Morphological and Performance Biomechanics Profiles of Draft Preparation American-Style Football Players

Abstract

Background/Objectives: Using advanced methodologies may enhance athlete profiling. This study profiled morphological and laboratory-derived performance biomechanics by position of American-style football players training for the draft. Methods: Fifty-five players were categorized into three groups: Big (e.g., lineman; n = 17), Big–skill (e.g., tight end; n = 11), and Skill (e.g., receiver; n = 27). Body fat (BF%), lean body mass (LBM), and total body mass were measured using a bioelectrical impedance device. Running ground reaction force (GRF) and ground contact time (GCT) were obtained using an instrumented treadmill synchronized with a motion capture system. Dual uniaxial force plates captured countermovement jump height (CMJ-JH), normalized peak power (CMJ-NPP), and reactive strength. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. Results: There was a between-group difference (F(26,80) = 5.70, p < 0.001; Wilk’s Λ = 0.123, partial η² = 0.649). Fisher’s least squares difference post hoc analyses showed that participants in the Skill group had greater JH, CMJ-NPP, reactive strength, and running GRF values versus Big players but not Big–skill players (p < 0.05). Big athletes had greater BF%, LBM, total body mass, and GCT values than Skill and Big–skill athletes (p < 0.05). Big–skill players had greater GCT asymmetry than Skill and Big players (p < 0.05). Asymmetries in running forces, CMJ eccentric, and concentric IMP were not different (p > 0.05). Morphological and performance biomechanics differences are pronounced between Skill and Big players. Big–skill players possess characteristics from both groups. Laboratory-derived metrics offer precise values of running and jumping force strategies and body composition that can aid sports science researchers and practitioners in refining draft trainee profiles.

1. Introduction

American-style football is a collision-based sport characterized by short bursts of power alternated with rest [1] in which players compete in different positions that have specific technical, tactical, and physical activity demands. The playing field is 91.4 by 48.7 m (100 by 53.3 yards). In the United States, the game is played at the university level in the National Collegiate Athletic Association (NCAA) and at the professional level in the National Football League (NFL). Players are divided into eight position groups: offensive linemen (OL), quarterback (QB), running backs (RBs), tight end (TE), wide receiver (WR), defensive backs (DBs), defensive linemen (DL), and linebackers (LBs), each with different tactical and physical demands [2,3] that require different body types to be successful [4,5,6]. Morphological and activity profiling has been conducted in American-style football at the NFL and NCAA division levels [4,5,6,7,8,9,10,11,12,13]. Since different player positions within the sport require different speed and movement demands, profiles are often position-specific as well. See

Table 1. General description of field-specific positional differences in American-style football.

Position	Description	Group
Offensive linemen	Protect the quarterback by blocking the defensive line of the opposition; typically, do not handle the ball (aside from the snap from the center lineman)	Big
Quarterbacks	Receives the ball to start the play, where he may run the ball himself, throw it to a player further up the field, or hand it to a ball carrier to run with it	Big–skill
Running backs	May be handed the ball to run with it, catch passes, or block	Skill
Tight ends	Hybrid position and may block or run routes and catch passes	Big–skill
Wide receivers	Run pass routes and catch the ball	Skill
Defensive backs	Cover wide receivers to break up passes and to make interceptions	Skill
Defensive linemen	Rush the quarterback, tackle runners to disrupt the play	Big
Linebackers	Tackle ball carriers, rush the quarterback	Big–skill

for a brief description of the sport-specific positional differences.

NFL try-outs are limited where players must meet strict eligibility requirements (e.g., be out of high school for at least three years and finish their NCAA eligibility). In the 2023 NFL draft, there were 259 players drafted across 32 teams [14]. Draft hopefuls train specifically for the combine where they will undergo a medical history and examination that includes body composition, psychological assessment, on-field position drills, and physical skills testing. However, players representing all positions are invited to the combine and, with very little exception, perform the same battery of tests. Specifically, the combine battery is made up of (a) a 36.6 m sprint with split times reported at 9.1 and 18.3 m, (b) vertical and horizontal jump distances, (c) an 18.3 m shuttle and three-cone drill times, and (d) a 102.1 kg bench press for maximum repetitions. The vertical jump and the 36.6 m sprint are indicators of lower body power and linear speed, respectively [15]. The vertical jump has been shown to predict success in the NFL, particularly in wide receivers [15]. The 36.6 m sprint is the most anticipated event and receives considerable media attention. Research shows that better performance has been associated with higher draft position [16] and that drafted players perform better than non-drafted players in the 36.6 m sprint [17].

