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Originally Posted by JJMartin
I use it for everything. The program I built, assembles the pp files including results files. I can then run a single file through one of multiples models, or build a database to back test a model against a whole year of data or whatever range I choose to use. Mostly I use it for the latter. I try to automate to the absolute maximum.
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What kind of data source do you use? From your description, it seems you are downloading PPs and results separately. I assume you create/generate your own track variants on the fly using the above data sources? That was a key issue in my own apps, and it took a bit of work to get right.
When you back test models, do you set filters mainly for accuracy (win%) or (possible) return (ROI)? Most lean toward the ROI side, to their disadvantage. In most cases, ROI (in smaller samples of whatever size) is derived from what are essentially anomalies. Rarely repeat going forward, yet everyone seems obsessed with the "woulda coulda shoulda" type modeling.
You might try parsing for winner attributes (ignoring ROI) and test that on future races. ROIs in the 90s (in past races) with relatively high win rates can be especially productive. My conjecture is that bettors "seeking value wagers" tend to avoid the obvious best choices for win, and those obvious best choices for win may often generate a decent profit going forward (that may be concealed or missing in the sample used to build the model).
The big advantage is that the results of a frequency in the 45% and up is much more likely to be productive than a lower frequency is likely to reproduce a paper ROI from a sample of past races.
