Heavy Topspin – Page 92 – The Tennis Abstract blog

Is Kevin Anderson Developing Into an Elite Player?

With his upset win over Andy Murray on Monday, Kevin Anderson reached his first career Grand Slam quarterfinal. At age 29, he’ll ascend to a new peak ranking, and with a bit of cooperation from the rest of the draw, one more win could put him in the top ten for the first time.

Anderson has been a stalwart in the top 20 for two years now, but this additional step comes as a bit of a surprise. Despite the overall aging of the ATP tour and the emergence of Stan Wawrinka as a multi-Slam champion, it’s still a bit difficult to imagine a player in his late twenties taking major steps forward in his career.

What’s more, Anderson’s game is very serve-dependent. With an excellent backhand, he isn’t as one-dimensional a player as John Isner, Ivo Karlovic, or perhaps even Milos Raonic, but it’s much easier to categorize him with those players than with more baseline-oriented peers.

In today’s game, it is very difficult to reach the very top ranks without a quality return game. Tiebreaks are too much of a lottery to depend on in the long-term; you have to consistently break serve to win matches. As I wrote in a post about Nick Kyrgios earlier this year, almost no players have finished a season in the top ten without winning at least 37% of return points. Anderson has achieved that mark only once, in 2010. Entering the US Open this year, he was winning only 34.2% of return points.

The only top-ten player this year with a lower rate of return points won is Raonic, at 30.2%. Raonic is a historical anomaly, and as his tiebreak winning percentage has tumbled, from a near-record 75% last year to a more typical 51% this year, his place in the top ten is in jeopardy as well. In other words, the only servebot in the top ten has to rely on plenty of luck–or outstanding, perhaps one-of-a-kind skills in the clutch–to remain among the game’s elite.

Anderson is a more well-rounded player than Raonic, and he wins more return points than that. But he still falls well short of the next-worst return game in the top ten, Wawrinka’s 36.7%. The 2.5 percentage points between Anderson and Wawrinka represent a big gap, almost one-fifth of the entire range between the game’s best and worst returners.

The less effective a player’s return game, the more he must rely on tiebreaks to win sets, and that’s one explanation for Anderson’s success this season. His 62%(26-16) tiebreak winning percentage in 2015 is the best of his career, and considerably higher than his career tiebreak winning percentage of 54%. Again, it sounds like a small difference, but take away three or four of the tiebreaks he’s won this year, and he no longer reached the final at Queen’s Club … or might not be preparing for a quarterfinal in New York.

Very few players have managed to spend meaningful time in the top ten while depending so heavily on winning tiebreaks. Another metric to help us see this is the percentage of sets won that are won in tiebreaks. Entering the US Open, just over 25% of Anderson’s sets won were won in tiebreaks. Only four times since 1991 has a player sustained a rate that high and ended the year in the top ten: Raonic last year, Andy Roddick in 2007 and 2009, and Greg Rusedski in 1998.

In fact, between 1991 and 2014, only 17 times did a player finish a season in the top ten with this rate above 20%. Roddick represents five of those times, and almost all, except for Roddick at his peak, were players who finished outside the top five. Wawrinka’s and Raonic’s 2014 seasons were the only occurrences in the last decade.

The one ray of light in Anderson’s statistical profile this season is a significantly improved first serve. His 2015 ace rate is over 18%, compared to the 2014 (and career average) rate of 14%. His percentage of first-serve points won is up to 78.8%, from last season’s 75.4% and a career average of 75.8%.

This is a major improvement, and is the reason why he is one of only five players on tour (along with Isner, Karlovic, Roger Federer, and Novak Djokovic) winning more than 69% of service points this year. In many ways, Anderson’s stats are similar to those of Feliciano Lopez, but the Spaniard–another player who has long stood on the fringes on the top ten–has never topped 68% of service points won for a full season.

