Category Archives: Surface speed

The Speed of Every 2013 Surface

Few debates get tennis fans as riled up as the general slowing–or homogenization–of surface speeds.  While indoor tennis (to take a recent example) is a different animal than it was fifteen or twenty years ago, it’s tough to separate the effect of the court itself from the other changes in the game that have taken place in that time.

Further, the “court effect” itself is multi-dimensional.  The surface makes a big difference, as grass will almost always play quicker than a hard court, which will usually play faster than clay.  But as we’ve seen with the persistence of Sao Paulo as one of the fastest-playing events on tour, altitude is a major factor, as is weather, which can slow down a normally speedy tournament, as was the case with Hurricane Irene at the 2011 US Open.  The choice of balls can influence the speed of play as well.

With all of these factors in play, what we often refer to as “surface speed” is really “court speed” or even “playing environment.”  It’s not just the surface.  That said, I’ll continue to use the terms interchangeably.

Because of there is only limited data available, if we want to quantify surface differences,  we must use a proxy for court speed.  What has worked in the past is ace rate–adjusted for the server and returner in each match.  On a fast court–a surface that doesn’t grip the ball; or one like grass with a low, less predictable bounce; or at a high altitude; or in particularly hot weather–a player who normally hits 5% of his service points for aces might see that number increase to 8%.  (Returners influence ace rate as well. A field with Andy Murray will allow fewer aces than a field with Juan Martin del Potro, so I’ve controlled for that as well.)

Aggregate these server- and returner-adjusted ace rates, and at the very least, we have an approximation of which courts on tour are most ace-friendly.  Since most of the characteristics of an ace-friendly court overlap with what we consider to be a fast court, we can use that number as an marker for surface speed.

2013 Court Speed Numbers

For the second year in a row, the high-altitude clay of Sao Paulo was the fastest-playing surface on tour.  The altitude also appears to play a role in making Gstaad quicker than the typical clay.

As for the slowing of indoor courts, the evidence is inconclusive.  The O2 Arena, site of the World Tour Finals, rated as slower than average in 2011 and 2012, on a level with some of the slowest hard courts on tour.  This year, it came out above average, and a three-year weighted average puts the O2 at the exact middle of the ATP court-speed range.

Valencia and the Paris Masters played about as fast as they have in the past, while Marseille remained near the top of the rankings. If there is evidence for a mass slowing of indoor speeds, it comes from some unlikely sources: Both Moscow and San Jose were among the quickest surfaces on tour in 2010 and 2011, but have been right in the middle of the pack for the last two years.

The table below shows the relative ace rate of every tournament for the last four years, along with a weighted averaged of the last three years.  The weighted average is the most useful number here, especially for the smaller 28- and 32-player events.  The limited extent of a 31-match tournament can amplify the anomalous performance of one player–as you can see from some of the bigger year-to-year movements.  But over the course of three years, individual outliers have less impact.

The “Sf” column is each event’s surface: “C” for clay, “H” for hard, and “G” for grass.  The numbers are multipliers, so Sao Paulo’s three-year weighted average of 1.58 means that players at that event hit 58% more aces than they would have on a neutral court.  Monte Carlo’s 0.67 means 33% less than neutral.

Event            Sf  10 A%  11 A%  12 A%  13 A%   3yr  
Sao Paulo        C    1.44   1.08   1.58   1.74  1.58  
Marseille        H    1.09   1.24   1.41   1.26  1.30  
Halle            G    1.20   1.39   1.26   1.20  1.25  
Wimbledon        G    1.36   1.18   1.24   1.25  1.24  
Shanghai         H    0.96   1.05   1.08   1.37  1.22  
Montpellier      H    1.28          1.40   1.16  1.21  
Brisbane         H    1.01   1.20   1.08   1.27  1.19  
Tokyo            H    1.35   0.98   1.17   1.26  1.18  
Gstaad           C    0.87   1.13   0.90   1.35  1.16  
Winston-Salem    H           1.20   1.10   1.18  1.16  

Chennai          H    0.75   0.77   1.21   1.25  1.16  
Valencia         H    1.02   1.10   1.12   1.19  1.15  
Zagreb           H    1.09   1.16   1.20   1.11  1.15  
Washington       H    0.96   0.93   1.34   1.10  1.15  
Vienna           H    1.42   1.22   1.01   1.19  1.14  
Santiago         C    1.23   1.21   0.86   1.29  1.13  
Sydney           H    1.08   1.14   0.94   1.25  1.13  
Atlanta          H    0.92   0.82   1.06   1.26  1.12  
Eastbourne       G    1.07   1.13   0.92   1.22  1.11  
Queen's Club     G    1.07   1.13   1.09   1.12  1.11  

