Which tennis players perform better on fast courts

How certain playing styles perform in quick conditions Betting strategies based on court conditions What is the quickest clay court on the ATP Tour? How to assess if a player…

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With the main clay court season underway, but the grass season starting in several months, the tennis calendar will witness a change from the generally slowest courts to the quickest. Our resident tennis expert Dan Weston has a look at some betting strategies based on player dynamics and anticipated conditions.

This will be the first of a two-piece strategy article series which assesses whether certain player dynamics benefit from specific court speeds and conditions in general. To start with, this first piece will look at how certain playing styles perform in quick conditions, and whether this is accurately accounted for by the markets.

Fast court betting strategy

When researching for this article, the first priority was to look at historical conditions of current events, in order to establish which courts are likely to exhibit the quickest conditions in the future.

There were 14 venues on tour with three-year service points won percentages of 65.2% or above, which is a key figure – given it is 1.5% above the overall ATP service points won mean.

Madrid is statistically the quickest clay court on the ATP Tour

Of these, five were grass courts – rather unsurprisingly – while three were three on indoor hard (New York, Paris and Marseille), five on outdoor hard (Atlanta, Auckland, Shanghai, Cincinnati and Brisbane), while a little less predictable was that this filter included a clay venue – Madrid, which is played at some altitude and is statistically the quickest clay court on the ATP Tour.

Having worked out which courts on tour have historically played the quickest, we can then assess how certain player dynamics perform in these conditions.

Here, there are several options to work out whether a player is serve or return orientated – either using a player’s basic service points won percentage, or using the difference between their serve and return points won percentages.

The two tables below illustrate how the profiled serve-orientated players performed from January 1, 2016 until April 23, 2019, with all bets using a hypothetical £100 flat stake:-

Filter one: Top 10 service points won players

Player Fast court performance, 2016+ Fast court performance, 2016+ Fast court performance, 2016+
Matches P/L ROI
Isner 63 346 5.49
Karlovic 32 -336 -10.50
Raonic 63 427 6.78
Federer 47 -65 -1.38
Kyrgios 59 276 4.68
Opelka 18 883 49.06
Anderson 44 686 15.59
Tsonga 36 -285 -7.92
Cilic 65 907 13.95
Querrey 54 3503 64.87

Filter two: Top 10 service points won – return points won difference players

Player Fast court performance, 2016+ Fast court performance, 2016+ Fast court performance, 2016+
 , Matches P/L ROI
Karlovic 32 -336 -10.50
Isner 63 346 5.49
Opelka 18 883 49.06
Klahn 20 649 32.45
Raonic 63 427 6.78
Kyrgios 59 276 4.68
Anderson 44 686 15.59
Copil 27 -180 -6.67
Kokkinakis 16 633 39.56
Querrey 54 3503 64.87

Evidently, these serve-orientated players performed very well in quick conditions across the last few years, using either filter.

Filter one – ATP players sorted by service points won percentages, yielded a profit of £6342 from a fairly reasonable sample size of 481 matches (ROI of 13.19%), while filter two generated even better profits, accumulating £6887 from 396 hypothetical bets (17.39% ROI).

It is, however, worth mentioning that Sam Querrey’s sample was heavily influenced by a 32.891 closing price win over Novak Djokovic in Wimbledon 2016, and naturally, this had an impact on the overall results – contributing around half the hypothetical profits from just one match. Having said, this, though, results were still strong even if we were to disregard this win for Querrey.

Boosting the theory that extreme serve-orientated players are undervalued by the market in quick conditions was the performances of return-orientated players in the same conditions. The tables below illustrates the 10 most return orientated-players, using the same filters:-

Filter three: Bottom 10 service points won players

Player Fast court performance, 2016+ Fast court performance, 2016+ Fast court performance, 2016+
 ‘ Matches P/L ROI
Nishioka 20 197 9.85
Albot 29 903 31.14
Andreozzi 2 -200 -100.00
Dzumhur 26 -44 -1.69
Daniel 18 -1001 -55.61
Fabbiano 24 -367 -15.29
Andujar 5 521 104.20
Berlocq 3 58 19.33
Olivo 4 -400 -100.00
Kavcic 11 -118 -10.73

Filter four: Bottom 10 service points won – return points won difference players

Player Fast court performance, 2016+ Fast court performance, 2016+ Fast court performance, 2016+
 , Matches P/L ROI
Simon 46 -428 -9.30
Nishioka 20 197 9.85
Berlocq 3 58 19.33
Fognini 28 -477 -17.04
Fabbiano 24 -367 -15.29
Kamke 19 -106 -5.58
Olivo 4 -400 -100.00
Dzumhur 26 -44 -1.69
Andujar 5 521 104.20
Schwartzman 24 -1019 -42.46

Immediately we can see that the reverse of the success from the serve-orientated players in these quick condition events is apparent. Blind-backing filter three – the lowest 10 service points won players – yielded a loss of -£451 from 142 matches (ROI of -3.18%), while the performance of players highlighted by filter four was even worse, with a loss of -£2065 from 199 matches (-10.38%) ensuing.

In short, return-orientated players both struggled in quick conditions, but also deigned to play considerably less at these events – perhaps a nod to avoiding these venues with their scheduling.

Conversely, it is obvious that serve-orientated players thrived in quick conditions, and generally attempted to play events at these venues as much as possible.

The second piece of this series looks at how both serve and return orientated players performed in the reverse conditions – the slowest venues on tour.

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