Author: jsackmann

A Closer Look at the Winner-Unforced Error Ratio

Italian translation at settesei.it

Few tennis statistics are more frequently cited than winners and unforced errors. Nearly every broadcast displays them, and the ratio between the two numbers is discussed during matches as much as any other metric in the game.

If we set aside the problems with unforced errors, the winner-unforced error (W/UFE) ratio does appear to have some value. Winners are unquestionably good, so more winners must be better than fewer winners. Errors are definitely bad, so fewer is better.

It’s one small step from those anodyne assumptions to the conventional wisdom that a player should aim to tally more winners than unforced errors, resulting in a ratio of 1.0 or more.

Like any metric, this one isn’t perfect. With the help of detailed stats from over 1,000 matches in Match Charting Project data, we can take a closer look.

Is the W/UFE ratio all it’s cracked up to be?

If you compare two players’ W/UFE ratio, you’ll find that the player with the better ratio almost always wins. No surprise there, since winners and unforced errors directly represent points won and lost.

It isn’t perfect, though. In both men’s and women’s matches, the player with the lower W/UFE ratio wins the match 11% of the time. Winners and unforced errors only represent about 70% of total points, so if the remaining 30% of points tilt heavily in one direction–especially in a close match–we’ll see an unexpected result.

Things get a little messier when we test the magic W/UFE ratio of 1.0. That’s the number commentators cite all the time, as if it is the line between winning and losing. W/UFE ratios differ quite a bit by gender, so we’ll need to look at men and women separately.

In the 512 men’s matches logged by the Match Charting Project, players recorded a ratio of 1.0 or better only 41.3% of the time. In over a quarter of those “successes,” though, they lost the match. That means we have plenty of false positives and false negatives:  losers who beat the target ratio as well as plenty of winners who failed to meet it.

Players who met or exceeded a 1.0 ratio won 74% of men’s matches. But the range just above the target–from 1.0 to 1.1–only resulted in wins about 60% of the time.

There’s no clear line separating a good ratio from a bad one: Even at 1.2 W/UFE, men only win about 70% of matches. As low as 0.8, they win nearly half.

Much of the problem here is that players influence each others’ numbers. Against a defensive baseliner, an average player will see his winners decrease and his unforced error count rise. In that hypothetical match, both players will have ratios below 1.0. Against an aggressive, big server, that same player will hit more winners, and because rallies end sooner, will tally fewer unforced errors. That scenario will often give you two ratios above 1.0.

A different story for women

In the sample of 552 women’s matches, players only recorded W/UFE ratios of 1.0 or better 26% of the time. Because the average ratio is so low–about 0.7–there aren’t very many false positives. Players who met the 1.0 standard won 89% of matches.

For women, a more reasonable target is in the 0.85 range. It’s roughly equivalent to 1.2 for men, in that a ratio at that level translates into about a 70% chance of winning.

There’s certainly no magic number. Even if we settle on revised targets like 0.85, winner and unforced error counts leave out too much data. In yesterday’s up-and-down match between Sara Errani and Jelena Ostapenko, Errani tallied 11 winners against 24 unforced. Ostapenko struck 54 winners against 49 unforced. A 0.46 ratio, like Errani’s, results in a win only 29% of the time, while a 1.1 ratio, like Ostapenko’s, is good for a victory 87% of the time. Yet, Errani is the one still standing.

Targeting the components

The Errani-Ostapenko match suggests another way of looking at the subject. Errani’s ratio was dreadful, but by keeping her unforced error rate low, she achieved at least half of the goal, leading to more Ostapenko errors. And while Ostapenko hit tons of winners, her own unforced error count was high enough to keep Errani in the match.

Looking at winners and unforced errors independently still doesn’t give us any magic numbers, but it does tell us more than the W/UFE ratio reveals by itself. Errani committed unforced errors on only 14% of points, which–taken by itself–results in a win about 70% of the time. Ostapenko’s error rate of 28% translates into success only 20% of the time.

By isolating the two components of the ratio, we can come up with clear targets for each. In women’s tennis, an error rate between about 14% and 16%–taken by itself–results in a 70% chance of winning. Consider winners independently, and we see that a winner rate of 19% to 20% also implies a 70% chance of victory.