Paris            H    1.38   0.97   1.16   1.12  1.11  
Cincinnati       H    1.09   1.02   1.08   1.13  1.10  
s-Hertogenbosch  G    1.13   1.08   1.03   1.15  1.10  
Auckland         H    1.01   1.08   1.06   1.12  1.09  
Memphis          H    1.08   1.12   0.95   1.09  1.05  
Stuttgart        C    1.09   1.05   1.04   1.06  1.05  
Bogota           H                         1.09  1.05  
Rotterdam        H    0.88   1.21   0.83   1.12  1.04  
Stockholm        H    0.93   0.96   1.15   0.99  1.04  
Basel            H    0.98   1.05   1.16   0.96  1.04  

Bangkok          H    1.20   1.12   0.73   1.19  1.03  
Australian Open  H    0.98   1.10   0.92   1.08  1.03  
US Open          H    1.14   0.93   1.06   1.04  1.03  
San Jose         H    1.21   1.23   0.96   0.99  1.02  
Moscow           H    1.28   1.12   1.01   0.99  1.02  
Dubai            H    1.13   1.07   1.14   0.92  1.02  
Doha             H    0.88   1.29   0.90   0.98  1.00  
Tour Finals      H    1.07   0.93   0.87   1.11  1.00  
Beijing          H    1.01   1.01   1.06   0.94  0.99  
Canada           H    0.99   1.02   1.04   0.95  0.99  

Madrid           C    0.76   0.86   1.19   0.89  0.98  
Kitzbuhel        C           1.12   0.70   1.12  0.98  
Metz             H    1.14   0.96   1.07   0.90  0.97  
Dusseldorf       C                         0.92  0.96  
Munich           C    0.77   0.82   0.91   0.97  0.92  
St. Petersburg   H    1.02   0.84   0.86   0.99  0.92  
Acapulco         C    0.88   0.89   1.06   0.84  0.92  
Delray Beach     H    0.98   1.07   0.92   0.85  0.91  
Newport          G    1.46   0.72   1.04   0.89  0.91  
Kuala Lumpur     H    0.96   0.97   0.81   0.94  0.90  

Miami            H    0.91   0.98   0.86   0.89  0.89  
Umag             C    0.56   0.74   0.67   1.04  0.87  
Hamburg          C    1.04   0.85   0.75   0.92  0.85  
Buenos Aires     C    0.84   0.86   0.93   0.74  0.82  
Indian Wells     H    0.92   0.90   0.86   0.77  0.82  
Roland Garros    C    0.82   0.86   0.81   0.78  0.81  
Barcelona        C    0.73   0.65   0.91   0.78  0.80  
Casablanca       C    0.82   0.91   0.77   0.75  0.79  
Estoril          C    0.62   0.73   0.79   0.71  0.74  

Houston          C    0.85   0.71   0.71   0.77  0.74  
Bucharest        C    0.61   1.08   0.62   0.68  0.73  
Rome             C    0.78   0.67   0.64   0.81  0.73  
Nice             C    0.88   0.84   0.79   0.64  0.72  
Bastad           C    0.93   0.74   0.86   0.58  0.70  
Monte Carlo      C    0.63   0.60   0.71   0.67  0.67

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If Surfaces are Converging…

Internet discussion has perked up about a post of mine from last month, The Mirage of Surface Speed Convergence.

Many people don’t like my results, and plenty of people just don’t like having someone challenge their preconceived notions–or those of the players they idolize.

Yet for all the chatter, no one has even attempted to address the question at the end of that post:

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?

Unless there is a valid answer to those questions, it really doesn’t matter how you felt after watching the Miami final, or what a top player said in some press conference.

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The Mirage of Surface Speed Convergence

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.

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How Fast is the Ice Rink in Sarajevo?

The Sarajevo challenger is considered to have one of the fastest surfaces on the tennis circuit.  James Cluskey, playing doubles there this week, tweeted, “fast is being very kind. Soo fast!”  Last year, some fans got the point across by calling the surface an ice rink.

The raw numbers agree.  In 13 of the 31 main-draw matches last year, aces made up at least 18% of all points.  Champion Jan Hernych won both his semfinal and final matches against players who scored aces on more than one in five service points.  Two years ago, titlist Amer Delic recorded a 21.6% ace rate for the entire tournament. That’s fast.

Here’s how fast.  The average player who competed in Sarajevo in any of the last three years hit 50% more aces in Sarajevo than his season average.  That’s higher than any other European challenger, a tick above Ortisei (+46%) and well ahead of the third-place fast court, the carpet in Eckental (+31%). (For more on methodology, click here.)

