ISSN: 1543-9518

Total Goalkeeper Performance (TGP): A Comprehensive Metric for Evaluating Modern Soccer Goalkeepers

Authors: Daniel J. Marcolongo1, Bret R. Myers2

1Graduate of Sports Industry Management Program, Georgetown University, Washington DC, USA

2Department of Management and Operations, Villanova University, Villanova, PA, USA

 

Corresponding Author:

Bret R, Myers, Ph.D.

Department of Management and Operations

Villanova School of Business

800 E Lancaster Avenue

Villanova, PA 19085

[email protected]

Daniel J. Marcolongo is a 2025 graduate of Georgetown University’s Sports Industry Management masters program. His focus is in soccer analytics, which he developed as a collegiate soccer player and lifelong student of the game.

Bret R. Myers, Ph.D. is a Professor of Practice in the Department of Management and Operations in the Villanova School of Business. His research interests focus on sports analytics, specifically, in the areas of team evaluation and managerial decision-making. He also is an active Analytics Consultant with 10+ years of experience working with professional teams and other sports organizations.

ABSTRACT 

The purpose of this study was to develop and validate a comprehensive metric for evaluating modern soccer goalkeepers that accounts for both defensive and offensive responsibilities. Total Goalkeeper Performance (TGP) was constructed using publicly available data from the English Premier League, incorporating shot-stopping, cross-stopping, sweeping, and distribution metrics. Analysis of 70 observations of goalkeeper performance revealed a moderate positive correlation between TGP and team success (r = 0.474, p < 0.001), with TGP explaining 22.5% of variance in expected team points per game (3 points for win/1 point for draw/0 points for loss). A one-unit increase in TGP corresponded to 1.75-4.64 additional expected points over a 38-match season. Year-over-year analysis showed moderate consistency in goalkeeper performance as measured by TGP. These findings suggest TGP effectively captures goalkeeper contribution to team success while accounting for the evolving multidimensional nature of the position. TGP provides a data-driven framework for recruitment, talent identification, and tactical planning that aligns with the demands of modern soccer.

Key Words: soccer analytics, goalkeeper metrics, player evaluation, player development

INTRODUCTION 

The goalkeeper is a unique position in sports. The player is often isolated from the rest of the team as the last line of defense. They even have different equipment from the rest of the team. In hockey, soccer, and more, one can immediately distinguish a goalkeeper from their teammates (3, 5). It leads to a feeling that the goalkeeper is isolated from the rest of the team. But no one is an island. There have been times when goalkeepers have done more than just protect their goal, which has been seen prominently in soccer (13).

This has been supported by several studies into the position. A study using machine learning algorithms showed that the difference between what they called elite and sub-elite goalkeepers was their ability with their feet (11). This suggests that the position has evolved so much that shot stopping is not even a goalkeeper’s main priority at the world’s best clubs. This was far from the only study to suggest that goalkeepers have seen an increase in their responsibilities. Goalkeepers are asked to do a lot more than just save shots in today’s game (13, 19). Soccer is not the only sport where this has occurred.

From roughly the mid-1990s to the mid-2000s, certain hockey goaltenders had a similar task. The most notable was Martin Brodeur. The New Jersey Devils, Brodeur’s team, employed a strategy called a neutral zone trap. The trap utilized a goalkeeper’s ability outside of the net to limit their opponent’s scoring chances. The Devils won three Stanley Cup titles from 1994 to 2003 with this strategy before the NHL introduced a new rule which severely limited what a goalkeeper could do outside of making saves (5).

In soccer, a goalkeeper is the only player on the team who can touch the ball with their hands and the player can only do this inside the 18-yard box (2). Historically, this led to goalkeepers not playing with their feet at all. But as the game evolved, this began to change, helped along by a rule change after the 1990 World Cup. The 1990 World Cup is considered one of the worst World Cups of all time due to the boring play. Part of the reason for the dullness was the goalkeepers who would waste time by holding the ball as long as legally allowed (14).

To combat this, the back-pass rule was introduced. This established the rule that goalkeepers could not pick up the ball if a player on their team passed it to them (14). With the change introduced to the game, it made goalkeepers’ ability to play with their feet more important (1). Following the success of goalkeepers like Manuel Neuer and Ederson, a goalkeeper’s distribution has become an essential skill (16-17). It is to the point that in some development teams, such as Chelsea, goalkeepers are judged more for their passing than their saves (4). Goalkeepers need to do so much more than just stop shots. However, that idea still hasn’t taken hold.

