Is There an Advantage To Serving First?

There’s no structural bias toward the player who serves first.  If tennis players were robots, it wouldn’t matter who toed the line before the other.

But the conventional wisdom persists.  Last year, I looked at the first-server advantage in very close matches, and found that depending on the scenario, the player who serves first in the final set may win more than 50% of matches–as high as 55%–but the evidence is cloudy.  And that’s based on serving first at the tail end of the match.  Winning the coin toss doesn’t guarantee you that position for the third or fifth set.

Logically, then, it’s hard to see how serving the first game of the match–and holding that possible slight advantage in the first set–would have much impact on the outcome of the match.  There’s simply too much time, and too many events, between the first game and the pressure-packed crucial moments that decide the match.

Yet, the evidence points to a substantial first-serve advantage.

In ATP main-draw matches this year, the player who served first won 52% of the time.  That edge is confirmed when we adjust for individual players.

39 players tallied at least 10 matches in which they served first and 10 in which they served second.  Of those 39, 21 were more successful when serving first, against 17 who won more often when serving second.  (Marcos Baghdatis didn’t show a preference.)  Weigh their results by their number of matches, and the average tour-level regular was 11% more likely to win when serving first than when serving second.  Converted to the same terms as the general finding, that’s 52.6% of matches in favor of the first server.

That’s not an airtight conclusion, but it is a suggestive one.  One possible problem would arise if lesser players–the guys who play some ATP matches against that top 39, but not enough to show up in the 39 themselves–are more likely to choose returning first.  Then, our top 39 would be winning 52.6% of matches against a lesser pool of opponents.

That doesn’t seem to be the case.  I looked at the next 60 or so players, ranked by how many ATP matches they’ve played this year.  That secondary group served first 51% of the time, indicating that the guys on the fringe of the tour don’t have any kind of consistent tendency when winning the coin toss.

For further confirmation, I ran the same algorithm for ATP Challenger matches this year.  That returned another decent-sized set of players with at least 10 matches serving first and 10 matches serving second–38, in this case.  The end result is almost identical.  The Challenger regulars were 9% more likely to win when serving first, which translates to the first server winning 52.2% of the time.

This is a particularly interesting finding, because in the aggregate, these 38 Challenger regulars prefer to serve second.  Of their 1110 matches so far this year, these guys served first only 503 times–about 45%.  Despite such a strong preference, the match results tell the story.  They are more likely to win when serving first.

When we turn our attention to the WTA tour, the results are so strong as to be head-scratching.  Applying the same test to 2013 WTA matches (though lowering the minimum number of matches to eight each, to ensure a similar number of players), the 35 most active players on the WTA tour are 28% more likely to win when serving first than when serving second.  In other words, when a top player is on the court, the first server wins about 56.3% of the time.  24 of the 35 players in this sample have better winning percentages when serving first than when serving second.

For something that cannot be attributed to a structural bias, a factor that can only be described as mental, I’m reluctant to put too much faith in these WTA results without further research.  However, the simple fact that ATP, Challenger, and WTA results agreed in direction is encouraging.  The first-server advantage may not be overwhelming, but it appears to be real.

Simpler, Better Keys to the Match

Italian translation at settesei.it

If you watched the US Open or visited its website at any point in the last two weeks, you surely noticed the involvement of IBM.  Logos and banner ads were everywhere, and even usually-reliable news sites made a point of telling us about the company’s cutting-edge analytics.

Particularly difficult to miss were the IBM “Keys to the Match,” three indicators per player per match.  The name and nature of the “keys” strongly imply some kind of predictive power: IBM refers to its tennis offerings as “predictive analytics” and endlessly trumpets its database of 41 million data points.

Yet, as Carl Bialik wrote for the Wall Street Journal, these analytics aren’t so predictive.

It’s common to find that the losing player met more “keys” than the winner did, as was the case in the Djokovic–Wawrinka semifinal.  Even when the winner captured more keys, some of these indicators sound particularly irrelevant, such as “average less than 6.5 points per game serving,” the one key that Rafael Nadal failed to meet in yesterday’s victory.

According to one IBM rep, their team is looking for “unusual” statistics, and in that they succeeded.  But tennis is a simple game, and unless you drill down to components and do insightful work that no one has ever done in tennis analytics, there are only a few stats that matter.  In their quest for the unusual, IBM’s team missed out on the predictive.

IBM vs generic

IBM offered keys for 86 of the 127 men’s matches at the US Open this year.  In 20 of those matches, the loser met as many or more of the keys as the winner did.  On average, the winner of each match met 1.13 more IBM keys than the loser did.

This is IBM’s best performance of the year so far.  At Wimbledon, winners averaged 1.02 more keys than losers, and in 24 matches, the loser met as many or more keys as the winner.  At Roland Garros, the numbers were 0.98 and 21, and at the Australian Open, the numbers were 1.08 and 21.

Without some kind of reference point, it’s tough to know how good or bad these numbers are.  As Carl noted: “Maybe tennis is so difficult to analyze that these keys do better than anyone else could without IBM’s reams of data and complex computer models.”

It’s not that difficult.  In fact, IBM’s millions of data points and scores of “unusual” statistics are complicating what could be very simple.

I tested some basic stats to discover whether there were more straightforward indicators that might outperform IBM’s. (Carl calls them “Sackmann Keys;” I’m going to call them “generic keys.”)  It is remarkable just how easy it was to create a set of generic keys that matched, or even slightly outperformed, IBM’s numbers.

Unsurprisingly, two of the most effective stats are winning percentage on first serves, and winning percentage on second serves.  As I’ll discuss in future posts, these stats–and others–show surprising discontinuities.  That is to say, there is a clear level at which another percentage point or two makes a huge difference in a player’s chances of winning a match.  These measurements are tailor-made for keys.

