I have a set of horse racing data. Specifically distances run by horses in races. For example, for a sample of 1000 horse races at a mile I have data for how far each horse ran during the course of the race. One might say that if it's a mile race, then each horse ran 5,280 feet. That would be incorrect because of ground loss. Ground loss can be attributed to a variety of factors (no pun intended): post position, running style, race distance, track configuration, etc.
Given this, would it be preferable to make total distance run the dependent variable and use race distance (in feet) along with other variables as the explanatory variables? Or
Would it be preferable to use ground loss (actual distance run - stated race distance).
Specifically, My concern is the high covariance between total distance run, say 5350 feet and an explanatory variable (stated distance) of 5280 may create a signal to noise issue and undermines the model's accuracy/validity. Or, is this an ideal situation that will lead to excellent results.
Thanks for your input.