# Multiple logistic regression and public behavior

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?

• Note that "multinomial" LR refers to models that predict the probability an observation is in 1 of >2 categories, NOT using multiple IVs to predict 2 categories (winning vs losing). Likewise mlogit is an R package for fitting such models. – gung - Reinstate Monica Oct 9 '14 at 19:21
• Right... I'm not a "stats" guy so I'm probably messing up the vocabulary. In STATA, the tool I use is "conditional logistic regression" ... those who do the same thing in this space have referred to it as multinomial, multiple etc. Essentially, a race is a group, the dependent is 1 or 0 for win or loss etc. – TravisVOX Oct 9 '14 at 19:41
• My comment wasn't intended as criticism; only as information. The terminology is not as intuitive (or consistently used) as one might prefer. – gung - Reinstate Monica Oct 9 '14 at 19:46
• I'm totally unfamiliar with betting on horse races. In a pari-mutuel, do the bettors know how much has already been wagered on each horse? – JenSCDC Oct 9 '14 at 20:06
• Yes, throughout the wagering, the \$ bet on each horse is known. I'm trying to forecast this value in advance of the race. – TravisVOX Oct 9 '14 at 20:13