ATP Injury analysis

In previous articles we have looked at how well the new upgraded ATP model has done against certain probabilties and overlays. This analysis can be found linked on the right hand side of this page:

However another interesting piece of information is how well the model goes based on our injury ratings. Sportpunter’s tennis model has four injury ratings and they are as follows

SurfaceNoPlay – Player has not played on this particular surface in last 2 months

LowPlay – Player has not won a match (or played) in last 2 months

Retire – Player has recorded at least 2 retirements or withdrawals in last 2 months

LossRetire – Player has not won a match since last retirement or withdrawal

Based on suggested bets from back to 2005, we can see how the model goes when betting on games where one of the above scenarios occur.

Shown below are the results of the ATP model for betting on players with the above scanerios (1), against a player with the above scenarios (-1) or when both players have, or do not have, the injury conditions.

The findings are quite substantial. Betting on a player who has not played on the particular surface recently resulted in an 8.1% loss from 310 bets, whilst a 9% ROI gain was made when the suggested bet was the other way around.

Similarly, when betting on a player who has not won or played a match recently, a 16.8% ROI loss was made, as compared to a 9% ROI gain the other way around.

There was little difference in the variables Retire and LossRetire for both betting for or against the injured player.

So what does this mean? Well, all these variables are in the model, and hence the model has accurately calculated the value of each one. However there still seems to be a bias there.

My conclusion is that the odds are biased in that they do not much enough for when a player has not played for a certain amount of time, or are “first up” in the last 2 months on a particular surface.

131013443.2% $58,571.28 -$4,720.81 -8.1%
09014379142.1% $1,387,954.09 $73,440.46 5.3%
-154425246.3% $119,623.03 $10,773.26 9.0%
TOTAL9868417742.3% $1,566,148.40 $79,492.91 5.1%
12056933.7% $37,172.96 -$6,232.67 -16.8%
09243384941.6% $1,408,208.11 $74,917.63 5.3%
-142025961.7% $120,767.33 $10,807.95 8.9%
TOTAL9868417742.3% $1,566,148.40 $79,492.91 5.1%
1922931.5% $15,231.53 -$3,054.57 -20.1%
09600406942.4% $1,514,310.56 $85,275.43 5.6%
-11767944.9% $36,606.30 -$2,727.95 -7.5%
TOTAL9868417742.3% $1,566,148.40 $79,492.91 5.1%
142018343.6% $78,125.39 $1,166.83 1.5%
09043382042.2% $1,416,602.14 $76,547.49 5.4%
-140517443.0% $71,420.86 $1,778.58 2.5%
TOTAL9868417742.3% $1,566,148.40 $79,492.91 5.1%

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2 Responses to ATP Injury analysis

  1. Jason says:

    Thanks for the great analysis Jon.

    The statement “Well, all these variables are in the model, and hence the model has accurately calculated the value of each one.” – does this mean that the probs of players with an injury rating are now reduced so it’s now ok to bet on them?

    If not, is there a way we can establish if a player has an injuy rating so we can manually decide not bet on them.

  2. admin says:

    Yes thats right Jason, a players probability will decrease if they fit into the injury categories above.

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