NFL teams do not actually use GPS to track players in games. The league's official tracking system, Next Gen Stats, is built on radio-frequency identification (RFID) technology supplied by Zebra Technologies, and its precision — accurate within roughly six inches, versus about 39 inches for GPS, according to Zebra — is what makes the resulting workload data trustworthy enough to shape practice plans. Team staffs then use the resulting speed, distance and acceleration figures to manage how much physical work each player absorbs across a season, with the aim of preparing athletes for game-day demands without over-preparing them and increasing injury risk.
Introduction: The Data-Driven NFL Field
Performance monitoring has reshaped how NFL teams train. As the league's own football operations documentation describes, the NFL expanded player and ball tracking to "capture real-time data for every player — on every play, anywhere on the field," with Next Gen Stats — developed with Zebra Technologies and Wilson Sporting Goods, running on Amazon Web Services — providing clubs data to analyze trends and player performance. Athlete-monitoring devices, which the league has described as GPS devices in the context of practice, measure both the quality of work, such as the speed of a rep, and its quantity, keeping players and coaches aware of workload during practices and offseason sessions.
Sports scientists, working under head strength and conditioning coaches, translate those numbers into daily coaching decisions, and reporting on teams like the Cincinnati Bengals shows the process in action: a practitioner tracks players during practice and hands the data to position coaches.
Beyond GPS: The Precision of RFID Technology
The popular shorthand "GPS tracking" obscures a meaningful technical distinction. In games, the NFL's system is RFID: two to three small tags are installed in each player's shoulder pads, with additional tags on officials, pylons, sticks, chains and inside the ball, communicating with receivers installed in every venue. Reporting by The Athletic, citing Zebra Sports client services supervisor Dominic Russo, quantifies the difference: RFID is accurate within six inches, while GPS tracking data is accurate within about 39 inches — a gap that matters when teams are drawing conclusions from individual movements.
The league worked with team equipment managers to determine the best size and location of the shoulder-pad tags, and quarterbacks, along with Wilson and the Competition Committee, tested devices in game balls to ensure the chips would not affect the ball's flight. Practice monitoring is a separate ecosystem: teams including the Bengals, Panthers and Chiefs have used STATSports devices, worn in jerseys during the season and compression shirts in the offseason, for their own athlete monitoring.
Stage 1: Capturing Real-Time Player Movement
The capture infrastructure is dense by design. Every NFL venue houses 20 to 30 ultra-wideband receivers, and the tags in shoulder pads, on officials, on the pylons, sticks and chains, and in the football add up to an estimated 250 tracking devices present in a venue on game day. The system samples player location, speed, distance traveled and acceleration ten times per second and charts individual movements within inches. Running it requires people as well as hardware — a team of three operators works every game to confirm the tracking systems are functioning properly. The same RFID approach extends beyond NFL games: Zebra also collects tracking data at spring all-star events like the Senior Bowl, using roughly 20 sensors around the field that communicate with nickel-sized tags players wear beneath their shoulder pads.
The Three Stages of NFL Player Tracking Data Utilization
| Stage | What happens | Evidence basis |
|---|---|---|
| 1. Capture | RFID tags in shoulder pads and the ball communicate with 20–30 ultra-wideband receivers per venue; roughly 250 devices per game sample movement ten times per second, charting position within inches. | NFL Football Operations documentation |
| 2. Metrics | Raw location data is converted into speed, distance traveled, acceleration and deceleration, plus more than 200 new data points per play via machine learning on AWS. | NFL Football Operations; sports science writing on GPS metrics in football |
| 3. Application | Sports scientists analyze average workloads and their variability, identify each position's most demanding metrics, and advise coaches on practice structure and intensity to manage fatigue and injury risk. | League reporting on team practice monitoring; sports science practice guidance |
This model is a synthesis across sources rather than a process any single document labels as three stages, but it traces the actual path the data travels: from receivers in the stadium rafters, through derived metrics, to the position coach deciding how hard Wednesday's practice should be.
Stage 2: Deriving Key Performance Metrics
Capture is only the beginning; the value comes from turning raw coordinates into numbers a coach can act on. The league's system uses the raw data to automate player participation reports, calculate performance metrics and derive advanced statistics through machine learning on AWS — more than 200 new data points are created on every play of every game. The core physical outputs are speed, distance traveled and acceleration, captured at ten samples per second.
These quantify the physical demands placed on athletes during activity, and as one sports science analysis of GPS metrics in American football puts it, monitoring the quality and quantity of training and competition stress is essential to understanding the dose-response relationship between loads and adaptations. That information feeds decisions on training periodization, on reverse-engineering a sport's physical demands, and on managing stress during the season so athletes peak for competition.
Stage 3: Applying Insights for Workload Management and Injury Prevention
The application layer is where sports science meets the practice schedule. Practitioners track players during sessions, download the data, and analyze it alongside coaching staff to structure practices that manage training load. League reporting on team practice monitoring describes exactly this workflow: during the season, monitoring systems are placed in players' jerseys and removed after practice, and the resulting data goes to position coaches. The analytical approach involves descriptive measures of central tendency and variability — averages and spread of a player's workload — so practitioners understand both typical exertion and its swings across a season. Crucially, metrics are chosen by position: on a practice day built around higher-volume running, for instance, running distance within different velocity bands becomes the priority metric, because it reflects the most significant physical demands of that day for the position group being trained.
Load management also means respecting ceilings. Sports science guidance on preparing athletes warns that progressing a player to extremely high workloads they may encounter only once per season carries opportunity costs — fatigue and increased risk of injury during training and practice — while many practitioners plan around a player's "worst case scenario," generally the highest physical workload an athlete might experience within a given metric. The goal is to prepare for that worst case without over-preparing for it.
Optimizing Performance Through Data-Driven Load Management
NFL teams can make informed decisions on player workload and practice intensity to optimize performance and mitigate injury risk by consistently analyzing player tracking data. The mechanism is straightforward even if the protocols are proprietary: precise RFID capture yields trustworthy movement data, that data becomes speed, distance and acceleration metrics, and sports scientists convert those metrics into advice on practice structure and intensity. The measurable indicator of whether the approach is working is changes in player performance metrics or injury rates over a season, reflecting the effectiveness of data-driven load management strategies — the same game-day tracking infrastructure that powers broadcast graphics like top speed and yards covered also gives clubs the raw material to keep their players available and peaking when it counts.
