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92 sats \ 10 replies \ @ken 10 Sep \ on: Degenerate Corner 860789 Stacker_Sports
I'm working on a machine learning model to forecast the win probability of NFL games. These are my predictions for this week. This is totally a work in progress and should NOT be used to make bets!
Team 1 | Team 1, Model Odds | Team 2 | Team 2, Model Odds |
---|---|---|---|
Buffalo Bills | -117 | Miami Dolphins | +117 |
Los Angeles Chargers | -257 | Carolina Panthers | +257 |
Dallas Cowboys | -270 | New Orleans Saints | +270 |
Detroit Lions | -300 | Tampa Bay Buccaneers | +300 |
Green Bay Packers | -113 | Indianapolis Colts | +113 |
Cleveland Browns | -163 | Jacksonville Jaguars | +163 |
San Francisco 49ers | -426 | Minnesota Vikings | +426 |
Seattle Seahawks | -355 | New England Patriots | +355 |
New York Jets | -122 | Tennessee Titans | +122 |
Baltimore Ravens | -355 | Las Vegas Raiders | +355 |
New York Giants | -150 | Washington Commanders | +150 |
Los Angeles Rams | -426 | Arizona Cardinals | +426 |
Pittsburgh Steelers | -194 | Denver Broncos | +194 |
Cincinnati Bengals | -100 | Kansas City Chiefs | +100 |
Houston Texans | -194 | Chicago Bears | +194 |
Philadelphia Eagles | -233 | Atlanta Falcons | +233 |
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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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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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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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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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