# NFL Season Simulation Methodology

This article gives an in-depth dive into our prediction and ranking system for NFL teams. Tune into our win projections, rankings, and Elo ratings throughout the 2017 NFL Season – our numbers going into Week 1 can be found here.

### Overview

Our NFL rankings are based upon projected win totals for the remainder of the season. As each week passes, we run a Monte Carlo Simulation using a Ridge Regression model that simulates the remainder of the regular season. Our predictive Model incorporates our custom Elo Ratings, along with other variables that will adjust throughout the season, to predict the probability of the home team winning each game in a given week. After calculating predicted probabilities from our model, we then simulate a random number between 0 and 1, and compare this to our prediction. If our predicted probability is greater than the random number, the home team wins, and if not, the away team wins. The Elo Ratings are then adjusted so that we can then make predictions on the ensuing weeks. As a quick example, say our model says the Vikings have a 70% chance of beating the Saints in Week 1. If the random number in this instance is > 0.70, we give the Saints the win, and if the random number is <= 0.70, we give the Vikings the win. Finally, the Elo Ratings for each team are then adjusted based on the result that was simulated.  This process is replicated for all games and all weeks to simulate a single season. By simulating a large number of seasons (say 10,000), we can converge around a mean number of predicted wins for each team.

### What is Included in our Preseason Predictive Model?

As the season goes on, our win / loss probability and spread models are able to garner more pertinent information on teams. In the preseason, however,  we do not have complete in-game season statistics for teams, and thus we make use of more generic statistics about the elements of the games as well as previous years statistics for predictions. Our Impact of Situational Factors on NFL Games article provides exploratory data analysis of some of the predictive variables used in our models. Here are some of the key predictors factored into our model:

• Model 284 Elo Ratings: Our Elo Ratings are one of the strongest and most heavily weighted predictors. For more information on our calculation of Elo Ratings see this methodology article.
• Days of Rest: There is a theory that Teams with more rest tend to perform better. Well, since 1994, Home Teams coming off of byes (roughly 14 days of rest) have a 61.4% win percentage compared to a normal week’s home team win percentage of 57.7%.
• Divisional Game: As one might imagine, divisional games seem to have a different element to them. For one, divisional teams are likely more familiar with each other since they play twice a season. And if teams remain fairly consistent year-over-year, this affect becomes amplified.
• Indoor/Outdoor: The elements of the game can have significant impact. For example, high octane offenses are more likely to thrive indoors whereas a stout defense might strive more in an outdoor cold weather-type game.
• *Previous Year Expected Offensive and Defensive Contribution: Going into Week 1, the only statistics we have are based on last season. So, to start the season we use last years expected offensive and defensive contribution statistics generated by Pro Football Reference. These give a proxy as to how good a team’s offense and defense were last season and have predictive power into how good an offense and defense will be this season.

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