Recently on Twitter, there has been detailed conversation about court speed, and the impact that it has in Tennis.
As a trader, let me make one point abundantly clear here – I don’t care why the conditions at a venue is fast or slow, just that it will be, and that it is possible to ascertain this information in advance. Advance knowledge of conditions will enable me to work out the likelihood of players holding serve, and a fast hard court could have over 10% influence over a slow hard court, for example.
Ex-player and Sky Sports commentator Rob Koenig tweeted on Monday that a rating, known as the CPR Rating, assessed the ATP 1000 Masters surfaces in the following order, from fast to slow, using 2014 data:-
https://twitter.com/RobKoenigTennis/status/587561221370544128
Shanghai
Toronto
Paris
Indian Wells
Miami
Cincinnati
Rome
Madrid
Monte Carlo
This caught my attention – effectively the surface last week, Monte Carlo, has been assessed as the slowest Masters 1000 surface. I was quite surprised at this, for several reasons:-
- I know for sure that recent hard court tournaments Indian Wells and Miami, played in March, have a bigger negative deviance to the mean than Monte Carlo. To explain further, Indian Wells and Miami are relatively slower to the hard court mean than Monte Carlo – a medium-slow court according to my records – is compared to the clay court mean.
- There are many slower clay venues than Monte Carlo on the ATP Tour. Casablanca and Umag are several examples.
I assess court speed by looking at the percentage of historical service holds compared to the surface mean. It’s not a perfect strategy, as it can be influenced by a tournament’s entry list, but it’s as good as it gets. Certainly, a Masters 1000 field is likely to have a very consistent entry list with very few players skipping these lucrative events without being injured.
Using this metric, I generated the following order for the Masters 1000 events, compared to the relevant surface mean:-
|
ATP Masters Tournaments |
2014 Data |
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Event |
Surface |
Service Hold % |
Surface Average Hold % |
Difference % |
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Madrid |
Clay |
80.5 |
75.7 |
4.8 |
|
Toronto |
Hard |
83.5 |
80.1 |
3.4 |
|
Shanghai |
Hard |
82.9 |
80.1 |
2.8 |
|
Rome |
Clay |
77.2 |
75.7 |
1.5 |
|
Cincinnati |
Hard |
80.3 |
80.1 |
0.2 |
|
Paris |
Indoor Hard |
80.8 |
80.7 |
0.1 |
|
Miami |
Hard |
79.4 |
80.1 |
-0.7 |
|
Monte Carlo |
Clay |
74.3 |
75.7 |
-1.4 |
|
Indian Wells |
Hard |
78.2 |
80.1 |
-1.9 |
Looking at this, the CPR Rating seems to be quite random! Shanghai, their quickest event, is indeed fast on my speed ratings, and so is Toronto, their second fastest, although I have them the other way around.
However it is the slower events which seem to be very different – Indian Wells and Miami are very slow hard courts and ranked below Cincinnati for speed, which isn’t the case with the CPR Ratings at all.
Furthermore, their ratings have the clay court events slowest, which is where I begin to understand how they work…
Madrid and Rome are traditionally fast clay court venues, so it appears that they have merely looked at the speed data without actually accounting for the surface.
Of course hard and indoor hard courts are likely to play faster than clay – it’s a quicker surface! But readers of the CPR Rating tweet – and there were plenty as it has been retweeted 75 times when I last checked – will automatically assume Madrid and Rome are slow courts, which isn’t the case at all! Trading thinking these events are slow is incredibly misleading and will undoubtedly lead to the poor house, which immediately illustrates the danger of taking random stats at face value without thinking about why these stats have occurred.
