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I don't want to sound negative or discouraging and I hope this is helpful.
You can't run such highly correlated variables as a multiple regression model. Check out a correlation matrix for the variables--if you have values higher than .7 or so you run into multi-colinearity which leads to an unstable model. Instability is tied to the correlation between error terms (Regression assumes a normal distriubtion for error terms--which the correlation violates). Also if you are running a stepwise regression, the first variable "sucks up" all the variance for explanation and doesn't leave enough for the following variables to be associated with. (not a very technical explanation)
You can run them individually as bivariate regressions (one regressor and the logit probability density function--just an S-shaped curve--as the dependent variable) and compare each of those models. So you can run, a model for speed figure one race back, then one for two races back etc. and compare them.
See which one gives you the best fit. That's assuming that is what you want to do. I think what you are trying to do is use the probability of winning as the dependent variable and speed figure as the independent variable.
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