Which tennis players perform better on slow courts?

  How certain playing styles perform in slow conditions Slow court betting strategy How do serve-orientated players perform on slow courts? What are the slowest tennis courts on tour? With…

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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.

In the previous article, we looked at how both serve and return orientated players performed at the quickest venues on tour, establishing that the market undervalues serve orientated players in quick conditions, and also overvalues return orientated players in the same conditions as well, giving us a workable strategy to take into the betting markets in the future.

This second part of the two-piece strategy article series looks at the reverse court conditions – the slowest venues on tour.

Again, initially, research was performed to analyse the historical conditions of current events, and there were 12 venues with a service points won percentage of 62.2% or below – the slowest venues on tour.

While the fast venues were a mixed bag of surfaces, with hard courts (both indoor and outdoor) and grass courts dominating, the slower venues based on this metric were exclusively clay court tournaments, including several major tournaments, including the Masters 1000 event venue in Monte Carlo, as well as the upcoming French Open.

Also included were the recent tournaments held in Barcelona and Budapest last week, won by Dominic Thiem and Matteo Berrettini, respectively.

A glance at Thiem’s performance at these 12 slowest venues on tour gives some insight into why he may have performed well in Barcelona. From 2016 onwards, he’s played 73 matches at these venues (the most across the entire ATP Tour) and has returned £1,203 profits, based on a hypothetical stake of £100 betted on him for every one of those matches.

This yielded a return on investment of 16.48%, aptly showing his ability in very slow conditions.

Despite being rather serve-orientated, Berrettini’s performances in slow conditions have also been solid – his small sample of 16 matches at these venues generated an ROI of 22.44%.

As with the first article in the series, our main objective is to assess how certain player dynamics perform in these slow conditions.

In the last article, we established that serve-orientated players outperformed market expectations in quick conditions, but the opposite was true in slow 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 at these slow venues from January 1, 2016 until April 28, 2019, with all bets using a hypothetical £100 flat stake.

Filter one: Top 10 service points won players

Player Slow court performance, 2016+ Slow court performance, 2016+ Slow court performance, 2016+
 , Matches P/L ROI
Isner 13 -213 -16.38
Karlovic 12 -214 -17.83
Federer 3 -63 -21.00
Raonic 14 -134 -9.57
Kyrgios 8 -163 -20.38
Anderson 17 199 11.71
Opelka 6 -42 -7.00
Tsonga 16 -17 -1.06
Cilic 20 -440 -22.00
Querrey 4 -178 -44.50

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

Player Slow court performance, 2016+ Slow court performance, 2016+ Slow court performance, 2016+
 , Matches P/L ROI
Karlovic 12 -214 -17.83
Isner 13 -213 -16.38
Opelka 6 -42 -7.00
Klahn 3 -126 -42.00
Raonic 14 -134 -9.57
Kyrgios 8 -163 -20.38
Anderson 17 199 11.71
Copil 21 -558 -26.57
Kokkinakis 4 -295 -73.75
Querrey 4 -178 -44.50

In the last article, we established that serve-orientated players outperformed market expectations in quick conditions, but the opposite was true in slow conditions. They couldn’t meet market expectations at all, with 113 matches in filter one returning an ROI of -11.19%, and 102 matches in filter two yielding an even worse return, of -16.90%.

Only Kevin Anderson, in both filters, was able to generate a positive return in slow conditions.

Filter three: Bottom 10 service points won players

Player Slow court performance, 2016+ Slow court performance, 2016+ Slow court performance, 2016+
 ,, Matches P/L ROI
Kavcic 3 -149 -49.67
Olivo 29 2668 92.00
Berlocq 41 -1052 -25.66
Andujar 17 297 17.47
Fabbiano 19 -883 -46.47
Daniel 44 -640 -14.55
Andreozzi 30 -506 -16.87
Dzumhur 26 -204 -7.85
Albot 22 -490 -22.27
Nishioka 6 -308 -51.33

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

Player Slow court performance, 2016+ Slow court performance, 2016+ Slow court performance, 2016+
 , Matches P/L ROI
Schwartzman 56 -332 -5.93
Andujar 17 297 17.47
Dzumhur 26 -204 -7.85
Olivo 29 2668 92.00
Kamke 12 -272 -22.67
Fabbiano 19 -883 -46.47
Fognini 51 532 10.43
Berlocq 41 -1052 -25.66
Nishioka 6 -308 -51.33
Simon 33 -582 -17.64

While we saw that big-servers struggled in slow conditions, it would appear from the filters that serve-orientated players were unable to outperform market expectations.

This is despite a clear preference from the players to play at these slower venues – evident from the volume of matches they competed in – and it would appear that the market has a fairly accurate handle on these return-orientated players.

Filter three returned -5.35%, while filter four generated -0.47%, so blind-backing return-orientated players in slow conditions was not a viable strategy.

However, as well as the conclusions drawn in the previous article, this data has helped us ascertain that there could well be some value in opposing big-servers when they participate at slow venues, and with them all participating at the French Open in several weeks’ time, this should give bettors food for thought in advance of the second Grand Slam of 2019.

 

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