Category: Surface speed

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.

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 The Speed of Every Surface, Redux

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.