Assuming you are using something like conditional or multinomial logistic regression for your model --
Classify your trip types. To keep things simple, letter codes like TripA and TripB, etc. should work just fine.
Imo, it doesn't matter what your trip types are (at least not at first.) As you move forward with your model, the data will tell you which of your trip types (if any) are significant and which you can safely discard.
The important thing is to classify your trip types, give each a distinct letter code, compile the data for each of your trip types in a consistent manner, and include a column for your trip types in the history table you are using to accumulate data for purposes of building your model.
For example purposes, below is a simple history table that contains data for Remington Park R1 on 09-03-2020.
The Track, rDate, Race, Surf, Dist, and Odds columns should be self explanatory.
The Horse column contains the horse's position in the starting gate from the rail out.
The Speed column contains the horse's HDW final time speed fig from its most recent running line. (Imo, there's nothing magic about last race running line speed fig. Just using it here for example purposes.)
The TripA column contains a value of 1 for True in cases where the horse qualifies as a Trip Type A. Otherwise it contains a value of 0 for False. In this case Trip Type A describes a poor start last out.
The TripB column contains a value of 1 for True in cases where the horse qualifies as a Trip Type B. Otherwise it contains a value of 0 for False. In this case Trip Type B describes a horse that was making an outside closing move on the far turn (or tying to) last out.
The Wnr column is assigned a value of 1 to indicate True this horse won this race. All other horses are assigned a value of 0 for False.
The table structure with data looks something like this:
Code:
Track rDate Race Surf Dist Horse Speed TripA TripB Odds Wnr
----- -------- ---- ---- ---- ----- ----- ----- ----- ----- ---
RPX 9/3/2020 1 1 1210 1 67 0 0 4.5 0
RPX 9/3/2020 1 1 1210 2 61 0 0 29.5 0
RPX 9/3/2020 1 1 1210 3 55 1 0 2.5 1
RPX 9/3/2020 1 1 1210 4 70 0 0 1.3 0
RPX 9/3/2020 1 1 1210 5 63 1 0 13.4 0
RPX 9/3/2020 1 1 1210 6 71 0 1 3.4 0
After your history contains data for a few thousand races, and if you've done a good job of compiling your trip type data in a consistent manner:
When you run the data through a third party stat tool such as SPSS, Stata, or one of the logistic regression packages in R:
The third party stat tool should be able to display significance for your trip types.
From there you should be in a position to make an informed decision whether or not to include your trip types in your model.
Hope I managed to type most of that out in a way that makes sense,
-jp
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