Quote:
Originally Posted by classhandicapper
I have a question on regression analysis.
Let's assume I have a set of data on each horse for over 1000 races. I want to know how to weight each of these factors to produce the most likely winner.
Let's say I want to use 5 factors to help predict the outcome
1. Speed Figure Last Race
2. Class Figure Last Race
3. Finish Position Last Race
4. Lengths Behind at Finish Last Race
5. Winning Margin Last Race (if the horse won)
I want to use those 5 factors to predict the finish position in today race (which I also have in my data along with Date, Track, and Race).
I've seen some very basic regression analysis done in Excel, but one thing I don't understand is how to make the analysis recognize that each race is it's own unit/group.
It has to understand that a 70 speed figure in one race might have been the top figure in one race and lead to a win but in another race that 70 speed figure may have been the worst speed figure and lead to a 9th.
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It sounds like you may want to try using multiple regression to solve the speed fig thing as to correlation. For example, what is the relative ranking of a 70 sp fig in relation all others in a particular race. Or how does a 70 relate to the average winning fig. relative to that class of race. A simpler way, although maybe not as accurate is to do a sort of all l.r. speed figs in a race, then have excel assign a ranking, 1,2,3, etc. The same technique can be used for class whether it's avg. $ per start, or some other class number.
