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. 2025 Aug 26;60(8):551–559. doi: 10.4085/1062-6050-0068.24

Hawaiian High School Football Player Positional Differences in Head-Impact Characteristics

Kiera D Glodowski *,✉, Bret Freemyer *, Jay L Myers †, JongSoo Lee ‡, Steven P Broglio §, Troy Furutani *, Nathan Murata *, Cris Stickley *, Erik E Swartz ‖
PMCID: PMC12418481  PMID: 39623558

Abstract

Context

The importance of analyzing head-impact exposure among football players is well established, but few authors have explored the differences across position groups in high school athletes. Better understanding of these differences may provide optimized intervention strategies for coaches and health care providers.

Objective

To compare differences in head-impact characteristics (eg, frequency, severity, and location) among position groups and differences in teams, session types, and seasons across 2 high school varsity football seasons in Hawaii.

Design

Cohort study.

Setting

High school athletic fields during fall sports seasons.

Patients and Participants

A total of 200 football players from 3 high school varsity teams, including 69 offensive and defensive linemen; 51 linebackers, running backs, and tight ends; and 80 cornerbacks, safeties, and wide receivers (age = 16.1 ± 0.9 years, height = 177.9 ± 7.8 cm, mass = 86.4 ± 22.7 kg), categorized as linemen, backers, and skill players, respectively.

Main Outcome Measure(s)

Head impacts per exposure (Imp/E) across positions, teams, session type, and seasons; accumulated total impact burden; and cumulative head-impact burden per location (front, top, right, left, and back) across position groups.

Results

Differences in Imp/E were found among position groups, with backers (mean = 3.77; 95% CI = 3.146, 4.395) having greater total Imp/E compared with linemen (mean = 1.47; 95% CI = 0.983, 1.96; P < .001) and skill players (mean = 1.56; 95% CI = 1.11, 2.01; P < .001). We observed a difference in total accumulated head-impact burden (F2,194 = 4.938, P < .008), with backers (mean = 4622.85g; 95% CI = 3077.43g, 6168.27g) having a greater total accumulated burden than linemen (mean = 2657.70g; 95% CI = 2045.61g, 3269.19g; P = .01) and skill players (mean = 2875.7g; 95% CI = 2216.38g, 3535.01g; P = .02). Accumulated front impact burden was different among position groups (F2,194 = 7.784, P < .001), with backers (mean = 1606.24g; 95% CI = 977.89g, 2234.58g) having greater accumulated burden than linemen (768.24g; 95% CI = 433.84g, 1102.64g; P = .008) and skill players (567.75g; 95% CI = 360.71g, 774.78g; P < .001).

Conclusions

Linebackers, running backs, and tight ends experienced more Imp/E and greater cumulative burden than other positions, which highlights the potential influence of specific positional requirements during football participation. Coaches and health care providers should be aware that players’ positions and roles during play may directly relate to changes in head-impact risk.

Keywords: head-impact frequency, head-impact burden, traumatic brain injury

Key Points

  • As a group, linebackers, running backs, and tight ends experienced more impacts per exposure than high school football players in other positions.

  • Total impact burden over a season was greater in linebackers, running backs, and tight ends.

  • Analyzing head impacts per exposure identified factors contributing to increased frequency of head impacts, which should lead to improved interventions to reduce risks across various teams and styles of play.

Head-impact exposure (HIE) in American football has been a topic of increasing concern due to its suspected relation to longer-term health consequences and neurocognitive sequelae later in life.1 Head-impact exposure is a multifactorial term, synonymous with subconcussive impacts or head-acceleration events, that may refer to the frequency of head impacts (eg, number of head impacts per session), the magnitude of the impacts (eg, peak linear acceleration), the location of the impacts, or the cumulative history of head impacts sustained over time.2 Various strategies have emerged to analyze, address, and mitigate HIE, including deploying tackling interventions, limiting live contact during practice sessions, and using technologies such as head-impact telemetry to monitor exposure.3–6 Head-impact telemetry devices have been recommended as a tool to identify and decrease head-first contact behavior in football exposure.6,7

The monitoring of head impacts in collision sports such as football has been an area of increased research focus, likely due to the emerging concerns with the health risks associated with repetitive head impacts.8–11 Researchers from youth, collegiate, and professional sports levels have monitored head impacts to describe the kinetic and kinematic properties such as impact location, frequency, linear and rotational velocity, and acceleration.2,12,13 Investigating and quantifying these values in varying populations associated with HIE enables stakeholders to guide prevention measures, improve helmet designs, and enhance understanding of brain injuries.10

