Assessment of WTA Lead Loss & Recovery Statistics


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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):-

Date

Leader

Loser

Combined

Situation

Small Movement

Large Movement

Break Back






78.57%

69.05%

59.52%

14/5

Sharapova

Jovanovski

100.18

S2

y

y

y

14/5

Dulgheru

Makarova

105.56

S2

n

n

n

14/5

Halep

Williams V

125.83

S2

n

n

n

14/5

Gavrilova

Bacsinszky

131.16

S2

y

y

y

15/5

Gavrilova

McHale

119.04

S2

y

y

y

15/5

Suarez Navarro

Kvitova

104.69

S2

y

y

n

16/5

Halep

Suarez Navarro

107.05

S2

y

y

y

16/5

Suarez Navarro

Halep

104.69

S3

y

y

y

17/5

Suarez Navarro

Sharapova

145.21

S3

y

y

y

17/5

Tsurenko

Lepchenko

104.44

S2

y

y

y

17/5

Rogers

Krunic

105.98

S2

n

n

n

17/5

Zhang S

Jovanovski

134.85

S2

y

y

y

18/5

Schiavone

Davis

121.43

S2

n

n

n

18/5

Arruabarrena

Babos

113.67

S2

y

y

y

18/5

Koukalova

Beck

152.56

S2

y

y

y

19/5

Nara

Voegele

104.55

S2

n

n

n

21/5

Nara

Vinci

102.38

S3

y

y

y

22/5

Knapp

Vinci

110.27

S3

n

n

n

24/5

Lucic-Baroni

Davis

105.96

S3

y

y

n

24/5

Safarova

Pavlyuchenkova

101.73

S2

y

y

y

24/5

Jovanovski

Tsurenko

129.23

S3

n

n

n

25/5

Pironkova

Strycova

107.88

S2

y

y

y

25/5

Kerber

Babos

115.90

S2

y

n

n

25/5

Errani

Riske

104.48

S2

y

y

n

25/5

Suarez Navarro

Niculescu

102.98

S2

y

n

n

25/5

Dulgheru

Gibbs

127.78

S2

n

n

n

25/5

Stephens

Williams V

119.48

S2

y

n

n

25/5

Gavrilova

Larsson

131.16

S2

y

y

y

25/5

Svitolina

Wickmayer

101.15

S2

y

y

y

25/5

Beck

Radwanska A

106.66

S3

y

y

y

26/5

Kuznetsova

Bertens

101.01

S3

n

n

n

26/5

Keys

Lepchenko

110.00

S2

y

y

n

27/5

Muguruza

Giorgi

107.97

S2

y

y

y

28/5

Kuznetsova

Schiavone

109.41

S2

y

y

y

28/5

Kuznetsova

Schiavone

109.41

S3

y

y

y

28/5

Stephens

Watson

109.31

S2

y

y

y

29/5

Beck

Svitolina

105.71

S3

y

y

y

30/5

Pennetta

Suarez Navarro

113.64

S2

y

y

y

30/5

Errani

Petkovic

123.85

S2

y

y

y

1/6

Muguruza

Pennetta

110.54

S2

y

n

n

1/6

Safarova

Sharapova

116.97

S2

y

y

y

4/6

Safarova

Ivanovic

114.92

S2

y

y

y

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.

Despite her run to the French Open final, the big-serving Lucie Safarova was successfully opposed a set and break up three out of three times using the data...

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.

Simona Halep's poor display against Mirjana Lucic-Baroni in the French Open would have hit many traders hard - not using this data...

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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