For a third key, I tried first-serve percentage.  It doesn’t have nearly the same predictive power as the other two statistics, but it has the benefit of no clear correlation with them.  You can have a high first-serve percentage but a low rate of first-serve or second-serve points won, and vice versa.  And contrary to some received wisdom, there does not seem to be some high level of first-serve percentage where more first serves is a bad thing.  It’s not linear, but he more first serves you put in the box, the better your odds of winning.

Put it all together, and we have three generic keys:

  • Winning percentage on first-serve points better than 74%
  • Winning percentage on second-serve points better than 52%
  • First-serve percentage better than 62%

These numbers are based on the last few years of ATP results on every surface except for clay.  For simplicity’s sake, I grouped together grass, hard, and indoor hard, even though separating those surfaces might yield slightly more predictive indicators.

For those 86 men’s matches at the Open this year with IBM keys, the generic keys did a little bit better.  Using my indicators–the same three for every player–the loser met as many or more keys 16 times (compared to IBM’s 20) and the winner averaged 1.15 more keys (compared to IBM’s 1.13) than the loser.  Results for other slams (with slightly different thresholds for the different surface at Roland Garros) netted similar numbers.

A smarter planet

It’s no accident that the simplest, most generic possible approach to keys provided better results than IBM’s focus on the complex and unusual.  It also helps that the generic keys are grounded in domain-specific knowledge (however rudimentary), while many of the IBM keys, such as average first serve speeds below a given number of miles per hour, or set lengths measured in minutes, reek of domain ignorance.

Indeed, comments from IBM’s reps suggest that marketing is more important than accuracy.  In Carl’s post, a rep was quoted as saying, “It’s not predictive,” despite the large and brightly-colored announcements to the contrary plastered all over the IBM-powered US Open site.  “Engagement” keeps coming up, even though engaging (and unusual) numbers may have nothing to do with match outcomes, and much of the fan engagement I’ve seen is negative.

Then again, maybe the old saw is correct: It’s all good publicity as long as they spell your name right.  And it’s not hard to spell “IBM.”

Better keys, more insight

Amid such a marketing effort, it’s easy to lose sight of the fact that the idea of match keys is a good one.  Commentators often talk about hitting certain targets, like 70% of first serves in.  Yet to my knowledge, no one had done the research.

With my generic keys as a first step, this path could get a lot more interesting.  While these single numbers are good guides to performance on hard courts, several extensions spring to mind.

Mainly, these numbers could be improved by making player-specific adjustments.  74% of first-serve points is adequate for an average returner, but what about a poor returner like John Isner?  His average first-serve winning percentage this year is nearly 79%, suggesting that he needs to come closer to that number to beat most players.  For other players, perhaps a higher rate of first serves in is crucial for victory.  Or their thresholds vary particularly dramatically based on surface.

In future posts, I’ll delve into more detail regarding these generic keys and investigate ways in which they might be improved.  Outperforming IBM is gratifying, but if our goal is really a “smarter planet,” there is a lot more research to pursue.

Avoiding Double Faults When It Matters

Italian translation at settesei.it

The more gut-wrenching the moment, the more likely it is to stick in memory.  We easily recall our favorite player double-faulting away an important game; we quickly forget the double fault at 30-0 in the middle of the previous set.  Which one is more common? The mega-choke or the irrelevancy?

There are three main factors that contribute to double faults:

  1. Aggressiveness on second serve. Go for too much, you’ll hit more double faults.  Go for too little, your opponent will hit better returns.
  2. Weakness under pressure. If you miss this one, you lose the point. The bigger the point, the more pressure to deliver.
  3. Chance. No server is perfect, and every once in a while, a second serve will go wrong for no good reason.  (Also, wind shifts, distractions, broken strings, and so on.)

In this post, I’ll introduce a method to help us measure how much each of those factors influences double faults on the ATP tour. We’ll soon have some answers.

In-game volatility

At 30-40, there’s more at stake than at 0-0 or 30-0.  If you believe double faults are largely a function of server weakness under pressure, you would expect more double faults at 30-40 than at lower-pressure moments.  To properly address the question, we need to attach some numbers to the concepts of “high pressure” and “low pressure.”

That’s where volatility comes in.  It quantifies how much a point matters by considering several win probabilities.  An average server on the ATP tour starts a game with an 81.2% chance of holding serve.  If he wins the first point, his chances of winning the game increase to 89.4%. If he loses, the odds fall to 66.7%.  The volatility of that first point is defined as the difference between those two outcomes: 89.4% – 66.7% = 22.7%.

(Of course, any number of things can tweak the odds. A big server, a fast surface, or a crappy returner will increase the hold percentages. These are all averages.)

The least volatile point is 40-0, when the volatility is 3.1%. If the server wins, he wins the game (after which, his probability of winning the game is, well, 100%). If he loses, he falls to 40-15, where the heavy server bias of the men’s game means he still has a 96.9% chance of holding serve.

The most volatile point is 30-40 (or ad-out, which is logically equivalent), when the volatility is 76.0%.  If the server wins, he gets back to deuce, which is strongly in his favor. If he loses, he’s been broken.

Mixing in double faults

Using point-by-point data from 2012 Grand Slam tournaments, we can group double faults by game score.  At 40-0, the server double faulted 3.0% of points; at 30-0, 4.2%; at ad-out, 2.8%.

At any of the nine least volatile scores, servers double faulted 3.0% of points. At the nine most volatile scores, the rate was only 2.7%.

(At the end of this post, you can find more complete results.)

