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by au_gambler
3169 days ago
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> A strategy intended to beat the bookmakers at predicting the outcome of sports games requires a more accurate model than the ones bookmakers have developed over many years of data collection and analysis. I disagree with this assumption and I think they have painted themselves into a corner because of it. To illustrate, imagine charting win rates against bins of price-implied-chances. $3 horses win roughly 33% of the time, $4 horses 25% for example. It resembles a noisy 1:1 linear relationship. Do the same for your selections and your line will be noisier, but crucially you're not taking bets where the price is worse than your estimate. This can leave a window of profitibility when you subtract the two, even when you are less 'accurate' as measured by win rate or KLD or other measures. The goal is profitibility, not accuracy. The problem with including the odds you are betting against as a feature for your ensemble is that it dampens that window. If you're right about your selections, you'll bet less and win less. * If you're concerned about the volitility that comes with being less accurate, there are better ways to address that. I've been doing this for a couple of years and in many ways it's a dream side-project. Location independent, no customers, automatable, and in some jurisdictions tax-free. It can be a little lonely at times though. I would love to chat with anyone else applying tech/math to beat the bookies. Sorry for the throwaway, I'll put a contact in my profile. |
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How's that contact information coming along? :-)