Dissecting NFL Next Gen Stats: Advanced Metrics Driving Football Analytics

Author: Rahul Chaudhary

In a recent presentation by Mike Band, senior research and analytics manager at NFL Next Gen Stats, he outlined a robust suite of advanced metrics derived from real-time player tracking data (Watch the full presentation here). This post aims to unpack the key points related to the data models that could reshape our understanding of player performance and game strategy.

NFL Next Gen Stats Overview

Next Gen Stats employs RFID technology in shoulder pads to capture millions of data points per game—tracking speed, direction, and position at intervals of every tenth of a second. By utilizing these rich datasets, Next Gen Stats goes beyond conventional metrics, moving into elaborate statistical modeling.

The Evolution of Metrics

Initially, the organization derived basic stats like top speed and distance traveled. However, over the years, they’ve developed 15 models that pull from both tracking data and traditional football statistics to produce more nuanced metrics. The groundbreaking models fall into categories like completion probability, expected rushing yards, and win probability, among others.

Example Models

1. Completion Probability

   • Model Type: XGBoost
   • Key Features: Distance traveled by the ball, the receiver’s separation from the nearest defender, the velocity of both the quarterback and receiver, and location on the field.
   • Training Set: Seasons 2018-2021, tested on the 2022 regular and postseason.
   • Quantitative Insight: In one instance, the model indicated a completion probability of 11.5% during a critical game moment, highlighting how it contextualizes game scenarios with high precision.

2. Expected Yards

   • Model Type: Convolutional Neural Network (CNN)
   • Situational Models: Five different models account for distinct play types—handoffs, scrambles, etc.
   • Training Insight: Each situation’s distribution varies, warranting separate models to avoid biases. In practice, the model expected a gain of 3 yards on a play while the actual result was 30 yards, illustrating its capacity to contextualize expectations against real performance.

3. Win Probability

   • Model Type: XGBoost
   • Features: Score differential, time remaining, team strength.
   • Example: During the Vikings’ comeback where their win probability sunk to 0.4%—quantifying how low odds can be re-evaluated as games progress.

Considerations for Model Deployment

Next Gen Stats faces multiple challenges in operation:

• Real-Time Predictions: Models must compute results almost instantaneously post-play.
• Interpretability: The more complex a model (like deep learning approaches), the harder it becomes to clarify how features contribute to outputs. Yet, human intuition remains pivotal in validating model decisions.
• Consistency and Maintenance: Continuous model retraining is necessary; outdated dependencies can skew metrics. This means that maintaining performance over seasons is as critical as developing new models.

Summary of Key Insights

The robust statistical framework being employed by NFL Next Gen Stats is a game-changer in how analytics can influence strategies on and off the field. They possess 15 active models, each with unique focuses and methodologies which collectively enhance the understanding of player and team dynamics—something traditional box scores can no longer accomplish effectively.

The discussion reflects a growing trend in NFL analytics where traditional paradigms meld with advanced data science techniques, making actionable insights more accessible for teams, analysts, and fans alike.