Originally Posted by
Carl-Haakon
I've been trying to model ice hockey totals for a few european leagues and my model seemed to be doing fairly well before I got hold of any odds.
I evaluated it by checking, for each game, how great of a probability the model had assigned to the correct outcome of the game (which in this case was over or under 5.5 goals scored). The average assigned probability for the correct outcomes was 53.3%, which seemed OK. I then scraped the market average odds for O/U 5.5 for these games and checked to see what the average bookmaker's average probability for a correct outcome was. This turned out to be a 51.7%; I didn't adjust their implied probabilities for overround either, so this should have been an indication that my model would be profitable.
Here's the weird part though: it's not. I've tried simulated betting for out-of-sample seasons with every staking strategy I know of (kelly, percentage, flat), and most of those simulations have ended up bankrupt. I thought that it might just be that the model itself is unprofitable and that my four test seasons where just lucky, but then I simulated betting over those seasons and ended up bankrupt there aswell! I've tried setting a higher threshold for the minimum edge the simulator should be willing to bet, but that doesn't work either.
Does anyone have any idea what I might be doing wrong? The model itself is calibrated: e.g. fourteen percent of all predictions that were deemed to be fourteen percent-likely came true. Is this enough to accurately gauge ones edge?
I'm going to hunt for bugs tonight again, but I'm pretty exasperated at this point. If anyone has any ideas, please share