To be a little more sophisticated about it, we can measure the correlation between double-fault rate and volatility.  The relationship is obviously negative, with an r-squared of .367.  Given the relative rarity of double faults and the possibility that a player will simply lose concentration for a moment at any time, that’s a reasonably meaningful relationship.

And in fact, we can do better.  Scores like 30-0 and 40-0 are dominated by better servers, while weaker servers are more likely to end up at 30-40. To control for the slightly different populations, we can use “adjusted double faults” by estimating how many DFs we’d expect from these different populations.  For instance, we find that at 30-0, servers double fault 26.7% more than their season average, while at 30-40, they double fault 28.6% less than average.

Running the numbers with adjusted double fault rate instead of actual double faults, we get an r-squared of .444.  To a moderate extent, servers limit their double faults as the pressure builds against them.

More pressure on pressure

At any pivotal moment, one where a single point could decide the game, set, or match, servers double fault less than their seasonal average.  On break point, 19.1% less than average. With set point on their racket, 22.2% less. Facing set point, a whopping 45.2% less.

The numbers are equally dramatic on match point, though the limited sample means we can only read so much into them.  On match point, servers double faulted only 4 times in 296 opportunities (1.4%), while facing match point, they double faulted only 4 times in 191 chances (2.2%).

Better concentration or just backing off?

By now, it’s clear that double faults are less frequent on important points.  Idle psychologizing might lead us to conclude that players lose concentration on unimportant points, leading to double faults at 40-0. Or that they buckle down and focus on the big points.

While there is surely some truth in the psychologizing–after all, Ernests Gulbis is in our sample–it is more likely that players manage their double fault rates by changing their second-serve approach.  With a better than 9-in-10 chance of winning a game, why carefully spin it in when you can hit a flashy topspin gem into the corner?  At break point, there’s no thought of gems, just fighting on to play another point.

And here, the numbers back us up, at least a little bit.  If players are avoiding double faults by hitting more conservative second serves on important points, we would expect them to lose a few more second serve points when the serve lands in play.

It’s a weak relationship, but at least the data suggests that it points in the expected direction.  The correlation between in-game volatility and percentage of second serve points won is negative (r = -0.282, r-squared = 0.08).  Complicating the results may be the returner’s conservative approach on such points, when his initial goal is simply to keep the ball in play, as well.

Clearly, chance plays a substantial role in double faults, as we expected from the beginning.  It’s also clear that there’s more to it.  Some players do succumb to the pressure and double fault some of the time, but those moments represent the minority.  Servers demonstrate the ability to limit double faults, and do so as the importance of the point increases.

Continue reading Avoiding Double Faults When It Matters

The Unlikeliness of Inducing Double Faults

Some players are much better returners than others.  Many players are such good returners that everyone knows it, agrees upon it, and changes their game accordingly.  This much, I suspect, we can all agree on.

How far does that go? When players are altering their service tactics and changing their risk calculations based on the man on the other side of the net, does the effect show up in the numbers? Do players double fault more or less depending on their opponent?

Put it another way: Do some players consistently induce more double faults than others?

The conventional wisdom, to the extent the issue is raised , is yes.  When a server faces a strong returner, like Andy Murray or Gilles Simon, it’s not unusual to hear a commentator explain that the server is under more pressure, and when a second serve misses the box, the returner often gets the credit.

Credit where credit isn’t due

In the last 52 weeks, Jeremy Chardy‘s opponents have hit double faults on 4.3% of their service points, the highest rate of anyone in the top 50.  At the other extreme, Simon’s opponents doubled only 2.8% of the time, with Novak Djokovic and Rafael Nadal just ahead of him at 2.9% and 3.0%, respectively.

The conventional wisdom isn’t off to a good start.

But the simple numbers are misleading–as the simple numbers so often are.  Djokovic and Nadal, going deep into tournaments almost every week, play tougher opponents.  Djokovic’s median opponent over the last year was ranked 21st, while Chardy’s was outside the top 50.  While it isn’t always true that higher-ranked opponents hit fewer double faults, it’s certainly something worth taking into consideration.  So even though Chardy has certainly benefited from some poorly aimed second serves, it may not be accurate to say he has benefited the most–he might have simply faced a schedule full of would-be Fernando Verdascos.

Looking now at the most recent full season, 2012, it turns out that Djokovic did face those players least likely to double fault.  His opponents DF’d on 2.9% of points, while Filippo Volandri‘s did so on 3.9% of points.  While these are minor differences when compared to all points played, they are enormous when attempting to measure the returners impact on DF rate.  While Djokovic “induced” double faults on 3.0% of points and Volandri did so on 3.9% of points, you can see the importance of considering their opponents.  Despite the difference in rates, neither player had much effect on their opponents, as least as far as double faulting is concerned.

This approach allows to express opponent’s DF rate in a more efficient way, relative to “expected” DF rate.  Volandri benefited from 1% more doubles than expected, Chardy enjoyed a whopping 39% more than expected, and–to illustrate the other extreme–Simon received 31% fewer doubles than his opponents would be predicted to suffer through.

You can’t always get what you want

One thing is clear by now. Regardless of your method and its sophistication, some players got a lot more free return points in 2012 than others.  But is it a skill?

If it is a skill, we would expect the same players to top the leaderboard from one year to the next.  Or, at least, the same players would “induce” more double faults than expected from one year to the next.

They don’t.  I found 1405 consecutive pairs of “player-years” since 1991 with at least 30 matches against tour-level regulars in each season. Then I compared their adjusted opponents’ double fault rate in year one with the rate in year two.  The correlation is positive, but very weak: r = 0.13.