These numbers probably understate just how speedy the Sarajevo surface is.  The players who show up for events like this generally have a game to match–they may not all know about the “ice rink” reputation, but they know it’s indoors.  That’s how you end up with Dustin Brown, Ilija Bozoljac, and Hernych in the late rounds last year.  Jerzy Janowicz was there as well.

Thus, the guys who play in Sarajevo are generally choosing fast surfaces.  So Sarajevo isn’t 50% faster than tour average, it’s 50% faster than the faster-than-average event that these types of players choose.  This is a much bigger factor on the challenger tour than at ATP level, because lower-level guys don’t all play the same events.  Clay-court specialists may show up for Valencia and the Paris Masters, but you won’t find a single South American playing in Bosnia this week.

So we can’t compare Sarajevo to Sao Paulo or Medellin.  (Due to the altitude, those are fast as well, but probably not to the same degree.)  But by any reasonable comparison we can calculate, Sarajevo is as fast as it gets–at least until some savvy promoter puts a tennis match on a real ice rink.

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The Fastest Surface on the ATP Tour

Last week, Rafael Nadal claimed that the indoor clay surface in Sao Paulo didn’t play like clay–it was faster than the surface of the US Open.  It also wasn’t up to standard, with frequent bad bounces and occasional slides gone wrong.

It’s easy to write off Rafa’s complaints as the whining of a once-dominant player who inexplicably loses sets to competitors who might otherwise never appear on television.  But what if he’s right?  What if some clay surfaces are faster than some hard surfaces?

In fact, I stumbled on this paradox when sharing some surface speed numbers last fall.  In the Brasil Open’s first year at a new venue in Sao Paulo, it’s main draw players hit 58% more aces than expected, the highest rate of any ATP tour event, comfortably ahead of European indoor events in Marseille and Montpellier.

Amazingly, this year, players in Sao Paulo hit 78% more aces than they would have on an average surface.  Some of the individual performances are impressive: Nicolas Almagro hit aces on 21% and 26% of service points in his two matches; Joao Souza cracked 27% in a qualifying match.  The raw numbers aren’t as eye-popping as they might be simply because most of the competitors prefer clay-courts for a reason.  Put Carlos Berlocq on an ice-skating rink and he still won’t hit many aces.  In fact, Berlocq’s ace rates last week account for three of the top eight of the 55 matches he played in the 52 weeks.

Ace rate doesn’t tell the whole surface speed story, but it’s an awfully good proxy.  It consistently places the expected indoor tournaments near the top of the rankings and traditionally slower clay events like Monte Carlo and Rome near the bottom.  So when a clay event spits out numbers like these, something wacky is going on.

Much has been written of the homogenization of surface speed, and certainly many hard courts have gotten slower.  But the clay courts in Sao Paulo aren’t drifting toward a bland average–they are going where few clay courts have gone before.  Perhaps, as more events are played on temporary surfaces, we’ll continue to see unexpected results like these.  Certainly, we cannot assume that all clay courts are created equal.

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The Speed of Every Surface, Redux

One of the most popular posts on this blog has been this one, which quantified the speed of every ATP tournament’s surface.  At the very least, it’s time to provide some updated numbers.  Beyond that, we can improve on the methodology and say more about how much we can learn from the numbers.

I was prompted to improve the methodology when I ran an update this week to see how fast the courts are at the O2 Arena in London.  The algorithm, which compares the number of aces (or service points won, or first service points won) to the number we’d expect from those players based on their season average, told me that London is much slower than average–almost 20% below average, on par with Roland Garros and the pre-blue clay Madrid Masters.

Counterintuitive conclusions are fun, but that’s just wrong.

Here’s the problem: Service stats aren’t only affected by servers.  Sure, when Milos Raonic is serving, there will be more aces than when Mikhail Youzhny is serving.  But how many aces Raonic hits is also influenced by the returning skills of the man on the other side of the net.  It’s clear why the algorithm got London so wrong: The eight or nine best players in the world got to where they are (in part, anyway) by getting more balls back.  No matter how fast the court, Mardy Fish wasn’t going to hit as many aces past Jo Wilfried Tsonga or Rafael Nadal in London as he did against Bernard Tomic in Shanghai or Tokyo.

I’ll be more succinct.  The goal is to compare the number of aces on a particular surface to the number of aces we’d expect on a neutral surface.  The number of Expected aces depends on more than just the man serving; it also depends on the man receiving.

(In my article last year, I used three different stats (ace rate, first serve winning percentage, and overall winning percentage on serve) to measure surface speed.  They track each other fairly closely, so there’s not a lot of additional value gained by using more than one.  From here on out, I’m measuring surface speed only by relative ace rate.)

Incorporating more data

To factor in the additional variable, we need each player’s ace rate for the season along with his ace against rate.  With those two numbers, together with the overall ATP average, we can apply the odds ratio method to get a better idea of each match’s expected aces.

