Article - WTA Break Back Percentages Based On Time Decay & Break Back Stats

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Following the launch of the WTA Ultimate In-Play Spreadsheet, I decided to do a similar detailed analysis of break back percentages in the WTA as I previously did in the ATP Tour, based on two variables.  

As mentioned in the ATP article, being able to estimate the percentage chances of a break back occurring in a set is a very valuable asset because it enables us to be on the right side of many swing trading opportunities.  An initial break lead reduces a player's price in-play significantly, and a break back will return the price to a similar level to where the price was before the break occurred.

The two variables are the time decay of the set, and the break back stats of the two competing players.  The data was derived from August 1st, 2013 until the end of the Tournament of Champions in Sofia, on November 3rd, 2013. 

The time decay of the set is measured by the amount of games expired immediately after the break lead occurs, and the break back stats are measured by adding the sum of the percentage that the relevant player loses a break lead in a set added to the percentage their opponent recovers a break deficit in a set (all stats derived from the Ultimate In-Play Spreadsheet).

The break back stats were then broken down into five categories:-

Sum of Under 75.00

Sum between 75.00 and 89.99

Sum between 90.00 and 104.99

Sum between 105.00 and 119.99

Sum Over 120.00

The percentages in 2013 for a top 100 (and a few notable others) WTA player, according to the stats on the Ultimate In-Play Spreadsheet were as follows:-

The average for giving a break lead up was 47.87%, and getting a break deficit back being 50.25% (this is higher due to the fact that they play players outside the top 100) the average sum can be considered to be 98.12% - almost the midpoint of the third, middle category.  We can also then assume that the average percentage in WTA matches for a break back to occur is 49.06% (98.12%/2).

As we can see based on the table at the bottom of the article (also available via the resource link here), the stats based on the combined sum of the two players show a huge trend towards the higher percentages, similar to the ATP.

When the two players combined score is under 75, there have been just 15.63% of break backs, which is well below the expectation of around 30-35% (there were very few combined scores below 60% and most were around the 70% region in this bracket).

When the combined score is between 75 and 89.99, and 90.00 and 104.99 the break-back figures were almost identical (46.60% and 46.54% respectively).  These figures were somewhat surprising considering the sample size was fairly decent in these areas. 

Considering the midpoint of each bracket is effectively double the expectation for the figures, we can see that the 75-90 bracket overachieved.  The midpoint expectation was 41.25% for this bracket so we can see the actual results indicated just over 5% more break-backs than expected.

The 90.00 to 104.99 bracket had a midpoint expectation of 48.75% so we can see that the actual results were slightly below expectation – 2.21% below the expected figure.

With all areas having below the average break-back percentage, it would not be recommended to lay a player a break up when the combined score of them giving up a break lead and their opponent recovering a break deficit is under 105.  This would be particularly the case if projected holds were also above average. 

As I detailed in the ATP analysis, at this point many readers may think this is fairly obvious, and would occur when top players are a break up.  However it also occurs in a great variety of other scenarios.  For example the likes of the highly-rated Eugenie Bouchard and top-20 player Kirsten Flipkens (both often start matches as a strong favourite) have very poor stats for getting a break deficit back and many of their matches would have been included these two categories.  There are also some lower-ranked players with strong break deficit recovery percentages.

The break back stats greatly increase when looking at the two brackets over 105. 

The 105.00 to 119.99 bracket showed a dramatic increase from the 90.00 to 104.99 bracket with break backs occurring 58.90% (12.36% above the 90.00 to 104.99 bracket).   This was also slightly above the midpoint expectation of 56.25%.  We can see with this large increase that situations are much more in our favour, with us now more likely to have a profit from our trade than a loss when we lay the player a break up in the set.

The final bracket which comprises match-ups with over 120.00 combined score had a fairly small sample but had very similar results to the 105.00 to 119.99 bracket with a break back percentage of 58.46%.  It’s worth mentioning that my expectation for this over 120.00 bracket was around 62-63% - I can’t remember there being many (if any) match-ups with a combined score over 130.

We can see from the table below that break backs occur at different percentages based on the time decay of the set (games completed).

Compared to the ATP data the WTA data for time decay is much less pronounced.  The ATP data showed there was a big discrepancy between the start and end of sets with there being 40.45% and 40.28% break-backs when the break lead was obtained in the 1st or 2nd game of the set, compared to 10.75% and 14.13% when the break lead was obtained in the 8th or 9th game of the set.

However the WTA data was less stark with there being just a 22.89% difference between the highest and lowest game percentages (1st game of the set = 58.33%, 7th game of the set = 35.44%).  The ATP largest difference was 29.70%, and also represents a much bigger percentage difference than the WTA data.

An average player that breaks in either the first (58.33%) and second (47.54%) games of the set has exactly a combined 54% chance of getting broken back - just under 5% above average - Unsurprisingly that’s the highest percentage for expired time due to the player having more time to 'defend' his break lead. 

However, I will re-iterate the point that the percentage decreased much less than I expected with time decay and laying the player a break up when the break comes in the latter stages of sets appears much more viable than in the ATP.

There are some circumstances where there is an extremely high chance of a break back occurring.