Nadal, one player who we would expect to have an effect on his opponents, makes for a good illustration.  In the last nine years, he has had six seasons in which he received fewer doubles than expected, three with more.  In 2011, it was 15% fewer than expected; last year, it was 9% more. Murray has fluctuated between -18% and +25%. Lots of noise, very little signal.

There may be a very small number of players who affect the rate of double faults (positively or negatively) consistently over the course of their career, but a much greater amount of the variation between players is attributable to luck.  Let’s hope Chardy hasn’t built a new game plan around his ability to induce double faults.

The value of negative results

Regular readers of the blog shouldn’t be surprised to plow through 600 words just to reach a conclusion of “nothing to see here.”  Sorry about that. Positive findings are always more fun. Plus, they give you more interesting things to talk about at cocktail parties.

Despite the lack of excitement, there are two reasons to persist in publishing (and, on your end, understanding) negative findings.

First, negative results indicate when journalists and commentators are selling us a bill of goods. We all like stories, and commentators make their living “explaining” causal connections.  Sometimes they’re just making things up as they go along. “That’s bad luck” is a common explanation when a would-be winner clips the net cord, but rarely otherwise.  However, there’s a lot more luck in sport than these obvious instances.  We’re smarter, more rational fans when we understand this.

(Though I don’t know if being smarter or rational helps us enjoy the sport more.  Sorry about that, too.)

Second, negative results can have predictive value. If a player has benefited or suffered from an extreme opponents’ double-fault rate (or tiebreak percentage) and we also know that there is little year-to-year correlation, we can expect that the stat will go back to normal next year. In Chardy’s case, we can predict he won’t get as many free return points, thus he won’t continue to win quite as many return points, thus his overall results might suffer.  Admittedly, in the case of this statistic, regression to the mean would have a tiny effect on something like winning percentage or ATP rank.

So at Heavy Topspin, negative results are here to stay. More importantly, we can all stop trying to figure out how Jeremy Chardy is inducing all those double faults.

The Mirage of Surface Speed Convergence

Italian translation at settesei.it

Rafael Nadal won Indian Wells. Roger Federer won on the blue clay. Even Alessio Di Mauro won a match on a hard court last week.

That’s just a sliver of the anecdotal evidence for one of the most common complaints about contemporary ATP tennis: Surface speeds are converging. Hard courts used to play faster, allowing for more variety in the game and providing more opportunities to different types of players. Or so the story goes.

This debate skipped the stage of determining whether the convergence is actually happening. The media has moved straight to the more controversial subject of whether it should. (Coincidentally, it’s easier to churn out columns about the latter.)

We can test these things, and we’re going to in a minute.  First, it’s important to clarify what exactly we mean by surface speed, and what we can and cannot learn about it from traditional match statistics.

There are many factors that contribute to how fast a tennis ball moves through the air (altitude, humidity, ball type) and many that affect the nature of the bounce (all of the same, plus surface). If you’re actually on court, hitting balls, you’ll notice a lot of details: how high the ball is bouncing, how fast it seems to come off of your opponent’s racket, how the surface and the atmosphere are affecting spin, and more.  Hawkeye allows us to quantify some of those things, but the available data is very limited.

While things like ball bounce and shot speed can be quantified, they haven’t been tracked for long enough to help us here.  We’re stuck with the same old stats — aces, serve percentages, break points, and so on.

Thus, when we talk about “surface speed” or “court speed,” we’re not just talking about the immediate physical characteristics of the concrete, lawn, or dirt.  Instead, we’re referring to how the surface–together with the weather, the altitude, the balls, and a handful of other minor factors–affects play.  I can’t tell you whether balls bounced faster on hard courts in 2012 than in 1992.  But I can tell you that players hit about 25% more aces.

Quantifying the convergence

In what follows, we’ll use two stats: ace rate and break rate.  When courts play faster, there are more aces and fewer breaks of serve.  The slower the court, the more the advantage swings to the returner, limiting free points on serve and increasing the frequency of service breaks.

To compare hard courts to clay courts, I looked for instances where the same pair of players faced off during the same year on both surfaces.  There are plenty–about 100 such pairs for each of the last dozen years, and about 80 per year before that, back to 1991.  Focusing on these head-to-heads prevents us from giving too much weight to players who play almost exclusively on one surface.  Andy Roddick helped increase the ace rate and decrease the break rate on hard courts for years, but he barely influences the clay court numbers, since he skipped so many of those tournaments.

Thus, we’re comparing apples to apples, like the matches this year between David Ferrer and Fabio Fognini.  On clay, Ferrer aced Fognini only once per hundred service points; on hard, he did so six times as often.  Any one matchup could be misleading, but combine 100 of them and you have something worth looking at.  (This methodology, unfortunately, precludes measuring grass-court speed.  There simply aren’t enough matches on grass to give us a reliable sample.)

Aggregate all the clay court matches and all the hard court matches, and you have overall numbers that can be compared.  For instance, in 2012, service breaks accounted for 22.0% of these games on clay, against 20.5% of games on hard.  Divide one by the other, and we can see that the clay-court break rate is 7.4% higher than its hard-court counterpart.

That’s one of the smallest differences of the last 20 years, but it’s far from the whole story.  Run the same algorithm for every season back to 1991 (the extent of available stats), and you have everything from a 2.8% difference in 2002 to a 32.8% difference in 2003.  Smooth the outliers by calculating five-year moving averages, and you get finally get something a bit more meaningful:

breakdiff

The larger the difference, the bigger the difference between hard and clay courts.  The most extreme five-year period in this span was 2003-07, when there were 25.4% more breaks on clay courts than on hard courts.  There has been a steady decline since then (to 16.9% for 2008-12), but not to as low a point as the early 90s (14.0% for 1991-1996), and only a bit lower than the turn of the century (17.8% for 1998-2002).  These numbers hardly identify the good old days when men were men and hard courts were hard.

