Point Data For In-Game Trading in the ATP & WTA


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11th June, 2015.


One of the beauties of Tennis trading is that there are many possible approaches which can all be successful, with some traders preferring to be exposed to risk, with others keener to take a more risk averse approach.


When laying a server, or the player a break up, we have previously discussed trading out if and when the break occurs.  However for the purpose of this article, I want to look at taking money out during a service game.


To make matters slightly easier, I grouped in-game scorelines into two brackets:-


Small price movements: 1 Point Receiver Leads (no break point).  This would be 0-15 and 15-30.

Larger price movements: 2+ Point Receiver Leads, or 1 point break point leads.  This would be 0-30, 0-40, 15-40, 30-40 or 40-A.


Typically, the price movement at 30-40 or 40-A will be bigger than at 0-15 or 15-30 due to the fact that it creates a break point opportunity for the receiver, whereas this is not the case at 0-15 or 15-30, which is why I have placed those scorelines in the second bracket.


I thought it would be pretty interesting to see how frequently these winning trade positions were hit during the average match (with no game selection) in various sets.  However, before moving onto the actual stats, I will say that each set and each Tour tends to have different dynamics.


The Game State spreadsheets illustrate this - for example, in the entire 2014 WTA season, the player a break down recovered the first break in the first set 52.8% of the time.  However, in the second set, when a player was a set and break down, this deficit was recovered just 40.3% of the time.  In the final set, this figure rose to the highest of all sets, 54.1%.


It is not entirely surprising that the second set, from a set and break position, was the lowest percentage, as this position exhibits the most dominance from all three scenarios.


The following table shows the percentage small and larger price movements were achieved from a break down in the WTA, in January 2015, and the ATP in March/April 2015:-


WTA Jan 2015


Player Break Down



Player Break Down






Got to only



Got to at least






0-15/15-30



0-30/15-40/30-40/40-A





Total Situations

Situations

Yes

No

Yes

2 Pts/BP %

1 Point at least %

Diff










Set 1

208

63

18

45

145

69.7

78.4

8.7

Set 2 S&B

150

62

23

39

88

58.7

74.0

15.3

Set 3

74

26

8

18

48

64.9

75.7

10.8

Overall

432

151

49

102

281

65.0

76.4

11.3


ATP 1/3/15 to 30/4/15


Player Break Down



Player Break Down






Got to only



Got to at least






0-15/15-30



0-30/15-40/30-40/40-A





Total Situations

Situations

Yes

No

Yes

2 Pts/BP %

1 Point at least %

Diff










Set 1

307

137

43

94

170

55.4

69.4

14.0

Set 2 S&B

236

118

44

74

118

50.0

68.6

18.6

Set 3

110

45

16

29

65

59.1

73.6

14.5

Overall

653

300

103

197

353

54.1

69.8

15.8


Unsurprisingly given the weaker serve of women, there were more positive price movements from laying the player a break up in the WTA than the ATP, with overall 76.4% of players a break down getting at least one point up (small movement) on their opponent's serve from this position, compared to 69.8% in the ATP.  This was also borne out by 65.0% of women getting two points or a break point up (larger movement) compared to 54.1% of men.  However, price movements in both instances will be smaller in the WTA, so just because numbers are higher doesn't mean performing this strategy in the WTA is better than the ATP.

On both tours the set two position laying the player a set and break up was the least successful, with the WTA 6.3% below mean for large movements and 2.4% below for small movements, and the ATP 4.1% and 1.2% respectively.  On this basis, laying ATP players in this spot might be slightly more advantageous than doing so trading the WTA.

Interestingly, set one had the biggest fightback success in the WTA, but it was set three in the ATP, highlighting the different dynamics of the two tours.  Whilst the WTA data showed comebacks were around average in set three, it was well above average in the ATP, giving us some scope to adapt different strategies for each tour.

What is also apparent is the difference between small and price movements per set.  Across both tours, set two (set and break lead) had the biggest difference between small and large scenarios and this would indicate that traders should look at clearing a bigger proportion of liability at small price movements from this scenario than in set one or three, where holding a bigger percentage of liability (particularly in set one in the WTA) is recommended.

Something else we can look at is the relationship between the success gaining small and large price movements and the mean break back combined score (the % a player loses a break lead + % opponent recovers a break deficit) for each tour.  This allows us to estimate the likelihood of a price movement in a match from the 'average' point of the set, a score something like *3-1 or *3-2.

The tables below show the ratios between small and large price movements and the combined score for each respective tour:-

WTA Jan 2015








Ave 2014 Comb Sc

2014 Ave Break Back




86.28

43.14


2 Pts/BP %

1 Point at least %

2 Pts/BP to CS Ratio

Min 1 Point to CS Ratio






Set 1

69.7

78.4

0.81

0.91

Set 2 S&B

58.7

74.0

0.68

0.86

Set 3

64.9

75.7

0.75

0.88

Overall

65.0

76.4




ATP 1/3/15 to 30/4/15








Ave 2014 Comb Sc

2014 Ave Break Back




59.2

29.6


2 Pts/BP %

1 Point at least %

2 Pts/BP to CS Ratio

Min 1 Point to CS Ratio






Set 1

55.4

69.4

0.94

1.17

Set 2 S&B

50.0

68.6

0.84

1.16

Set 3

59.1

73.6

1.00

1.24

Overall

54.1

69.8




So, to estimate the likelihood of a small or large price movement we can use the following equation for each relevant set:

Combined score of player a break up (available in the daily spreadsheets) * relevant ratio.

Rafael Nadal recovered a break deficit to win the first set against Marcos Baghdatis.

As an example, looking at ATP matches today, Marcos Baghdatis' combined score was high at 68.41 against Rafael Nadal.  Using ratios, we can see that if Baghdatis led by the first break in the first set (he did), he'd be (68.41*.94) = 64.05% to have a large positive price movement when laying him a break up, and (68.41*1.17) = 79.72% to have a small positive price movement.  

In other words, you'd make at least small profit 79.72% laying Baghdatis a break up, and a larger profit 64.05%.  In the match, Baghdatis led *3-2 and was broken to love for 4-4.  Out of interest, if the Cypriot led by a break in set 3, a small positive price movement would be generated (68.41*1.24) = 84.49%, making this a superb trading opportunity.

Laying Klara Koukalova when she took the first break in set 1 against Annika Beck was 94.81% likely to generate small positive price movement in the set.

In the WTA today, Klara Koukalova's combined score against Annika Beck was very high at 104.19.  Using the ratios above, we can see that in the first set, a small positive price movement from laying Koukalova when she takes the first break in the set would be generated (104.19*.91) = 94.81% of the time, and a larger one (104.19*.81) = 84.39%.  As it transpired, Koukalova threw away a break lead twice in the set at *1-0 and *2-1 to lose the set 4-6.

Hopefully this should give you a good idea of pricing in-game point by point scenarios and will allow you to make some calculations on the likelihood of positive price movements based on the combined score for each player.



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