If Anderson can sustain this new level of first-serve effectiveness, he will–at the very least–continue to see a bit more success in tiebreaks. A tiebreak winning percentage higher than his career average of 54% (though still probably below his 2015 rate of 62%) will help keep him in the top 15. However, even for the best servers, tiebreaks are often little more than coin flips, and players don’t join the game’s elite by relying on coin flips.

As his quarterfinal appearance at the Open shows, Anderson is moving in the right direction. It’s easy to see a path for him that involves ending the season in the top ten. But to move up to the level above that, following the path of someone like Wawrinka, he’ll need to start serving like peak Andy Roddick, or–perhaps just as difficult–significantly improve his return game.

Break Point Persistence: Why Venus is Better Than Her Ranking

Some points matter a lot more than others. A couple of clutch break point conversions or a well-played tiebreak make it possible to win a match despite winning fewer than half of the points. Even when such statistical anomalies don’t occur, one point won at the right time can erase the damage done by several other points lost.

Break points are among the most important points, and because tennis’s governing bodies track them, we can easily study them. I’ve previously looked at break point stats, with a special emphasis on Federer, here and here. Today we’ll focus on break points in the women’s game.

The first step is to put break points in context. Rather than simply looking at a percentage saved or converted, we need to compare those rates to a player’s serve or return points won in general. Serena Williams is always going to save a higher percentage of break points than Sara Errani does, but that has much more to do with her excellent service game than any special skills on break points.

Once we do that, we have two results for each player: How much better (or worse) she is when facing break point on serve, and how much better (or worse) she is with a break point on return.

For instance, this year Serena has won 2.8% more service points than average when facing break point, and 7.5% more return points than average with a break point opportunity. The latter number is particularly good–not only compared to other players, but compared to Serena’s own record over the last ten years, when she’s converted break points exactly as often as she has won other break points.

Serena’s experience isn’t unusual. From one year to the next, these rates aren’t persistent, meaning that most players don’t consistently win or lose many more break points than expected. Since 2006, Maria Sharapova has converted 1% fewer break points than expected. Caroline Wozniacki has recorded exactly the same rate, while Victoria Azarenka has converted 2% fewer break points than expected.

On serve, the story is similar, with a slight twist. Inexperienced players seem to perform a little worse when trying to convert a break point against a more experienced opponent, so most top players save break points about 4% more often than they win other service points. Serena, Sharapova, Wozniacki, Azarenka, and Petra Kvitova all have career rates at about this level.

Unlike in the men’s game, there’s little evidence that left-handers have a special advantage saving break points on serve. Angelique Kerber is a few percentage points above average, but Kvitova, Lucie Safarova, and Ekaterina Makarova are all within one percentage point of neutral.

While a few marginal players are as much as ten percentage points away from neutral saving break points or converting them, the main takeaway here is that no one is building a great career on the back of consistent clutch performances on break points. Among women with at least 250 tour-level matches in the last decade, only Barbora Strycova has won more than 3% more break points (serve and return combined) than expected. Maria Kirilenko is the only player more than 3% below expected.

This analysis doesn’t tell us anything very interesting about the intrinsic skills of our favorite players, but that doesn’t mean it’s without value. If we can count on almost all players posting average numbers over the long term, we can identify short-term extremes and predict that certain players will return to normal.

And that (finally) brings us to Venus Williams. Since 2006, Venus has played break points a little bit worse than average, saving 2% more break points than typical serve points (compared to +4% for most stars) and winning break points on return 3% less often than other return points.

But this year, Venus has saved break points 17% less often than typical service points, the lowest single-season number from someone who played more than 20 tour-level matches. That’s roughly once per match this year that Venus has failed to save a break point that–in an average year–she would’ve saved.

There’s no guarantee that saving those additional break points would’ve changed many of Venus’s results this year, but given the usual strength of her service game, holding serve even a little bit more would make a difference.

