Laying at Low Prices - ATP


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I've been asked a few times whether blanket laying a player at low prices would show a long-term profit.

My reply generally is that the markets are usually quite efficient in this regard and that identifying player traits and then looking at this would be a much better approach.

There's an obvious attraction in laying players at low prices, as your potential reward, and hence potential trading profit, is very high compared to the risk outlaid.  This low-risk, high-reward trading style is something I like and something I would recommend to new tennis traders.

There are two general situations where players will be generally trading at low prices (notwithstanding heavy pre-match favourites).  This is either when they are a set and a break up in the second set, or a break up in the deciding set.  However, should a player let this lead slip, they would trade significantly higher.  Therefore, if we managed to lay at a low price, and the match turned in our favour, we'd be able to manage a significant profit when hedging our position.

For example, in the Madrid Masters Final in May between Kei Nishikori and Rafael Nadal, Nishikori led by a set and a break.  The Japanese talent was priced at 7.53 prior to the match (Nadal started at 1.13) but when a set and break up, Nishikori was trading at around 1.35-1.40.  Therefore, it's not difficult to see that even the biggest pre-match underdog will be trading as a favourite when a set and break up in the second set.

Ultimately, Nishikori was broken back by Nadal, who went on to take the second set, and the trophy, when Nishikori retired when losing in the deciding set.

*To clarify here following feedback - this match was selected merely as an illustration of how a heavy underdog's price moves from starting price to when a set and break up in-play.  The fact that Nishikori suffered an injury was irrelevant - his projected hold was 70.3% (5.2% below ATP clay mean at that time) with a combined score of 80.8, and based on the criteria of this article would have fitted scenario 5, and would be laid when a set or break up. *


Despite starting the match as heavy underdog, Kei Nishikori was a heavy favourite against Rafael Nadal when a set & break up...

What I decided to do was evaluate a number of different scenarios, based on the statistics in the Tier Two Daily Spreadsheets.


The two main variables that I looked at were projected holds (compared to the ATP surface mean) and the combined break up/down score of the relevant player, to see if we could get an edge over the market, using this data to our advantage.

The following categories were generated:-

1. Projected Hold between 5-10% below surface mean but combined score below 71 (the level I feel it is viable to consider laying a player a break up).
2. Projected Hold over 10% below surface mean but combined score below 71.
3. Projected Hold between 5-10% below surface mean but combined score not available (due to lack of in-play data on at least one player).
4. Projected Hold over 10% below surface mean but combined score not available.
5. Projected Hold between 5-10% below surface mean and combined score above 71.
6. Projected Hold over 10% below surface mean and combined score above 71 (The 'best' situation statistically).

Research was performed for all these scenarios, both when a player was a set and break up, and also when a player was a break up in the final set.  The dates of this research were between the 1st April 2014 and 31st July 2014.  I felt this was a very 'fair' sample because it included data from three surfaces - clay, hard and grass courts, so an unfair weighting to one surface, which may have skewed results, was not obtained.

Please note that the current average percentage for ATP top 100 players losing a break is 30.84%, so fewer than one in three break leads are lost so that the set goes back on serve in the ATP.

The following was the data generated for the scenario when a player was a set and break up:-

ATP 1/4/14 -31/7/14
Set & Break
PH 5-10% PH 10%+ PH 5-10% PH 10%+ PH 5-10% PH 10%+ Overall %
below mean below mean below mean below mean below mean below mean
CS <71 CS <71 CS N/A CS N/A CS >71 CS >71
Yes No Yes No Yes No Yes No Yes No Yes No Yes No
31.25 27.78 45.24 43.90 43.48 55.56 42.41
5 11 5 13 19 23 18 23 10 13 10 8 67 91

As we can see from the sample, the 42.41% overall figure is way higher than the 30.84% ATP average, so overall, using projected hold percentages and combined score percentages to filter out the 'bad' trades had an 11.57% greater success than average for the ATP.  This in itself is a fine figure and would be easily be enough to beat the market on a consistent basis.

Furthermore, if we were to filter out the first two categories, where combined score was below 71, we would have had 57 winning trades and 67 losing ones (45.97%), increasing our edge by a further 3.56%.

Based on these numbers, there can no doubt whatsoever that using my projected hold figures and break-back percentages gets a significant edge over the market.

The following data was generated when a player was a break up in the final set:-

ATP 1/4/14 -31/7/14
Break Up 
In 3rd Set
PH 5-10% PH 10%+ PH 5-10% PH 10%+ PH 5-10% PH 10%+ Overall %
below mean below mean below mean below mean below mean below mean
CS <71 CS <71 CS N/A CS N/A CS >71 CS >71
Yes No Yes No Yes No Yes No Yes No Yes No Yes No
50.00 12.50 50.00 55.00 36.36 55.00 46.99
5 5 1 7 7 7 11 9 4 7 11 9 39 44

First of all, what is most apparent is the overall figure increases to an even higher figure than from the set and break up scenario. 

This is logical because a player who is a break up in the final set has exhibited a lower level of dominance over their opponent than a player who is a set and break up in the second set.  

All previous research has found that conditions tend to continue in tennis, with several examples being the following:-

* A player is more likely to win a break point than a 'normal' return point (2.8% more in the ATP and 1.8% more in the WTA).
* A player is more likely to hold their next service game than an average service game, if they held their previous service game to love.

Therefore, it's not a surprise to see a player loses a break lead slightly more in deciding sets than when they are a set and break up in the second set.

The 46.99% overall figure is an incredible 16.15% above the ATP mean for break-backs, and should we have filtered out the matches where combined score was below 71, as we did in the set and break up scenario, we would have had 33 winning trades and 32 losses for a success rate of 50.77% - a huge 19.93% above expectation.

Scenarios 3-6 have clearly generated a scenario whereby laying a player at odds on the final set is more likely to produce a big win than a smaller loss, and furthermore there will be plenty of other opportunities to trade out at a profit or clear liability, such as when a player is a break up but 0-30, 0-40 or 15-40 down.  

The final table shows the statistics via projected hold and combined score differences:-

Total CS <71 CS >71 CS N/A PH 5-10% PH >10%
Overall %
43.98 30.77 48.61 47.01 43.10 44.80

Overall there were 106 winning trades and 135 losing ones (90 winning and 99 losing = 47.62% if the combined score of less than 71 was filtered out) for an overall success rate of 43.98% - 13.14% above the ATP mean.

As we can see, the combined score of less than 71 merely represented figures around the mean, so using the scenarios where the combined break-back score is high generated much more lucrative opportunities.  

What was a little surprising was there was only a slightly better success rate as projected holds got further away from the ATP surface mean - only 1.70% difference between a projected hold between 5 and 10% below the mean and when projected hold was over 10% below the average.  

It can therefore be assumed that whilst projected holds are very important here (no doubt, if we had assessed high projected holds, the figures would have been much worse), combined scores are what really makes this method and using the two statistics in conjunction with each other is a long-term winning scenario.

All statistics used in this article can be found on a daily basis via the Tier Two Daily Spreadsheets, sent daily to subscribers.


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