Each NFL regular season, teams fight through a 16 game slate which pits them against their rivals across the league. The length of the season provides a large sample to truly distinguish the bad, good, and great teams. Yet it is always difficult to recognize who is most responsible for their team’s success. The NFL anoints a “Most Valuable Player” at the end of each season to honor the player who is perceived to have contributed the most to his team. In 2017, the New England Patriots finished with a league best 13-3 record, and their Quarterback, Tom Brady, was awarded the MVP honor. This award is voted on by members of the media. However, these members are subject to biases and each member’s vote may be influenced by personal opinion, group think or player exposure due to the market they play in (consider a player in a bigtime city like New York vs in Kansas City). Throughout the season, many players emerged as candidates for the MVP as their teams entered hot streaks, emphasizing the fact that the team might be the more responsible for the award versus the player themselves. Therefore, we pose the question: Did Tom Brady deserve the 2017 NFL MVP?

To answer this question, we defined metrics that aim to analyze a player’s contribution to his team. We use our metrics to statistically break down a set of quarterbacks who we consider MVP candidates from the 2017 NFL Season. It is important to note that we selected metrics for analysis before looking at the dataset to reduce any biases we may exhibit. Furthermore, we make this analysis available to you, the reader, through interactive graphs that allow you to perform your own analysis. In each section, we will explain our methodology, including how we processed the data. Finally, for those who are not as familiar with football but are fans of numerical arguments, we will provide notes like this to supplement your knowledge of football as you read.

MVP Candidates

Our set of NFL MVP candidates only includes quarterbacks (QBs). We make this decision because QBs inherently have more value in today’s NFL as passing has taken over offensive play calling . Quarterbacks touch the ball on every play and make the most important decisions that influence his team’s success, such as audibles , checking the defense and modifying the play , and getting the ball from the center to the running back or receiver . Furthermore, 22.5 of the last 30 MVP Awards have gone to QBs (0.5 being attributed to Co-MVPs). Although fans of LA Rams Running Back Todd Gurley may take issue with our exclusion of running backs , in order to complete a fair assessment of Brady, we must compare him using metrics relevant to Brady, and as such, we can only compare him against QBs. Our NFL MVP candidate set includes 6 QBs who were selected to the Pro Bowl this past season.

Tom Brady

New England Patriots NE Carson Wentz

Philadelphia Eagles PHI Ben Roethlisberger

Pittsburgh Steelers PIT Philip Rivers

Los Angeles Chargers LAC Russell Wilson

Seattle Seahawks SEA Drew Brees

New Orleans Saints NO

Metrics of Analysis

We will compare Brady to each of the other 5 QBs and gauge his value on 3 metrics:

1. Passing Effectiveness

This dimension analyzes QB accuracy by completion percentage. We break down effectiveness into accuracy by pass distance, down , and a game’s point spread. Each of these graphs highlight a QB’s ability to perform based on pass difficulty (distance), short-term difficulty (down), and long-term pressure (point spread).

2. Efficiency

We gauge a QB’s efficiency based on a series of drive metrics, which includes time per drive , yards per drive , points per drive , and plays per drive . This evaluates a QB on the quality of the drives he plays in by looking at scoring (points per drive), speed (time per drive), length (yards per drive), and rate of progress (plays per drive).

3. Impact

A player’s value can also be quantified by looking at his impact on his team’s win probability. By comparing offense and defense average win probability (WP), the change from defense WP to offense WP may indicate how much that given player impacts their team.

Methodology

To compute our metrics for analysis, we rely on the nflscrapR-data dataset, which contains a play by play documentation across the 2017 NFL Season. The author originally scraped all the data from the NFL API and joined many datasets together to form this one. We filter the quarterbacks we compare in our visualizations by their total number of passes across the 2017 NFL Season. A QB is included if they have attempted at least 100 passes, as this provides a reasonable body of work at around 4 games of playing time. Any quarterback who has not attempted this many passes is not included in our graphs. This narrowed our set of valid passers to 45 QBs, each of which can be seen across every visualization. In addition, for each visualization we compute the NFL Average using the 45 valid QBs. Our work for this project can be found here, which includes our data preprocessing and graph generation.