This type of analysis can’t say whether a rough patch like Venus’s is due to bad luck, mental lapses, or something else entirely, but it does suggest very strongly than she will bounce back. In fact, she already has. In her successful US Open run, she’s won about 66% of service points while saving 63% of break points. That’s not nearly as good as Serena’s performance this year, but it’s much closer to her own career average.

Like so many tennis stats that fluctuate from match to match or year to year, this is another one that evens out in the end. A particularly good or bad number probably isn’t a sign of a long-term trend. Instead, it’s a signal that the short-term streak is unlikely to last.

Sabr Metrics: The Case For the Hyper-Aggressive Return

Italian translation at settesei.it

Roger Federer has made waves the last few weeks by occasionally moving way up the court to return second serves. While the old-school tactic was nearly extinct in today’s game of baseline attrition, it seems to be working for Fed.

At least in one sense, it’s too early to say whether the kamikaze return is an effective tactic. Federer has used it sparingly for only a handful of matches, and in that tiny sample, he’s missed plenty of returns. But in the view of many pundits, the hyper-aggressive return gets in his opponents’ heads, making the tactic more valuable than simply changing the result of a few points. Presumably Roger agrees, since he keeps using it.

I agree that the tactic is a good one, though for a different reason. By taking greater risks, Fed is generating more unpredictability, or streakiness, on his opponents’ service games, which is valuable even if he doesn’t win any more return points.

Watching and waiting

To win a match, a player usually needs to break serve, and in the contemporary men’s game, that’s not an easy thing to do. On average, servers win about 64% of points and hold about 80% of service games. On hard courts, the equivalent numbers are even higher. Against a good server–let alone John Isner, Fed’s opponent tonight–they are higher still.

Returners who stand well behind the baseline and try only to put the ball back in play are basically crossing their fingers and hoping for the best. Maybe their opponent will miss several first serves, or the server will make a couple of errors against those weak returns. It can work, and for a brilliant returner such as Novak Djokovic, hitting moderately aggressive returns and winning some of the ensuing rallies is usually good enough for several breaks per match.

For most players, however, breaks of serve rely more on the server’s occasional lapses. To put it in numerical terms: A passive returner is playing the lottery in every return game–a lottery with only a 10% to 20% chance of winning.

Generating the coin flip

The best way to earn more breaks of serve, of course, is to win more return points. But unless you’re spending the offseason at Djokovic’s training camp, that’s unlikely.

The alternative is to change the rules of the lottery. Instead of accepting a steady rate of 35% of return points, a hyper-aggressive strategy is more likely to make the point-by-point results more streaky, even if the overall rate doesn’t change.

To see why this is effective, we need to oversimplify a bit. A player who wins 35% of return points will, on average, break in 17% of his return games. If we introduce a slight variation in the rate of return points won, we see a slight improvement in break rate, as well. If that same player wins 30% of return points in half of his games and 40% of return points in the other half, he’ll break serve 18% of the time.

That one percent improvement is barely noticeable. It probably represents what’s already going on in most matches, often because servers are a bit streaky already. The more volatility we introduce, though, the more the odds tilt toward the returner.

Double the variation and say that the returner wins 25% of return points half the time and 45% the other half. Now he’ll break serve in 21% of games, or one extra break per 25 return games. Still not overwhelming, but that’s one extra break in a five-setter.

The real magic happens when we expand the variation to an even split between 20% of return points and 50% of return points. In that scenario–when, remember, our returner is still winning 35% of points–the break rate improves to 26%, almost one more break per ten return games. On average, that’s an extra break per best-of-three match, and closer to two extra breaks in a typical best-of-five match.

Back to reality

A hyper-aggressive return game is going to result in more return errors as well as more return winners. That’s true regardless of return position: Mikhail Kukushkin managed to break Marin Cilic four times on Friday by going for return winners, even if he stayed in the general area of the baseline.

