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Interesting. You do have a few divergences from the odds.
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87 sats \ 3 replies \ @ken 10 Sep
I will mention that I'm using 2023 end-of-season statistics to generate the odds (until I have at least a few weeks of 2024 data). So if the team went through significant change, the odds could be way off.
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Are you familiar with the Elo model 538 used to use? The way they handled that is by moving each team partway back towards the mean from where they ended the previous season.
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48 sats \ 1 reply \ @ken 11 Sep
Elo model
Thanks, I've never heard of this! That's an interesting approach.
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I know they added a bunch of bells and whistles to it over the years, but I liked the simplicity of the initial model. The problem with simplicity is that it performs really poorly around major personnel changes.
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What are your inputs for the model?
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51 sats \ 3 replies \ @ken 10 Sep
Most of the inputs come from Pro Football Reference. I basically use a bunch of general performance indicators (win/loss ratio, total points scored, etc) along with offensive/defensive performance data (passing, rushing, penalities, etc) and do a binary classification.
I trained the model using statistics from every game since 2004.
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Neat. I've been wanting to do something similar to this for basketball (and eventually baseball, hockey, soccer, etc.).
Was the data fairly accessible?
I didn't get very far in looking into it, but it seemed like the gamelogs were behind a paywall.
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38 sats \ 1 reply \ @ken 11 Sep
I've built a similar model for NCAA basketball, and it looks like professional basketball data is available:
The basic data can be scraped from the tables for free. Deeper information might be behind a paywall, but I think you could create a basic model from the data that is freely available.
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Thanks. I only actually need the game logs and I'll eventually want to integrate college and international into the model.
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