These findings also cast a bit of light on another frequent question: Which is more important, increasing winners or decreasing errors? Based on this evidence, the answer is decreasing errors, but only by a whisker–and only in women’s matches. The player with more winners claims 68% of contests, while the player with fewer errors wins 73% of matches. A more sophisticated look, in which I separated all matches into buckets based on winner rate and error rate, suggests an even narrower margin. The relationship between error rate and winning percentage was very slightly stronger (r^2 = 0.92) than the relationship between winner rate and winning percentage (r^2 = 0.90).

Men’s components

For men, the 70% thresholds are different. Taken alone, a winner rate of about 22% will get you a 70% chance of winning. An unforced error percentage of 15% will achieve the same goal.

The relative importance of winners and unforced errors is different on the ATP tour, perhaps because aces–which are counted as winners–are such a large part of the game. Again, the difference is minor, but here, the relationship between winner rate and winning percentage is a bit stronger (r^2 = 0.94) than the relationship between error rate and winning percentage (r^2 = 0.92).

I’m almost done

Most men play plenty of matches in which they meet the W/UFE target of 1.0 and still lose. Most women fail to reach the 1.0 standard much of the time, and some players, like Errani, put together excellent careers despite almost never reaching it. We could do a lot better.

For a generic rule-of-thumb, the W/UFE target ratio of 1.0 isn’t horrible. But as we’ve seen, a slightly more nuanced view–one that takes into account the differences between men and women, as well as the independent value of winner rate and error rate–would be considerably more valuable.

The Myth of the Tricky First Meeting

Italian translation at settesei.it

Today, both Roger Federer and Stan Wawrinka will play opponents they’ve never faced before. In Federer’s case, the challenger is Steve Darcis, a 31-year-old serve-and-volleyer playing in his 22nd Grand Slam event. Wawrinka will face Hyeon Chung, a 19-year-old baseliner in only his second Slam draw.

For all those differences, both Federer and Wawrinka will need to contend with a new opponent–slightly different spins, angles, and playing styles than they’ve seen before.  In the broadcast introduction to each match, we can expect to hear about this from the commentators. Something along the lines of, “No matter what the ranking, it’s never easy to play someone for the first time. He’s probably watched some video, but it’s different being out there on the court.”

All true, as even rec players can attest. But does it matter? After all, both players are facing a new opponent. While Darcis, for example, has surely watched a lot more video of Federer than Roger has of him, isn’t it just as different being out on the court facing Federer for the first time?

Attempting to apply common sense to the cliche will only get us so far. Let’s turn to the numbers.

Math is tricky; these matches aren’t

Usually, when we talk about “tricky first meetings,” we’re referring to these sorts of star-versus-newcomer or star-versus-journeyman battles. When two newcomers or two journeymen face off for the first time, it isn’t so notable. So, looking at data from the last fifteen years, I limited the view to matches between top-ten players and unseeded opponents.

This gives us a pretty hefty sample of nearly 7,000 matches. About 2,000 of those were first meetings. Even though the sample is limited to matches since 2000, I checked 1990s data–including Challengers–to ensure that these “first meetings” really were firsts.

Let’s start with the basics. Top-tenners have won 86.4% of these first meetings. The details of who they’re facing doesn’t matter too much. Their record when the new opponent is a wild card is almost identical, as is the success rate when the new opponent came through qualifying.

The first-meeting winning percentage is influenced a bit by age. When a top-tenner faces a player under the age of 24 for the first time, he wins 84.6% of matches. Against 24-year-olds and up, the equivalent rate is 88.0%. That jibes with what we’d expect: a newcomer like Chung or Borna Coric is more likely to cause problems for a top player than someone like Darcis or Joao Souza, Novak Djokovic‘s first-round victim.

The overall rate of 86.4% doesn’t do justice to guys like Federer. As a top-tenner, Roger has won 95% of his matches against first-time opponents, losing just 8 of 167 meetings. Djokovic, Rafael Nadal, and Andy Murray are all close behind, each within rounding distance of 93%.

By every comparison I could devise, the first-time meeting is the easiest type of match for top players.

The most broad (though approximate) control group consists of matches between top-tenners and unseeded players they have faced before. Favorites won 76.9% of those matches. Federer and Djokovic win 91% of those matches, while Nadal wins 89% and Murray 86%. In all of these comparisons, first-time meetings are more favorable to the high-ranked player.

A more tailored control group involves first-time meetings that had at least one rematch. In those cases, we can look at the winning percentage in the first match and the corresponding rate in the second match, having removed much of the bias from the larger sample.