In previous research within youth football, investigators described the influence of years of experience on HIE, suggesting that seasonal head impacts appeared to increase along with increasing levels of play.14 However, HIE of youth football players compared with high school players varies from season to season due to positions, levels of physical maturation changing throughout the years, and overall skill development.15 In a study of youth football players followed for 8 consecutive seasons, Dorman et al concluded that the number of head impacts players sustained decreased in their last season compared with the first season of data collection, implying the possible influence of safe tackling and behavior modification statewide, starting at a young age.16

Researchers have observed HIE and head-impact biomechanics in high school football players and noted that position groups depict general differences seen in biomechanical properties of HIE, such as linear and rotational acceleration, overall frequency, and head-impact telemetry severity profile.4,12 However, the independent positions or player groups were not involved in a head-impact frequency comparison.4,12 Across several studies, both offensive and defensive lineman have been found to have the most head impacts during games or practice sessions.12,17,18 Head impacts within collegiate football players vary by position, but linemen consistently have the most head impacts, often twice as many as players in other positions.2,19 This suggests that the likelihood of head-impact frequency, force, and location can be influenced by player position at any level of competition.20

Better understanding of specific position-group differences may allow for potential behavior modifications and head-, neck-, and spine-injury risk reduction in the sport. Therefore, the primary purpose of our study was to compare head-impact characteristics (eg, frequency, severity, and location) among position groups across 2 high school varsity football seasons in Hawaii. Our secondary purpose was to explore differences among teams, session types (games versus practice), and seasons, where appropriate.

METHODS

Study Design and Participants

We implemented a prospective observational design (Clinicaltrials.gov ID: NCT0404020874). This cohort study was part of a larger investigation that involved a tackling and blocking training intervention deployed among high school football participants in Hawaii between 2019 and 2022.

We recruited participants across 2 seasons of play and included varsity-enrolled football players from 3 high school teams (2 private and 1 public) in Honolulu, Hawaii. Players and parents provided written informed assent and consent, respectively, and the study was approved by the Institutional Review Board of the University of Hawai‘i at Manoa.

Procedures

Participants were issued new, properly fitted Riddell SpeedFlex helmets (Riddell Sports Group, Inc) embedded with a Riddell InSite head-impact system sensor. The InSite system consists of an overliner sensor that lies atop the helmet air bladders underneath the outer shell. Sensors connect via Bluetooth (Bluetooth Special Interest Group) to a handheld monitor that communicates real-time head-impact data triggered by impacts >14g. This system is used to record impact frequency, magnitude level (low, 15g–19g; medium, 20g–28g; high, 29g–43g; alert [95th percentile alert], 44g–63g; and high alert [99th percentile alert], >63g), and location (front, top, back, and right and left sides); has been reported as highly correlated with Hybrid III head form impact measurements; and has been cited in related studies.21–23 An on-site field research assistant (not an author) fitted helmets and collected relevant time data for each session (practices and games) throughout the season, such as start and stop times during any session when helmets were worn (eg, full versus limited contact practices), to standardize impact frequency (ie, head impacts per session). This step permitted the identification and elimination of spurious impacts occurring outside of monitored session time windows, such as before practice or during halftime. Head-impact data were regularly exported from the InSite system and filtered for spurious impacts using Excel (Microsoft 365 MSO version 2502 build 16.0.18526.20168; Microsoft Corporation). Exports were then cross-referenced with participant-exposure start and stop times and overall team attendance. A participant-exposure was defined as an athlete who wore the sensor helmet and participated in one team session for any length of time. Any participant data collected when the participant did not participate in a team session but still wore a helmet (eg, athlete return-to-sport protocols after injury or acclimatization periods) were excluded from the analysis.

Impact data were reduced and organized according to participants’ primary self-reported position and divided into position categories: linemen (offensive and defensive linemen), backers (tight ends, running backs, and linebackers), and skill players (cornerbacks, safeties, and wide receivers). These groupings and position assignments represent 3 zones of distance from the ball at the line of scrimmage. Linemen are generally in close-quarter engagement with blocking and tackling at the line of scrimmage, whereas backers and skill players are more spread out with open-field play at variable closing distances on the ball. Tight ends were assigned to the backer category because they are more midfield players with a mix of blocking, receiving, and ball carrying. The total number of impacts was divided by the total number of exposures for standardization. Accumulated impact burden was compiled by summing the midpoint magnitude value (eg, 17g [low]) in all 5 levels for each of the 5 impact locations over the weeks of the season.24