There is no way to rank soccer goalkeepers in a way that accounts for what they do with the ball. There isn’t even a statistic to accurately rank a goalkeeper defensively. Some statistics individually look at saves, cross-stopping, and sweeping, but no statistics takes all those aspects into account (6). In fact, some awards recognize players for a single performance statistic. For example, the Premier League Golden Glove award is given to the goalkeeper who has had the most ‘clean sheets’ (i.e., a game where they did not allow a goal) (10). The way goalkeepers are ranked has not kept up with the times. There should be a new statistic that accurately rates a goalkeeper based on everything they have to do, their Total Goalkeeper Performance.

The purpose of this study is to develop and advance a comprehensive metric for evaluating modern soccer goalkeepers that captures both their defensive and offensive responsibilities. First, the study outlines the methodology, including the acquisition of player performance data from the English Premier League for men’s professional soccer. Second, the offensive and defensive statistics used to construct the Total Goalkeeper Performance (TGP) metric are defined, and the procedures for calculating these statistics are detailed. Third, the data analysis plan—featuring correlation analysis and regression modeling—is described. The results are then presented and interpreted, followed by the study’s conclusions. Finally, the practical applications of this metric within sports analytics, particularly in organized soccer, are discussed.

METHODS 

Dataset and Sampling

The data used in the paper spans eight seasons from the Premier League (2017-2018 through 2024-2025), a period where modern goalkeeper statistics are publicly available. All of the data comes from FBRef.com, with the exception of the data on punches which came from the Premier League’s website. In all, 70 goalkeepers were examined.

TGP features several different parts of a goalkeeper’s responsibilities. These can be divided into defensive and offensive statistics. Based on the data available the following performance statistics are used to construct TGP.

Defensive Statistics

Defensively, the main responsibility a goalkeeper has is shot stopping, making saves to prevent goals, but that’s not their only task. Goalkeepers also must defend the goal when balls come into their area. This can be from either crosses that a goalkeeper must deal with inside the 18-yard box or passes that force a goalkeeper to leave the box (18). These skills will be called cross-stopping and sweeping.

  1. Shot Stopping

Shot stopping will be measured with expected goals (xG). xG tracks how likely a goal is to be scored from the moment it is struck on a scale of 0-1 with a better shot being ranked closer to 1 (8). To make this stat useful for a goalkeeper, one must track how much xG a goalkeeper faces and then subtract the total number of goals allowed to get the post-shot (PS) expected goals minus goals allowed (GA). In order to standardize this for all goalkeepers, the statistic will be converted into a per 90 minutes through using the minutes played by each goalkeeper (PSxG-GA/90).

2. Cross Stopping

Cross Stopping is another skill needed to be quantified. Goalkeepers typically face several crosses being put into their box during a game. The best way to stop a cross is to claim it, catching the cross before the opposition can get to it. Punching a cross away can also be beneficial but is not preferable to catching as the ball could go back to the opposition but it is still preferable to leaving the cross to the opposition (9). When making cross-stopping into a statistic, one must factor in both crosses claimed (CC), crosses punched (CP), and total crosses faced (TC), but claiming and punching crosses are not equal.. Because of that, TGP weighs a punch as half of a claim when measuring cross-stopping. The final stat to measure cross stopping for TGP is: Cross Stopping = (CC+.5xCP)/TC.

3. Sweeping

Sweeping is the easiest of the three defensive skills to quantify. The statistic – defensive actions outside of the penalty area – measures sweeping well. This tracks how often a goalkeeper comes outside of his goal to help his team (6). The more often a goalkeeper does this, the better they are at sweeping. To fairly measure these statistics in comparison, it must be looked at on a per-90-minute basis as well. TGP will use defensive actions outside the penalty area per 90 (DAOP/90) minutes to measure sweeping.

From an interview with US International goalkeeper Tyler Miller, shot-stopping is the most important, followed by cross-stopping, then sweeping (12). TGP will weigh the skills 3:2:1 in that order. The stats will then be added together to make a defensive score.

Offensive Statistics

Days 1-4 focused on primary lift progression (Front Squat, Bench Press, Deadlift, Overhead Press) with integrated plyometric, conditioning, and movement quality components. Day 5 emphasized pulling strength and unilateral work. Day 6 focused on coordination, explosive power, and metabolic conditioning. All days included Tabata rowing (20 seconds work/10 seconds rest × 8 rounds) for conditioning stimulus and mental toughness development.

