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[Dave Schwartz]

Originally Posted by classhandicapper

I don’t know anything about how AI works, but it seems similar to running PP data through an Excel regression, getting weights for each factor, using the weights to make selections going forward, adding new data as races are run, and rerunning the regression to get updated weights for each factor and so on.

If you were doing this with Excel, you might then break it up by dirt/turf, sprint/route, high class races and low class races and so on until you had a different model for each type of race.

That’s sort of the Excel project I started this year that I’ve posted data on where I was getting 36% winners and outperforming the track take significantly in dirt stakes over 8 1/2 years. That was with pretty basic data and not even broken up by sprint/route yet.

The difference being that AI is doing all the work and learning behind the scenes. That’s obviously one hell of a lot easier and will probably work better on raw data.

What you have described is not even close to the capabilities of a robust, deep-learning/deep understanding AI engine.

What you have described is going to be purely linear.

That is, the best horse, is always the best horse.

To my knowledge, all the whale teams use the same approach, which is much as you describe.

Not necessarily the precise tools but some form of iterative regression

Chat.GPT v4
me: Please explain iterative regression
then...
me: Pease simplify your answer for 8th grade level

GPT4: Iterative regression means repeatedly adjusting a mathematical model to better fit a set of data. This is done by using the model to make predictions, comparing the predictions to the actual data, and then adjusting the model accordingly. This process is repeated until the model fits the data well enough, based on certain rules. Iterative regression can help improve the accuracy of the model, especially when the data is complex or there are unusual points, but it can also take a long time to run.

To be clear, iterative regression WORKS WELL for the whales.

If you begin now you will (essentially) be doing what they are doing.

But lest one gets the idea that, since the answer for them was/is found in ItReg Analysis, remember that they have a 15+ year head start.

IOW, if you do what they are doing but are not doing it as well, you will be eaten alive.

A sophisticated AI engine (which is NOT what's described in the article) would be able to say, "In this race we bet the best bet because it's the best bet. But in the next race, we bet the worst bet because the best bet will be hammered down to the -3% level by the whales."

IOW, a deep learning system is not linear.

In fact, it is capable of understanding that there are times for linearity, but other times you need anti-linear, reverse linear, or even chaotic randomness.