More than five years after I first started trying to use ATP match stats to estimate surface speed, the issue remains a contentious one. Most commentators agree that surface speeds have converged and generally gotten slower. The ATP has begun to release a trickle of court speed data, but it raises more questions than answers.
It’s been three years since I’ve published surface speed numbers, so we’re due for an update. Before we do that, it’s important to understand what exactly these figures mean, as well as their limitations.
Court surfaces–and, more broadly, the environments in which pro matches are played–have a variety of characteristics. Some courts are faster or slower and some cause higher or lower bounces. Tournaments use different balls, are played at a range of elevations, and take place in all sorts of weather conditions. All of these factors, and more, affect how matches are played.
Due to the limits of available tennis data, however, we can’t isolate those different factors. It would be great to know which surfaces allowed for the most effective slice approaches or the deadliest drop shots, but we don’t have the data to even begin trying to answer those questions. The Match Charting Project is a step in the right direction, but with only a few hundred men’s matches per year, there isn’t quite enough to compare surfaces while controlling for different players and playing styles.
So we work with what we have. Faster surfaces are more favorable to the server, which shows up in ace counts and service breaks. The ATP publishes those basic stats for every match, so that’s what we’ll use. When I first researched this issue, I discovered that there isn’t much difference between counting aces and counting service breaks, except that there’s a wider variation in ace rates between faster and slower surfaces, so the resulting numbers are easier to understand.
At the risk of repeating myself: Measuring surface speed by ace rate ignores a lot of court characteristics. It is far from complete and certainly imperfect. It does, however, give us an idea of how tournaments compare in one important regard.
That said, simply counting aces–for example, 6.8% of points in Buenos Aires this year and 11.2% of points in Los Cabos–isn’t good enough. Players make scheduling choices based on their strengths and preferences, so the guys who show up for clay court events tend, on average, to be weaker servers than those who play on hard and grass courts. To take an extreme example, Gilles Muller managed to play only two matches on clay this season. As it turns out, the courts in Buenos Aires and Los Cabos had almost identical effects on ace rates–the difference is entirely due to the mix of players in each draw.
So we adjust for the makeup of the field. For every player with at least three tour-level matches on clay and another three on hard or grass, I calculated their season average ace rates on clay and hard/grass,which I then weighted (one-third clay, two-thirds hard/grass) so that the numbers give us idea of what their ace rate would’ve been had they played an “average” (that is, unbiased by scheduling preferences) season. I’ve lumped hard and grass together here, not because they are the same–of course they’re not–but because the small number of grass court events makes it difficult to treat on its own.
With player averages in hand, we can go through every match of the season (between players who meet our minimums) and, using their ace rates and the rates at which players hit aces against them, calculate a “predicted” ace rate for the match, given a neutral surface. Then, by comparing the match’s actual ace rate to the neutral prediction, we get one data point regarding the surface’s effect on aces. If the actual ace rate is greater than the prediction, it suggests the surface is faster than average. If the prediction is greater than the ace rate, it implies the surface is slower than average.
No single match can tell us about a court’s tendency, but by aggregating all the matches at an event, we get a fairly good idea. With that final step, we get a single number per event. A neutral surface rates at 1, faster surfaces are greater than 1, and slower surfaces are less than 1. For instance, this algorithm rates the 2016 Paris Masters as 1.18, meaning that there were 18% more aces than we would expect on a neutral surface, rating Bercy as faster than all but 10 other events this season.
Whew! Here are the ace-based surface ratings for the last three seasons of every current tour-level event listed from fastest to slowest:
Tournament Surface 2016 Ace% 2016 2015 2014 Shenzhen Hard 12.9% 1.54 1.20 1.49 Quito Clay 11.9% 1.50 0.89 Metz Hard 12.6% 1.43 1.28 1.37 Marseille Hard 15.3% 1.38 1.28 1.26 Stuttgart Grass 13.3% 1.38 1.32 0.89 Chengdu Hard 11.7% 1.27 Australian Open Hard 12.3% 1.25 1.19 1.12 Queen's Club Grass 14.3% 1.25 1.27 1.26 Washington Hard 19.5% 1.24 1.12 1.25 Cincinnati Masters Hard 14.2% 1.18 1.04 1.17 Paris Masters Hard 13.7% 1.18 1.03 1.03 Brisbane Hard 12.2% 1.16 1.20 1.23 Canada Masters Hard 12.6% 1.16 1.08 1.00 Halle Grass 12.2% 1.16 1.12 1.31 Nottingham Grass 12.0% 1.15 1.21 Gstaad Clay 10.1% 1.12 0.84 0.77 Basel Hard 10.1% 1.12 1.01 1.20 Tokyo Hard 11.5% 1.12 1.00 1.06 Chennai Hard 10.3% 1.12 0.91 0.65 Auckland Hard 12.9% 1.11 1.21 1.01 Tournament Surface 2016 Ace% 2016 2015 2014 Doha Hard 8.8% 1.11 1.06 0.83 Sydney Hard 10.5% 1.11 1.32 1.27 Montpellier Hard 9.7% 1.10 1.29 1.29 Shanghai Masters Hard 10.7% 1.10 1.05 1.34 Kitzbuhel Clay 6.9% 1.09 0.85 0.81 s-Hertogenbosch Grass 13.2% 1.08 1.06 1.05 Winston-Salem Hard 10.4% 1.07 1.33 1.10 Newport Grass 11.0% 1.07 1.26 1.23 Tour Finals Hard 9.5% 1.06 0.99 0.89 Wimbledon Grass 11.8% 1.06 1.20 1.35 Rotterdam Hard 9.8% 1.04 1.19 1.08 Vienna Hard 11.8% 1.02 1.39 1.26 Memphis Hard 8.7% 1.00 1.19 0.94 Miami Masters Hard 10.0% 1.00 0.86 1.04 Sofia Hard 8.4% 1.00 Beijing Hard 9.4% 0.99 1.05 0.81 Atlanta Hard 15.5% 0.97 1.35 0.90 St.Petersburg Hard 8.1% 0.97 0.98 Marrakech Clay 8.5% 0.95 Olympics Hard 7.1% 0.95 Tournament Surface 2016 Ace% 2016 2015 2014 Moscow Hard 6.6% 0.94 1.08 1.12 Antwerp Hard 8.6% 0.93 Delray Beach Hard 9.2% 0.92 0.88 0.93 US Open Hard 8.9% 0.91 1.10 1.10 Dubai Hard 9.4% 0.88 0.93 0.81 Madrid Masters Clay 8.6% 0.86 0.85 0.94 Los Cabos Hard 11.2% 0.85 Buenos Aires Clay 6.8% 0.85 0.78 0.64 Houston Clay 11.5% 0.84 0.76 0.70 Sao Paulo Clay 7.1% 0.83 1.03 1.20 Acapulco Hard 10.5% 0.83 0.67 0.98 Indian Wells Masters Hard 8.2% 0.83 0.99 0.90 Stockholm Hard 7.6% 0.82 1.13 1.15 Rio de Janeiro Clay 7.4% 0.81 0.80 0.77 Estoril Clay 7.4% 0.80 0.63 0.62 Nice Clay 6.3% 0.79 0.64 0.74 Geneva Clay 8.3% 0.77 0.78 Umag Clay 5.4% 0.77 0.67 0.76 Roland Garros Clay 7.6% 0.77 0.72 0.71 Rome Masters Clay 7.2% 0.76 0.94 0.74 Bucharest Clay 5.9% 0.71 0.59 0.51 Munich Clay 6.3% 0.71 1.01 0.87 Monte Carlo Masters Clay 6.2% 0.70 0.63 0.64 Istanbul Clay 5.7% 0.67 0.83 Barcelona Clay 5.4% 0.65 0.70 0.72 Bastad Clay 5.3% 0.65 0.64 1.07 Hamburg Clay 5.7% 0.60 0.62 0.79
As usual, we have an interesting mix of usual suspects and surprises. The top of the list is primarily indoor hard and grass courts, along with the high-altitude clay in Quito and Gstaad. However, in both of the latter cases, those tournaments had lower-than-expected ace rates in 2015. The surface ratings for 250s are particularly volatile because, in addition to the small number of matches, many of these matches must be discarded because one or both of the players didn’t meet our minimums. For the 2015 Quito event, we have only 11 matches to work with.
The sample size problem doesn’t apply to larger events, however, so we can have a fair amount of confidence in the ratings for the Australian Open, showing up here as the fastest of the Grand Slams–considerably faster than Wimbledon, which is only a few ticks above neutral.
Ace ratings and Court Pace Index
Last month, TennisTV released some data on court speed for this season’s Masters events. Court Pace Index (CPI) is a commonly-accepted measure of the speed of the surface itself–that is, the physical makeup of the court. As I’ve said, that’s far from the only factor affecting how a court plays, but it is an important one.
Here’s how my surface ratings compare to CPI:
Tournament Surface TA Rating CPI Cincinnati Masters Hard 1.18 35.1 Paris Masters Hard 1.18 39.1 Canada Masters Hard 1.16 35.2 Shanghai Masters Hard 1.10 44.1 Tour Finals Hard 1.06 40.6 Miami Masters Hard 1.00 33.1 Madrid Masters Clay 0.86 22.5 Indian Wells Masters Hard 0.83 30.0 Rome Masters Clay 0.76 24.0 Monte Carlo Masters Clay 0.70 23.7
It’s noteworthy that Madrid is, by my measure, the most ace-friendly of the three clay-court Masters, while its CPI is the lowest. Altitude could account for the difference.
The biggest mismatch, though, is the Tour Finals. The O2 Arena has one of the highest CPIs, but it doesn’t rate very far above average in aces. The Tour Finals has always been a bit problematic, as there is an unusually small number of matches, and the level of returning is very, very high. My algorithm takes into account how well each player prevents aces, but perhaps that issue is more complex when our view is limited to only the very best players.
TennisTV also showed CPI for the last several years of Tour Finals:
Compared to my ratings:
Year TA Rating CPI 2016 1.06 40.6 2015 0.99 34.0 2014 0.89 33.6 2013 0.90 32.8 2012 1.18 33.9
If the table cut off after 2013, it would look like a relatively good fit. As it is, the relationship between CPI and my rating for 2012 wouldn’t be out of place in the previous table, which included a 35.1 CPI for Cincinnati to go with an ace-based rating of 1.18.
I hope that this is a sign of more data to come. If so, we can move beyond approximations based on ace rate to get a better sense of what factors influence play at the ATP level. More data won’t settle the age-old surface speed debates, but it will make them a whole lot more interesting.