When we turn to ace rate, the trend provides even less support for the surface-convergence theory.  Here are the same 5-year averages, representing the difference between hard-court ace rate and clay-court ace rate:

acediff2

Here again, the most diverse results occurred during the 5-year span from 2003 to 2007, when hard-court aces were 51.3% higher than clay-court aces.  Since then, the difference has fallen to 46%, still a relatively large gap, one that only occurred in two single years before 2003.

If surfaces are converging, why is there a bigger difference in aces now than there was 10, 15, or 20 years ago? Why don’t we see hard-court break rates getting any closer to clay-court break rates?

However fast or high balls are bouncing off of today’s tennis surfaces, courts just aren’t playing any less diversely than they used to.  In the last 20 years, the game has changed in any number of ways, some of which can make hard-court matches look like clay-court contests and vice versa.  But with the profiles of clay and hard courts relatively unchanged over the last 20 years, it’s time for pundits to find something else to complain about.

Warming Up and Losing Out

Italian translation at settesei.it

This week’s pair of ATP warmups for the Australian Open provide quite the contrast.

In Sydney, only one seeded player (the hardly automatic Andreas Seppi) reached the semifinals, and only one other even made the quarters. Across the ditch in Auckland, three of the final four are among the top four seeds, and the fourth, Gael Monfils, would typically sport a ranking in the same range.

Sydney fits a conventional narrative, while Auckland confounds it. The week before a Grand Slam, many of the top players are out of action, while those who are in action … well, let’s just say warmups don’t always appear to be their top priority.

Winning in 250s

The ATP schedule gives us a convenient natural experiment in order to determine whether slam warmups really are different.

(For convenience, I’m using the term “warmups.” However, we’re only looking at tournaments the week before a slam starts. Sydney is included, but not Brisbane, even though events two weeks before Austrlian and Wimbledon are generally called “warmups.”)

Since 2009, all of the lowest rung of tour-level events have been worth 250 points to the winner. Conveniently, all tourneys the week before slams have fallen into this category.

To see if players seem to treat slam warmups differently from other events, we can simply compare results from warmups to those from other 250s. It isn’t perfect, since a few 250s have draws of more than 32 players and the field quality isn’t identical in all tourneys at this level, but by looking at a few different metrics, we can limit the impact of those quibbles.

Who cares?

Let’s start by simply counting wins and losses of seeded players. In slam warmups from 2009 through 2012, seeds won about 61% of matches against unseeded opponents (224 of 365), while in other 250s, seeds win over 70% of those matches (1499 of 2129). That’s a substantial difference.

To eliminate the quirks of the bigger 250 draw at Queen’s Club, and perhaps toss out some first-round retirements as well, let’s consider the records that seeds have posted in specific rounds.

In the round of 16 at slam warmups, seeds have gone 71-50, for a winning percentage of 58.7%. At other 250s, seeds have won 591 against 223 losses, a percentage of 72.6%.

In the quarterfinals of slam warmups, seeds have beaten unseeded players in 33 of 46 matches–71.7% of encounters. In other 250s, similar matchups have gone to the seeded player 200 of 275 times, or 72.7% of the time.

It seems that many top-ranked players show up at slam warmups with the intent of getting one or two matches under their belt. (Or perhaps fulfilling an obligation to a sponsor.) Those players don’t perform up to their usual standard. But as shown by the comparable records in quarterfinals, those who come to compete play at their usual level.

A few other looks

One issue that seems to have a particular impact in slam warmups is last-minute withdrawals, like that of second-seed Gilles Simon in Sydney this week. Those don’t show up in the won-loss records.

To consider the overall picture, including withdrawals, we can count the number of seeds who reach the semifinals in our different categories of ATP 250s.

In slam warmups, the semifinal fields in the last four years have consisted of 53 seeds and 43 nonseeds–about 55% top-ranked players. In other 250 semifinals, we’ve seen 365 seeds against 191 nonseeds–66% seeds.

Yet another angle is the performance of the top four seeds. In 250s, the 5 through 8 seeds are often barely distinguishable from the rest of the pack. For example, in Sydney this week, those last four seeds are Florian Mayer, Radek Stepanek, Jeremy Chardy, and Marcel Granollers. Not much difference between those guys and unseeded semifinalists Julien Benneteau, Kevin Anderson, and Bernard Tomic.

There’s no clear line between first-rank guys and the rest of the pack, but taking the top half of the seeds seems as good as any other option.

The results are similar to what we saw with the larger pool of seeds. Overall, when a top-four seed played a non-top-four opponent in a slam warmup, he won 65% of matches (129 of 199). In other 250s, he won 74% (978 of 1321).

In the round of 16, top-fours went 51-24 in slam warmups, for a record of 68%, compared to 76% (366-114) in other 250s.

Where the top four seeds differ from other seeds is in the quarterfinal round. In slam warmup QFs, top-fours went 31-20, winning 61% of matches. In other tourneys, they won 71% (261-105). Perhaps the first-round bye in many slam warmups means that top seeds want two warmup matches, but no more.

As mentioned, these experiments give us imprecise results, as they don’t take into account the exact field quality of the various 250s. While they may not be the final word on this question, these numbers do strongly indicate that higher-ranked players don’t view slam warmups as particularly important. Against a similar pool of opponents, they win far more matches in 250s at other times throughout the year.

Perhaps that’s one reason why winning an Aussie Open warmup doesn’t forecast any particular level of success in Melbourne–these are tournaments where some of your most highly-ranked opponents just aren’t trying as hard as usual.

