In late September I took a trip to Las Vegas for a friends birthday, and because I am too good at math to enjoy gambling in the casinos, I had to figure out a better plan. So I decided to take some of the code I have created and try to apply it to betting on football. In early September I spent a few hours putting together two models, one for NCAA football and one for the Pros.
My NCAA model looks at the top 25 ranked teams, and applies various factors to the team and their opponent, then spits out a prediction on whether or not the spread is too high. I placed some mock trades the first two weeks of the season, and had a success rate of 60% or higher each week.
My pro football model was a little different. I do a confidence pool each week with a bunch of friends where we assign a weight to each team we think will win, giving our most confident pick a 16 and our least confident pick a one. I had eight years of data from this pool, ran a few regressions, but didn’t find any strong correlations to find a winner. I finally decided on the method of comparing the consensus picks each week from this pool, comparing those to the spread and betting on the game with the highest divergence. Using this method, the first couple weeks performed pretty well – in the 60 – 70% accuracy range (against the point spread). To be clear, nobody in this pool is a clear expert (definitely including myself), so it is an interesting sample of football fan sentiment.
Off I went to Vegas, armed with a spreadsheet of recommended picks. The results:
- For the top 8 NCAA games that I thought were most incorrectly priced, I won 5 out 8 (62.5%). Not great, but still positive factoring in the 10% casino take. Even better, of the 5 games I actually bet on using my spreadsheet, 4 of them were winners (80%). One of my games I arrived too late to bet, and two others I laid off because I wasn’t at all familiar with the teams. I also made two ‘hunch bets’ on the under of two games, won 1 and lost 1 of those. So my spreadsheet did outperform my hunches.
- For the pro games, my model picked 5 games that were considered most mispriced by the spread. Of those games, it was correct on 4 out of 5 (80%). The good news for me was I only made 4 bets and won 4 out of 4 (100%). I laid off the one loser only because I predicted in our confidence pool against my spreadsheet, so I didn’t trust the spreadsheet. The bad news is I made another ‘hunch’ bet on a game, and it was wrong, so I still ended up 4 out of 5 (80%).
What did I learn from this experience? I am not sure. I only spent a few hours of spreadsheet work coming up with these formulas, so I find it hard to believe after that I found the magic formula to sports betting riches. And the sample set of 3 weeks is too small to make any firm determination. However, I do think its quite possible that the sports betting market is much more inefficient than the stock market, so with more analytics it may be possible to come up with a consistent winning strategy. I would guess most sports bets are made on emotion and hunches. During my research I found a whole lot of data that could be used to create algorithms to find patterns, and finding games where the spread is distorted by emotion.
So for now, I am going to toss the task of a sports betting model on the pile of software projects in my personal backlog. But you never know, it could be a whole new career ahead of me.