So a new return tactic is unlikely to make a player much better in general. And of course, it’s unlikely to generate anything like the neat, theoretical examples shown above, when one game is better and one game is worse.

However, I suspect that higher-risk shots are more likely to be streaky, which would result in something like those neat examples. And if the pundits are right, that Fed’s kamikaze return unnerves his opponents, that ought to make his return games even streakier still, as his opponents deal with a new challenge mid-match.

Whenever there’s an opportunity to change the nature of the game and make it less predictable, the underdog should take it. Odd as it is to think of Federer as the underdog, he–like everyone else on the men’s tour–is in fact fighting an uphill battle in every return game. Hyper-aggressive tactics are a small step toward leveling the field.

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.

Measuring WTA Tactics With Aggression Score

Editor’s note: Please welcome guest author Lowell! He’s a prolific contributor to the Match Charting Project, and the author of the first guest post on this blog.

The Problem

Quantifying aggression in tennis presents a quandary for the outsider. An aggressive shot and a defensive shot can occur on the same stroke at the same place on the court at the same point in a rally. To know whether one occurred, we need information on court positioning and shot speed, not only of the current shot, but the shots beforehand.

Since this data only exists for a fraction of tennis matches (via Hawkeye) and is not publicly available, using aggressive shots as a metric is untenable for public consumption. In a different era, net points may have been a suitable metric, but almost all current tennis, especially women’s tennis, revolves around baseline play.

Net points also can take on a random quality and may not actually reflect aggression. Elina Svitolina, according to data from the Match Charting Project, had 41 net points in her match against Yulia Putintseva at Roland Garros this year. However, this was not an indicator of Svitolina’s aggressive play so much as Putintseva hitting 51 drop shots in the match.

The Match Charting Project does give some data to help with this problem however. We can use the data to get the length of rallies and whether a player finished the point, i.e. he/she hit a winner or unforced error or their opponent hit a forced error. If we assume an aggressive player would be more likely to finish the point and would be more likely to try to finish the point sooner rather than later in a rally, we can build a metric.

The Metric

To calculate aggression using these assumptions, we need to know how often a player finished the point and how many opportunities did they have to finish the point, i.e. the number of times they had the ball in play on their side of the net. To measure the number of times a player finished the point, we add up the points where they hit a winner or unforced error or their opponent hit a forced error. For short, I will refer to these as “Points on Racquet”.

To measure how many opportunities a player had to finish the point, we calculate the number of times the ball was in play on each player’s side of the net. For service points, we add 1 to the length of each rally and divide it by 2, rounding up if the result is not an integer. For return points, we divide each rally by 2, rounding up if the result is not an integer. These adjustments allow us to accurately count how often a player had the ball in play on their side of the net. For brevity, I will call these values “Shot Opportunities”.

If we divide Points on Racquet by Shot Opportunities we will get a value between 0 and 1. If a player has a value of 0, they never finish points when the ball is on their side of the net. If the player has a value of 1, they only hit shots that end the point. As the value increases, a player is considered more aggressive. For short, I will call this measure an “Aggression Score.”

The Data

Taking data from the latest upload of the Match Charting Project, I found women’s players with 2000 or more completed points in the database (i.e. all points that were not point penalties or missed points). Eighteen players fitted these criteria. Since the Match Charting Project is, unfortunately, a nonrandom sample of matches, I felt uncomfortable making assessments below a very large number of data points. Using 2000 or more data points, however, an overwhelming amount of data would be required to overcome these assessments, giving some confidence that, while bias exists, we get in the neighborhood of the true aggression values.

The Results

Below are the results from the analysis. Tables 1-3 provide the Aggression Scores for each player overall, broken down into serve and return scores and further broken down into first and second serves. They also provide differences between where we would expect the player to be more aggressive (Serve v. Return, First Serve v. Second Serve and Second Serve Return v. First Serve Return).