Statistical Analyses

The independent variables were position group (linemen, backers, and skill players), session type (total, games, and practices), helmet-impact location (front, top, back, and left and right sides), team (A, B, and C), and season (2019 [year 1] and 2021 [year 2]; no data were collected during 2020 because of the COVID-19 pandemic). Data were collected for teams A and B in year 1 and teams B and C in year 2. The dependent variables were impacts per exposure (Imp/E), accumulated total impact burden (in gravitational acceleration), and accumulated impact burden per location (in gravitational acceleration). Normality was assessed using the Shapiro-Wilk test, and equality of variance was assessed via the Levene test. A 3-way analysis of variance with a follow-up Tukey post hoc honestly significant difference test was performed to identify any differences across the independent variables and adjust the level of significance as needed. The α level was set at .05 for all analyses. All statistical analyses were completed using SPSS software (version 28.0.1.1; IBM Corporation).

RESULTS

A total of 200 football players (age = 16.1 ± 0.9 years, height = 177.9 ± 7.8 cm, mass = 86.4 ± 22.7 kg) from 3 high school varsity teams across 2 seasons participated. Included were 69 linemen, 51 backers, and 80 skill players. Participants with <20 head impacts (n = 5) and those who appeared to have malfunctioning head-impact sensor equipment (n = 2) were excluded from analysis. The maximum potential total exposures from practices and games available for participants was 97 (86 practices and 11 games) in season 1 and 102 (91 practices and 11 games) in season 2.

Normality Assessments

We observed a difference in the variances of each dependent variable (total, game, and practice Imp/E) based on the Levene test (P < .001). The distribution of Imp/E for each variable is presented in Figure 1. Results of the Shapiro-Wilk analysis confirmed that the dependent variables were not normally distributed (P < .001). This result was consistent across both seasons.

Figure 1.

Figure 1

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Head-impact frequency histograms scaled by A, total, B, game, and, C, practice exposures.

Head-Impact Frequency per Exposure

A difference was found for total Imp/E (ie, combined practice and game exposures) by position groups (F2,188 = 19.777, P < .001; Figure 2A). The post hoc analysis confirmed that backers (mean = 3.77; 95% CI = 3.146, 4.395) had greater total Imp/E than linemen (mean = 1.47; 95% CI = 0.983, 1.96; P < .001) or skill players (mean = 1.56; 95% CI = 1.11, 2.01; P < .001), whereas linemen and skill players were not different from each other (P = .80). We observed a difference for game Imp/E by position (F2,185 = 12.991, P < .001). Post hoc analysis confirmed that game Imp/E was greater for backers (mean = 7.62; 95% CI = 6.01, 9.23; P < .001) than linemen (mean = 2.77; 95% CI = 1.51, 4.03; P < .001) and skill players (mean = 2.96; 95% CI = 1.78, 4.13; P < .001; Figure 2B). A similar pattern was found for practice Imp/E (F2,188 = 16.805, P < .001). Backers (mean = 3.19; 95% CI = 2.67, 3.72) had greater practice Imp/E than linemen (mean = 1.30; 95% CI = 0.89, 1.71; P < .001) and skill players (mean = 1.38; 95% CI = 1.00, 1.76; P < .001), with no difference between skill players and linemen (P = .781; Figure 2C).

Figure 2.

Figure 2

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Impacts per exposure by position group categorized by A, total, B, game, and, C, practice exposures. Within the whisker box plot, the median is represented with a thick line in the middle. Quartile 1 and Quartile 3 are the bottom and top of the box, respectively, and the minimum and maximum are the bottom and top lines of the whiskers.

Group Interactions in HIE by Position, Team, and Year

An interaction was found between position groups and individual teams for total Imp/E, indicating that each team exhibited different patterns by position (F4,188 = 4.489, P = .002). Total Imp/E differed between team B (mean = 2.32; 95% CI = 1.96, 2.68) and team A (mean = 3.14; 95% CI = 2.41, 3.87; P = .048) and team C (mean = 1.27; 95% CI = 0.63, 1.94; P = .007). We observed differences in total Imp/E between team A and team C (P < .001; Figure 3A) and between year 1 (mean = 2.71; 95% CI = 2.26, 3.16) and year 2 (mean = 1.83; 95% CI = 1.42, 2.24; P = .005).

Figure 3.

Figure 3

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Combined interaction of head impacts per exposure by position group and team, categorized by A, total, B, game, and, C, practice exposures.

