Numbers Never Lie: How NFL Teams Are Using Analytics to Evaluate Player Performance
The roar of the crowd, the clash of helmets, the unpredictable bounce of the pigskin – these are the visceral elements that define the NFL. But beneath the surface of gridiron glory lies a world increasingly governed by numbers. In a league where fractions of a second and inches can determine victory or defeat, teams are turning to analytics like never before to evaluate player performance and gain a competitive edge. Just ask the Kansas City Chiefs, whose calculated fourth-down gamble in Super Bowl LVII, based on win probability models, ultimately sealed their championship victory.
The Evolution of NFL Analytics
The use of data in football isn’t new. Coaches have always studied game film and tracked basic statistics. However, the explosion of computing power and the development of sophisticated algorithms have revolutionized the field. Teams now have access to a vast ocean of data, from player tracking information (Next Gen Stats) to detailed breakdowns of play types and formations.
Pioneering teams like the Baltimore Ravens, under the guidance of coaches like John Harbaugh, were early adopters of analytics, using data to inform decisions on everything from draft strategy to in-game play calling. Now, nearly every team in the league has a dedicated analytics department, staffed with data scientists, statisticians, and former coaches.
Beyond the Box Score: Unveiling Hidden Value
Traditional statistics like passing yards, rushing touchdowns, and tackles still hold value, but analytics allows teams to dig deeper and uncover hidden insights. For example, Pro Football Focus (PFF) grades every player on every play, providing a more nuanced evaluation of individual performance than simply looking at box score numbers.
Expected Points Added (EPA) and Completion Percentage Over Expected (CPOE) are two other key metrics that are gaining traction in the NFL. EPA measures the impact of a play on the expected point value of a drive, while CPOE compares a quarterback’s completion percentage to the average completion percentage for similar throws. These metrics help teams identify players who are consistently making positive contributions, even if those contributions aren’t always reflected in traditional stats.
Play Calling: The Art of the Possible
Analytics is also transforming the way teams approach play calling. Coaches are using data to identify tendencies in opposing defenses and to determine the most effective plays to run in specific situations. For instance, the Los Angeles Rams, under Sean McVay, have been known to use data to identify mismatches in the secondary and exploit them with targeted passing plays.
Fourth-down decision-making has also been heavily influenced by analytics. Teams are now more willing to go for it on fourth down, even in their own territory, if the data suggests that it’s the optimal strategic move. The Philadelphia Eagles, under Nick Sirianni, have been particularly aggressive on fourth down, relying on analytics to guide their decisions.
Player Evaluation: Finding the Right Fit
Perhaps the most significant impact of analytics has been in the area of player evaluation. Teams are using data to identify undervalued players in the draft and free agency, and to make more informed decisions about contract extensions and trades.
The “Moneyball” approach, popularized by the Oakland Athletics in baseball, has found its way into the NFL. Teams are looking for players who excel in specific areas, even if they don’t possess the traditional attributes of a star player. For example, a team might target a wide receiver who is particularly good at getting open against zone coverage, even if he lacks blazing speed.
The New England Patriots, known for their shrewd personnel decisions under Bill Belichick, have long been rumored to be heavy users of analytics in player evaluation. They have a knack for finding players who fit their system and contribute at a high level, even if those players weren’t highly touted prospects.
Case Studies: Analytics in Action
Several recent NFL success stories can be attributed, at least in part, to the use of analytics. The Baltimore Ravens’ decision to draft Lamar Jackson in 2018 was based on a belief that his unique skillset could be maximized through innovative offensive schemes. The Ravens’ analytics team played a key role in developing those schemes, which helped Jackson win the MVP award in 2019.
The Los Angeles Rams’ Super Bowl LVI victory was also fueled by analytics. The Rams used data to identify and acquire key players like Matthew Stafford and Von Miller, and to develop game plans that exploited their opponents’ weaknesses.
The Human Element: Finding the Right Balance
While analytics is becoming increasingly important in the NFL, it’s important to remember that it’s just one piece of the puzzle. Football is still a game played by human beings, and factors like leadership, chemistry, and intangibles can’t be easily quantified.
The best teams are able to strike a balance between data-driven decision-making and the human element. They use analytics to inform their decisions, but they also rely on the judgment of their coaches and scouts to evaluate players and make strategic calls.
The Future of Analytics in the NFL
As technology continues to evolve, analytics will only become more sophisticated and pervasive in the NFL. We can expect to see teams using artificial intelligence and machine learning to develop even more advanced models for player evaluation, play calling, and injury prevention.
The teams that embrace analytics and find ways to integrate it effectively into their operations will have a significant competitive advantage in the years to come. The numbers never lie, and in the NFL, they are increasingly telling the story of success.