Averaging Down - Reducing 'Train' Percentage


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14th April, 2014:-

Following on from the recent article Trading the Second SetI have recently added the train % of players since July 2014 to the lead loss/recovery sheets, and this information is superb at highlighting match-ups where this approach can be used.

Using the overall data on the lead loss/recovery sheets, from 1/7/14 to 10/4/16, 46.98% of second sets on the ATP Tour ended up in trains - effectively situations where the winner of the first set won the second set without either going a break down or losing a set and break lead to get back on serve.

34.06% (927/2722) of first set losers took the first break of the second set, so it is easy to work out that 18.96% of scenarios resulted in the first set loser going a break down and then getting this break back to get back on serve.  It's important to note this isn't the set and break recovery percentage, which is 28.76%.

Three matches were highlighted today in the lead loss/recovery sheets as having very low potential for a train.  With players on the lead loss sheets 'training' 46.98% when they won set one, and players on the recovery sheets found their opponents 'training' 44.28%, we can add the two figures together to get a combined average figure, (46.98+44.28) = 91.26.  

Looking at match-ups where the first set winner train % and first set loser opponent train % combined to be less than 75, I found three matches in Monte Carlo today:-

Benoit Paire vs Andy Murray:-

Player

Leading Situations

Set 2 Train %







1/7/14 to 10/4/16

8042

46.98




Paire

67

28.95


Player

Losing Situations

Set 2 Opponent Train %







1/7/14 to 10/4/16

6859

44.28




Murray

65

28.85


If Paire won the first set, the two scores combined two generate 57.80 (28.95% + 28.85%) - well below the 91.26 average.  It is clear that based on historical data, Paire taking the first set would make him much more vulnerable than average to struggle to win set two easily.  That's all we need really as situational traders - we just want to take advantage of price movements in the Paire price, as opposed to worrying who will actually win the match.

Benoit Paire struggles to win second sets easily when he wins the first set...

According to the lead loss/recovery spreadsheets, the other two match-ups with potential for averaging down were the following:-

Dominic Thiem (set 2 train 40.74%) vs Rafael Nadal (set 2 opponent train 25.71%) = 66.45 combined.
Marcel Granollers (set 2 train 26.32%) vs David Goffin (set 2 opponent train 30.77%) = 57.09 combined.

Prior to doing this, I checked out six months of historical lead loss/recovery data from October 2015 to April 2016.

ATP CS

Set 1 Loser

%

Set 1 Loser Goes 

%

Set 1 Winner

%


Breaks 1st S2


S&B Down, Breaks Back


Trains


<50

3

100.0


0.0


0.0

50-55

1

50.0


0.0

1

50.0

55-60

3

37.5

3

37.5

2

25.0

60-65

5

45.5

2

18.2

4

36.4

65-70

4

30.8

2

15.4

7

53.8

70-75

11

52.4

6

28.6

4

19.0

Overall

27

46.6

13

22.4

18

31.0


It's worth noting that there were some scenarios also where players won the first set that weren't covered by the lead loss/recovery sheets when their opponent was (e.g. their ATP main draw sample was too small due to mainly playing Challengers) and I'll talk about how to incorporate these players later in the article.  Adding these players to the sample using expectation data would have boosted the sample significantly.

Overall, we can see that of the filtered first set losing players, 46.6% took the first break of set two, with 22.4% recovering from a set and break down, and 31% losing to their opponent via a train.

This is much better than the overall lead loss/recovery data established earlier with 34.06% taking the first break of set two, 18.96% recovering from a set and break down, and 46.98% losing to their opponent via a train.  Effectively using these filters we've been able to reduce the train percentage from almost 47% to 31%, although this should be able to be further improved by filtering further for first set losing players whose stats reflected competitiveness in the opening set.

The train percentage in the WTA in my sample was also low at 27.3%, compared to the overall lead loss/recovery spreadsheet percentage of 37.28%:-

WTA CS

Set 1 Loser

%

Set 1 Loser Goes 

%

Set 1 Winner

%


Breaks 1st S2


S&B Down, Breaks Back


Trains


<35

1

100.0


0.0


0.0

35-40

1

50.0

1

50.0


0.0

40-45

6

75.0

1

12.5

1

12.5

45-50

5

31.3

4

25.0

7

43.8

50-55

13

59.1

5

22.7

4

18.2

55-60

13

46.4

6

21.4

9

32.1

Overall

39

50.6

17

22.1

21

27.3


Calculating Expectation Ratios:-

Earlier in the article I mentioned that it would be useful to calculate expectations for the Challenger players playing on the main tour who aren't included in the lead loss/recovery spreadsheets.  This isn't a perfect method, but it's as good as there will be...

In the time period of the lead loss/recovery sheets, the mean ATP service hold was 79.7% across all surfaces, and there were 46.98% of second set trains.  We can divide 79.7% by 46.98% to get a ratio of 1.70.  

This would indicate the following:-

Player with mean hold percentage of 90% would train 52.94% of the time (90/1.70).
Player with mean hold percentage of 70% would train 41.18% of the time (70/1.70).

We can do the same with the WTA, where mean service hold was 64.4% and the mean train percentage was 37.28%.  Here we get a ratio of 1.73 - almost identical to the ATP ratio.

We can now use these ratios for players who don't have lead loss/recovery stats due to sample size issues when they play against players included in the lists - now around 130 players on each of the ATP and WTA Tours.




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