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Something that I've mentioned numerous times over the past few years is the importance of formulating a trading plan or script so that you trade in a methodical and reasoned manner, as opposed to using a disorganised or emotional approach.
Whilst improving organisation and removing as much emotion as possible won't give you guaranteed success, it will certainly reduce a trader's potential for heavy losses. Furthermore, if a trader possesses solid data - particularly data that the majority of the market does not have - as well as a balanced, rational approach, then they are well on their way to beating the markets.
One way of doing this is the possession of player specific data, which gives superb insights into a player's tendencies, enabling users to establish whether a player is strong or weak in given scenarios, and in particular highlights situations where 'bracketed' players by the market (e.g. big servers) are good or bad when the market thinks they are not. When situations like this are established, we can formulate a script which allows us to trade if a given situation occurs in a match.
A case study of data for several ATP and WTA players illustrates how the same player could be strong in one situation, or weak in another. This is far more advantageous than knowing a general 'break-back' percentage, which was unique and groundbreaking when I first generated those numbers.
Case Study 1 (WTA Front-Running) - Angelique Kerber:-
Above are Kerber's stats when she is leading in a match - either when a break up in the first set, a set and break lead in the second set, or a break up in the final set.
General data shows that she has lost a break lead across all sets 45.92% (final column), just 2.29% below the WTA mean of 48.21%. This data, treated in isolation, would indicate that - considering her strong world ranking - perhaps she is a little under-performing as a front-runner and might be vulnerable at losing leads against players who are strong at recovering deficits.
However, micro-analysis creates an entirely different picture...
It's easy to see where she is strong - from a set and break up in a match. She's only lost 27.78% of set and break leads since July 2014, which is an improvement of 13.45% on the WTA average - she's not nearly as vulnerable from this position as she is in the first and third sets. Given this information, laying Kerber when a set and break up should only be considered against players who are very strong at recovery - for example, Maria Sharapova, when fit.
Kerber is much weaker at retaining leads in the first, and particularly third sets, losing the first break lead in those sets on 54.55% and 61.11% of occasions, respectively. Against even a player who is average at recovery, we can consider opposing her when a break up, especially in the final set.
Instead of thinking that Kerber was just average at break lead retention (the overall all set figure - the final column) we can now use this information to formulate an action plan for trading her matches - looking for opportunities to lay her in sets one and three, and avoiding opposing her when a set and break up in matches.
Case Study 2 (WTA Recovery) - Zarina Diyas:-
Based on previous data I must admit that I have always pegged Diyas as a mediocre player at recovering deficits in matches. It's easy to understand why - she's only recovered 34.48% of dominant (set two and three) deficits. Certainly I've been in no rush to back her in these spots.
However, using the micro-detail, we can see a clear division in her stats between set one and sets two and three.
Diyas is superb at recovering break deficits in set one, doing so 23/34 (67.7%) of the time, compared to 10/29 (34.48%) deficits in sets two and three combined. Reasons why this would be the case are speculative, although it wouldn't be a huge shock if it was either fitness or mentality based, or both.
These reasons aren't particularly important in all honestly - the knowledge that the polarisation exists is more than enough. Backing Diyas to recover a break deficit in the first set should be strongly preferred by traders, whilst backing her when a set and break down should only be considered against the worst of the WTA Tour front-runners.
Case Study 3 (ATP Front-Running) - Simone Bolelli:-
Here is an example of a small-name player (current rank 55) who is superb at retaining leading positions throughout every scenario.
Bolelli consistently loses leads much less than the average ATP player, losing dominant (set two and three) deficits just 15.00% (14.44% better than ATP average) of the time (3/20) and lost a lead across all sets 8/45 (17.78%), also around 14% better than the ATP average.
Despite his relatively lowly ranking and frequently non-favourite starting price status, it is clear that Bolelli should only be opposed when leading in matches against the best recoverers on the ATP Tour - the likes of Roger Federer and David Ferrer.
Case Study 4 (ATP Recovery) - Kevin Anderson:-
The final case study is on the 'big serving' South African, Kevin Anderson.
Almost certainly based on his matches against Tomas Berdych, somewhat unfairly Anderson has gained a reputation on social media as a bit of a choker with a mediocre return game. His lead retention stats say otherwise about his choking tendencies. His recovery data shows a player with a strong propensity to recover deficits, particularly as matches get longer.
Anderson's worst area for deficit recovery was without doubt the first set - just five of 19 break deficits were recovered (26.32%), a figure which is 8.31% below the ATP average.
However, the data clearly shows that as the match progresses, Anderson improves at being able to recover break deficits - he's recovered a set and break deficit 6/15 (40.0%) since July 2014 and a break deficit in the final set 50.0% (3/6 occasions). Combined, this set two and three break recovery percentage of 42.86% is 12.13% above the ATP mean, whereas his all set recovery percentage of 35.00% is a mere 2.39% above average.
Given this, it's unlikely that we will want to back Anderson in the first set when a break down, but it would certainly be a move that should be contemplated in the latter two sets - particularly given that many traders will have written him off at this stage.
Overall, these case studies illustrate how detailed player analysis can generate numerous pre-planned and non emotional strong entry points for in-play traders but also highlight situations worth avoiding, which many traders without an array of data like this may not be aware of.
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