Biomechanical markers of linear speed (disregarding the start) include short ground contact times (GCTs) and large vertical ground reaction forces (vGRFs) [18,19]. Mean GCTs of 0.102 s and 0.168 s have been reported for male elite sprinters [19] and university American-style football players [20], respectively. We selected average rather than peak vGRFs because they are subject less to intraindividual differences [21]. Bilateral deficit, or asymmetry in force production, is also a recommended biomechanical marker to study for sport performance and training optimization [22]. Subtle asymmetries are not always perceptible to the coach’s naked eye, even in experts, nor are they necessarily apparent to the athlete [23]. Thus, an advanced analysis of a player’s running pattern is necessary to assess symmetry in biomechanical factors such as vGRF and GCT. The same could be stated for the biomechanical variables of the vertical jump test.

The vertical jump has long been used to determine lower limb muscular power and is particularly accurate when force plates are used [24]. In addition to jump height, dual force plates allow for measurement of additional variables that provide useful insight into a player’s jump strategy, such as the modified reactive strength index (RSImod), peak power, which can be normalized to body mass for comparisons, and impulse asymmetry in both the eccentric (loading) and concentric (propulsion) phases [24,25,26]. RSImod provides greater insight into the neuromuscular and stretch-shortening cycle (SSC) function than jump height alone since it accounts for the time to take-off during a CMJ [24]. Participants with high RSImod values have been shown to produce a larger force, power, and velocity both eccentrically (during loading) and concentrically (during propulsion) and generate an impulse characterized by high force within a short time [24]. Physics dictates that impulse is equal to the change in momentum of an object. Thus, inquiry into the symmetry of impulse generation between player groups seems prudent.

Profiling can be a valuable means of identifying talent, strengths, and weaknesses, and helps in the design of optimal strength and conditioning programs [4,5,6,7,8,13,15]. Holistic testing that involves morphology, biomechanics, and performance improves the profile [7]. To our knowledge, we are not aware of any study documenting laboratory-based running kinetics in addition to jump biomechanics and body composition metrics in this population. This is relevant for strength and conditioning specialists responsible for designing, implementing, and supervising training programs for draft-eligible American-style football players [4,7,8]. Further, with the global growth of American-style football, results may provide scouts, coaches, and medical personnel with valuable data. Therefore, the purpose of this study was to characterize the morphologic and performance biomechanics characteristics of draft-eligible American-style football players. We hypothesized that Skill players would be characterized as the leanest and lightest with the shortest ground contact times and have the greatest jump heights, jump reactivity, run forces, and asymmetries in direct contrast to Big players. Finally, we expected a hybrid of characteristics in the Big–skill players.

2. Materials and Methods

Fifty-five players (age, 22.9 ± 1.0 yrs; height, 1.87 ± 0.06 m; mass, 103.5 ± 18.3 kg) who were in their first week of specialized training for the NFL draft at an off-campus performance center volunteered for this study. Participants represented three position groups, Big (e.g., offensive linemen; n = 17), Big–skill (e.g., tight end; n = 11), and Skill (e.g., wide receiver; n = 27), and had competed for different university teams in the United States. All participants had just completed their collegiate football season, were active players training 5–6x per week, and were cleared by licensed medical staff to participate in the study. Participants arrived at the lab in groups of three at a designated time. They wore their own compression shorts, running shoes, and T-shirt (shirt not worn for running trials). All data were collected over two days in the Sports Performance and Gait Science Laboratory at Nova Southeastern University. Figure 1 depicts a schematic of the protocol. The study was approved by the University’s Institutional Review Board (#2022-570) and conducted in accordance with the ethical standards of the Helsinki Declaration, and participants provided written informed consent.

2.1. Morphological Measurements

Body fat percentage (BF%), lean body mass (LBM; kg), and total body mass (kg) were measured with an InBody 270 multifrequency bioelectrical impedance device (InBody USA, Cerritos, CA, USA). InBody 270 is automatically calibrated upon device activation and uses its level indicator to ensure the unit is balanced. Participants were instructed to remove jewelry, void prior to testing, and refrain from eating or exercising within three hours prior to testing. Procedures were in accordance with the manufacturer’s guidelines where participants stood on the platform of the device barefoot with the soles of their feet on the electrodes. Participants then grasped the handles of the unit with the thumb and fingers while maintaining direct contact with the electrodes. The elbows were fully extended and the glenohumeral joints were abducted to approximately 30 degrees. See Figure 2. The duration of the test was less than one minute. Only 1 measurement was taken.

