NFL次世代統計の10年間の革新

A decade of NFL Next Gen Stats innovation

Every NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed.

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Every snap in the NFL triggers a deluge of physical data. Twenty-two players accelerate, collide, and change direction in fractions of a second, while the ball traces a path through the controlled chaos.

Yet for most of the sport’s history, much of that complexity went unmeasured. “Football, for 100-plus years, has been a box score game: you've got yards, you've got touchdowns, you've got tackles … ,” says Mike Band, senior manager of research and analytics with NFL’s Next Gen Stats.

Those numbers could capture only a sliver of what actually unfolded on the field. Coaches pored over game recordings and made educated guesses. Fans argued from the stands and the sofa. Officials occasionally made judgment calls based on partial, often obstructed views. “Looking at box score stats, you didn’t even know which 22 players were on the field for a given play,” says Mike Lopez, senior director of NFL Football Data and Analytics.

In 2015, the NFL decided to expand beyond box scores by launching Next Gen Stats (NGS). RFID chips were placed in every set of shoulder pads and inside the football, and more than 20 ultrawideband receivers were mounted around each stadium. The system began streaming the coordinates of all 22 players (10 times a second) and the ball (25 times per second). For the first time, the league was capturing comprehensive player location data, accurate to a few inches, for every moment of every play.

RFID chips were placed in every set of shoulder pads and inside the football, and more than 20 ultrawideband receivers were mounted around each stadium. At first, each club could access only its own tracking data. That shifted in 2018, when teams gained league-wide access, putting coaches, scouts, and analysts on common analytic footing. Also that year, the league formalized and deepened its partnership with AWS, marking the start of the gradual transformation of NGS from a tracking experiment into critical NFL infrastructure, with live broadcasts only its most visible expression.

Today, NGS underpins decision making across the league, from how clubs evaluate players and design game plans to how the NFL studies officiating, player safety, and rule changes. Every team, and much of the league itself, now works from the same continuously expanding data backbone.

But it started simply, says Band. “Our early metrics were low-hanging fruit — player separation, speed, and time to throw — easily derivable from the data we had. Modeling more-complex game metrics takes much more effort, and that’s where AWS came in.”

The first complex stat the partnership delivered, in 2018, was completion probability. It was built to answer a simple question: can the difficulty of a pass be quantified?

The answer came, in part, courtesy of an XGBoost machine learning (ML) model hosted on Amazon’s SageMaker platform. It blended the factors that shape a throw’s outcome, from quarterback pressure to throw depth, receiver separation, and sideline proximity. The model returned a single percentage that captured both likelihood and difficulty. “That became our entry point into machine learning,” Band says.

Beyond SageMaker, the NFL’s analytics work has expanded into a broad suite of AWS tools, including Amazon Quick, which the League uses to deliver real-time, interactive visualizations and answers to fans, analysts, and broadcast partners. Lopez says the members of the league’s football data analytics group “call ourselves an AWS shop.” By 2018, with league-wide access in place and AWS’s ML pipelines running, NGS began to illuminate deeper questions across the sport.

Every NFL game generates millions of raw-tracking data points, yet the raw feed is only the substrate. The real data growth comes from the models that convert coordinates into usable football insight. Pressure probability, for example, estimates how likely a defender is to affect the quarterback at each moment of a pass rush and produces more than a dozen secondary metrics.

Band estimates that NGS now produces between 500 and 1,000 stats — per play. Keeping the system responsive depends on AWS infrastructure to ingest the feed, run the models, return results within seconds for teams and broadcasters, and store the wider data trove for deeper analysis.

Overview of Amazon Quick Sight dashboard. The NFL uses the Amazon Quick’s agentic AI and BI capabilities to deliver real-time, interactive visualizations and answers to fans, analysts, and broadcast partners. Using the capabilities of Amazon Quick, the NFL provided unprecedented access to insights on player performance and draft predictions to over a million fans with their Combine IQ, Draft IQ, and Draft IQ Assistant experiences.

The roots of that deeper analysis extend back to 2018, with the inaugural Big Data Bowl. Led by Lopez, it became the league’s first large-scale effort to open player-tracking data to external researchers, inviting them to tackle questions such as which defenders close space most effectively or how to predict post-throw player movement.

Structured as a months-long hackathon, the annual competition challenges participants to train ML models on historical tracking data and test their ability to generalize to unseen plays. The emphasis is increasingly on prediction — models that can anticipate what would happen next.

An early success was the 2020 development of rush yards over expectation (RYOE). The metric measures the difference between actual yards gained and expected rushing yards, or what a league-average player would be predicted to gain on the same carry, considering the location, speed, and direction of blockers and defenders. It helps contextualize how strong a given run was and, when aggregated, how well a back performed over a game or season.