Against opponents they would face again, top-tenners won their first meetings 85.1% of the time. In their second meeting, that success rate fell to 80.2%. It’s tough to say exactly why that rate went down–in part, it can be explained by underdogs improving their games, or learning something in the first match–but to make a weak version of the argument, it certainly doesn’t provide any evidence that first matches are the tough ones.

It may be true that first matches–no matter the quality of the opponent–feel tricky. It’s possible it takes more time to get used to first-time opponents, and that those underdogs are more likely to take a first set, or at least push it to a tiebreak. That’s a natural thing to think when such a match turns out closer than expected.

Whether or not any of that is true, the end result is the same. Top players appear to be generally immune to whatever trickiness first meetings hold, and they win such contests at a rate higher than any comparable set of matches.

Certainly, Fed fans have little to worry about. Most of his first-meeting losses were against players who would go on to have excellent careers: Mario Ancic, Guillermo Canas, Gilles Simon, Tomas Berdych, and Richard Gasquet.

His last loss facing a new opponent was his three-tiebreak heartbreaker to Nick Kyrgios in Madrid, only his third first-meeting defeat in a decade. As a rising star, Kyrgios fits the pattern of Fed’s previous first-meeting conquerors. Darcis, however, looks like yet another opponent that Federer will find distinctly not tricky.

Will the US Open First-Round Bloodbath Benefit Serena Williams?

After only two days of play, the US Open women’s draw is a shell of its former self.

Ten seeds have been eliminated, only the fifth time in the 32-seed era that the number of first-round upsets has reached double digits. Four of the top ten seeds were among the victims, marking the first time since 1994 that so many top-tenners failed to reach the second round of a Grand Slam.

Things are particularly dramatic in the top half of the draw, where Serena Williams can now reach the final without playing a single top-ten opponent. In a single day of play, my (conservative) forecast of her chances of winning the tournament rose from 42% to 47%, only a small fraction of which owed to her defeat of Vitalia Diatchenko.

However, plenty of obstacles remain. Serena could face Agnieszka Radwanska or Madison Keys in the fourth round, and then Belinda Bencic–the last player to beat her–in the quarters. A possible semifinal opponent is Elina Svitolina, a rising star who took a set from Serena at this year’s Australian Open.

The first-round carnage didn’t include most of the players who have demonstrated they can challenge the top seed. Five of the last six players to beat Serena–Bencic, Petra Kvitova, Simona Halep, Venus Williams, and Garbine Muguruza–are still alive. Only Alize Cornet, the 27th seed who holds an improbable .500 career record against Serena, is out of the picture.

What’s more, early-round bloodbaths haven’t, in the past, cleared the way for favorites. In the 59 majors since 2001, when the number of seeds increased to 32, the number of first-round upsets has had little to do with the likelihood that the top seed goes on to win the tournament.

In 18 of those 59 Slams, four or fewer seeds were upset in the first round. The top seed went on to win five times. In 22 of the 59, five or six seeds were upset in the first round, and the top seed won eight times.

In the remaining 19 Slams, in which seven or more seeds were upset in the first round, the top seed won only five times. Serena has “lost” four of those events, most recently last year’s Wimbledon, when nine seeds fell in their opening matches and Cornet defeated her in the third round.

This is necessarily a small sample, and even setting aside statistical qualms, it doesn’t tell the whole story. While Serena has failed to win four of these carnage-ridden majors, she has won three more of them when she wasn’t the top seed, including the 2012 US Open, when ten seeds lost in the first round and Williams went on to beat Victoria Azarenka in the final.

Taken together, the evidence is decidedly mixed. With the exception of Cornet, the ten defeated seeds aren’t the ones Serena would’ve chosen to remove from her path. While her odds have improved a bit on paper, the path through Keys, Bencic, Svitolina, and Halep or Kvitova in the final is as difficult as any she was likely to face.

The Unalarming Rate of Grand Slam Retirements

Italian translation at settesei.it

Yesterday, Vitalia Diatchenko proved to be even less of a match for Serena Williams than expected. She retired down 6-0, 2-0, winning only 5 of 37 points. She also sparked the usual array of questions about how Grand Slam prize money–$39,500 for first-round losers–incentivizes players to show up and collect a check even if they aren’t physically fit to play.

Diatchenko wasn’t the only player to exit yesterday without finishing a match. Of the 32 men’s matches, six ended in retirement. On the other hand, none of those were nearly as bad. All six injured men played at least two sets, and five of them won a set.