Following the morphological measurements, anthropometric measures were obtained according to the specifications of the Vicon Nexus Plug-in Gait lower body model (Vicon, Centennial, CO, USA). In brief, this included measuring pelvis, knee, and ankle widths using an anthropometer (Lafayette Instruments, Lafayette, IN, USA) and leg length using a standard fiberglass tape measure.

In groups of three, the participants then underwent a standardized 25 min. warm-up administered by a single coach that consisted of dynamic stretching, muscle readiness, and reactivity exercises on an outdoor runway next to the laboratory. After the warm-up, sixteen 14 mm retroreflective markers were placed bilaterally on each participant’s pelvis and lower limbs according to the specifications of Vicon’s Plug-in Gait lower body model.

2.2. Running Kinetics

Running kinetics were captured in a laboratory using an instrumented split-belt treadmill (Bertec Corporation, Columbus, OH, USA) collecting at 1000 Hz that was synchronized with a 10-camera 3D motion analysis system (Vicon, Centennial, CO, USA) sampling at 120 Hz. The motion capture system was calibrated at the start of each testing day using an active wand with light-emitting diodes that was waved in the capture space, which had three markers on the treadmill, to indicate the global coordinate system. The calibration acceptance threshold for each camera was ≤0.2 mm. Further, each participant underwent local calibration after their markers were applied by standing still in the capture volume (on the left side of the treadmill) with their arms folded across their chest and their feet at hip’s width apart for 1–2 s while the cameras captured the marker coordinates. After local calibration, the participant was instructed to step off the treadmill. At this time, the treadmill plates were zeroed on the amplifiers and within the Nexus software (ver. 2.15) for calibration. Following treadmill calibration, the participant was instructed to step back on the treadmill on the right, non-collection side.

The running trial took place on the left side of the treadmill over a single moving belt. The belt’s speed was started by the researchers at 1 m/s (walking velocity). The participant then transferred to the moving belt when comfortable while holding onto the handrail fixed at chest height of the treadmill. Then the treadmill speed increased 1 m/s in one-second increments until 6.5 m/s was reached, which was the maximum afforded by the treadmill. Participants were instructed to release their grip when they were comfortable, and vGRFs and contact times were collected for 5 sec at 6.5 m/s. All participants in this study ran at 6.5 m/s. See Figure 3. Five seconds was selected to mimic the 36.6 m run durations at the NFL combine. Participants then transferred their weight to the non-moving belt, and the treadmill belt was decelerated to a stop. Only one running trial was collected from each subject that included 6–8 steps from both the right and left sides. All trials were initially processed with Vicon Nexus software (ver. 2.15) with a low-pass third-order Butterworth filter with a cut-off frequency of 30 Hz to minimize the attenuation of impact GRFs [27]. A custom MATLAB (MathWorks Inc., Natick, MA, USA) program was used to obtain the peak vGRF and the GCT. The GCT was a measure of continuous foot–ground contact time in milliseconds (ms) with the treadmill when the vGRF exceeded 40N [19]. vGRF data were normalized using participants’ body weights (BWs). Interlimb asymmetries in percentages were calculated between the vGRF and GCT using the following Equation [26,28]:

$$Asymmetry=ABS\left[{\displaystyle \frac{(Right-Left)}{0.5(Right+Left)}}\right]\times 100$$

2.3. Countermovement Jump Measurements

Countermovement jump kinetics were captured in the laboratory at a separate station using dual uniaxial force plates (FD Lites, Vald Performance, Queensland, Australia) sampling at 1000 Hz. The force plates were calibrated prior to each trial for each participant according to manufacturer’s guidelines, which included zeroing the plates and weighing the participant. Familiarity with force plates by the participants was varied and was not documented for this study. However, all participants were highly experienced with countermovement jumping.

