Beyond the Box Score: Using Advanced Metrics to Predict NFL Game Outcomes

The roar of the crowd, the clash of helmets, the elation of a touchdown – these are the visceral experiences that define NFL Sundays. But beneath the surface of every game lies a complex web of data, a silent narrative woven from advanced metrics that can unlock deeper insights and, perhaps, even predict the future. As the 2025 season approaches, teams and fans alike are increasingly turning to these sophisticated tools to gain a competitive edge. Did you know that the Detroit Lions averaged 33.1 points per game last season, 2.5 more than the next closest team, the Buffalo Bills? This kind of dominance isn’t just about talent; it’s about leveraging every available advantage.

The Evolution of NFL Analytics

Gone are the days when wins and losses were solely determined by basic stats like passing yards and rushing touchdowns. Today’s NFL is driven by data. Teams are using advanced metrics to evaluate player performance, strategize game plans, and even make critical in-game decisions. This evolution has been fueled by technological advancements that allow for the collection and analysis of vast amounts of data, providing a more nuanced understanding of the game.

Next Gen Stats, developed in collaboration with Zebra Technologies, Wilson Sporting Goods, and Amazon Web Services, capture player data such as location, speed, distance traveled, and acceleration ten times per second. This granular data feeds into a new suite of advanced metrics designed to revolutionize how we evaluate tackling, point after touchdown decisions and pre-snap offensive dynamics. Tackle Probability, Offensive Shift and Motion Classification, and an updated Field Goal Probability 2.0 model are just a few examples of how these metrics are providing new insights into player performance and game dynamics.

Key Advanced Metrics and Their Impact

Several key advanced metrics are now widely used to predict NFL game outcomes. Understanding these metrics is crucial for any serious NFL fan or analyst:

DVOA (Defense-Adjusted Value Over Average): This metric evaluates a team’s performance by comparing each play to a league-average baseline, considering the context of each play, such as down, distance, and opponent strength. DVOA provides a more nuanced view of team performance than traditional stats.
PFF Grades (Pro Football Focus): PFF grades assess individual player performance and team strengths by evaluating every player on every play. These grades offer a comprehensive view of player contributions beyond basic statistics.
EPA (Expected Points Added): EPA measures the impact of a play on the game’s expected point total. This metric helps to understand the true value of a play beyond just yards gained or points scored.
Completion Percentage Over Expectation (CPOE): CPOE contextualizes quarterback performance by considering factors such as air distance and target separation, providing a better evaluation of challenging throws.
Adjusted Net Yards Per Attempt (ANY/A): ANY/A weighs all elements of quarterback play, including passing yardage, sacks, touchdowns, and interceptions, offering a more complete picture of quarterback performance.

How Teams Are Using Advanced Metrics

NFL teams are increasingly using advanced metrics to make data-driven decisions in various aspects of the game:

Game Planning: Metrics like Tackle Probability and Offensive Shift and Motion Classification assist in analyzing and improving team performance. These tools can identify players who consistently position themselves effectively or analyze the effectiveness of pre-snap movements.
Player Evaluation: Advanced metrics provide a more precise assessment of player performance, helping teams identify strengths and weaknesses and make informed decisions about player acquisitions and development.
In-Game Strategy: The Next Gen Stats PAT Decision Guide utilizes rest-of-game simulations to compare the odds of winning under different scenarios, whether a team goes for two or kicks the extra point. The model considers factors such as the projected drives remaining in the game, the likelihood of future scores across those possessions, and the probabilities of successful two-point conversions and extra points.
Player Health & Safety: Next Gen Stats data can be leveraged to enhance player safety initiatives.

Case Studies: Advanced Metrics in Action

Several teams have successfully integrated advanced metrics into their decision-making processes. The Baltimore Ravens, under head coach John Harbaugh and GM Eric DeCosta, have remained remarkably consistent and successful by leveraging data analytics. The Ravens’ defense is well-distributed, and the team has very few major holes, making them a contender in the AFC.

The Kansas City Chiefs, led by Andy Reid and Patrick Mahomes, have also embraced analytics to maintain their dominance. Despite a lopsided loss in Super Bowl LIX, the Chiefs are expected to remain competitive, with their defense receiving reinforcements through the draft. Reid’s ability to adapt and innovate, combined with Mahomes’ exceptional talent, makes them a perennial Super Bowl contender.

The Rise of Predictive Models

Predictive analytics leverages data and statistical models to forecast future events, and it can be a powerful tool for NFL betting. By analyzing past performance, player statistics, and various other factors, predictive analytics can help bettors make more informed decisions.

Several platforms offer NFL prediction models, including:

Rithmm: Rithmm’s app offers AI-driven insights and customizable parameters to create NFL predictive models. The app displays recommended bets, marked with green stars, which indicate where the model predicts the best chances of success.
Betfair: Betfair’s internal team of Data Scientists has created an NFL Predictions Model that utilizes a vast array of variables and proven statistical methods to predict NFL outcomes.
nfelo: nfelo is a prediction model built on top of 538’s Elo framework that uses unique dynamics about the NFL to improve prediction accuracy. It is one of the most accurate public models available on the internet.

These models analyze historical data, player statistics, team metrics, and various other factors to predict game results. By using a predictive model, fans can move beyond gut feelings and make informed decisions based on statistical probabilities and trends.

Coaching and the Human Element

While advanced metrics provide valuable insights, they are not a substitute for coaching expertise and the human element of the game. The best coaches understand how to interpret and apply data to make informed decisions, while also considering the intangible factors that can influence game outcomes.

Ben Johnson, the new head coach of the Chicago Bears, is a prime example of a coach who can blend analytics with on-field leadership. Johnson’s success as the offensive coordinator for the Detroit Lions, where he guided the team to yearly averages of 26.6, 27.4, and 33.1 points per game, demonstrates his ability to maximize offensive talent. The Bears are hoping that Johnson can bring the same success to Chicago and help quarterback Caleb Williams reach his full potential.

The Future of NFL Analytics

As technology continues to evolve, the use of advanced metrics in the NFL will only become more sophisticated. New metrics will emerge, providing even deeper insights into player performance and game strategy. AI and machine learning will play an increasingly important role in analyzing data and predicting outcomes.

The NFL is also seeing a trend in lighter and faster defensive players. In 2024, the average playing weight among NFL defensive linemen and linebackers who played 200 or more snaps was 271.2 pounds, a decrease from 276.5 pounds in 2013. This shift reflects the need for better pass rushers and coverage defenders in a more pass-heavy league.

Conclusion

In the high-stakes world of the NFL, every advantage matters. Advanced metrics provide teams and fans with a deeper understanding of the game, enabling them to make more informed decisions and gain a competitive edge. As the 2025 season approaches, expect to see even greater emphasis on data-driven analysis, as teams continue to push the boundaries of what’s possible in their quest for victory.