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7th July 2016.
Something I've been spending some time looking at recently in tennis trading is the effect that projected hold has - compared to combined score - on the likelihood of a player being broken back in a set of tennis.
To those who aren't aware of these concepts, which are discussed at depth in previous articles on the website, as well as being quantified for each and every ATP and WTA match in the daily ATP/WTA spreadsheets, I'll run through a quick explanation:-
Projected Hold % = The projected likelihood that a player will hold an individual service game (based on data available prior to the match starting).
Combined Score = The % that a player, when a break up, gives up the first break lead in across any set of the match, plus the % that their opponent recovers this as a deficit.
Generally speaking, I consider a player to have a high projected hold when their projected hold percentage is 5% or greater than the relevant tour surface mean, and for a player to have a low projected hold when their projected hold percentage is 5% or less than the relevant tour surface mean.
Using the lead loss/recovery spreadsheets that I produce, we can look at the mean combined score for the WTA players (131 are included in the WTA sheets) which is 48.63% for lead loss, and 50.29% for recovery in the last update. Combined, this works out at 98.92, which is a very accurate figure for the mean combined score in the WTA. On a side note, it's perfectly reasonable for the recovery percentage to be slightly higher than the lead loss percentage, as some of the recovery successes will be against players not included in the sheets (low ranked players), where naturally the recovery percentage will be higher.
On this basis, I made an assessment that a combined score of 105.00-114.99 (6.08-16.07 above the mean) was high (leading player was more vulnerable than average when leading by a break of serve) whilst a combined score of 115.00 or more was very high (leading player was highly vulnerable to lose a break lead when leading by a break of serve).
Now that boundaries were assessed for both metrics, we can look at samples of data for the WTA.
Across all sets, the following data was produced for matches in the 2016 season (upto and including 7th July):-
The table above shows the effect of projected hold on the likelihood to lose the first break lead of any set when the player is leading, if we assume that the combined score was 105 or greater.
We can see that there was a slight increase when the break leader's projected hold percentage dropped to greater than or equal to 5% below the surface mean, with this figure producing 56.59% of break-backs compared to the 52.20% when the projected hold percentage was within 5% of the surface mean.
It is clear from this data that the projected hold percentage of the player a break up in a set has some impact on the likelihood of a player losing the break lead.
The second sample I took was again, across all sets, looking at scenarios when the projected hold percentage of the leading player was at least within 5% of the surface mean, and the combined score was at least 105:-
We can see that when combined score is at least 115.00, the likelihood of the leading player being broken back rose to 59.92%, the highest of any point in this sample. Furthermore, it is also apparent that combined score is a bigger driver towards break-back likelihood than the projected hold percentage of the leader, with there being a 10.42% difference between the break-back percentage when combined score is only slightly high at 105.00-114.99 compared to when it is 115.00+.
On this basis, tennis traders would be well advised to value combined score as more valuable than the projected hold percentage when assessing whether a player is likely to be vulnerable when a break up.
I also established individual set data, and also evaluated each individual situation (e.g. projected hold >=5% below mean and combined score 115+) to generate even more powerful data, but due to the obvious value and sensitivity of this individual set/scenario data, I cannot publish this in a free article.
However, if you are keen on obtaining this data, please feel free to email me via email@example.com to discuss your requirements.
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