Participants were given 1–2 submaximal practice CMJs using a dowel held horizontally across the posterior shoulders. Then, two maximal effort CMJ trials with the dowel were performed and recorded. The time between test jumps was approximately 2–3 s. See Figure 4. ForceDecks software (ver. 1.8.6, Vald Performance, QLD, Australia) was used to calculate the averages of both CMJs for the variables that are described in

Table 2. Description of countermovement jump variables collected using dual uniaxial plates.

Variable	Units	Definition
Jump height	cm	Maximal displacement of the participant’s center of mass (CoM); (take-off velocity)2/2(9.81 m/s2)
Reactive strength index, modified	m/s	Ability to change quickly from eccentric to concentric contraction; jump height/contraction time
Normalized peak power	W/kg	Highest power output during the jump relative to a participant’s body mass
Concentric impulse asymmetry	%	Absolute difference in the right and left limbs in total work from the point of the lowest portion of the CoM to the point of take-off, or the propulsion phase
Eccentric impulse asymmetry	%	Absolute difference in the right and left limbs in total work from the point the movement starts to the lowest point of the CoM depth

. ForceDecks uses a 20 N offset from the measured bodyweight, which was quantified before all jump trials to define the start of the movement. The end of the eccentric phase and start of the concentric phase was defined as the minimum absolute displacement (zero velocity), and take-off was defined as the time point at which the total vertical force fell below the threshold of 30 N below bodyweight [29]. Interlimb asymmetries in the concentric and eccentric impulse (IMP) were calculated using the same equation as for the running kinetics. The investigators used the absolute value of the asymmetry results since the side of the asymmetry was not of interest in this study. The CMJ landing was not analyzed in this study.

2.4. Statistical Analysis

All statistical analyses were performed using the Statistics Package for Social Sciences (ver. 28; IBM Corporation, New York, NY, USA). Descriptives (means ± standard deviations (SDs) and 95% confidence intervals (CIs) were calculated by position groups (Skill, Big–skill, and Big). A multivariate analysis of variance (MANOVA) with Least Square Difference (LSD) post hoc for multiple pairwise comparisons was used to calculate differences in the morphological and biomechanics performance characteristics between groups (p ≤ 0.05). Effect sizes (d) were also calculated for the pairwise comparisons. An effect size of less than 0.2 was considered trivial, 0.2–0.6 a small effect, 0.6–1.2 a moderate effect, 1.2–2.0 a large effect, 2.0–4.0 a very large effect, and 4.0 and above an extremely large effect [30].

3. Results

Table 3. Morphological characteristics (means ± SDs; 95% CIs) by player position group and pairwise comparison results.

Player Position Group	Total Body Mass (kg)		Lean Body Mass (kg)		Body Fat (%)
Skill (n = 27)	89.3 ± 7.6 b (85.8, 92.7)		80.9 ± 6.9 b (78.6, 83.3)		9.3 ± 2.8 b (7.6, 10.9)
Big–skill (n = 11)	105.6 ± 4.0 b (100.2, 111.0)		93.8 ± 4.6 b (90.1, 97.5)		11.1 ± 3.4 b (8.6, 13.7)
Big (n = 17)	126.7 ± 12.5 a (122.4, 131.1)		102.3 ± 5.5 a (99.5, 105.3)		18.8 ± 6.2 a (16.7, 20.8)
Pairwise Comparisons
	p value	d	p value	d	p value	d
Skill vs. Big–skill	<0.001	2.68	<0.001	2.20	0.233	0.58
Skill vs. Big	<0.001	3.62	<0.001	3.55	<0.001	1.97
Big–skill vs. Big	<0.001	2.27	<0.001	1.77	<0.001	1.54

Note: Group means with same lowercase letter are not statistically different from one another, p > 0.05.

,

Table 4. Running biomechanics characteristics (means ± SDs; 95% CIs) by player position group and pairwise comparison results.

