Mar 19, 2015
Mar 19, 2015

Assessing the formula for a tennis betting upset

Assessing the formula for a tennis betting upset

With big-price underdogs winning throughout the tennis season, it would be favourable for bettors to identify any patterns or trends that occur. This article looks at historical tennis data to identify if specific circumstances can lead to an upset?

Shock results happen in every sport, and tennis is no exception. The biggest recently being Rafael Nadal losing at Wimbledon in 2012 to Lukas Rosol (35.520) and again in 2013 to Steve Darcis (41.60).

These huge upsets may be rare, however numerous matches where the winner is priced in advance of 3.00 (heavy underdog), occur with greater regularity throughout the calendar – Andy Murray was recently knocked out of Dubai by Borna Coric, who was available at 20.250.

Despite the likes of Darcis, Rosol and Coric offering significant financial rewards to bettors who have backed them pre-match against an elite opponent, the vast majority of the market will not have sided with these players prior to the match starting. 

For tennis bettors it would be useful to determine whether a shock result is more likely in some circumstances than others. There are three areas we investigate in this article. 

  • Opponent playing style
  • Age of the underdog
  • Round of match

Player style

Previously we've looked at how playing style affects handicaps and the conditions they are suited towards. To bracket the players into big-servers or returners we used the formula below:

(service hold % - break opponent %)

The highest figure indicated a big-sever, while return orientated players were at the lower end of the results. The top ten players - outside of the elite - for each style were then added to a list.

The following table illustrates the records of big servers and strong returners, in best of three set matches since 2013, when starting the match priced over 3.00 with Pinnacle.

For clarity, only completed matches were used in the sample. A £100 hypothetical bet was applied to each player’s matches:

PlayerMatchesWin %Won A Set In Match %Handicap Cover %Profit/LossROI
Karlovic 21 42.86 52.38 76.19 2562 122.00
Isner 10 20.00 60.00 30.00 192 19.20
Groth 12 8.33 58.33 58.33 -889 -74.08
De Schepper 22 18.18 36.36 36.36 -667 -30.32
Raonic 10 10.00 40.00 30.00 -610 -61.00
Lopez 16 31.25 43.75 43.75 114 7.13
Johnson 15 26.67 53.33 53.33 -115 -7.67
Tomic 13 7.69 30.77 30.77 -911 -70.08
Querrey 7 14.29 42.86 42.86 -311 -44.43
Tsonga 9 22.22 44.44 33.33 109 12.11
Big Servers13522.2245.9345.93-526-3.90
Andujar 37 27.03 45.95 37.84 680 18.38
Monaco 10 10.00 40.00 20.00 -567 -56.70
Lorenzi 24 16.67 50.00 37.50 -1010 -42.08
Fognini 25 28.00 48.00 32.00 718 28.72
Ferrer 14 21.43 71.43 42.86 650 46.43
Berlocq 21 33.33 52.38 47.62 1461 69.57
Ebden 14 7.14 28.57 21.43 -1067 -76.21
Falla 17 29.41 52.94 64.71 415 24.41
Garcia-Lopez 25 28.00 64.00 40.00 333 13.32
Gabashvili 15 6.67 26.67 26.67 138 9.20
Returners20222.7749.0138.1217518.67

What the data suggests

  • Strong returners perform better than big-servers as large underdogs
  • Ivo Karlovic (big server) had the best individual record
  • Big-servers cover the -3.5 game handicap more regularly
  • Returners won at least one set in the match more often than big-servers

With seven of ten strong returners recording positive records when priced 3.00 or above, it appears that these grinders are better equipped to cause a shock when starting the match as a heavy underdog. 

This may surprise readers, who may have expected big-servers to be able to achieve better results, especially given they are more likely to keep sets closer.

This data is very useful to tennis bettors, making it apparent that backing underdog big servers on the game handicap is a better prospect than the set handicap, and vice versa for strong returners.

Youngsters

Now we have information on player style we can look at how the youngest ten players in the current ATP top 100 performed when priced over 3.00 since 2013. 

Again, only completed matches were included, and Challenger matches were not sampled. A £100 hypothetical bet was applied to these players.

PlayerRankAgeMatchesWinsMatch Win %Profit/LossROIBest Win
Dimitrov 11 23 18 2 11.11 -155 -8.61 Djokovic, 11.92
Tomic 35 22 19 2 10.53 -1187 -62.47 Llodra, 3.89
Krygios 37 19 12 3 25.00 991 82.58 Nadal, 10.29
Vesely 45 21 8 1 12.50 -431 -53.88 Monfils, 3.69
Thiem 46 21 15 3 20.00 140 9.33 Wawrinka, 6.15
Carreno-Busta 55 23 22 4 18.18 -703 -31.95 Monfils, 4.54
Sock 58 22 17 5 29.41 911 53.59 Raonic, 11.52
Coric 60 18 16 6 37.50 2465 154.06 Murray, 20.25
Schwartzman 63 22 9 2 22.22 -200 -22.22 Janowicz, 3.64
Dzumhur 91 22 8 3 37.50 476 59.50 Brown, 4.61
Young Players1443121.53230716.02

A superb 16.02% return on investment was generated backing the ten youngest top 100 players when priced over 3.00, with Coric (wins over Murray and Nadal) and Nick Kyrgios, who memorably got the better of Nadal at Wimbledon last year, achieving the best results. 

Jack Sock, and quite surprisingly, the unheralded Damir Dzumhur, also impressed, but Bernard Tomic and Jiri Vesely struggled.

How do elite players perform in the opening round?

The final area to research is how top-level players perform in their opening round matches.

The following table illustrates the records of the current ATP top ten in first round matches in their careers, with a £100 hypothetical bet used.

PlayerRankWin %ROI %Round 1 Win %ROI %
Djokovic 1 81.2 3.7 87.7 3.7
Federer 2 82.1 -0.1 84.6 3.4
Nadal 3 82.5 -2.1 87.5 -0.7
Murray 4 74.9 -0.5 80.6 -0.8
Nishikori 5 67.7 23 64.4 5.6
Raonic 6 64.4 2.1 66.3 2.9
Wawrinka 7 63.2 2.7 64.3 2.8
Ferrer 8 67.9 2 69.9 9.9
Berdych 9 66 -2 69.9 -12.6
Cilic 10 64.9 1.5 69.7 -2.1
Overall1.190.11

We can see that overall, the current ATP top ten achieved a career return of investment of 0.11% in first round matches, compared to 1.19% in all other rounds of events. 

Therefore it's reasonable to assume that top players are slightly more vulnerable to an upset in the first round than any other time in the tournament.

There are a number of factors to consider as to why this may be, such as motivation, injuries and complacency.  

Tennis betting takeout

This research has determined that the following conditions are best suited for causing a shock result, and should be factored into a bettors analysis and research:

  • Player is more return than serve orientated
  • Player is young
  • Match is played in the 1st round

Obviously tennis bettors should realise that these conditions will not guarantee an upset, but could create a ‘perfect storm’ for a surprise outcome.

Bettors are advised to use this data alongside there own research when trying to predict future tennis betting upsets.  

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