We observed an interaction between positions and teams for game Imp/E (F4,185 = 4.257, P = .003). Team A (mean = 7.05; 95% CI = 5.18, 8.92) was different from team B (mean = 3.95; 95% CI = 3.01, 4.89) and team C (mean = 2.84; 95% CI = 1.15, 4.53). Teams B and C were not different (P = .26). No interaction existed between position and year for game Imp/E (P = .16). Post hoc comparison showed that game Imp/E differed between year 1 (mean = 5.32; 95% CI = 4.17, 6.48) and year 2 (mean = 3.58; 95% CI = 2.51, 4.65; P = .03) of data analysis.

We did not observe an interaction overall between positions and years (P = .58) for practice Imp/E; however, practice Imp/E differed by positions and teams (P = .02). Team C (mean = 1.14; 95% CI = 0.59, 1.69) was different from team A (mean = 2.39; 95% CI = 1.78, 3.00; P = .003) and team B (mean = 2.152; 95% CI = 1.85, 2.46; P = .002). Practice Imp/E differed between teams A and B (P = .003) and between years 1 (mean = 2.26; 95% CI = 1.88, 2.63) and 2 (mean = 1.66; 95% CI = 1.310, 2.00; P = .02).

Accumulated Head-Impact Burden

We observed a difference for total accumulated burden of head impacts (all locations combined; F2,194 = 4.938, P = .008). Post hoc analysis revealed that backers (mean = 4622.85g; 95% CI = 3077.43g, 6168.27g) had greater total impact burden than linemen (mean = 2657.70g; 95% CI = 2045.61g, 3269.19g; P = .01) and skill players (mean = 2875.7g; 95% CI = 2216.38g, 3535.01g; P = .02; Figure 4).

Figure 4.

Figure 4

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Total impact burden by position group. Within the whisker box plot, the median is represented with a thick line in the middle. Quartile 1 and Quartile 3 are the bottom and top of the box, respectively, and the minimum and maximum are the bottom and top lines of the whiskers.

A difference for accumulated front impact burden existed among position groups (F2,194 = 7.784, P < .001; Figure 5). Post hoc analysis confirmed that backers (mean = 1606.24g; 95% CI = 977.89g, 2234.58g) had greater accumulated burden than both linemen (mean = 768.24g; 95% CI = 433.84g, 1102.64g; P = .008) and skill players (mean = 567.75g; 95% CI = 360.71g, 774.78g; P < .001).

Figure 5.

Figure 5

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Cumulative impact burden per helmet location by position group.

DISCUSSION

We investigated total, game, and practice HIE by position groups of high school football players. The most important finding was that backers experienced more Imp/E and nearly twice the accumulated burden compared with other positions. These patterns displayed across teams may indicate that athletes running the ball or receiving short-yardage passes and those who defend them sustain greater HIE (impact frequency and force) compared with other positions. This finding contrasts with findings of previous researchers, who reported that linemen experienced fewer total head impacts compared with other position groups at the high school level across games and practices.2,11 One reason for this difference may be that the previous literature is now ≥10 years old and football practices are conducted under newer guidelines for the number of sessions allowed during preseason or the days or times per week that can be used for full-contact training. For example, Broglio et al reported 24.54 ± 22.41 impacts in games and 9.16 ± 8.64 impacts in practices, much higher than what we recorded.12 Another reason our findings may differ could be that our data were standardized by exposures, whereas previous researchers typically examined total impacts as an average estimate across a season.2,11

Standardizing impact frequency by athlete-exposures (ie, athlete session attendance) factors the number of sessions a player is susceptible to sustaining impacts, in contrast to just reporting season averages without considering whether players missed time due to injury or other reasons. In previous literature comparing position group HIE characteristics, researchers did not standardize impacts by session attendance (ie, athlete-exposure), making it difficult to compare our results.4,12 To our knowledge, head impacts by exposure at the high school level have been assessed in only 3 other studies.3,5,24 Broglio et al standardized HIE similarly to the way we did for this study; however, they did not compare head-impact characteristics or outcomes by position group.4,5 Although Swartz et al reported that participation in a helmetless tackling intervention decreased overall load using Imp/E, position group was not a main outcome measure.3,24 Nevertheless, researchers have reported greater HIE in defensive linemen than in offensive linemen, offensive skill players, and defensive skill players.2,13,14 Defensive line players sustained the most impacts per session, with impacts occurring 1 to 2 times more frequently than those sustained by the offensive linemen, offensive skill players, and defensive skill players.2 These previously reported values contrast with our findings, as we observed that the backers group had twice the HIE of other position groups in total, games, and practices. These differences may be due to variations in the grouping to each position category, as well as natural differences that would be expected among teams with different players, coaches, game strategies, and opponents.25

