{"id":976,"date":"2012-11-07T14:51:15","date_gmt":"2012-11-07T19:51:15","guid":{"rendered":"http:\/\/heavytopspin.com\/?p=976"},"modified":"2012-11-07T14:51:15","modified_gmt":"2012-11-07T19:51:15","slug":"the-speed-of-every-surface-redux","status":"publish","type":"post","link":"https:\/\/www.tennisabstract.com\/blog\/2012\/11\/07\/the-speed-of-every-surface-redux\/","title":{"rendered":"The Speed of Every Surface, Redux"},"content":{"rendered":"

One of the most popular posts on this blog has been this one<\/a>, which quantified the speed of every ATP tournament’s surface. \u00a0At the very least, it’s time to provide some updated numbers. \u00a0Beyond that, we can improve on the methodology and say more about how much we can learn from the numbers.<\/p>\n

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. \u00a0The 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\u00a0slower<\/em> than average–almost 20% below average, on par with Roland Garros and the pre-blue clay Madrid Masters.<\/p>\n

Counterintuitive conclusions are fun, but that’s just wrong.<\/p>\n

Here’s the problem: Service stats aren’t only affected by servers. \u00a0Sure, when Milos Raonic<\/a> is serving, there will be more aces than when Mikhail Youzhny<\/a> is serving. \u00a0But how many aces Raonic hits is also influenced by the returning skills<\/a> of the man on the other side of the net. \u00a0It’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. \u00a0No matter how fast the court, Mardy Fish<\/a> wasn’t going to hit as many aces past Jo Wilfried Tsonga<\/a> or Rafael Nadal<\/a> in London as he did against Bernard Tomic<\/a> in Shanghai or Tokyo.<\/p>\n

I’ll be more succinct. \u00a0The goal is to compare the number of aces on a particular surface to the number of aces we’d expect on a neutral surface. \u00a0The number of Expected<\/em> aces depends on more than just the man serving; it also depends on the man receiving.
\n<\/i><\/p>\n

(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. \u00a0They track each other fairly closely, so there’s not a lot of additional value gained by using more than one. \u00a0From here on out, I’m measuring surface speed only by relative ace rate.)<\/p>\n

Incorporating more data<\/strong><\/p>\n

To factor in the additional variable, we need each player’s ace rate for the season along with his ace\u00a0against\u00a0<\/em>rate. \u00a0With those two numbers, together with the overall ATP average, we can apply the odds ratio method<\/a> to get a better idea of each match’s expected aces.<\/p>\n

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. \u00a0The tournament-wide average gives us an estimate of how fast the courts played at that event.<\/p>\n

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. \u00a0A 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.<\/p>\n

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. \u00a0At 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.<\/p>\n

Multi-year averages and (extremely mild) projections<\/strong><\/p>\n

If we want to know exactly what happened in one edition of a tournament, the single-year number is instructive. \u00a0Perhaps the weather, or the lighting, was very bad or very good, causing an unusually high or low number of aces. \u00a0Just because a tournament’s number for 2012 doesn’t match its numbers for any of the previous three years doesn’t mean it’s\u00a0wrong.<\/em><\/p>\n

However, the variety of effects that give us this year-to-year variance\u00a0do<\/em>\u00a0warn us that last year’s number will not accurately predict this year’s number.<\/p>\n

The year-to-year correlation of relative ace rate (as I’ve described it above), is not very strong (r = .35). \u00a0One way to modestly improve it is to use a three-year weighted average. \u00a0A 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).<\/p>\n

Another way of looking at these more reliable forecasts is that they get closer to isolating the effect of the surface. \u00a0As I noted in last year’s article, the weather effects of Hurricane Irene dampened the ace rate at last year’s US Open. \u00a0By 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. \u00a0The 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. \u00a0That 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.<\/p>\n

Below the jump, find the complete list of all tour-level events that have been played in 2011 and\/or 2012. \u00a0The first four numerical columns show the relative ace rate for each year from 2009 to 2012. \u00a0For instance, in Costa Do Sauipe this year, there were a staggering 61% more aces than expected. \u00a0The final two columns show the weighted averages for 2011 and 2012. \u00a0Each event’s “2012 Wgt” is my best estimate of the current state of the surface and how it will play next year.<\/p>\n