The prominence of Serena’s first-round match, combined with the sheer number of Monday retirements, is sure to keep pundits busy for a few days proposing rule changes. As we’ll see, however, there’s little evidence of a trend, and no need to change the rules.

Men’s slam retirements in context

Before yesterday’s bloodbath, there had been only five first-round retirements in the men’s halves of this year’s Grand Slams. The up-to-date total of 11 retirements is exactly equal to the annual average from 1997-2014 and the same as the number of first-round retirements in 1994.

The number of first-round Slam retirements has trended up slightly over the last 20 years. From 1995 to 2004, an average of ten men bowed out of their first-round matches each year. From 2005 to 2014, the average was 12.2–in large part thanks to the total of 19 first-round retirements last season.

That rise represents an increase in injuries and retirements in general, not a jump in unfit players showing up for Slams. From 1995 to 2004, an average of 8.5 players retired or withdrew from Slam matches after the first round, while in the following ten years, that number rose to 10.8.

Retirements at other tour-level events tell the same story. At non-Slams from 1995-2004, the retirement rate was about 1.3%, and in the following ten years, it rose to approximately 1.8%. (There isn’t much of a difference between first-round and later-round retirements at non-Slams.)

Injury rates in general have risen–exactly what we’d expect from a sport that has become increasingly physical. Based on recent results, we shouldn’t be surprised to see more retirements in best-of-five matches, as most of yesterday’s victims would’ve survived to the end of a best-of-three contest.

Women’s slam retirements

In most seasons, the rate of first-round retirements in women’s Grand Slam draws is barely half of the corresponding rate in other tour events.

In the last ten years, just over 1.2% of Slam entrants have quit their first-round match early. The equivalent rate in later Slam rounds is 1.1%, and the first-round rate at non-Slam tournaments is 2.26%. Diatchenko was the fifth woman to retire in a Slam first round this year, and if one more does so today, the total of six retirements will be exactly in line with the 1.2% average.

One painful anecdote isn’t a trend, and the spotlight of a high-profile match shouldn’t give any more weight to a single data point. Even with the giant checks on offer to first-round losers, players are not showing up unfit to play any more often than they do throughout the rest of the season.

Is Serena Williams Taking Advantage of a Weak Era?

tl;dr: No.

Serena Williams is, without question, the best player in women’s tennis right now. She’s held that position off and on for over a decade, and it’s easy to make the case that she’s the best player in WTA history.

The longer one player dominates a sport, the tougher it is to distinguish between her ability level and the competitiveness of the field. Is Serena so successful right now because she is playing better than any woman in tennis history, or because by historical standards, the rest of the pack just isn’t very good?

As we’ll see, the level of play in women’s tennis has remained relatively steady over the last several decades. While there is no top player on tour these days who consistently challenges Serena as Justine Henin or peak Venus did, the overall quality of the pack is not much different than it has been at any point in the last 35 years.

Quantifying eras

Every year, a few new players break in, and a few players fade away. If the players who arrive are better than those who leave, the level of competition gets a bit harder for the players who were on tour for both seasons. That basic principle is enough to give us a rough estimate of “era strength.”

With this method, we can compare only adjacent years. But if we know that this year’s field is 1% stronger than last year’s, and last year’s field was 1% stronger than the year before that, we can calculate a comparison between this year’s field and that of two years ago.

Since 1978, the level of play has fluctuated within a range of about 10%. The 50th-best player from a strong year–1995, 1997, and 2006 stand out–would win 7% or 8% more points than the 50th-best player from a weak year, like 1982, 1991, and 2005. That’s not a huge difference. One or two key players retiring, breaking on to the scene, or missing substantial time due to injury can affect the overall level of play by a few percentage points.

The key here is that a dominant season in the mid-1980s isn’t much better or worse than a dominant season now. Perhaps Martina Navratilova faced a stiffer challenge from Chris Evert than Serena does from Maria Sharapova or Simona Halep, but that difference is at least partially balanced by a stronger pack beyond the top few players. Serena probably has to work harder to get through the early rounds of a Grand Slam than Martina did.

Direct comparisons

So, Serena’s great, and her greatness isn’t a mirage built on a weak era. Using this approach, how does she compare with the greats of the past?

