How the NFL Uses Data & Stats to Evaluate Players

Introduction

The National Football League (NFL) has undergone a significant transformation in how it evaluates players. The traditional methods of relying solely on subjective scouting reports, game film, and combine drills have been augmented, and in some cases, supplanted, by the burgeoning field of data analytics. This shift is driven by the desire to gain a competitive edge in every aspect of the game, from draft and free-agent acquisitions to in-game strategy. The integration of data and statistics provides a more objective and granular view of player performance, allowing teams to make more informed decisions. This is made possible by a wide array of tools and metrics, from advanced tracking technology to complex machine learning models.

10 Ways the NFL Uses Data & Stats to Evaluate Players

1. Next Gen Stats (NGS) Player Tracking: The NFL, in partnership with companies like Zebra Technologies, uses radio-frequency identification (RFID) chips embedded in player shoulder pads and the football. These chips track player and ball location, speed, distance traveled, and acceleration at a rate of 10 times per second. This data provides a level of detail previously unimaginable, moving beyond simple box-score stats to show how a player performed on every single play.
2. Expected Points Added (EPA): This is a metric that provides context to a player’s performance. Instead of just looking at yards, EPA measures the change in a team’s win probability on a given play. For example, a two-yard run on 3rd-and-1 is highly valuable, while a two-yard run on 3rd-and-10 is not. EPA accounts for factors like down, distance, and field position to determine the true value a player or play contributed to a team’s scoring chances.
3. Completion Percentage Over Expected (CPOE): For quarterbacks, CPOE goes beyond a simple completion percentage by accounting for the difficulty of each throw. The model uses factors like air yards, pass location, and pressure to calculate the expected completion probability for a pass. A quarterback’s CPOE is the difference between their actual completion percentage and their expected completion percentage, providing a more accurate measure of their passing accuracy and decision-making.
4. Player-Specific Advanced Metrics: Data analytics has given rise to a new generation of statistics tailored to specific positions. For example, pass rushers are evaluated not just on sacks, but on metrics like “Pass Rush Win Rate” or “Pressures” which measure their effectiveness at beating their blockers, even if a sack doesn’t occur. Similarly, wide receivers can be evaluated on “Separation” (how open they get from a defender) or “Yards After Catch Over Expected” (YAC/OE), which measures their ability to generate extra yards after the catch.
5. Predictive Modeling for the Draft: NFL teams are increasingly using data analytics to supplement their scouting efforts for the NFL Draft. They create predictive models that use a player’s college stats, physical measurements, and even high school track and field data to project their future performance at the professional level. This helps teams identify undervalued talent and mitigate the risk associated with draft selections.
6. PFF Player Grades: Third-party services like Pro Football Focus (PFF) have become influential by meticulously grading every player on every play. The grades are based on a subjective but data-informed analysis of a player’s “contribution to production.” This provides an additional layer of insight into player performance that goes beyond traditional box scores.
7. In-Game Strategy and Play-Calling Optimization: Data is not just for evaluating players in a vacuum; it’s used to optimize on-field strategy. Teams analyze data to identify play types and formations that are most effective against specific opponents or in certain game situations. Analytics departments use win probability models to inform critical decisions like whether to go for a two-point conversion or punt on fourth down.
8. Risk Assessment and Injury Prediction: The vast amount of data collected, including player load and acceleration, is used for player health and safety. Teams can analyze this information to monitor fatigue and identify patterns that may lead to injury, allowing them to adjust practice schedules and workload to keep players healthy.
9. Cognitive and Reactionary Testing: Beyond physical measurements, teams are using data from cognitive assessments to evaluate a player’s ability to process information and react to stimuli. These tests, like the S2 and AIQ, provide insight into a player’s mental acuity, which is a crucial, though often overlooked, aspect of on-field performance.
10. Composite Scores and Machine Learning: The most sophisticated teams don’t rely on any single metric. They use machine learning and data science techniques like Principal Component Analysis (PCA) and K-Means Clustering to combine a wide range of data points into composite scores. This allows them to group players by their unique play styles and evaluate them more holistically, providing a more nuanced and comprehensive picture of their talent.

Conclusion

The use of data and statistics has fundamentally changed how the NFL evaluates players. While the human element of scouting and coaching remains vital, analytics provides an objective lens that removes much of the guesswork from player assessment. The future of the NFL is one where data-driven insights are a cornerstone of every major decision, from the draft and free agency to in-game strategy. This evolution will continue to push the boundaries of what’s possible, leading to more precise player evaluation and, ultimately, a more competitive and data-informed league. Sources