I’ve also created a prettier, sortable version<\/a> of the same table.<\/p>\n

<\/p>\n

Event                 Surface  09 A%  10 A%  11 A%  12 A%  11 Wgt  12 Wgt  \nCosta Do Sauipe       Clay      1.03   1.37   1.20   1.61    1.23    1.43  \nMarseille             Hard      1.31   1.09   1.30   1.47    1.23    1.35  \nHalle                 Grass     1.09   1.20   1.44   1.22    1.30    1.29  \nMontpellier           Hard      1.33   1.28          1.44            1.27  \nWimbledon             Grass     1.32   1.37   1.26   1.22    1.31    1.26  \nZagreb                Hard      0.95   1.09   1.29   1.22    1.17    1.22  \nWashington            Hard      1.01   0.96   0.99   1.39    0.99    1.18  \nSan Jose              Hard      1.11   1.21   1.41   0.93    1.30    1.14  \nDoha                  Hard      0.87   0.88   1.54   0.94    1.21    1.13  \nVienna                Hard      1.10   1.42   1.25   0.95    1.28    1.13 \nParis Masters         Hard      1.36   1.39   1.01   1.13    1.19    1.13 \nTokyo                 Hard      1.35   1.35   1.01   1.14    1.18    1.13  \nBrisbane              Hard      0.86   1.01   1.20   1.10    1.08    1.12  \nValencia              Hard      0.98   1.02   1.15   1.13    1.08    1.12  \nBasel                 Hard      1.34   0.98   1.11   1.15    1.10    1.11  \nAuckland              Hard      1.05   1.01   1.13   1.13    1.07    1.11  \nQueen's Club          Grass     1.20   1.07   1.13   1.08    1.12    1.10  \nWinston-Salem         Hard                    1.19   1.07    1.09    1.10  \nCincinnati Masters    Hard      1.21   1.09   1.09   1.08    1.11    1.08  \nDubai                 Hard      1.09   1.13   1.08   1.04    1.10    1.07  \ns-Hertogenbosch       Grass     1.28   1.13   1.04   1.04    1.11    1.06  \nStuttgart             Clay      0.97   1.09   1.15   0.99    1.10    1.06  \nSydney                Hard      0.95   1.08   1.18   0.96    1.11    1.05  \nMoscow                Hard      1.36   1.28   1.17   0.89    1.24    1.05  \nShanghai Masters      Hard      1.08   0.96   1.03   1.07    1.02    1.04  \nStockholm             Hard      1.18   0.93   0.92   1.15    0.97    1.03  \nAtlanta               Hard             0.92   0.90   1.15    0.92    1.03  \nBeijing               Hard      1.05   1.01   0.99   1.04    1.01    1.02  \nNewport               Grass     0.80   1.46   0.69   1.09    0.97    1.02  \nDelray Beach          Hard      0.77   0.98   1.19   0.92    1.05    1.02  \nUS Open               Hard      0.97   1.13   0.93   1.05    1.00    1.02  \nMetz                  Hard      1.18   1.14   0.95   1.02    1.06    1.02  \nCanada Masters        Hard      1.11   0.99   1.06   1.01    1.04    1.02  \nEastbourne            Grass     1.03   1.07   1.08   0.95    1.07    1.01  \nMemphis               Hard      1.20   1.09   1.08   0.94    1.10    1.01  \nAcapulco              Clay      1.12   0.89   0.93   1.11    0.95    1.01  \nAustralian Open       Hard      0.98   0.98   1.13   0.92    1.06    1.00  \nChennai               Hard      0.99   0.76   0.84   1.19    0.84    1.00  \nMadrid Masters        Clay      0.81   0.76   0.92   1.13    0.85    1.00  \nRotterdam             Hard      1.20   0.88   1.28   0.80    1.14    0.98  \nSantiago              Clay      0.80   1.22   1.32   0.66    1.20    0.97  \nGstaad                Clay      0.94   0.87   1.17   0.85    1.03    0.96  \nMiami Masters         Hard      0.92   0.91   1.04   0.88    0.98    0.93  \nBangkok               Hard      1.16   1.21   1.06   0.75    1.12    0.93  \nMunich                Clay      0.75   0.78   0.98   0.92    0.87    0.92  \nIndian Wells Masters  Hard      0.94   0.91   0.96   0.87    0.94    0.91  \nBuenos Aires          Clay      0.69   0.87   1.00   0.84    0.90    0.90  \nKuala Lumpur          Hard      0.95   0.96   1.02   0.75    0.99    0.87  \nCasablanca            Clay      0.99   0.82   0.96   0.81    0.92    0.86  \nRoland Garros         Clay      0.82   0.83   0.87   0.81    0.85    0.83  \nBarcelona             Clay      0.89   0.74   0.70   0.94    0.74    0.83  \nHamburg               Clay      0.85   1.04   0.85   0.75    0.91    0.83  \nLos Angeles           Hard      0.68   0.81   0.71   0.90    0.74    0.82  \nSt. Petersburg        Hard      0.89   1.02   0.86   0.71    0.92    0.81  \nNice                  Clay             0.89   0.80   0.78    0.86    0.80  \nBelgrade              Clay      0.54   0.72   0.64   0.92    0.65    0.79  \nKitzbuhel             Clay                    1.05   0.50    1.03    0.77  \nBucharest             Clay      1.06   0.61   0.97   0.62    0.86    0.74  \nHouston               Clay      0.83   0.85   0.71   0.69    0.78    0.73  \nEstoril               Clay      0.69   0.62   0.64   0.78    0.64    0.71  \nRome Masters          Clay      0.61   0.79   0.72   0.67    0.73    0.71  \nBastad                Clay      0.79   0.93   0.72   0.59    0.80    0.69  \nUmag                  Clay      0.86   0.56   0.66   0.74    0.66    0.68  \nMonte Carlo Masters   Clay      0.57   0.63   0.62   0.71    0.62    0.67  \nDusseldorf            Clay      0.77   0.70   0.47   0.63    0.60    0.59  \nTour Finals           Hard      1.18   1.08   0.97           1.04            \nJohannesburg          Hard      1.06   1.13   1.13           1.12<\/pre>\n","protected":false},"excerpt":{"rendered":"
One of the most popular posts on this blog has been this one, which quantified the speed of every ATP tournament’s surface. \u00a0At the very least, it’s time to provide some updated numbers. \u00a0Beyond that, we can improve on the methodology and say more about how much we can learn from the numbers. I was … Continue reading The Speed of Every Surface, Redux<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[109,126],"tags":[],"class_list":["post-976","post","type-post","status-publish","format-standard","hentry","category-surface-speed","category-world-tour-finals"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/976","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/comments?post=976"}],"version-history":[{"count":0,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/posts\/976\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/media?parent=976"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/categories?post=976"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tennisabstract.com\/blog\/wp-json\/wp\/v2\/tags?post=976"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}