Given an estimate of each season’s “pack strength,” we can rate every player-season back to 1978. For instance, if we approximate Serena’s points won in 2015 (based on games won and lost), we get a Dominance Ratio (the ratio of return points won to serve points lost) of 2.15. In layman’s terms, that means that she’s beating the 50th-ranked player in the world by a score of 6-1 6-1 or 6-1 6-2. The 2.15 number means she’s winning 115% more return points than that mid-pack opponent. If the pack were particularly strong this season, we’d adjust that number upwards to account for the level of competition.

Repeat the process for every top player, and we find some interesting things.

Serena’s 2.15–the second-best of her career, behind 2.19 in 2012–is extremely good, but only the 21st-ranked season since 1978. By this metric, the best season ever was Steffi Graf‘s 1995 campaign, at 2.42, with Navratilova’s 1986 and Evert’s 1981 close behind at 2.38.

Graf has seven of the top 20 seasons since 1978, Navratilova has four, and Evert has three. Venus’s 2000 ranks sixth, while Henin’s 2007 ranks tenth.

It seems to have become harder to post these extremely high single-season numbers. In the last ten years, only Serena, Henin, Sharapova, Kim Clijsters, and Lindsay Davenport have posted a season above 2.0. Serena has done so four times, making her the only player in that group to accomplish the feat more than once.

Best ever?

As we’ve seen in comparing Serena’s best seasons to those of the other greatest players in WTA history, it’s far from clear that Serena is the greatest of all time. Graf and Navratilova set an incredibly high standard, and since the greats all excelled in slightly different ways, against different peer groups, picking a GOAT may always be a matter of personal taste.

Assigning a rating to the current era, however, isn’t something we need to leave up to personal taste. I’m confident in the conclusion that Serena is not simply padding her career totals against a weak era. If anything, her own dominance–during an era when dominating the women’s game seems to be getting harder–is making her peers look weaker than they are.

Ivo Karlovic and His Remarkable 10,466* Aces

Italian translation at settesei.it

Here’s the official story: This week, Ivo Karlovic crossed the much-heralded 10,000-ace milestone. Next up is the all-time record of 10,183 aces, held by Goran Ivanisevic.

Karlovic is one of the greatest servers in the game’s history, and he has in fact hit more than 10,000 aces. Ivanisevic was really good at serving, too, and he might even hold the all-time record. But when it comes down to the details in this week’s ATP press releases, all the numbers are wrong.

Last year, Carl Bialik laid out the two main problems with ATP ace records:

  • The ATP doesn’t have any stats from before 1991. (Ivanisevic started playing tour-level matches in 1988.)
  • ATP totals don’t include aces from Davis Cup matches, even though Davis Cup results are counted toward won-loss records and rankings.

I’ll add one more: There are plenty of other matches since 1991 with no recorded ace counts, too. By my count, we don’t have stats for 14 of Ivanisevic’s post-1991 matches. (They’re not on the official ATP site, anyway.) That doesn’t count Davis Cup, the Olympics (also no stats), and the now-defunct Grand Slam Cup.

If you like tracking records and comparing the best players from different eras, tennis might not be your sport. All of these problems exist for players who retired only recently, and some of the issues persist to the present day. And if you want to compare Federer or Ivanisevic with, say, Boris Becker or–it’s tough to write this without laughing–Pancho Gonzalez, you’re completely out of luck.

We’ll probably never find ace totals from all of the missing matches. But it seems silly to pretend we can identify the true record-holder and celebrate when these “records” are broken when we so obviously cannot.

Approximate* career* totals*

What we can do is estimate the number of missing aces for each of the top contenders. In Ivanisevic’s case, his 1988-90 seasons, combined with Davis Cup and other gaps in the record, total nearly 200 matches. Even if we can’t pinpoint the exact number of uncounted aces, we can come up with a number that demonstrates just how far ahead of Karlovic he currently stands.

To fill in the gaps, I calculated each player’s rate of aces per game for each surface for every season he played. For 1988-90, I used 1991 rates. (This post at First Ball In, which I discovered after writing mine, suggests that players improve their ace rates the first few seasons of their careers, so we should adjust a bit downward. That may be right. A 5% penalty for Goran’s 1988-90 knocks off about 60 aces from his total below.)

Once we crunch the numbers, we get an estimated 2,368 aces in Ivanisevic’s 195 “missing” matches. That gives him a career total of 12,551–a mark Karlovic couldn’t achieve until the end of 2017, if then.

But wait–Ivo has some missing matches, too! The gaps in his record only amount to 21 matches, mostly Davis Cup. The same approximation method adds 466 aces to his record, meaning he hit that 10,000th ace back in June, in his second-rounder against Alexander Zverev. Even with those nearly 500 “extra” aces, Ivanisevic’s record is almost surely out of reach.