Table 1: Aggression Scores

Name         Overall  On Serve  On Return  S-R Spread  
S Williams     0.281    0.3114     0.2476      0.0638  
S Halep       0.1818    0.2058     0.1537      0.0521  
M Sharapova   0.2421    0.2471     0.2358      0.0113  
C Wozniacki   0.1526    0.1788     0.1185      0.0603  
P Kvitova     0.3306     0.347      0.309       0.038  
L Safarova    0.2475    0.2694     0.2182      0.0512  
A Ivanovic    0.2413     0.247     0.2335      0.0135  
Ka Pliskova    0.256    0.2898     0.2095      0.0803  
G Muguruza     0.231     0.238     0.2214      0.0166  
A Kerber      0.1766    0.2044     0.1433      0.0611  
B Bencic      0.1742    0.1784     0.1687      0.0097  
A Radwanska   0.1473    0.1688     0.1207      0.0481  
S Errani      0.1232    0.1184     0.1297     -0.0113  
E Svitolina   0.1654    0.1769     0.1511      0.0258  
M Keys        0.3017    0.3284     0.2677      0.0607  
V Azarenka    0.1892    0.1988     0.1762      0.0226  
V Williams    0.2251     0.247     0.1944      0.0526  
E Bouchard    0.2458    0.2695     0.2157      0.0538  
WTA Tour       0.209    0.2254     0.1877      0.0377

Table 2: Serve Aggression Scores

Name          Serve  First Serve  Second Serve  1-2 Spread  
S Williams   0.3114       0.3958        0.2048       0.191  
S Halep      0.2058       0.2298        0.1587      0.0711  
M Sharapova  0.2471       0.2715        0.1989      0.0726  
C Wozniacki  0.1788       0.2016         0.121      0.0806  
P Kvitova     0.347       0.3924        0.2705      0.1219  
L Safarova   0.2694       0.3079        0.1983      0.1096  
A Ivanovic    0.247       0.2961        0.1732      0.1229  
Ka Pliskova  0.2898       0.3552        0.1985      0.1567  
G Muguruza    0.238       0.2906        0.1676       0.123  
A Kerber     0.2044       0.2337        0.1384      0.0953  
B Bencic     0.1784       0.2118        0.1218        0.09  
A Radwanska  0.1688       0.2083        0.0931      0.1152  
S Errani     0.1184       0.1254        0.0819      0.0435  
E Svitolina  0.1769       0.2196         0.105      0.1146  
M Keys       0.3284       0.3958        0.2453      0.1505  
V Azarenka   0.1988       0.2257        0.1347       0.091  
V Williams    0.247       0.3033        0.1716      0.1317  
E Bouchard   0.2695       0.3043        0.2162      0.0881  
WTA Tour     0.2254       0.2578        0.1679      0.0899

Table 3: Return Aggression Scores

Name          Serve  1st Return  2nd Return  Spread  
S Williams   0.2476      0.2108      0.3116  0.1008  
S Halep      0.1537      0.1399      0.1778  0.0379  
M Sharapova  0.2358      0.2133      0.2774  0.0641  
C Wozniacki  0.1185      0.1098       0.132  0.0222  
P Kvitova     0.309      0.2676      0.3803  0.1127  
L Safarova   0.2182      0.1778      0.2725  0.0947  
A Ivanovic   0.2335      0.1952      0.3027  0.1075  
Ka Pliskova  0.2095      0.1731      0.2715  0.0984  
G Muguruza   0.2214      0.1888      0.2855  0.0967  
A Kerber     0.1433      0.1127       0.191  0.0783  
B Bencic     0.1687      0.1514       0.197  0.0456  
A Radwanska  0.1207      0.1049      0.1464  0.0415  
S Errani     0.1297      0.1131      0.1613  0.0482  
E Svitolina  0.1511      0.1175      0.1981  0.0806  
M Keys       0.2677      0.2322      0.3464  0.1142  
V Azarenka   0.1762      0.1499      0.2164  0.0665  
V Williams   0.1944      0.1586       0.255  0.0964  
E Bouchard   0.2157      0.1757      0.2837   0.108  
WTA Tour     0.1877      0.1609      0.2341  0.0732

The first plot shows the relationship between serve and return aggression scores as well as the regression line with a confidence interval (note: since there are only 18 players in the sample, treat this regression line and all of the others in this post with caution).

