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30th December, 2016.
The third set of a best of three set tennis match is usually the most volatile stage of the match.
A break lead is highly valuable, considering that having this lead requires a player to hold their service games to be guaranteed to win the match, and conversely, a break deficit means that a player needs to fight back in this set to have a chance of winning.
Despite this, a lot of traders avoid final sets, which seems strange given its frequent volatility – surely a trader should be keen to take advantage of the swings which are often available, if you can control risk.
Player data is obviously very useful, and the TennisRatings lead loss/recovery data spreadsheets have a wealth of valuable information filtered on a set-by-set basis, including the final set.
An example of this is Phillip Kohlschreiber, who for the matches that I can get point by point data for, has yet to recover a break deficit in the third set since I started collecting data at 1st July, 2014, despite the fact that his recovery data in other sets is slightly above average. Certainly, backing the ageing German when losing in the final set of a best of three set match does not seem to be a viable proposition, as a screen shot of his recovery data from the lead loss/recovery spreadsheets illustrates:-
I thought it would be pretty interesting to see whether age played a role in deciding set volatility – would an older player tire more in the final set, making their lead loss relatively high and deficit recovery relatively low, or would their experience enable them to get a win in a tight match?
As always, I used data to find out…
The table below illustrates the ATP first break lead loss data for third sets in best of three set matches from July 2014 onwards, for matches I can get point by point data for:-
|
Age |
Loses 3rd Set Break Lead |
Loses 3rd Set Break Lead |
Loses 3rd Set Break Lead |
|
Yes |
No |
% |
|
|
<=22 |
20 |
45 |
30.77 |
|
23-28 |
131 |
263 |
33.25 |
|
29-32 |
133 |
292 |
31.29 |
|
33+ |
42 |
146 |
22.34 |
Quite surprisingly, the older players in the sample were much better than the other age groups for protecting break leads – in short, they were able to hold the lead they had much more frequently. This would indicate that older players possess better game management skills than their more inexperienced rivals.
However, players aged 33 or over were worst at recovering break deficits, having much worse recovery data than their younger competitors:-
|
Age |
Recovers 3rd Set Break Deficit |
Recovers 3rd Set Break Deficit |
Recovers 3rd Set Break Deficit |
|
Yes |
No |
% |
|
|
<=22 |
27 |
46 |
36.99 |
|
23-28 |
129 |
250 |
34.04 |
|
29-32 |
125 |
207 |
37.65 |
|
33+ |
47 |
115 |
29.01 |
Again, there wasn’t much between the other three categories, indicating a clear game style change as players get older.
Looking at Roger Federer, we can see how clear this is. In the last three years, he’s held serve an overall 91.2% and broken opponents 25.7%, yet in 2013 his numbers were 86.9/25.9%, 2012 was 90.9%/26.1%, 2011 was 89.0/28.4%, 2010 was 89.3%/27.2%.
In short, as Federer has aged, he’s improved his serve slightly (this also be possibly attributed to better game management skills getting him a hold of serve in tough spots) and his return data has dropped by 2-3%.
Roger Federer’s playing style has become more serve-orientated as he has got older…
We can also look at player rank to see how good lead loss and deficit recovery is. Here is the same data filtered for player rank, as opposed to player age:-
|
Rank |
Loses 3rd Set Break Lead |
Loses 3rd Set Break Lead |
Loses 3rd Set Break Lead |
|
Yes |
No |
% |
|
|
1-25 |
123 |
323 |
27.58 |
|
26-50 |
100 |
207 |
32.57 |
|
51-75 |
62 |
130 |
32.29 |
|
76-100 |
41 |
86 |
32.28 |
|
Rank |
Recovers 3rd Set Break Deficit |
Recovers 3rd Set Break Deficit |
Recovers 3rd Set Break Deficit |
|
Yes |
No |
% |
|
|
1-25 |
112 |
174 |
39.16 |
|
26-50 |
102 |
201 |
33.66 |
|
51-75 |
72 |
158 |
31.30 |
|
76-100 |
42 |
85 |
33.07 |
We can see here that the highest ranked players had a positive deficit difference of 11.58% (39.16-27.58) whilst there was little discernible difference between the other three ranking brackets.
The overall implications of this data seem pretty clear:-
1) Top players are much better at recovering deficits and protecting leads, which is utterly logical given that they are ranked the highest.
2) Matches involving older players have less in-set swings in the final set than those involving younger players.
3) Considering that there was little difference between the other three ranking brackets, it is clear that player tendencies are key, as opposed to general traits. Blanket strategies (e.g. lay all players at 1.10) are rarely successful in Tennis trading, and this is a good example – knowing player tendencies and having detailed data on their playing style is vital for success in the markets.