Player Position Group	Running GCT (sec)		Running GCT Asymmetry (%)		Running Force (BW)		Running Force Asymmetry (%)
Skill (n = 27)	0.15 ± 0.02 b (0.14, 0.16)		3.3 ± 3.6 b (1.8, 4.6)		2.94 ± 0.31 a (2.82, 3.1)		5.2 ± 4.4 a (2.8, 3.1)
Big–skill (n = 11)	0.16 ± 0.02 b (0.15, 0.18)		4.3 ± 3.1 a (2.2, 6.4)		2.73 ± 0.28 ab (2.54, 2.92)		7.6 ± 11.3 a (2.6, 3.0)
Big (n = 17)	0.20 ± 0.03 a (0.19, 0.21)		1.5 ± 2.4 b (0.2, 2.8)		2.68 ± 0.35 b (2.52, 2.83)		5.4 ± 4.7 (2.5, 2.8)
Pairwise Comparisons
	p value	d	p value	d	p value	d	p value	d
Skill vs. Big–skill	0.430	0.50	0.485	0.30	0.233	0.71	0.381	0.28
Skill vs. Big	<0.001	1.96	<0.001	0.59	<0.001	0.79	0.020	0.04
Big–skill vs. Big	0.001	1.57	<0.001	1.01	<0.001	0.16	>0.999	0.25

Notes: Group means with same lowercase letter are not statistically different from one another, p > 0.05. GCT = ground contact time. BW = body weight.

and

Table 5. Countermovement jump biomechanics characteristics (means ± SDs; 95% CIs) by player position group and pairwise comparison results.

Player Position Group	CMJ Height (cm)		RSImod (m/s)		Normalized Peak Power (W/kg)		Eccentric Impulse Asymmetry (%)		Concentric Impulse Asymmetry (%)
Skill (n = 27)	46.7 ± 5.8 a (43.2, 47.6)		0.62 ± 0.12 a (0.57, 0.66)		63.2 ± 4.7 a (61.2, 65.1)		9.0 ± 7.4 a (6.5, 11.6)		5.7 ± 6.5 a (3.4, 8.1)
Big–skill (n = 11)	42.3 ± 3.5 ab (38.8, 45.5)		0.52 ± 0.07 ab (0.45, 0.60)		59.0 ± 2.0 ab (56.0, 61.9)		9.5 ± 5.9 a (5.6, 13.5)		6.5 ± 5.7 a (2.8, 10.1)
Big (n = 17)	38.9 ± 6.7 b (36.7, 45.7)		0.48 ± 0.13 b (0.42, 0.54)		55.3 ± 6.4 b (52.9, 57.7)		7.9 ± 5.5 a (3.1, 9.0)		6.1 ± 5.6 a (4.7, 11.1)
Pairwise Comparisons
	p value	d	p value	d	p value	d	p value	d	p value	d
Skill vs. Big–skill	0.430	0.92	0.106	1.02	0.063	1.16	>0.999	0.07	>0.999	0.13
Skill vs. Big	<0.001	1.24	<0.001	1.12	<0.001	1.41	>0.999	0.17	>0.999	0.07
Big–skill vs. Big	0.001	0.64	0.957	0.38	0.180	0.78	>0.999	0.28	>0.999	0.07

Notes: Group means with same lowercase letter are not statistically different from one another, p > 0.05. CMJ = countermovement jump. RSImod = modified reactive strength index.

display the results for the morphological characteristics, running biomechanics, and CMJ biomechanics for each position group, respectively.

3.1. MANOVA Group Differences

There was a significant between-group difference in the characteristics (F(26,80) = 5.70, p < 0.001; Wilk’s Λ = 0.123, partial η² = 0.649), indicating that player position group moderately affects morphology and speed running and jumping biomechanics performance. LSD post hoc analyses showed Big players had greater BF%, LBM, total body mass, and GCT values than Skill (large effects) and Big–skill (large effects) players (p < 0.05). Big–skill players had greater GCT asymmetries than Skill (small effect) and Big (large effect) players (p < 0.05). Skill players had significantly greater JH, CMJ-NPP, reactive strength, and running force values than Big (moderate and large effects) players (p < 0.05) but not Big–skill players (p > 0.05). Asymmetries in running forces, CMJ eccentric, and concentric IMP were not different between groups (p > 0.05).

3.2. Radar Plot

Figure 5 is a radar plot constructed from the Z-scores of each of the 12 characteristics to visually display player position group differences.