What about Pete Sampras? Officially, Pete is fifth on the all-time list, with 8,858 aces. But like Goran, he played a lot of matches before record-keeping began in 1991. His ace record is missing nearly 200 matches, as well.

In Sampras’s case, we can estimate that he hit 1,815 aces that aren’t reflected in his official total. (In line with the caveat regarding Goran’s total above, we might want to knock that total down by 50 to reflect the possibility that he hit more aces in 1991 than in 1988-90.)

Making similar minor adjustments to the other members of the top five, Federer and Andy Roddick, here’s what the all-time list should look like, at least in general terms:

Player      Official  Est Missing  Est Total  
Ivanisevic     10183         2368      12551
Sampras         8858         1815      10673  
Karlovic       10022          466      10488  
Federer         9279          524       9803  
Roddick         9074          694       9768

Coincidentally, Karlovic is officially within 200 aces of  Ivanisevic’s all-time record, and while he really isn’t anywhere near the record, he is that close our estimate of Sampras’s second-place total.

We can be confident that Ivo is a great server. But if we can’t be sure of his own ace total, mostly amassed in the last decade, it seems foolish to pretend that we’ll know when–or even if–he breaks the all-time record.

The Match Charting Project hits 1,000!

In less than two years since I first introduced the Match Charting Project and asked for the help of volunteer contributors, we’ve reached a major milestone: 1,000 matches!

I can hardly tell you how excited I am about this. When the concept behind the project was first suggested to me in 2012, I hesitated to act, in part because I didn’t think I could convince enough other people of the project’s merits to build a dataset of this size. I’ve been proven hugely wrong. Even at the beginning of 2015, I figured we’d be lucky to hit the four-figure barrier by the end of the calendar year. Instead, we’ve added matches at a faster pace than ever.

 

Thanks to MCP contributors, the tennis research community now has access to a standardized dataset of 144,000 points and 580,000 shots. Nothing like this has ever existed in a form that is available to anyone who wants to pursue their own research projects.

I want to take this opportunity to thank all of the 50+ MCP contributors. Special mention is owed to Lowell, who with 141 matches is our most prolific charter and who is a big reason why the WTA is even more extensively represented in the database than is the ATP. I’d also like to single out Edo, who started contributing less than three months ago and has already added 43 matches to the tally, including many Grand Slam finals.

The first 1,000 is, I hope, just a beginning. Please consider contributing to the project–download the spreadsheet and read more about how it works here.

To keep up with the project, you can always find the full list of charted matches here, or a list organized by player here. I plan to post a bit more about the Match Charting Project next week here at Heavy Topspin, as well.

Nick Kyrgios and the Minimum Viable Return Game

Italian translation at settesei.it

No matter how well a player serves, he still needs to win some return points. While one-dimensional ATPers such as Ivo Karlovic and John Isner have demonstrated that an unbreakable serve alone can get you a steady paycheck and some quality time in the top 20, their playing style has never translated into a prolonged stay in the top ten.

Nick Kyrgios isn’t quite as tall as Isner or Karlovic, but his numbers are similar. In the last year, he has won 31.7% of return points, third-worst among the top 50, ahead of only those two players. In fact, since 1991, only five players have lasted a full season at tour-level while winning a lower percentage of return points. To make an impact in the upper echelon of the men’s game, the Australian will need to improve his return game in a big way.

To win matches, you need to break serve or win tiebreaks, and most players don’t demonstrate any particular tiebreak skill. That leaves breaks of serve, and to break serve, you need to win return points. Almost all ATP tour regulars win between 29% and 43% of return points, so a single percentage point or two is a meaningful distinction. While Milos Raonic‘s rate of return points won over the last 52 weeks is a Kyrgios-comparable 32.1%, no other top-ten player is below 36%.

If Kyrgios is to crack the top ten without any substantial improvement in his return game, Raonic is the model. Last year, Milos finished the season at #8 in the rankings despite having won only 33.7% of return points. That’s the lowest rate on record for a player with a year-end ranking in the top ten, and only the seventh time since 1991 that a RPW% below 35% earned someone a spot in the top ten.