  1. Pass Completion Percentage (in buildup)

Offensive skills are more difficult to track due to the limitations of data on goalkeepers’ offensive abilities. One skill to track is a goalkeeper’s ability in buildup, making short passes to help his team up the field. In addition, a goalkeeper’s ability to make decisive passes that can start an attack on his team is important as well. The best widely available statistic to track a goalkeeper’s ability in buildup is completion percentage (PC) , how accurate they are as a passer.

2. Long Pass Completion Percentage

Similarly, long pass completion percentage (LP) shows how effective a goalkeeper is with long passes that are more likely to lead to an attack (6). Both statistics, completion percentage and long pass completion percentage, will be weighed equally to make an offensive score.

Component Weighting and Possession-Based Adjustments

The TGP metric is a weighted average of offensive and defensive scores.  However, weights are conditionally applied based on possession characteristics of the team. Teams with more possession tend to take more advantage of the offensive skills of the goalkeeper through more time with the ball. Meanwhile, teams with less of the ball have much less of a use for an offensively minded goalkeeper. (18). Because of that, possession, the statistic for how much of the ball a team has per game, is a way to weigh how important a goalkeeper’s offensive skills are for a team (6).

To ensure that the statistic is applicable across different seasons, a player’s score in different statistics will be weighed against the league’s average score. This includes the league average scores on shot-stopping (μPSxG – GA/90), cross stopping (μ(CC + .5CP)/TC)), sweeping (μDAOP/90), pass completion percentage(μPC), and long pass completion percentage (μLP).

For a team with 62.5 percent possession (P) or more, a number chosen for ease of calculations though it is a number only the most ball dominant teams can reach, the offensive and defensive scores will be weighted equally. For a team with 37.5 percent possession or below, a number chosen for the same reasons but for the least ball dominant teams, it will be weighted 3:1 defensive score to the offensive score (7). For example a player on a team with 62.5% possession or more his defensive and offensive scores would remain the same for calculations. In a team with 37.5% possession or below his defensive score would be multiplied by 1.5 and his offensive score multiplied by 0.5. For a team with between 37.5 and 62.5 percent possession the weight would slide between those ratios. For example, in a team with .531 percent possession a goalkeeper would have his defensive score multiplied by 1.188 and his offensive score multiplied by .812.

The overall TGP formula for a goalkeeper per match can be expressed as follows:

 TGP = (DS*(2 – (2P – 0.25))) + (OS*(2P – 0.25))) / 2

where:
 DS = 1.47*(((PSxG – GA/90 + 0.52)/(μPSxG – GA/90 + 0.52)*5.09) + 0.97*(((CC + .5CP)/TC)/μ(CC + .5CP)/TC)*5.15) + 0.62*((DAOP/90)/μ(DAOP/90)*4.02) OS = (0.90*((PC*/μPC*)/10.12) + 1.01*((LP*/μLP*)/9.92)/(4/3)

*μ represented the mean levels of the performance metric by league.

Here is a sample calculation for a high performing goalkeeper with the following measures:

Nick Pope 2023-24: TGP=(19.37*1.206 + 16.05*0.794) / 2 = 18.03

DS=1.47*(.58/0.44)*5.09 + (0.97*0.98)/0.83)*5.15) + 0.62*(1.87/1.29)*4.02=19.37

OS=(0.99(76.9/72.9)*10.12+1.01*(47.9/44.27)*9.92)/(4/3)=16.05

Here is a sample calculation for a low performing goalkeeper with the following measures:

James Trafford 2023-24: TGP (14*1.302 + 12.09*.698) / 2 = 13.36

DS=1.47*(0.31/0.44)*5.09 + 0.97*(0.95/0.83)*5.15) + 0.62*(1.54/1.29)*4.02=14.00

OS=(0.99(65.5/72.9)*10.12+1.01*(32/44.27)*9.92)/(4/3)=12.16

The formula is created with a score of 15 to be the league average for every season. The numbers each individual statistic is multiplied by is there to ensure that no stat is weighted more than any other.