Responding to Pressure at 5-5

In a post last week, I presented some data that suggested that servers weaken a bit under the pressure of a tiebreak.  It’s not a strong effect, but it’s a consistent one.  A possible explanation–that all that time between points gives servers a chance to psych themselves out, yet may not affect returners the same way–would apply almost as much to games toward the business end of a set, such as at 5-5 or 5-6.

In other words, if players don’t serve as well (or they return better) when things get tight, we’d expect to see more breaks toward the end of a set–more breaks than expected at 5-5, but perhaps fewer breaks than expected at 2-2.

This also opens up a possible method for evaluating players, as Carl Bialik has suggested.  If someone is losing more sets 5-7 than they are winning 7-5, it may be that they are wilting under the pressure of 5-5 more than the average player.  It would make sense if the players who consistently exceed tiebreak expectations also regularly outperform 7-5 expectations as well.

Within the constraints of the ATP’s Matchstats, 7-5 sets are a great way to identify these patterns.  While some 6-4 sets end with a break (or a break followed by a set-sealing hold), a 6-4 set doesn’t necessarily end that way.  But a 7-5 set must have reached 5-5 before one player took control.

If the hypothesis is correct that players get tighter on serve as the end of the set approaches, we would expect more 7-5 sets in the real world than simulations would imply.

To estimate the number of sets that should end 7-5, we need to take each player’s service points won from each match.  With that, we can calculate the probabilities that sets will end at any given score.  Repeat the process for every match over a period of time and we get a general idea of how often we should see 7-5 sets.

As it turns out, 7-5 sets should make up about 7.8% of all sets.  In fact, 8.8% of sets end 7-5.  Not a huge difference, but one that is fairly consistent from year to year.  Every year since 1991, where this dataset begins, there have always been more 7-5s than expected.  It certainly adds more weight to the claim that the balance of power swings to the returner toward the end of a tight set.

(My set-prediction model doesn’t exactly replicate reality, since players win more games than their service winning percentages predict, in large part because almost all servers are better in either the deuce or ad court, and the variance between them makes it more likely that the player wins a given service game.  When applying a crude adjustment for this, the crumbling-server hypothesis looks even better–the more games servers are predicted to win, the fewer predicted 7-5 sets.)

Identifying the unbreakable

This type of discussion must make you wonder: Which players are good as this stuff?  If it is true that late-set pressure results in more breaks, it seems obvious that some players are more prone to that pressure, and that other players take advantage of that pressure.

In an ideal world, we’d be able to identify some great 7-5 records, point out some 5-7 records, and have some great new insights into players.

As it is … we might.

As we saw last week with tiebreak analysis, we can’t simply count up a player’s 7-5 sets and compare that total to his 5-7 set losses.  Over the last three years, Andy Roddick won more than 55% of his 7-5 and 5-7 sets, but given the players he faced in those sets and their performances in those matches, he should have won 62%.

There are two ways to quantify player accomplishments in this department.  The first evaluates how well a player avoids losing 5-7 when he reaches 5-5; the other compares his ability to break for 7-5 against his proneness to being broken for 5-7.

Let’s call the first stat Five-Seven AVoidance, or FSAV.  For any player, we first add up the sets that reached 5-5, then count the sets that he won 7-5 or reached a tiebreak.  Then we use the general method described above to estimate how many times the player should have reached 5-5, and how many of those times he should have avoided 5-7.   Since the beginning of 2010, Kei Nishikori has avoided a 5-7 finish in about 92% of the sets in which he reached 5-5.  My model would have expected him to avoid 5-7 only about 84% of the time.  (The model expects that most players will avoid 5-7 about 82-90% of the time they reach 5-5.)

From those numbers, we discover that Nishikori lost 5-7 less than half as often as we would have expected him to.  No other player comes close to that mark. In everyday language, FSAV approximates how often a player was able to hold serve at 5-5 or 5-6.  Important skill, that.

The second stat is more narrowly focused on 5-5 sets that do not reach a tiebreak.  Let’s call this one the Seven-Five Outperformance Rate, or SFOR, similar to the TBOR (TieBreak Outperformance Rate) I introduced last week.

Here, instead of comparing 5-7s to all 5-5 sets, we compare 5-7s to 7-5s.  In other words: Is the player more likely to break for 7-5 or be broken for 5-7?  As with the previous stat, after calculating the simple rate (that is, number of 7-5 sets divided by total number of 7-5 and 5-7 sets), we compare that to the results that the model would have expected the player to post.

Bizarrely enough, our three-year leader in SFOR is Ernests Gulbis, who has won about 73% of his 7-5 and 5-7 sets, compared to the 50% the model expects of him.  (It’s even more impressive when compared to the 7% that I personally would have expected from him.)

As the highlighting of Gulbis suggests, these stats probably don’t yet belong in our everyday toolbox.  There simply aren’t very many 7-5 sets, even if–as I established above–there are a few more than we would expect.  For reference, there are almost twice as many tiebreaks as 7-5s.

And to keep Gulbis in the spotlight, it may be that winning 7-5 sets is more a function of getting to 5-5 when you shouldn’t.  Perhaps many of those 7-5s racked up by the Latvian came when he should have put the set away 6-2.  Once 5-5 came along, he finally decided to get serious.  As Gulbis himself might tell you, it’s anybody’s guess.

Follow the jump for FSAV and SFOR on about 50 or so of the most active players (including all tour-level matches (but excluding Davis Cup) since the beginning of 2010, sorted by FSAV) and decide for yourself.

Continue reading Responding to Pressure at 5-5

How Good is Brian Baker?