Even at 33.7%–two percentage points higher than Kyrgios’s current rate–it took a remarkable run of tiebreak success for Raonic to win as many matches as he did. Milos won 75% of tiebreaks last year, a rate that almost no one has ever sustained beyond a single season. In other words, if Raonic is to continue winning matches at the same pace, he’ll probably need to post better return-game results.

To earn a place in the elite of the top five, the return-game threshold is even higher. Only two players–Pete Sampras and Goran Ivanisevic–have finished a season in the top five with a RPW% below 36%, and only two more–Andy Roddick and Stanislas Wawrinka–have done so with a sub-37% RPW%. Roger Federer, the most serve-oriented of the big four, hasn’t posted a RPW% below 38% in fifteen years.

The difference between 32% and 36% is enormous. To use a baseball analogy, a similar gap in batting average would be, roughly, from .240 to .280. The effects are equally meaningful. At 32%, a player is breaking serve roughly once per eight return games–considerably less than once per set. At 36%, he’s breaking serve almost once per five return games. Improve a few more percentage points to 39%, and he’s breaking every fourth game, almost twice as often as Kyrgios is now.

Those break rates are simply a way of quantifying what we already know at a general level: Players with strong return games have the power to decide matches. The more one-dimensional the playing style, the more likely a match is decided by just a few key points. And the smaller that number of points, the more that luck plays a part.

Of course, luck cuts both ways. It’s what makes players like Isner and Kyrgios so dangerous. Someone like Novak Djokovic or Rafael Nadal can usually dictate play, but against an unbreakable opponent, it all comes down to a few points in a couple of tiebreaks. So big servers tend to rocket into the top 30 or 40. A fifty-fifty winning percentage, especially coupled with a big upset and an occasional deep run at a big tournament, is plenty good enough to earn a spot that high in the rankings.

But without at least a mediocre return game, it’s tough for a big server to get beyond that level. Isner has managed it by winning tiebreaks at one of the best rates of all time, and even he has barely dipped his toe in the top ten. Raonic is a substantially better returner than the American, and it remains to be seen whether he can sustain his impressive tiebreak winning percentage and keep a spot among the game’s best.

Fortunately, Kyrgios has plenty of time to improve and break out of the mold of a one-dimensional big server. If he hopes to make a mark beyond the occasional upset and a home at the fringes of the top 20, that’s exactly what he’ll need to do.

Kei Nishikori’s Unbeatable Run in Deciding Sets

Italian translation at settesei.it

When Kei Nishikori defeated Roberto Bautista Agut in last week’s Barcelona quarterfinals, it was the seventh time in a row that he won a deciding set. By Nishikori’s standards, that’s nothing special. It is the fifth time in his career he’s put together a string of at least seven straight deciding-set wins, three of which he’s recorded since the beginning of last season.

The wider the perspective, the more impressive Kei’s deciding-set record. Since last year’s Australian Open, he’s won 27 of 30 matches that went the distance, including a 13-match winning streak from Halle to London. Back in 2011-12, he won 16 deciding sets in a row, including four against top-ten players.

In his career on tour, Nishikori has won 75 deciding-set matches and lost 20, for a winning percentage of 79%. Using any reasonable minimum number of matches, no other player has come close to that mark. You might recognize some of the other names on this list, ranked by record in deciding sets (minimum 80 matches):

Kei Nishikori   78.9%  
Bjorn Borg      74.7%  
Novak Djokovic  74.1%  
Jimmy Connors   69.8%  
Rafael Nadal    69.5%  
Andy Murray     69.4%  
Rod Laver       68.4%  
John McEnroe    68.1%  
Pete Sampras    68.0%

Kei’s career accomplishments don’t quite stack up with those of this crowd, but in terms of deciding-set performance, we’re looking at much more than an early-career fluke. While his numbers are a bit padded by matches that shouldn’t have gone the distance (like his early-round hiccups in Memphis this year against Ryan Harrison and Austin Krajicek), he has been almost as good when facing the best players in the game. Against top-ten opponents, he’s 17-6, good for a 74% winning percentage–a mark that would still put him near the top of the list.

Let’s return to Nishikori’s outrageous recent record of 27-3 in his last 30 deciding sets. Sure enough, no one has ever done better. Nine other players  have posted an equal mark in a span of 30 deciding-set matches, including Novak Djokovic, Rafael Nadal, Roger Federer, and Nishikori’s coach Michael Chang. Amazingly, Kei himself had already gone 27-3, back in 2011-12.