Analyses and Visualizations

Three key areas in this study are explored: 1) Relationship between TGP and Team Success, 2) Individual TGP Rankings and Year-Over-Year Repeatability, 3) TGP vs. Player Market Value. In order to evaluate Team Success, Team EPL Points per Game (PPG) will be used (3 points for team 1, 1 point for team draw, 0 points for team loss). Python is used to carry out correlation and regression analyses exploring the relationship between TGP and Team Success based on n = 70 qualifying goalkeepers from the EPL. Specifically, the scipy library is used for correlation analysis and statsmodels library is used for regression analysis. Furthermore, data visualization is carried out using Python’s matplotlib library. Correlation analysis and data visualization (also from Python) are also used to explore year-over-year repeatability of TGP scores based on n = 10 qualifying goalkeepers, Similar methods are also used to help examine the relationship between TGP and Player Market value.

RESULTS

Relationship between TGP and Team Success

In order to assess the relationship between TGP and team success, a Pearson correlation analysis was performed comparing TGP to PPG for 70 observations across the 2022-2023, 2023-2024, and 2024-2025 English Premier league seasons. The data set is representative of 39 distinct goalkeepers that qualify by having played at least 10 matches.

The analysis revealed a moderative positive correlation between TGP and PPG (r = 0.474 and p < 0.001). This indicates that goalkeepers with higher TGP scores tend to play for teams that earn more points per match. Figure 1 displays the scatterplot with a fitted regression line which demonstrates the positive, linear trend between TGP and team performance. While correlation does not imply causation, the statistically significant relationship suggests that the multidimensional TGP metric captures aspects of goalkeeper performance that contributes directly to winning.

Figure 1

Note. Scatterplot depicting relationship Points per Game and TGP across 2022-2023, 2023-2024, and 2024-2025 seasons in the English Premier League.

Furthermore, a simple linear regression was performed to help understand the magnitude of the contribution to team success. The analysis was performed using the statsmodels library in Python and the results are included in Figure 2.

Figure 2

Note. Ordinary Least Squared Regression Results for TGP vs. Team Performance

The model was statistically significant, F(1,68)=19.74, p<0.001, and explained 22.5% of the variance in PPG (R² = 0.225). The resulting regression equation was:

PPG=0.143+0.084×TGP

The TGP coefficient was positive and significant (β=0.084, t=4.44, p1<0.001), with a 95% confidence interval ranging from 0.046 to 0.122. To put it more in practical terms, every 1 unit increase in TGP is expected to increase points per game from 0.046 to 0.122. In the context of a 38 match EPL season, a 1 unit increase in TGP exhibited by GKs would lead to 1.75 to 4.64 additional points.

Individual TGP Rankings and Year-over-Year Analysis

The Total Goalkeeper Performance (TGP) results for the 2024–2025 Premier League season are summarized in Table 2 below. The top-performing goalkeeper was Guglielmo Vicario of Tottenham Hotspur, who achieved a TGP score of 19.93 across 24 league appearances. Based on the established regression model, this corresponds to an expected points-per-game (PPG) value of approximately 1.81. In contrast, Alphonse Areola of West Ham recorded the lowest TGP score of 11.50 over 26 matches, translating to an expected PPG of roughly 1.10. When extrapolated over a full 38-match season, the difference in expected point contribution between a high-performing and low-performing goalkeeper equates to 26.98 points (68.78 vs. 41.80). While overall team success depends on multiple factors—including defensive structure and attacking capabilities—this analysis demonstrates that goalkeeper performance, as captured by TGP, is a significant independent driver of team outcomes.

Table 2

2024-2025 TGP Rankings in the EPL

RankingPlayerClubEffective Matches played (per 90)TGP
1Guglielmo VicarioTottenham2019.93
2EdersonMan City21.818.95
3Nick PopeNewcastle2318.28
4Robert SánchezChelsea2717.92
5Arijanet MuricIpswich1816.93
6David RayaArsenal3316.78
7AlissonLiverpool22.916
8Mark FlekkenBrentford31.415.87
9Kepa ArrizabalagaBournemouth2615.7
10Emiliano MartínezAston Villa3115.57
11Jordan PickfordEverton3314.98
12Łukasz FabiańskiWest Ham11.914.54
13Mads HermansenLeicester25.514.43
14Stefan OrtegaMan City11.214.12
15Bart VerbruggenBrighton3114.1
16André OnanaMan United3213.53
17Bernd LenoFulham3313.45
18Dean HendersonCrystal Palace3313.38
19José SáWolves2512.37
20Aaron RamsdaleSouthampton2512
21Matz SelsNottingham Forest3211.68
22Alphonse AreolaWest Ham21.111.5

There is also good evidence of the repeatability of TGP ratings year over year. That is – there is slight to moderate positive correlation between seasons. Table 3 represents the TGP performance of 10 GK who had qualifying minutes in the 2022-2023, 2023-2024, and 2024-2025 seasons.

