Someone tell my wife I will see her in February, the NFL regular season starts tonight! (not really... see you about 5:30)
If you read this blog regularly, you know that back in early summer I developed model to predict playoff teams and end-season team records. (first post, final post) I assumed I would be posting about the performance more during the season, and I will in fact, on three specific areas:
- Preseason Model Picks: The preseason model I created also can be used to predict individual games, though because it relies on last season's data, the predictions are not great. Should be fun to watch though, and it's predictive capabilities will decay as the season progresses and more things happen that the model doesn't know about (injuries, over-performing players, etc). This model is effectively: if we just looked at last season's performance, how would this game turn out.
- In-Season Model Picks: This is another model I've created that predicts results of weekly games, based on prior season AND in-season results. This model should be more predictive than the preseason model, and more predictive as the season progresses.
- Performance Evaluation: Each week I will evaluate the performance of the picks compared to a "null" model (random picks) and how well certain sportswriters do in weekly team picks. I am considering Tom Keegan from the Lawrence Journal World as a benchmark pundit, but not sure.
Ok, on to the real picks
Let's get this started, my first week's picks:
A couple of notes on terminology and form.
- The yellow highlighted teams are my picks for the week.
- The odds ratio is the odds ratio of the home team winning the game. (e.g. the Patriots are 2.01:1 favorites effectively)
- The certainty level speaks to how certain we are about the prediction. Keep in mind that NFL games are still relatively unpredictable, and even highly probable favorites will lose a quarter of the time or greater.