I'm trying to develop a model to forecast the behavior of the public… specifically, in horse racing.
Most models in horse racing use whether or not the horse won as the dependent variable and then use a variety of predictive variables within the independent set.
The public does the same thing as a whole, however, they tend to over bet and/or under bet certain variables. What are some techniques for capturing this behavior in a logit model?
The core problem I'm having… If I were to use a variable the public tends to over bet, the model would automatically discount it slightly and thus nullify any potential advantage a fundamental model would have over it.
Make sense? Any thoughts?
You should look into the Brunswik lens model.
To better understand how people are betting, use what they bet to win as your response variable, not what actually won. Then the parameters estimate the log odds they are intuitively using.