Table 3

Year-over year TGP performances of qualifying Goalkeepers

Player24-25 TGP23-24 TGP22-23 TGP
Emiliano Martínez15.5720.9819.42
Ederson18.9519.5316.57
Nick Pope18.2818.0317.41
Alisson16.0016.0620.72
David Raya16.7816.6717.72
Robert Sánchez17.9217.3313.96
Bernd Leno13.4514.4718.67
Jordan Pickford14.9816.5514.88
José Sá12.3717.4312.96
Dean Henderson13.3811.7012.90

To accompany this table, Figure 3 below shows a correlation matrix that summarizes the strength of the pairwise association between each of the last three seasons in terms of TGP performance.

Figure 3

Note. Correlation matrix of TGP performance for 2022-2023, 2023-2024, and 2024-2025 seasons

TGP vs. Player Market Value

Player evaluators and scouts need to be in tune with the market value of players. One common method is to use Transfermkt (https://www.transfermarkt.com/), a highly reputable site used to estimate player market value based on performance, potential, age, and other market trends. Accordingly, the player market values from the recent 2024-2025 season were collected and paired against TGP values. The relationship between the two variables is depicted in Figure 4.

Figure 4

Note. TGP vs. Player Market Value for the 2024-2025 season.

As you can see, there is a baseline positive relationship between TGP and player market value. The scatterplot also labels the player with a color-coding system such that players above the expectation of performance by salary are in green, while those at expectation are in yellow, and those below expectation in red. Given the typical club operates on player budgets for wages, it is a common goal to try to acquire players that deliver at or above expectations in terms of performance.

DISCUSSION

Interpretation of Findings

The results of this study provide compelling evidence for the utility of the Total Goalkeeper Performance (TGP) metric as a comprehensive evaluation tool for modern soccer goalkeepers. The correlation (r = 0.474, p < 0.001) between TGP and PPG is evidence of a moderate, positive association between goalkeeping performance (as measured by TGP) and team performance. Furthermore, it can be said that 22.5% of the variation in PPG can be explained by the TGP metric. Given that there are 11 players on the field that contribute to team performance, 22.5% in the goalkeeping position signifies how critical the position is to team success.

The regression model also indicates that a single unit increase in TGP corresponds to an additional 1.75 to 4.64 points over a 38-match season. This finding quantifies the tangible impact a high-performing goalkeeper can have on a team’s league position. The substantial 26.98-point difference in expected contribution between the highest and lowest TGP scores in our sample (Vicario at 19.93 vs. Areola at 11.50) underscores the potential competitive advantage teams can gain through goalkeeper selection and development.

The year-over-year analysis reveals moderate consistency in goalkeeper performance as measured by TGP, suggesting that while the metric captures some stable aspects of goalkeeper ability, performance also fluctuates due to contextual factors such as team defensive structure, managerial approach, and opposition quality. This temporal stability adds credibility to TGP as a metric that identifies genuine skill rather than merely capturing random variation.

Tactical or Practical Implications

The TGP metric offers several practical applications for soccer professionals. First, it provides a data-driven framework for recruitment and talent identification that aligns with the multidimensional demands of the modern goalkeeper position. Clubs can use TGP to identify goalkeepers whose specific skill profiles match their tactical approach, rather than relying on traditional metrics that may not capture relevant abilities.

For teams with high possession percentages, our findings suggest that investing in goalkeepers with strong distribution skills yields tangible benefits. Conversely, teams that typically have less possession might prioritize shot-stopping and cross-claiming abilities. This contextual approach to goalkeeper evaluation enables more nuanced decision-making in the transfer market. Our analysis shows how TGP can be paired with player valuations, which can enable front offices to make smarter decisions.

The year-over-year analysis provides insights for player development specialists. The moderate temporal stability of TGP scores suggests that while goalkeeper performance has a skill component that persists across seasons, there is also room for improvement through targeted training. Development programs could use TGP component scores to identify specific areas for improvement in young goalkeepers.

From a tactical perspective, managers can use TGP to inform game strategy. Understanding the relative strengths of opposition goalkeepers across different dimensions could influence pressing approaches, crossing strategies, and shot selection. Similarly, awareness of one’s own goalkeeper’s TGP profile might influence defensive organization and build-up patterns.

