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The last update of the Premium Product - WTA lead loss & recovery statistics - was performed on the 13th May, and I thought it would be interesting to see how the data performed in the time since the last update and the end of the French Open (6th June).
As yesterday's article "Point Data For In-Game Trading in the ATP & WTA" established, when laying the player a break up, the mean WTA figures were 76.4% for small positive price movement (small profit), 65.0% for larger positive price movement (either two points up on return or at least one break point - larger profit), or 43.1% for a break-back (even larger profit).
Using the WTA lead loss & recovery statistics we can classify a dominant lead as the following scenarios:-
1) First Set & Break Up in Set 2
2) First Break Up in Set 3
Grouping these two scenarios together we can get a bigger sample on player lead loss from the first dominant position of a set and player recovery from first bad situation. The mean figures for the 110 players analysed in the WTA since 1st July, 2014, was 44.97% for lead loss for the first dominant position of the set, and 46.62% for the first recovery when an opponent has a dominant scenario (combined 91.59).
Having these figures allows us to filter for situations where the combined figure is better than 91.59 - effectively situations where historical data shows players are bad at defending dominant leads, or opponents are good at fighting back from a very negative position, or even both. I decided that a figure over 100.00 would signify this well. Only players with at least 15 leading scenarios (when leading) or losing scenarios (when losing) were assessed, and the data is shown below (with each player's percentages filtered out to protect the sensitivity of the information):-
From the data we can see that a small movement was achieved 78.57% of the time (2.2% above WTA mean) with a larger movement achieved 69.05% (4.1% above mean). These figures show that with game selection the mean figures can be boosted, giving a solid edge to the figures.
Interestingly, the data truly came into its own for break-back percentages. 59.52% of scenarios laying the player who was a first break up in the set from either a set and break lead, or a break up in the final set resulted in a break-back, a huge 13.72% above the 45.80% WTA first break loss mean (the previously mentioned combined score of 91.59/2) Using this data would have resulted in massive profit laying the player a break up, and on this basis with game selection, holding onto a position until the break-back occurs, as opposed to clearing large chunks of liability in-game in advantageous positions, would be the way forward.
Not only this, we can see that the players laid as leader were far from nobodies - top ten players Maria Sharapova, Simona Halep (on two occasions) and Carla Suarez Navarro (4) were filtered in, as well as the big-serving Lucie Safarova in three matches. In addition to this, whilst the likes of superb recoverers Sharapova and Ana Ivanovic were filtered in when losing, low ranked players such as Bojana Jovanovski, Aleksandra Krunic and Timea Babos and Nicole Gibbs were also included. This shows the data is far from 'obvious'.
Furthermore, it avoided the traders disaster that was Mirjana Lucic-Baroni vs Simona Halep at the French Open. Lucic-Baroni 'trained' in the second set, winning it 6-1, with no positive price movement from the first set and break lead. The data showed Lucic-Baroni is very solid when in a dominant position, so this match was filtered out as only a neutral expectation lay position on her.
Using the WTA lead loss and recovery statistics as a way of filtering for players to lay a break up either as a set and break up in set two, or a break up in set 3, is clearly an excellent method. Considering very few traders have this information, it provides a clear edge statistically, as well as over the competition.
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