So I started modeling college hoops which is extremely tough in itself, but it looks like I have found an edge in picking sides, but not totals. My question is: my previous assumption was that it would be easier to find an edge in picking totals than sides...is this true? If I am constructing a model to compute a score for two teams, why would it have successful results picking sides but not totals?
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human brain tends to find patterns too easily, consider your sample size firstComment -
The way some model it, they are basically modeled together so that neither is more difficult than the other. Ie. they predict total pts for Team A and total pts for Team B and then the difference is the spread and the sum is the total.
But I would still answer it should be a bit easier to go after totals for no other reason than the sharpest guys are more focused on the bigger markets, and totals markets are smaller.Comment -
How many seasons' worth of data are you using for backtesting?Comment -
I used three seasons of data for my model (2005-2007) and backtested it on those years and 2008-2010.
Mathdotcom: The model you speak of is what I am doing (predicting two teams scores). The sides outperformed totals by a lot.Comment -
You shouldn't backtest it on 2005-2007 because that's the data you used to create the model.Comment -
Regardless, the 2008-2010 has the same results which makes me question the model and my database. Or maybe the model is only only successful with sides.Comment -
There is no one way to model anything, so if you have something that seems to work for totals then stick with it. If it's not working for sides then change that one up. If you're modeling anything reasonably complicated you always have to be re-evaluating your model.Comment -
Thanks guysComment