NFL Big Data Bowl Explained | AWS Events RYOE moved from the Big Data Bowl to national broadcasts quickly. Lopez recalls the moment he first saw it appear, during the 2021 NFC Championship Game between the Buccaneers and Packers: “Leonard Fournette had a good run, and immediately a graphic popped up with his rush yards over expectation. That was less than 10 months after we got the winning solution.” He adds: “I took a photo of my TV screen, and colleagues were sending me theirs. It was a proud moment.”

That pipeline has turned the Big Data Bowl into a proving ground for both ideas and data science talent. In its first decade, the Big Data Bowl has become a central part of the league’s analytics ecosystem. As then New Orleans Saints coach Sean Payton quipped in 2015 about the rise of real-time data on the sidelines, “ I think it means there are going to be more MIT grads coaching.”

Over the past decade, NGS has grown into a portfolio of more than 75 ML models, spanning offense, defense, special teams, and game strategy. Among those, tackle probability and defensive alerts perhaps best illustrate how raw tracking data can be converted into clearer insights for teams, broadcasters, and fans.

Tackle probability estimates the likelihood of a defender completing a tackle at the moment of contact, factoring in speed, angle, distance, leverage, and pursuit. That data allows NGS to identify true tackle opportunities, quantify missed tackles, and calculate the yards a defender saves or concedes.

Defensive alerts assess defensive alignment and movement before the snap to predict which players are likely to rush. The model uses acceleration patterns and presnap shifts, combines them with situational context such as down, distance, and game state, and then applies generative AI to predict likely rushers, who are highlighted with red circles for viewers.

“Defensive alerts had a big impact, from a broadcast perspective,” says Dashiell Flynn, AWS’s principal sports consultant. He highlights how the model exposes deliberate misdirection: “Sometimes the prediction is wrong because the defense itself is using misdirection, trying to trick the offense into thinking a blitz is coming.” Those moments give game commentators a natural way to discuss disguised defensive pressure and the intent behind it.

Together, these metrics show how NGS models can turn fast, ambiguous moments into clear visual and tactical explanations.

Overview of how Next Gen Stats uses data to make accurate predictions.

Player safety and rule changes

The same tracking foundation that fuels performance analysis also gives the league clearer visibility into player safety. By capturing every player’s speed, spacing, and movement, it gives the league a concrete understanding of the dynamics behind plays long considered risky.

The new dynamic kickoff, introduced for the 2024 season, is a clear example. Kickoffs were producing too many dangerous, high-speed collisions. NGS helped quantify and ultimately change that.

“The season before, we were showing Next Gen Stats animations of the space and relative speeds of the players, and that analysis became a critical part of the rules change,” says Lopez.

The NFL Competition Committee tested alternative formations and identified a design that reduced high-speed contact without removing the competitive element. Two seasons of data show the dynamic kickoff is working: the 2025 return rate jumped to 75% (from 32% in 2024), and even with 1,157 more plays, lower-extremity injuries dropped 35% while concussion rates remain below the old kickoff format. The change is delivering both more action and fewer injuries.

The infrastructure for the next major advance — optical tracking — is already embedded in every NFL venue. Rather than recording only a player’s two-dimensional location, the system uses 4K cameras to capture the full three-dimensional position of key joints such as shoulders, elbows, knees, hips, and hands.

The result is pose estimation, a digital skeletal model for every player on every play. This season marks the first year the league has had what Band calls “full installation, full capture” across every game, although the data remains internal while it is validated, structured, and stored for future use.

For the NGS team, pose estimation arrives at the right moment. A decade of two-dimensional tracking has deepened understanding of the game, Band says, “but this new skeletal data is going to unlock the next level. It’s an inflection point.”

The scale of the data capture is worth pausing over. Standard location tracking collects a single x,y coordinate for each player 10 times per second. Optical tracking captures high-resolution video from 16 angles to derive x,y,z coordinates for 29 body parts per player, 60 times a second. “The explosion in the volume of data can be daunting,” says Flynn. “But once folks wrap their heads around it, the ideas start flowing very quickly.”

The pipeline behind optical tracking runs in three stages: local capture, on-site processing, and cloud analysis. High-bandwidth video from 4K cameras cannot be sent to the cloud fast enough, so each stadium hosts AWS servers that process the data within about 700 milliseconds. The processed, simplified data is then sent to the cloud, where ML models run in under 100 milliseconds and return analysis to the production team. This keeps the full capture-to-analysis pipeline under a second. And because broadcasts such as Thursday Night Football operate with a roughly two-second delay, Next Gen Stats derived