General Betting
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23 Oct 12
14:59
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14 Aug 07
If you had a system that was returning 54% winners on even money chances, how many trials would make it statistically likely to be a true edge?
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I would want to see several thousand trials.
Although, the sample mean of the sampling distribution is approximately equal to the mean of the sampled population for all n >=30 The approximation tightens as the sample size increases. |
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I tried a new (to me) method for this (Bayes), I think you can say:
After 264 trials (138wins/118losses) you can be 90% sure that your edge is positive (ie true strike rate >50%). After 422 trials you can be 95% sure that your edge is positive (ie true strike rate >50%). Also, out of interest: After 1022 trials you can be 90% sure that your edge is true strike rate >52%. After 1684 trials you can be 95% sure that your edge is true strike rate >52%. |
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Okay, i've been trying to do a bit more working out. I make it that 100 trials gives the probability of the success continuing as 82%, after 250 trials 91%, 500 - 97 and 1000 - 99%.
Does that sound right? |
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Doh! bit slow there!
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Interesting, we have come at it slightly differently but the figures seem roughly similar.
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JLivermore,
Can you post the Bayes' formula used? It's been 9 years since I've seen one! I always thought the best way to trial such an edge's significance was to calculate the p-value [p < 0.01] |
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Steeringjob - do you really think it needs thousands of trials to be confident?
Also, can you explain the following in simpleton terms  as I don't really understand!Although, the sample mean of the sampling distribution is approximately equal to the mean of the sampled population for all n >=30 |
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^ Bayesian formula even
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I asked a Ph.D statistican the sample size required forany such type of experiement - and the answer was "5,000 trials".
It tallies with JL's analysis above i.e. the level of significance increases as the number of trials rises. What my comment means is this:- - take the average answer from a sample of trials [i.e.at least 30 bets]; - the trials should be taken from the phenomenon you are looking to model [i.e. your edge]; - the average answer for your trials will approximate the average answer for the phenomenon as a whole; - the scope for error falls as the number of trials rise |
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I used an Beta distribution, which I *think* is the distribution of the Bayesian posterior.
So for the probability your strike rate is > 50%: =1-BETA.DIST(0.5, |
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ty - yes, that tallies.
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Okay, I'm not sure I get that. When you say take the average answer from a sample of trials [i.e.at least 30
bets]; are you saying, to take average answers from trial runs of the system over different numbers of sample bets?  Also, when you say - I always thought the best way to trial such an edge's significance was to calculate the p-value [p < 0.01] are you saying to calculate the number of trials until there is a |
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it didn't print all my formula - here is what I meant:
=1-BETA.DIST(0.5, number of wins, number of losers, TRUE) |
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Not sure what happened there, half my post disappeared.
Okay, I'm not sure I get that. When you say take the average answer from a sample of trials [i.e.at least 30 bets]; are you saying, to take average answers from trial runs of the system over different numbers of sample bets?Crazy Also, when you say - I always thought the best way to trial such an edge's significance was to calculate the p-value [p < 0.01] are you saying to calculate the number of trials until there is a |
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It did it again!!
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If you are saying to calculate the figure so that the chances of the true figure being less is
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If you are saying to calculate the figure so that the chances of the true figure being less is smaller than 1% then I calculate that number to be 839
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got called away from my desk............
Thanks for the earlier replies, i'm still not really sur of a number I shoul aim for though. What complicates the matter is that the system only throws up a bet approx 2-3 times a week and there is no way to back-test it prior to March this year so waiting for meaningful stats could be a long time!!! |
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statistical significance can be a pretty high bar.
If I was you I would bet it with my uncertainty about the edge taken into account and track the edge as you get more bets. |
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Judge R
You say " What complicates the matter-----only throws up a bet 2-3 times a week-----so waiting for meaningful stats could be a long time ". Now I'm no statistician, but couldn't you therefore just keep tracking all future events and if they ( being those that do not throw up bets) keep running at a flat stake bet loss ( as they must do if the ones that throw up a bet strike at 52% rate), then your system is still theoretically intact, despite not getting any actual bets for the period concerned. Or is that false logic ? |