In his remarkable comeback this year, Brian Baker has already recorded two top-20 scalps, along with seven other victories against players in the top 100.   In the same span of six months, he’s also lost to a player barely inside the top 400, and suffered another six defeats against guys outside the top 100.

This is inconsistency of historic magnitude.  The list of players he’s beaten may actually be more impressive than the list of those who have beaten him!  Adding to the confusion, we don’t have any other recent results from him.  We can’t just wave our hands and point to his 2011 performance level as an accurate indicator of his current level.

One measurement of player ability, the ATP ranking system, places him at #78, a number that seems just as ridiculous when he’s beating Philipp Kohlschreiber at a Masters event as when he’s losing to Maxime Authom at a challenger.  But overall, the ATP estimate doesn’t seem too far-fetched.  It’s certainly better than what jrank (my rating system) spits out.  That algorithm doesn’t know what to do with such a limited track record, so it places him far outside the top 100.

We can do better.  As we’ll see, Baker’s results suggest he belongs on the cusp of the top 50.

Uniquely limited results

Imagine a completely unknown player is given a wild card into a major event.  We don’t know where he came from or who he might have beaten in the past.  He’s a completely blank slate.  If we wanted to estimate his ability level, we would have to wait until we got some results.

If that player won an opening-round match against the 17th-best player in the world, our best guess would be that he is better than #17, but we wouldn’t know how much.  If he lost that opening round match, we would assume he is worse than #17.  We might use statistics from that match to estimate how much better or worse than #17.

As our unknown kept playing more matches, we would update our estimate, using additional data as it came in.

(You might protest that in the early going, we should regress our estimate to the mean, since if some random guy came out of nowhere, he probably isn’t one of the 16 best tennis players in the world–there was a reason he was nowhere.  And, in such a real-world scenario, you would be right.  But such a case, what is the mean?  If a baseball player is called up from Triple-A, an intelligent observer, such as a scout or team executive, considers him at least marginally MLB-level, so we would regress our estimate to the level of marginal MLB players.  But if a player receives a wild card into a tennis tournament, what do we know?)

Few tennis players in history have come closer to this unknown than Brian Baker.  Sure, everyone has to start somewhere, but usually “somewhere” is a long string of futures tournaments, followed by an even longer string of challengers.  By the time a player bags his first top-20 scalp, we have lots and lots of data to work with.

When other players were racking up several dozen matches every year, Brian Baker was rehabbing injuries and coaching college tennis.  We can only judge him based on a small number of recent results.  And those results are particularly contradictory.

Working backward

Intuitively, it’s tough to accept that a single player has beaten a bunch of good players and lost to several weaker ones.  No matter how good that guy is, such a set of outcomes is unlikely.

But how unlikely?  That question is the key to estimating Baker’s current level.

Rather than assuming Baker is playing at a certain level (like that of #78) and scratching our heads at his inconsistency, we can work backwards–take his results and determine the likelihood that he is playing at various levels.

For instance, we could assume that Baker is #5 in the world.  If so, some of his results would be very predictable (like the two wins against Blake Strode) and others would be particularly jarring.  We could go further and calculate the probability that the #5 player in the world would amass Baker’s specific match record.  Those odds, of course, are vanishingly small.

If you repeat the process for every possible ranking, you get a probability that #5, or #12, or #77 would win the matches Baker has won and lose the matches he has lost.  One of those probabilities will be higher than the others, and that’s our best guess of how highly we should regard the American.

(If you’re interested in methodology, click “Continue Reading” below.)

Using this method, we discover that Baker has played at the level of someone with about 820 ATP ranking points, putting him around #54, in a tight pack with Grigor Dimitrov, Gilles Muller, Alejandro Falla, and Lukas Lacko.  With every match he plays, we can continue to fine-tune our estimate.

There are many factors we need to ignore to do an analysis like this, largely because of the limited data that led us to the topic in the first place.  Many of Baker’s worst results have come on hard courts; perhaps he will prove over a longer period to be stronger on clay and grass.  If his ability level has changed over the last six months, as seems very likely, this approach fails to take it into consideration.

But because of the unique nature of Baker’s comeback, which makes it difficult to assume anything about his ability level–this approach allows us to a make a reasonably good guess.  And with such a strange mix of great wins and rough losses, a good guess is all we can hope for.

Continue reading How Good is Brian Baker?

Tommy Haas: Old and Winning

For all the talk of 30-somethings at the top of the modern men’s game, tennis players decline quickly.  30 may be the new 20, but 35 is still the same old 35, and 35-year-old tennis players are usually found on the champions tour, the doubles court, or national television.

Yet Tommy Haas, aged 34 years and 5 months, is enjoying a resurgence, having reached three finals in the last two months–on three different surfaces.  He’s one of the hottest players on tour of any age.

34-year-olds don’t do things like that.  In the last ten years, players 34 and older have accounted for fewer than 1% of wins on the ATP tour.  From 2008 to 2011, all 34-year-olds–combined–won a total of 17 tour-level matches.  In the five months since his birthday, Haas has won 22.

To find a point of comparison, we need to go back five years, to the 2007 campaign of Fabrice Santoro, and slightly earlier, to Andre Agassi‘s 2004 season.  Agassi at 34 was better than Haas at 34, winning 37 tour-level matches and reaching two grand slam quarterfinals.  Agassi was the best “old” player since Jimmy Connors and the only man in the discussion since the 1970s.

Yet already, Haas is among the best 34-and-overs in ATP history.  His 22 wins since his 34th birthday are good for 28th on the all-time list, ahead of Fred Stolle and just behind Roy Emerson.  But that understates Haas’s accomplishment.  With the exceptions of Santoro, Agassi, and Connors (whose 178 wins-past-34 are good for 2nd on the all time list, behind Ken Rosewall), everyone on the list retired more than 20 years ago.