When the combined score is over 105, and the break lead originated in the first or second game of the set, the player a break down got the set back on serve 71.97% of the time (95/132).  Furthermore, when the combined score went over 120, whilst sample size was small, the player a break down got the set back on serve 84.21% of the time (16/19).

These situations provide us with some very high expectation opportunities for laying the player a break up.

The ATP article spent some time discussing the phenomenon of the 11th game of the set.  In the ATP, when the break came in the 11th game of the set (so a player is *6-5 up) the break-back came much less than expectation, and I discussed the reasons behind that.

This was not the case in the WTA with the break-back percentage being 37.50%.  This is pretty much exactly the same as the average WTA service break across all surfaces of 36.70%, so this appears to be a solely ATP based trend.

How can we use this data?

Based on my research, it's clear to see that combining time decay and the break back data provided in the Ultimate In-Play Spreadsheet will generate many positive expectation opportunities for profit.  If you laid the player blindly a break up when the combined score was over 105, you'd have had 357 entry points in the 3 months sampled, and had 210 winning trades (58.82%).   This figure is well above the average break-back percentage of 49.06%.

We can also look to the set betting markets – if the data indicates there is an above average chance of a break-back, we can look to back the player a break down to win the set, often at a big price.

Finally, Twitter followers may have noticed that yesterday I did a social experiment.  I asked for some estimates for a break back percentage for the following situation and asked for some reasons as to why.

‘A WTA player that recovers break deficits 60% goes *1-0 down to a player that loses a break lead 55%’. 

I wanted to get a handle on what people’s perceptions were, both for the percentage and for the reasons why – a bit of a ‘market’ sample, if you like.

Not one reply mentioned the time decay of the set (or it being at its lowest point) and the average of the Twitter sample was 57.8%.  The highest estimate was 80%, and the lowest 33%. 

We can see from the data below that the figure from my sample was around the 71.43% from the 105.00 to 119.99 combined score bracket (the sample had a 115 combined score – not far from the midpoint of that bracket). 

This experiment also lends weight to the fact that the market expects the break-back to occur less than it actually does – which is exactly what we want and need for this type of trade to show a long-term profit. 


The complete data can be seen below.




Break After Game x of set = A player leads by a break immediately after that game of the set

Combined Score = The sum of the % the relevant player loses a break lead in a set added to the % their opponent recovers a break deficit in a set (all stats derived from the Ultimate In-Play Spreadsheet).

The figure in red for each game of the set is the overall break back percentage for that set.


WTA Stats August 1st  2013 Average =49.06%
to 3rd November Sample Average = 49.19
Break After Game 1 of set Break After Game 2 of set Break After Game 3 of set Break After Game 4 of set
Combined Back on Serve % Combined Back on Serve % Combined Back on Serve % Combined Back on Serve %
Score Yes No Score Yes No Score Yes No Score Yes No
58.33 47.50 56.55 51.18
<75 0 5 0.00 <75 2 11 15.38 <75 1 0 100.00 <75 1 4 20.00
75-89.99 44 35 55.70 75-89.99 18 30 37.50 75-89.99 18 18 50.00 75-89.99 20 11 64.52
90-104.99 70 63 52.63 90-104.99 41 49 45.56 90-104.99 42 32 56.76 90-104.99 25 30 45.45
105-119.99 50 20 71.43 105-119.99 29 14 67.44 105-119.99 29 19 60.42 105-119.99 16 14 53.33
120+ 11 2 84.62 120+ 5 1 83.33 120+ 5 4 55.56 120+ 3 3 50.00
Break After Game 5 of set Break After Game 6 of set Break After Game 7 of set Break After Game 8 of set
Combined Back on Serve % Combined Back on Serve % Combined Back on Serve % Combined Back on Serve %
Score Yes No Score Yes No Score Yes No Score Yes No
51.69 38.14 35.44 38.10
<75 0 2 0.00 <75 0 3 0.00 <75 0 0 #DIV/0! <75 0 1 0.00
75-89.99 11 13 45.83 75-89.99 8 14 36.36 75-89.99 4 10 28.57 75-89.99 6 13 31.58
90-104.99 35 23 60.34 90-104.99 17 26 39.53 90-104.99 15 29 34.09 90-104.99 11 20 35.48
105-119.99 10 16 38.46 105-119.99 9 15 37.50 105-119.99 6 7 46.15 105-119.99 7 3 70.00
120+ 5 3 62.50 120+ 3 2 60.00 120+ 3 5 37.50 120+ 0 2 0.00
Break After Game 9 of set Break After Game 11 of set Overall
Combined Back on Serve % Combined Back on Serve % Combined Back on Serve %
Score Yes No Score Yes No Score Yes No
40.96 37.50
<75 0 1 0.00 <75 1 0 100.00 <75 5 27 15.63
75-89.99 10 10 50.00 75-89.99 5 11 31.25 75-89.99 144 165 46.60
90-104.99 11 27 28.95 90-104.99 9 18 33.33 90-104.99 276 317 46.54
105-119.99 10 7 58.82 105-119.99 6 5 54.55 105-119.99 172 120 58.90
120+ 3 4 42.86 120+ 0 1 0.00 120+ 38 27 58.46
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