To break the tie among these accomplishments, we might look at the difficulty of the 30-match span, as measured by deciding sets against top-tenners. When Djokovic went 27-3, between 2011 Dubai and 2012 Canada, he played 15 of those matches against top-ten opponents,winning 14 of them. (Novak is also 27 of his last 30, including 15 of 17 against top-tenners.) When Nadal had his run, between 2008 Dubai and 2009 Paris, he faced 12 top-tenners, beating 10. Kei has faced only six, winning five.

It’s clear that Nishikori’s deciding-set prowess is a skill, not just a statistical fluke built on easy draws and luck. And based on the performance of the other players who have put together equally impressive deciding-set streaks, we can expect Kei to win most of his upcoming three- and five-setters.

Including streaks that overlap, there have been 27 instances in ATP history when a player won 27 of 30 deciding-set matches, excluding Kei’s and Novak’s current spans. In the ten deciding-set matches that followed each of those streaks, in each instance the player won at least five, and the average was just under seven.

Only once in ATP history has a player gone 27-3 in deciding-set matches and followed it up by winning nine of his next ten. If Nishikori is to match or better that mark, at least he’s assembled the right team: The player he’s chasing is Michael Chang.

Novak Djokovic and the Best Fifty-Match Stretches

Few players have ever been as dominant as Novak Djokovic is right now. Over his last fifty matches, he has posted stats that are almost too good to be believed:

Armed with stats going back 25 years, we can see how Djokovic’s current performance compares with the best in recent ATP history. In some categories, he is indeed atop the list. In others, he’s merely very close to the best ever.

Let’s start with the simple matter of won-loss record. 47 wins in 50 matches is excellent by any standard. Only four players–Roger Federer, Rafael Nadal, Thomas Muster, and Djokovic himself–have done better. Pete Sampras also won 47 of 50 in a stretch in 1993-94.

The category in which Djokovic most clearly stands out is his performance against top-10 opponents. His 21 top-10 wins in a 50-match stretch outpaces the best of Nadal (18, in 2013), Federer (17, in 2006-07), and Andre Agassi (17, in 1994-95). Only 12 different players have won ten top-10 matchups in a 50-match stretch, let alone 20. Novak’s 23 top-10 matches is also the highest on record.

Then there are the bagels. In this span, Djokovic has won 13 sets by a 6-0 score. That’s not quite the best: Federer won 14 in his 2006-07 stretch. Sergi Bruguera (1993) and Agassi (1992-93) also show up here, with 13 bagels over the course of 50 matches.

Finally, let’s turn to aggregate statistics. Dominance Ratio (DR) is the ratio of return points won to serve points lost, and serves as a simple yardstick for–you guessed it–dominance. A DR of 1.0 indicates the two players were equal, 1.1 is a narrow win, and anything in the 1.5 range is a comfortable victory.

As Carl noted in that tweet, Djokovic has maintained a DR of nearly 1.5 over his last 50 matches. That’s not the best of all time–in fact, it’s not even Novak’s best. From 2013 Cincinnati to the second round of 2014 Monte Carlo, Djokovic posted a cumulative DR of 1.49, just edging out his current streak.

But neither mark is number one on the list. As with so many other categories, this one belongs to 2006 Federer. From the 2006 Halle final through the end of the 2007 Australian Open, Fed won 49 of 50 matches, 16 of 16 matches against the top 10, served 14 bagels, and posted an overall DR of 1.54. It would take an extremely strong performance from Djokovic over the next few weeks–even by his own standards–to reach those heights.

If you prefer the more traditional metric of total points won, Fed is still your number one, at 56.84% over that 2006-07 span. A different streak of Novak’s–his historic 2011 run–comes in a very, very close second, at 56.77%. Nadal put together a stretch in 2012-13 of 56.6%. The entire top ten is dominated by these three guys; the only other player who has won more than 56% of total points over 50 matches is Guillermo Coria, who did so in 2003.

Comparing Novak’s current streak to the rest of the field merely emphasizes how much distance he has placed between himself and the pack. Federer’s DR over his last 50 matches is a very respectable 1.37, with Nadal not far behind at 1.29. Kei Nishikori and Milos Raonic aren’t far behind in the official rankings, but by this measure they have an immense amount of ground to make up, with cumulative DRs of 1.17 and 1.16, respectively.

For Djokovic right now, a number that starts with 1.1 is a bad day. In his last 50 matches, he has sunk below 1.2 only seven times. Whichever metric you prefer, we’re watching one of the great performances of modern tennis history.