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I don't mean to sound rude, but I don't believe the results in your $1 * 26000 races graph. I spent a few years in a hedge fund building algorithmic trading st

I don't mean to sound rude, but I don't believe the results in your $1 * 26000 races graph. I spent a few years in a hedge fund building algorithmic trading strategies, and whenever I saw a graph (we called them 'account curves') like that, it inevitably meant that there was a bug somewhere. Usually the strategy is somehow non-tradable, either because the market is closed or the system is borrowing data from the future. The graph shows you making about $55,000 over 26,000 races (i.e. about a 100% return) with no big swings. If the effect was that big, wouldn't people know about it already? Despite the fact that you can't apply the strategy everywhere, couldn't you settle for making a little money at your local betting shop? Your theory is that there are more informed bettors betting at the last minute. That suggests that the market odds spike towards the 'correct' odds as betting is about to close. If someone proposed your 'bet on the favourite' strategy to me, I would have asked to see evidence of that spike. Are you able to get access to real-time odds to test the theory?

surfsvammel 8y ago
Not rude at all! Appreciate the comment. I don't rule out that there is a bug in there. I did not spend much time on that part of it. It was definitely a side track of that project and had nothing to do with the main model. I do have access to real-time data, but not historic data. So cannot back-test it, that would be interesting. Real-time data can be scraped from ATG.se, the only bookie for harness racing in Sweden. Also, I dont suggest anyone go out and place real money on anything like this. The main point of the article is that its not that hard to get the basics of machine learning and actually doing things with it.
beezle 8y ago
Betting $1 on 26,000 races: Stated win rate betting on the post time favorite is 37% This equates to a loss of 16,380 of original 26,000. Earnings must then come from the 9,620 bet on favorites that won. Assuming the 55,000 is winnings (and not a total including original 9,620), this would equate to an average odds of better than 11/2. If reduced by the 9,620 then odds come down to a bit over 9/2 My experience in horse racing (regular and harness) is that odds on the favorite of 11/2 (or even 9/2) are extremely rare and certainly not the average. A realistic figure is 3/2 over the various field sizes (smaller fields tend to go close to even money, larger to 2/1 or so). So yeah, the data as presented appear flawed.

surfsvammel 8y ago

Not rude at all! Appreciate the comment. I don't rule out that there is a bug in there. I did not spend much time on that part of it. It was definitely a side track of that project and had nothing to do with the main model. I do have access to real-time data, but not historic data. So cannot back-test it, that would be interesting. Real-time data can be scraped from ATG.se, the only bookie for harness racing in Sweden. Also, I dont suggest anyone go out and place real money on anything like this. The main point of the article is that its not that hard to get the basics of machine learning and actually doing things with it.

beezle 8y ago

Betting $1 on 26,000 races: Stated win rate betting on the post time favorite is 37% This equates to a loss of 16,380 of original 26,000. Earnings must then come from the 9,620 bet on favorites that won. Assuming the 55,000 is winnings (and not a total including original 9,620), this would equate to an average odds of better than 11/2. If reduced by the 9,620 then odds come down to a bit over 9/2 My experience in horse racing (regular and harness) is that odds on the favorite of 11/2 (or even 9/2) are extremely rare and certainly not the average. A realistic figure is 3/2 over the various field sizes (smaller fields tend to go close to even money, larger to 2/1 or so). So yeah, the data as presented appear flawed.