Comparisons to Haas’s contemporaries do a better job of illustrating how unusual he is.  The only two older men to have won a match on tour this year are Arnaud Clement and Ruben Ramirez Hidalgo, neither of whom are a factor anywhere but the challenger tour.  The other 34-year-old to win some matches this season is hyper-fit warrior Michael Russell, who took advantage of the weak draws in Atlanta and Los Angeles.

As long as he stays healthy, Haas is far from finished.  According to Jrank, he’s the 11th-best hard court player in the game right now. He may not have another grand slam final ahead of him, as Agassi did at the same age, but he has more wins in his future than most players a decade his junior.

The Hangover Effect of a Marathon Fifth Set

Italian translation at settesei.it

Marathon sets are again the talk of tennis.  We won’t soon forget Roger Federer’s 19-17 third-set win over Juan Martin Del Potro … or Roger’s weak performance in the match that followed.

The unusual Olympic format–best-of-three, no final-set tiebreak–brought several issues to the fore.  Should best of three be enough for slams?  It certainly gave us plenty of dramatics last week.  And is it finally time to end the no-tiebreak madness?  For all of the occasional drama, do we really need to see even more service holds in John Isner matches?

Peter Bodo makes the case for a marathon-free world:

[M]y main reason for embracing the final-set tiebreaker is not the obvious one that would be cited by most time-sensitive television producers. The real problem with deuce sets is that when a match goes as long as Federer v. Delpo or even Jo-Wilfried Tsonga v. Milos Raonic (that one went 25-23, for Tsonga) the reward for the winner’s heroic feat is almost always a quick subsequent loss.

As Bodo goes on to illustrate, this seems anecdotally true.  But who cares about anecdotes?  This is a testable hypothesis.

As we’ll see, there is a noticeable hangover effect when a player has fought through a marathon fifth set.  But the alternative–a fifth-set tiebreak–produces nearly the same hangover.

There have been 146 marathon fifth sets–matches in which the final set reached 6-6–in Grand Slam tennis since the beginning of 2001.  The record of those 146 winners in their next round is dreadful: 43-103, or 29.5%.  It’s even worse than that, actually.  Four times, two marathon men went on to play each other, so four of those wins were inevitable.

However, that isn’t the end of the story.  To prove that fifth-set marathons significantly weaken their winners, we need to establish two things: (1) They had a decent shot at beating their next opponents anyway, and (2) if a fifth-set tiebreak were played, their chances would have been better.

Post-marathon underdogs

The first issue is a bit sneaky.  If a player has to go deep into the fifth set to win in the early rounds, he’s hardly a dominating presence in the draw.  Consider the extreme case of Yen Hsun Lu, who in 2010, beat Andy Roddick in a 9-7 fifth set, advancing to play Novak Djokovic in the Wimbledon quarters.  Sure, Lu was tired, but what were the odds of an upset even if Roddick lost in three?  Top players rarely need five hours to push through an early-round opponent.

To quantify this, we can turn to jrank-driven predictions.  Using these measures of each player’s ability level at the time of the match, we can estimate the actual chances of our 146 marathon men.

The marathon men would have been underdogs in their next match no matter what.  On average, each one had a 43.4% chance of winning, meaning that of the 146 matches, they should have won 63 of them.  Even adjusting for their underdog status, they seem to have suffered from their marathons–they won 43 of those matches, barely two-third the number that they “should” have won.

Almost-but-not-quite marathons

We’ve established that once a player enters the uncharted territory beyond 6-6, his chances of winning the next match are substantially weakened.  But surely the fatigue didn’t set in right at the moment the chair umpire called “6-6.”  Even if the fifth set is a bagel, simply playing five sets of professional-level tennis is exhausting, and might impact one’s performance a day or two later.

The most relevant set of matches for comparison are US Open five-setters that went to a final-set tiebreak.  Since 2001, we have 40 of those.  In their next matches, the winners of the almost-marathons went a dismal 11-29 (27.5%)–worse than the marathon men!

Compared to their expectations, though, they did a bit better.  Those forty men, on average, had a 38% chance of winning their next matches, meaning we would expect them to win about 15 of the 40.  Relative to the predictions we would have made at the time, this small sample of fifth-set-tiebreak winners outperformed the marathon men, but just barely.

For a bigger sample, we can turn to the slightly shorter–but still epic–matches that end 7-5 in the fifth.  Of the 95 such matches since 2001, the 7-5 winners went on win 49, or 51.5% of their next matches!  This despite the fact they were collective underdogs, expected to win only 48%, or 46 of those matches.

What now?

Since the 7-5 group performed so differently in their next matches, it’s tempting to speculate why they did so.  My best guess: If a player manages a break before the set goes 6-6, he’s relatively fresh, physically and mentally.  The sort of player who can break at 5-5 or 6-5 is one who can come back a day or two later and plow through another three or four hard-fought sets.

By contrast, matches that get to 6-6–whether they end in a tiebreak or not–are usually battles of attrition.  Think Isner-Mahut: The longer it lasted, the less likely either player could challenge the other’s serve.  That brand of tennis had set in before 6-6 in the fifth: If one of the players pulled out a 7-4 tiebreak, it wouldn’t say much about his fitness or mental stamina, simply that someone is bound to get lucky for a point or two.

Based on the limited data we have, there just isn’t much difference between the after-effects of fifth-set marathons and fifth-set tiebreaks.  In both cases, the marathon men weren’t going to be favored anyway, and their fatigue hurts them even more.  Changing format to fifth-set tiebreaks would have little effect on future outcomes–it would just make those matches a bit more dependent on a lucky bounce.