The second and third plots show the relationships between players’ aggression scores on first serves and their aggression scores on second serves for serve and return points respectively as well as the regression lines with confidence intervals.

The fourth and fifth plots show the relationship between the spread of serve and return aggression scores between first and second serve and the more aggressive point for the player, i.e. first serve for service points and second serve for return points as well as the regression lines with confidence intervals.

We can take away five preliminary observations.

Sara Errani knows where her money is made. The WTA is notoriously terrible for providing statistics. However, they do provide leaderboards for particular statistics, including return points and games won. Errani leads the tour in both this year. She also uniquely holds a higher Aggression Score on return points than serve points. From this information, we can hypothesize that Errani may play more aggressive on return points because she has greater confidence she can win those points or because she relies on those points more to win.

Maria Sharapova is insensitive to context; Elina Svitolina is highly sensitive to context. She falls outside of the confidence interval in all five plots. More specifically, Sharapova consistently is more aggressive on return points, second serve service points and first serve return points than her scores for service points, first serve service points and second serve return points respectively would predict. She has also lower spreads on serve and return than her more aggressive points would predict.

This result suggests that Sharapova differentiates relatively little in how she approaches points according to whether she is serving or returning or whether it is first serve or second serve. Svitolina exhibits the opposite trend as Sharapova. Considering anecdotal thoughts from watching Sharapova and Svitolina, these results make sense. Sharapova’s serve does not seem to vary between first and second and we see a lot of double faults. Svitolina can vary between aggressive shot-making and big first serves and conservative play. Hot takes are not always wrong.

Lucie Safarova, meet Eugenie Bouchard; Ana Ivanovic, meet Garbine Muguruza. Looking at the plots, it is interesting to note how Safarova and Bouchard seem to follow each other across the various measures. The same is true for Ivanovic and Muguruza. A potential application of the aggression score is that it can point us to players that are comparable and may have similar results. Players with good results against Safarova and Ivanovic may have good results against Bouchard and Muguruza, two younger players whom they are much less likely to have played.

Serena Williams and Karolina Pliskova serve like Madison Keys and Petra Kvitova, but they are very different. Serena, Pliskova, Keys and Kvitova are all players that are known for their serves as their weapons. Serena and Pliskova have the third and fourth highest Aggression Scores respectively. However, they also have wide spreads on serve and return scores and they have much lower second serve service point scores than their first serve scores would predict, whereas Keys is about where the prediction places her and Kvitova is far more aggressive than her first serve points would predict.

While Serena is still a relatively aggressive returner, she rates lower on first serve return aggression than Maria Sharapova. Pliskova falls to the middle of the pack on return aggression. Kvitova and Keys, in contrast, are both very aggressive on return points. My hypothesis for the difference is that while Serena and Pliskova are aggressive players, their scores get inflated by using their first serve as a weapon and they are only somewhat more aggressive than the players that score below them. Kvitova and Keys, on the other had, are exceptionally aggressive players.

The WTA runs through Victoria Azarenka and Madison Keys. Oddly, the players who seemed to best capture the relationships between all of the aggression scores and spreads of aggression scores were Victoria Azarenka and Madison Keys. Neither strayed outside of the confidence interval and often ended up on the best-fit line from the regressions. They define average for the WTA top 20.

These thoughts are preliminary and any suggestions on how they could be used or improved would be helpful. I also must beseech you to help with the Match Charting Project to put more players over the 2,000 point mark and get more points for the players on this list to help their Aggression Scores a better part of reality.

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.