In addition, total HIE reported in the literature varies across position groups during practices and games, with defensive linemen sustaining the most impacts within a season, followed by tight ends, linebackers, and running backs.15 Savino et al reported that linemen sustained 10.7 ± 9.7 impacts per session compared with 3.1 ± 4.0 for quarterbacks, 4.5 ± 4.9 for the skills group, and 7.1 ± 8.6 for the backers group.15 The number of impacts doubled during game situations.15 However, in a study conducted specifically on youth football players, Broglio et al found that mean HIE differed by games and practices (5.8 versus 6.7).12 Our results indicated that differences exist across position groups in total, game, and practice HIEs for all teams combined. We do not know which factors affected the larger number of impacts for the backers group. Again, coaching or player technique, position-group coaching style, years of experience, or other factors may influence the number of head impacts sustained.

Although research completed in a specific cohort or state is not always directly applicable to an entire population, the outcomes can still be used to guide clinical practice and care of athletes. For example, in a group of collegiate football players participating in a 5-minute helmetless tackling intervention, Swartz et al noted a 30% decrease in head impacts per athlete-exposure throughout a season.3 This intervention was replicated to elicit fewer head impacts in high school football players.24 In National Collegiate Athletic Association Division I football, researchers studied a rule change regarding kickoffs, consistent with the National Football League’s implementation of the same rule during the 2011 season, resulting in overall fewer concussions after kickoffs.26,27 In our study, the larger number of Imp/E sustained by the backers emphasizes the need for individual strategies to reduce overall HIE in position groups. In addition, the importance of teaching safer tackling techniques across position groups based on style of play should be highlighted by stakeholders in high school football.

Our study also provides an appreciation of the impact severity, or force, that cumulative impacts deliver to players’ helmets, heads, and, ultimately, brains. Although we did not directly measure impact force sustained at the level of the brain tissue, we used the head-impact telemetry system to measure the estimated acceleration range for each impact and aggregate those values by their respective locations. A heat map of the impact burden across the regions of the helmet indicates the proportion of burden focused on the front of the helmet for all players irrespective of position played (Figure 6). This provides further evidence not only of pervasive head-initiated contact in football but also of the potential severity of the forces imparted through the skull to the brain tissue via the frontal lobe throughout a season.

Figure 6.

Figure 6

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Heat map visualization of cumulative impact burden across the locations per interquartile range.

Our study had limitations due to the nature of the analysis. Tracking athletes who played both offense and defense during a given session would have been difficult, as multiple researchers would have been needed to manually calculate total plays, units, and substitution patterns that may have occurred across all practices, games, and scrimmages. In addition, some impacts may have occurred and been counted for a skill-position player when the player might have been playing out of the primary position. In these cases, head impacts would have been counted for a skill-position athlete if the athlete had been playing linebacker or tight end for a few plays. These data were not normally distributed and lacked equality in variance, but this is to be expected for a larger sample size. We took steps to ensure the authenticity of the data as outlined previously. Data collections occurred in the 2019 and 2021 seasons, a timeframe influenced by the COVID-19 pandemic. The 2021 season featured a mandatory pause on public schools’ sports and activities, which affected the research teams differently. Team B continued to practice, which resulted in more exposures, as it had an extended season in comparison. Team A participated in only one season of data analysis due to staffing changes.

CONCLUSIONS

Our findings highlight the differences in HIE biomechanics across individual positions and teams. Athletes in the backers group (running backs, linebackers, and tight ends) experienced the highest impact frequency and burden compared with athletes in other position groupings. Stakeholders need to continue their attempts to decrease head-impact frequency of youth participants. Coaches, clinicians, and researchers should use this knowledge to be vigilant with focused tackling or blocking instruction across respective positions and, when necessary, plan behavior modifications that promote head-safe participation.

ACKNOWLEDGMENTS

This study was funded by a grant from the Gary O. Galiher Foundation (Ms Glodowski, Dr Freemyer, Dr Myers, Dr Lee, Dr Broglio, Mr Furutani, Dr Murata, and Dr Swartz). This cohort study was part of a larger investigation involving a tackling and blocking training intervention, HuTT (University of New Hampshire), that was developed when Dr Swartz was employed at the University of New Hampshire. The term HuTT is a registered trademark with the US Patent and Trademark Office under the University of New Hampshire. Under this larger investigation, data were collected and further analyzed for this study. We thank Ross Oshiro, MS, LAT, MPT; Darian Brothers, MS, ATC; Elissa Roblero, MS, ATC; Ivet Lloansi, MS, ATC; and Greg Taguchi for their assistance in data collection.

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