My Kansas Predictions Revisited

Over on NBC Sports, I made Kansas predictions using only 2022 data.

A table comparing my predictions with the results for the fall Kansas race

So here’s a table comparing my algorithm’s prediction in the leftmost columns and drivers’ actual finish positions in the right.

I colored my Kansas predictions for 1-10 green, and did the same for the actual finishing positions. I repeated the process with yellow for positions 11-20.

I didn’t include some drivers in my prediction because I didn’t have enough data for them. (That’s Ty Gibbs, Noah Gragson, etc.)

How’d I do?

Much to my surprise, eight out of the 10 drivers who rose to the top in my algorithm finished in the top 10. The two who didn’t — Kyle Busch and Kevin Harvick — had problems that put them either out of the race or well back in the field.

It was a very simple algorithm that weighted drivers’ finishes this year at Kansas, Las Vegas and Michigan. I modified that with a factor that took the driver’s last five finishes into account.

So pretty good results for a really, really basic algorithm.

My attempt to predict who the first four drivers out would be will be far less successful.

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