NFL Analysis & Research

Passer rating is an efficiency statistic with each component measured on a per attempt basis. As a result, the number of completions, passing yards and touchdown passes required for a perfect passer rating change depending on the number of pass attempts. However, a minimum "rate" for each component can be calculated. The minimum requirements for a perfect passer rating are: 77.5% Completion Percentage 12.5 Yards per attempt 11.9% Touchdown Rate 0% Interception Rate (ie zero interceptions)

NFL Passer Rating and QBR (also known as Total Quarterback Rating) are rating systems used to evaluate the performance of NFL quarterbacks. While they both measure overall quarterback performance, there are notable differences between the two in terms of how they incorporate context, and, more importantly, how they attribute credit to quarterbacks for the team's overall production.

A deep dive into the relative predictiveness of quarterback metrics like EPA, ANY/A, QBR, and PFF Grades, to determine which is the best for measuring the performance of NFL QBs.

In this post, we'll first explore how Over Expected metrics are calculated using CPOE as an example. With a baseline understanding in place, we'll then look at the relative effectiveness of other Over Expected metrics like RYOE and YACOE to explore how and when these types of metrics break down.

By looking at replacement performance versus expectation, market expectation for replacements, and the few natural experiments created by starting QBs changing teams, we see that “team and scheme” are, perhaps, more influential towards individual QB performance than previously thought

The analysis in this post digs deeper into the nuances of HFA by exploring the impacts of three additional factors--field surface, time of day, and bye weeks. These factors are then combined to create a new model for predicting HFA in NFL games, finding the following results: * Opponent familiarity in divisional games reduces HFA by nearly 50% * Bye weeks provide diminishing marginal benefit to home teams and are actually worth more to away teams * Different surfaces are a strong driver of HFA and are worth as much as a bye week * Timezones both positively and negatively impact HFA depending on kickoff time

If you follow the NFL, and especially if you follow football analytics, Expected Points Added (EPA) is a metric you’ve seen being used more and more. But what is EPA? This post will help explain the intuition behind Expect Points, how Expected Points Added is calculated from Expected Points, and how to think about both in the context of analytics. 

This post uses the work of nflfastR’s Win Probability (WP) model to answer a new question--what is the probability that a team covers their pre-game spread given the current game state?

Regressing a predictive NFL model to market spreads creates a model that reliably beats the Vegas line

By using data to model key numbers in NFL outcomes, the expected value (EV) of a bet can be calculated more accurately

Weighting Expected Points Added (EPA) yields a model that predicts future NFL team performance better than point differential and DVOA

Homefield advantage is declining in the NFL, but its recent disapearance is likely just short term variance

The best game grades describe how well teams played in a game *and* predict how well those teams will perform in the future. This post explores the descriptive and predictive characteristics of metrics like Weighted EPA, DVOA, and PFF grades.