4. Discussion

This is the first study to provide a detailed profile of laboratory-based morphological and performance biomechanics characteristics of NFL draft-eligible American-style football players. Previous investigators have detailed the characteristics of morphological and/or field-based performance in junior college [31], NCAA Division I [6,7,32,33], Division II [33], Division III [34,35], and professional [4,8,13] players by position, providing context for the current study. The inclusion of speed running kinetics derived from an instrumented treadmill in the current study is unique and adds greater detail to the force application strategy used by players of different positions. The main finding of the study is that there are distinctions in laboratory-based measurements between Skill, Big–skill, and Big groups of American-style football players that may strengthen profiling. Big players possessed the greatest morphology characteristics, whereas Skill players produced better performance characteristics, such as the smallest speed running GCT and the highest CMJ height, power, and reactive strength. The Big–skill group possesses a combination of the Big and the Skill group characteristics. Specifically, regarding the morphology measurements, we hypothesized that players in the Big group would be heavier (greater total body mass and LBM) and display greater BF% values when compared to the Big–skill and Skill players. This hypothesis was confirmed. For the speed running biomechanics, we hypothesized that Big players would have longer GCTs and greater GCTs and force asymmetries but smaller normalized forces when compared to the other two groups. This was partially supported, as Big players had significantly longer GCTs than Big–skill and Skill players. Further differences will be discussed in the next section.

For the CMJ performance biomechanics, we hypothesized that Skill players would have the highest JH, RSImod, CMJ-NPP, and IMP asymmetries when compared to Big–skill and Big players. This hypothesis was generally supported, but comparisons were not statistically significant. The profiles will be discussed in more detail in the following sections.

Collection of such information is currently uncommon due to equipment expense and accessibility, time constraints, and required expertise. However, with the emergence of markerless motion capture technology, highly trained sport scientists working with teams, and the desire by coaches and athletes for greater specificity of performance, we anticipate data from the current study being used by future practitioners and researchers. Further, in addition to CMJ peak power, we have presented impulse asymmetry measurements that may not only be of interest to the sports science and performance staffs, but also to athletic trainers and sports physiotherapists.

4.1. Big Player Group

Big players are offensive and defensive linemen. Strength and large physical characteristics are advantageous for linemen given their field position requirements. It is widely recognized that body sizes in American-style football have been increasing over time [10,36]. The weight at all positions except kickers has significantly increased over time, with offensive linemen experiencing the greatest change [36]. One key factor is that there are no weight limits in the NFL. If they have the strength to hold the line or push through the line of scrimmage, a larger weight would be more difficult for the opponent to stop. However, a balance between increasing body fat percentage and lean body mass is essential. Increasing one’s overall body mass correlates with decreased endurance performance [37,38,39,40,41], whereas increasing one’s lean body mass also correlates with regional increases in strength [25,39]. To be an effective lineman, players need the impulse (force x time) and muscular strength to push against a force that is also pushing against them and compete throughout all four quarters of the game. While body mass is important for these players, it comes at a cost to their agility. These players tended to exhibit longer running ground contact times with a lower CMJ height, CMJ reactive strength, and CMJ normalized peak power, suggesting there is a trade-off between fast speed and a higher vertical jump and large mass and increased strength. Examining participants at the NFL combine from 2005 to 2009, the Big players scored better than all other positions on tests involving strength, such as the maximum repetition bench press and predictive measure of maximal strength, while scoring lower than all other positions in the speed and jump tests [13]. An interesting biomechanical finding within the Big players is their running GRFs when they are normalized to body weight. These players have the largest weight and are expected to have a greater absolute GRF, but when normalized, they tend to have the lowest relative GRF, suggesting a possible dampening of the force. Previous studies have noticed this finding in obese recreational runners [42] and a similar group of American-style football players [43]. However, future studies need to be conducted to further investigate this trend since this player group has shown significant increases in mass over the last 50 years but is running faster at the combine [7]. Big player training focuses on increasing weight and strength. Even though these players need to be well-rounded in all their performance measures, they need to excel at strength and physical explosiveness tests to compete with the other linemen.

