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I don't think that there is a major issue in the difference between probit or logit regression, at least the way I was taught it many years ago in at Univ. of Wisconsin-Madison, by Draper. He was one of the heavy hitters in Applied Regression Analysis. The issue is that you can't regress against a dichotomous dependent variable (0,1 or win, lose). It violates error term distribution assumptions. So, you use a distribution curve (normally S-shaped such as a normal distribution) referred to as a probability density function. Thus, you can predict a probability of the occurance with the regression model. Actually this is a simple but intuitive approach. My Ph.D. dissertation many years ago used logistic regression and i had to write a chapter on methods, which is why I remember this.
My take on this thread is that we have been discussing a new approach which simply uses a logistic (or it could be probit) dependent variable in the form of some type of S-shaped curve. I think most of the questions and uncertainty is on the independent variable side of the equation. With the one exception of the use of two dependent variables, which I'm absolutely clueless on.
If any of the above is incorrect please correct me. This is the best of my rusty and outdated knowledge.
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