Amos is a machine learning model created to predict the outcome of each regular season and playoff NFL game. Amos uses team level stats from 2011 to 2020 to learn what wins games, and uses that information to calculate the probability of a given home team winning their matchup.
Amos’ and peer model’s weekly predictions are presented as probabilities of the home team winning. A larger assigned percentage can be interpreted as a greater likelihood of the home team winning. For those viewing from a mobile device, this page is best viewed in landscape orientation.
|Game Time||Home Team||Away Team||Amos||FiveThirtyEight||Bing||FPI|
Cumulative Performance represents the number of correct predictions out the number of games played over the entire season to-date.
Weekly Performance represents the number of correct predictions out of the number of games played in a given week.
Amos is not alone the NFL prediction space; there are a number of sources for game predictions. Three peers were identified, Microsoft’s Bing (Bing), ESPN’s Football Power Index (FPI) and FiveThirtyEight’s quarterback-adjusted Elo Forecast (FiveThirtyEight), as benchmarks for comparison to Amos this year. The selection is due to the probabilistic approach to each of these prediction methods, which provides similar, but not exact, foundations for comparison.
It’s important to note the methodology used to track NFL season performance and its limitations. When a model predicts a home team win likelihood above 50%, this is counted as a prediction that the home team will win. Conversely, when a model predicts a home team win likelihood of below 50%, this is counted as a prediction that the away team will win. The models therefore increase performance by correctly predicting whether a home or away team will win and decrease performance by incorrectly predicting whether a home or away team will win.
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See Amos’ predictions from the 2018 NFL season.
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