4.2. Skill Player Group

Athletes categorized in the Skill player group, such as wide receivers and defensive backs, have completely opposite characteristics. Opposed to favoring a large body mass habitus like the Big players, Skill players tend to display biomechanics associated with improved speed and agility. They have lower ground contact times and greater CMJ heights, reactivities, and normalized peak powers, which are ideal qualities for the quick movements required of their positions. From previous NFL combine data, cornerbacks and wide receivers performed better than all other positions in the 36.6 m sprint, and similarly, cornerbacks, free safeties, and strong safeties all performed better than all other positions on the CMJ vertical jump [13]. Improving one’s speed, specifically for wide receivers during the 9.1 m (10 yards) and 18.3 m (20 yards) sprints, is correlated with a better draft position [43], while having a better CMJ could help increase the catch radius for these players. Each Skill player position has a unique job on the football field, but collectively, they all require quickness and mobility to compete effectively. On the other hand, their body mass characteristics tend to be smaller than the those of the players in the Big group. There are some inter-positional differences in body characteristics, but when examining intra-positional differences, offensive and defensive positions that mirror each other on the football field share similar body composition measures, such as total fat mass and lean body mass [11]. Having a similar body mass and biomechanics between mirroring positions allows for competitive physicality when going after the football. If a cornerback is slower or cannot jump as high as the wide receiver, the offense would continuously outperform the opponent’s defense. It is important to note that the body mass characteristics may be lower than the larger linemen, but their mass is generally greater than age-matched controls [36]. The combination of exercise and weightlifting places increased metabolic stress on the body, and these players need to routinely replenish the lost calories with a high-calorie diet. Like the linemen, a balance should exist within their training. Strength training is important, but improving biomechanics relating to speed and agility may help the player stand out from others in the same position.

4.3. Big–Skill Player Group

The Big–skill players, like tight ends, are athletes who have qualities similar to both linemen and wide receivers in addition to their mirrored defensive counterparts. Their body mass characteristics and CMJ attributes were in between the two groups. Tight ends, specifically, weigh significantly less but match a similar height as the linemen, and when compared to wide receivers, they are significantly taller and heavier [11]. These traits make this position very versatile. They have the mass to block linebackers and other defensive players but also can run quick passing routes as a receiver. In predictive models, tight ends with an increased height, BMI, bench press, total college yards, and decreased forty-yard dash times are predicted to have a better draft order [44,45]. Many variables play into the selection process, but a true balance between large morphologic characteristics, strength, and improved biomechanics for speed and agility are important for players in the Big–skill position. Interestingly, this study found a unique characteristic of greater asymmetry amongst positions in the Big–skill group. They produced the greatest percentage of asymmetries for eccentric impulse, concentric impulse, running GCT, and running force. This may reflect the variability of their field position requirements. However, reducing these asymmetries may improve performance at the NFL combine for the 36.6 m sprint and the vertical jump.

4.4. Limitations

This study presents novel and precise measurements obtained through advanced methods. However, within the context of laboratory-derived measurements, it is not without limitations. The first limitation is the control of the instrumented treadmill speed. This was used for two reasons: (a) the treadmill can only achieve 6.5 m/s, and (b) to control for the effects of interindividual speed differences that would influence the ground contact times and ground reaction force magnitudes [19,21,27]. However, 6.5 m/s may not have been the maximum speed participants could attain, and the results may not transfer to higher running speeds. The second limitation is the restricted performance test selection. Only the biomechanics of running and vertical jumping were chosen. Including broad jump, agility, and/or upper body performance biomechanics utilizing force plates and/or velocity-based measurement units would strengthen the profiles and allow for greater generalization to the NFL combine test battery. Finally, the countermovement jump was performed with a leg-only focus without an arm swing. We selected this variation to reduce any negative influence of coordinating the arm swing with the jump. Including the arm swing in future analyses will provide more construct validity, as it will resemble the vertical jump test at the NFL combine. Within the context of these limitations, this is the first study to provide a laboratory-based analysis of NFL draft preparation players. Results indicated that the player group characteristics of morphology and performance are typical of American-style football players. However, the results also highlight the kinetic asymmetries in the Big–skill group that could be targeted for improvement prior to combine testing.

5. Conclusions

This study described differences in the body compositions and laboratory-based biomechanics markers of speed-running and countermovement jumping between three position groups (Big, Big–skill, Skill) in American-style football players training for the NFL draft. The morphological differences are similar to those shown by university and professional players, with Big players having more mass and greater body fat percentages than Big–skill and Skill players. Jump height and reactivity measurements were also similar to previous research findings, with Skill players jumping higher and being more reactive and powerful (when power was normalized to body mass). The asymmetry in the eccentric and concentric phases of the CMJ were novel measurements in profiling, but they did not yield differences between player position groups. The running kinetics obtained on an instrumented treadmill were also novel and showed value in distinguishing Big players as having longer GCTs, larger GCT asymmetries, and lower normalized GRFs. Big–skill players had the largest GRF asymmetries. Future studies may consider additional laboratory-based tests that are associated with the combine battery (e.g., broad jump, agility) as well as longitudinal monitoring to determine if profiles change with training.