## 2 Materials and Methods  
NGS tracking data from 768 regular season games from the 2018-2020 seasons were retrospectively analyzed. Two RFID tags were affixed to the epaulet in the shoulder pads of every player; linemen had a third tag on their shoulder pads centered across the back. Tags were sampled at 10 Hz and used to quantify the locomotor activity of players during each game. While this technology was initially designed to capture movement during discrete plays, a substantial amount of activity is performed between plays as players run on and off the field. Therefore, the locomotor activity was collected while each individual was on the field of play starting with the opening kickoff through the end of the game. For this paper, the field of play was defined as within 0.91 m of the sideline and 4.57 m from the back of the endzone. Furthermore, a player had to participate in at least one play during the game to be included. To explore inter-positional movement demands, players were categorized into positions as designated by their respective NFL franchise: cornerbacks (CBs; \(n=455\)), safeties (Ss; \(n=450\)), defensive linemen (DLs; \(n=732\)), linebackers (LBs; \(n=667\)), offensive linemen (OLs; \(n=779\)), quarterbacks (QBs; \(n=182\)), running backs (RBs; \(n=418\)), tight ends (TEs; \(n=325\)), and wide receivers (WRs; \(n=663\)).  
Metrics calculated from the tracking data included distance, maximum velocity, high-velocity efforts and distance, acceleration efforts and distance. The operational definitions used to quantify these variables are detailed in Table 1. Values were summarized on a player-game basis to describe game demands while the player is on-field.  
The prior literature [22] found positional differences in maximum velocities; however, most authors used absolute thresholds to define sprinting and running velocities. An absolute threshold may indicate that certain positions never achieve sprinting velocities, failing to consider the individual sprinting capacity of the player. To account for distinct high-velocity capacities by positions, thresholds were determined relative to positional maximum velocities. Position-specific maximum velocity was defined as the 75 th percentile value observed in all player-games in order to estimate the top velocity capabilities of all players within a position. This percentile was chosen over an average or median to account for different opportunities for players to achieve high velocities. Some players may have only participated in a limited number of plays and were not afforded the opportunity to achieve their true maximum velocity during a game.  
Data are represented as mean \(\pm\) standard deviation. Distributions illustrate the movement demands of all NFL players. One-way ANOVA determined the positional group main effects for each metric. Post hoc Bonferroni tests were used for all the main effects detected by one-way ANOVA to determine the statistical significance between positions. Alpha intervals were set an a priori at \(p\)-value \(\leq\) 0.05 as the level of significance for statistical tests. Data were analyzed using the SciPy (1.5.1) and StatsModels (0.12.2) modules in Python v3.8.3.