Quote:
Originally Posted by Dark Horse
A method objectively divides 7 points between two teams in a contest. When these 7 points are divided as 4-3, the results are:
Home favorite 4-3: 9-29 ATS
Home dog 4-3: 13-3 ATS
Road dog 4-3: 3-12 ATS
What is the chance that these ATS results are the result of luck?
And if they're not the result of luck, what type of strength (or bet size) would you assign to such a method?
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Haven't we had this discussion before? As I recall you never seem to be satisfied with my answer.
Anyway, just as last time the, most important issue is of data sampling methodology. Did you go into this expecting these types of results, looking at only this one scoring method? Or did you look at many different scoring method, with this one happening to be the one (out of sever candidates) that worked? The former is indicative of a model substantially more likely than the latter to possess predictive power. The latter represents what's known as "data mining" and is to be avoided. (The idea being that if you look at a randomly generated set of data long enough, you'll eventually find patters, which although they might describe set itself, have no predictive ability whatsoever.)
I also can't help but ask why you're only presenting scores of 4-3. If points are generally a "bad" thing for a team, then in general wouldn't we expect that
more points would be indicative of a
greater probability of defeat? Why would an even attribution of points (4-3) ostensibly represent a higher probability of loss (or victory) than a more lopsided one (such as 7-0, or 0-7)? Now obviously there
might be a good explanation for this, but at first blush my initial reaction would be that this is indicative of data mining.