.6%            
2011 Wimbledon         79.4%   59.4%            
2010 Wimbledon         79.3%   61.6%            
2006 Wimbledon         77.9%   62.1%            
2012 Wimbledon         77.3%   61.5%            
2012 Australian Open   76.8%   56.7%

You might notice a pattern at the top of this list: Those are slams that he went on to win. The 2010 US Open was his first hard court major title, sealed with a four-set win over Djokovic, his most dominant non-clay victory over his long-time rival. 2008 Wimbledon was his first title there, in the memorable final against Federer. The 2013 US Open was another relatively tidy triumph over Djokovic. All the Wimbledons that clutter the bottom half of this list are inflated a bit by the surface, and it is revealing that Rafa’s next-best performance at the Australian Open sits so far down the list, with his 76.5% first-serve mark in 2012. That fortnight didn’t end in his favor, but it took nearly six hours for Djokovic to beat him.

This is all encouraging and, at the very least, it will make for an interesting aspect of tomorrow’s final, between the newly dangerous serving of Nadal and the ever-brilliant return game of Djokovic. But with only six matches on record, it’s tough to push the analysis much further. Rafa was dominant against Tsitsipas, but barely better than he was against the Greek when they met in Canada last summer. In Australia, he won 80.3% of service points, including 85% of his firsts; in their previous meeting, he won 78.9% of service points and 93.8% of his firsts. A more positive comparison is between his fourth-round win over Tomas Berdych (75.3% service points won, 80.4% firsts) and his previous hard court meetings with the Czech (66.6%, 72.7%). On the other hand, they hadn’t played since 2015 and Berdych is returning from injury, so we can’t put too much weight on the comparison.

Nadal’s more pessimistic fans will be keeping an eye on his second serve in Sunday’s final, as that delivery has not demonstrated the same jump in effectiveness. In the six Melbourne matches, Rafa has won 58.0% of second-serve points, just barely above his career average of 57.3% at hard court majors. That relative weakness was exploited by Alex De Minaur, the best returner of his Aussie Open opponents, who held Nadal to a measly 36.4% of second serve points won. Djokovic is even better, neutralizing bigger second-serve weapons than Rafa’s, so it remains a concern.

If Nadal wins the title, his new serve will rightfully take much of the credit. Not only has it improved his effectiveness on that side of the ball, it has helped keep his matches short and his body ready for the challenges of hard court tennis. Years ago, I bucked the conventional wisdom and argued that Rafa could reach 17 slams. Since then, Federer has shifted the goalposts, but a bigger-serving Nadal makes 20 or 21 look more realistic than ever before.

Petra Kvitova’s Current Status: Low Risk, High Reward

Italian translation at settesei.it

For more a decade, Petra Kvitova has been one of the most aggressive women in tennis. She aims for the corners, hits hard, and lets the chips fall where they may. Sometimes the results are ugly, like a 6-4 6-0 loss to Monica Niculescu in the 2016 Luxembourg final, but when it works, the rewards–two Wimbledon titles, for starters–more than make up for it.

She’s currently riding another wave of winners. Her 11-match win streak–which has involved the loss of only a single set–puts her one more victory away from a third major championship. The 28-year-old Czech has gotten this far by persisting with her big-hitting style, but with a twist: In Melbourne, she’s not missing very often. While she’s ending as many points as ever on her own racket, she’s missing less often than many of her more conservative peers.

In her last five matches at the Australian Open, from the second round through the semi-finals, 7.9% of her shots (including serves) have resulted in unforced errors. In the 88 Petra matches logged by the Match Charting Project, that’s the stingiest five-match stretch of her career. In charted matches since 2010, the average WTA player hits unforced errors on 8.0% of their shots. So Kvitova, the third-most aggressive player on tour, is somehow making errors at a below-average rate. It’s high-risk, high-reward tennis … without the risk.

And it isn’t because her go-for-broke tactics have changed. In Thursday’s semi-final against Danielle Collins, her aggression score–an aggregate measure of point-ending shots including winners, induced forced errors, and unforced errors–was 30.5%, the third-highest of all of her charted matches since her 2017 return to the tour. Her overall aggression score in Melbourne, 28.2%, is also higher than her career average of 27.1%.

In other words, she’s making fewer errors, and the missing errors are turning into point-ending shots in her favor. The following graph shows five-match rolling averages of winners (and induced forced errors) per shot and unforced errors per shot for all charted matches in Kvitova’s career:

Even with the winner and error rates smoothed out by five-match rolling averages, these are still some noisy trend lines. Still, some stories are quite clear. This month, Kvitova is hitting winners at close to her best-ever rate. Her average since the second round in Melbourne has been 20.3%, as high as anything she’s posted before with the exception of her 2014 Wimbledon title. (I’ve never tried to adjust winner totals for surface; it’s possible that the difference can be explained entirely by the grass.)

And most strikingly, this is as big a gap between winner rate and error rate as she’s achieved since her 2014 Wimbledon title run. In fact, between the second round and semi-finals at that tournament, she averaged 8.1% errors and 20.0% winners. Both of her numbers in Australia this year have been a tiny bit better.

Best of all, the error rate has–for the most part–seen a steady downward trend since 2016. The recent error spike is largely due to her three losses in Singapore last October and a bumpy start to this season in Brisbane. We can’t write those off entirely–perhaps Kvitova will always suffer through weeks when her aim goes awry–but she appears to have put them solidly behind her.

None of this is a guarantee that Petra will continue to avoid errors in Saturday’s final against Naomi Osaka. I could’ve written something about her encouraging error rates before the tour finals in Singapore last fall, and she failed to win a round-robin match there. And Osaka is likely to offer a stiffer challenge than any of Kvitova’s previous six opponents in Melbourne, even if her second serve doesn’t. That said, a stingy Kvitova is a terrifying prospect, one with the potential to end the brief WTA depth era and dominate women’s tennis.