For each server in each match, we compare his actual aces to his expected aces, and then take the average of all of those ratios.  The tournament-wide average gives us an estimate of how fast the courts played at that event.

The improved algorithm still insists that aces were 3% lower than on a neutral surface at the 2011 Tour Finals, but counters that with the conclusion that aces were 18% and 8% more than on a neutral surface in 2009 and 2010, respectively.  A weighted average of those three seasons (more on that in a bit) estimates that the O2 Arena gives us 4% more aces than a neutral surface.

The variance from year to year–in some cases, like that of London, suggesting that a surface is faster than average one year, slower than average the next–is a bit worrisome.  At the very least, we can’t simply take a one-year calculation for a single tournament and treat it as the final word, especially when the event only includes 15 matches.

Multi-year averages and (extremely mild) projections

If we want to know exactly what happened in one edition of a tournament, the single-year number is instructive.  Perhaps the weather, or the lighting, was very bad or very good, causing an unusually high or low number of aces.  Just because a tournament’s number for 2012 doesn’t match its numbers for any of the previous three years doesn’t mean it’s wrong.

However, the variety of effects that give us this year-to-year variance do warn us that last year’s number will not accurately predict this year’s number.

The year-to-year correlation of relative ace rate (as I’ve described it above), is not very strong (r = .35).  One way to modestly improve it is to use a three-year weighted average.  A 3/2/1 weighted average of 2011, 2010, and 2009 numbers gives us a better forecast of how the surface will play in the following year (r = .5).

Another way of looking at these more reliable forecasts is that they get closer to isolating the effect of the surface.  As I noted in last year’s article, the weather effects of Hurricane Irene dampened the ace rate at last year’s US Open.  By my new algorithm, the ace rate last year was 7% lower than a neutral surface, while this year it was 5% higher than a neutral surface.  The three-year weighted average would have been able to look past Irene; using data from 2009-11, it estimated that courts in Flushing were exactly neutral.  That not only turned out to be a better projection for 2012 than the -7% of 2011, it also probably better described the influence of the court surface, as separate from the weather conditions.

Below the jump, find the complete list of all tour-level events that have been played in 2011 and/or 2012.  The first four numerical columns show the relative ace rate for each year from 2009 to 2012.  For instance, in Costa Do Sauipe this year, there were a staggering 61% more aces than expected.  The final two columns show the weighted averages for 2011 and 2012.  Each event’s “2012 Wgt” is my best estimate of the current state of the surface and how it will play next year.

I’ve also created a prettier, sortable version of the same table.

Continue reading

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How Does the Blue Clay Play?

If someone told you about an event where Rafael Nadal crashed out to a non-contender, Milos Raonic made a statement, and the final pitted Tomas Berdych against Roger Federer, you’d be forgiven for assuming the event was played on a very fast court. All of those things happened last week in Madrid on a surface that has at least some things in common with clay.

Given the tournament results, it’s no surprise to discover that statistically, the Madrid courts didn’t play like the old-fashioned red stuff. The stats from this year’s event at Caja Majica are a significant departure from those in past years, and suggest that the blue clay resembles a hard court more than it does European dirt.

Let’s start with aces. Aces are the stat most affected by surface, given the small difference in serve speed and bounce trajectory that can turn a returnable offering into an unreachable one. Of the 29 ATP tournaments played so far this year, Madrid ranks 10th in ace percentage after making adjustments for the players in the field and how many matches each one played. In fact, taking these adjustments into account, the ace rate in Madrid was almost indistinguishable from that of the indoor San Jose tourney!

(For a bit more background on methodology and more tourney-by-tourney comparison, see this article from last September.)

This is a huge departure for Madrid. The tournament has always had a reputation for playing a bit fast, given the altitude compared to Monte Carlo, Barcelona, Rome, and Paris, but that has long been a minor difference, at least when it comes to ace counts. In 2011, Madrid’s ace rate ranked 22nd of the season’s first 29 events, just ahead of Acupulco and behind Munich, Casablanca, and Santiago. 2010 was almost exactly the same, with Madrid coming in 23rd of these 29 events.

Another way of estimating court speed is by looking at the percentage of points won by the server. Even on points where the returner gets the ball back in play, a fast court should generate weaker returns and more third-shot winners. In this department, Madrid once again ranks among this year’s faster events. As in ace rate, it is #10 of 29 on the list, just behind San Jose and ahead of the hard court events in Chennai, Auckland, and Brisbane.

I can’t say whether it’s right or wrong to have a Masters-level event on an unusual surface, but I can say, based on these numbers, that the blue clay hardly plays like clay at all.

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