Limitations

Several limitations must be acknowledged when interpreting these results. First, while our dataset includes three seasons of Premier League data, it represents only one league.. Goalkeeper requirements may differ substantially across leagues with different tactical tendencies, and the TGP weightings established here may not generalize perfectly to other contexts.

Second, our reliance on publicly available data limits the granularity of our analysis. More sophisticated tracking data could provide additional insights into goalkeeper positioning, command of area, and communication—aspects that are difficult to quantify with event data alone. The offensive component of TGP is particularly constrained by data availability, as metrics like pass completion percentage do not fully capture the quality and tactical significance of goalkeeper distribution.

Third, while we adjusted for team possession, other contextual factors like defensive structure, opposition quality, and score state may influence goalkeeper performance in ways not fully accounted for in the TGP metric. A goalkeeper playing behind a well-organized defense may face fewer high-quality shots, potentially affecting their PSxG-GA/90 component.

Finally, our weighting system, while informed by intuitive insight, introduces a subjective element to the metric. Different experts might propose alternative weightings based on their philosophical approach to the position. Future research could explore the sensitivity of TGP to different weighting schemes or develop data-driven approaches to component weighting. Despite these limitations, TGP represents a significant advancement in goalkeeper evaluation methodology and provides a foundation for future refinements as data availability and analytical techniques continue to evolve.

CONCLUSION 

This study demonstrates that the Total Goalkeeper Performance (TGP) metric is a robust and comprehensive tool for evaluating modern goalkeepers. By integrating both defensive and offensive contributions into a single, possession-adjusted framework, TGP captures the multidimensional nature of the position more effectively than existing measures. The results show a clear and statistically meaningful relationship between TGP and team success, as well as moderate year-over-year consistency, establishing TGP as a credible and practical benchmark for goalkeeper performance.

TGP should be recognized as a new standard for goalkeeper evaluation. It provides clubs, coaches, and analysts with a powerful framework for recruitment, player development, and tactical decision-making. The metric moves beyond traditional, outdated statistics such as clean sheets and instead delivers a data-driven, holistic assessment that reflects the modern demands of the position.

While future refinements—particularly improved offensive data, expanded league coverage, and longitudinal tracking—will further strengthen its utility, the evidence presented here is clear: TGP represents a decisive advancement in goalkeeper analytics. By adopting this framework, the soccer industry can better align evaluation practices with the realities of today’s game and gain a competitive edge in identifying and developing top goalkeepers.

APPLICATIONS IN SPORT

TGP provides practical value for multiple stakeholders in professional soccer. For technical directors and recruitment teams, it offers a multidimensional framework for goalkeeper evaluation that aligns with modern tactical demands, enabling more informed transfer decisions by identifying goalkeepers whose specific skill profiles match a team’s playing style. For coaches and tactical analysts, TGP components can inform game strategy by highlighting opposition goalkeeper weaknesses across different dimensions. Teams might adjust pressing approaches against goalkeepers with poor distribution or increase crossing volume against those who struggle with aerial control. Player development specialists can utilize TGP component scores to create targeted training programs addressing specific goalkeeper weaknesses, allowing youth academies to track development progress across all relevant goalkeeper skills rather than focusing exclusively on traditional shot-stopping metrics.

This type of expanded analysis has proven transformative in other sports. In American football, quarterback evaluation has evolved far beyond simple counting statistics such as touchdowns or interceptions. Advanced metrics like Expected Points Added (EPA), Completion Percentage Over Expectation (CPOE), and QBR now provide a multidimensional assessment of quarterback decision-making, efficiency, and contextual performance. In baseball, the introduction of Wins Above Replacement (WAR) revolutionized how players are valued, combining offensive, defensive, and baserunning contributions into a single comprehensive number. These examples illustrate the power of moving past one-dimensional measures to holistic frameworks that better reflect player impact.

Soccer goalkeepers are a natural candidate for this type of approach, but they are not alone. Other sports positions that blend defensive and offensive responsibilities—such as catchers in baseball, liberos in volleyball, or goaltenders in lacrosse and hockey—could benefit from similar metrics that capture their multifaceted roles. Expanding evaluation frameworks in this way allows teams across sports to more accurately quantify player value, align talent acquisition with tactical systems, and design targeted development programs that reflect the true demands of the position.

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2025-12-03T09:26:11-06:00May 20th, 2026|General, Sport Training, Sports Coaching, Sports Exercise Science|Comments Off on Total Goalkeeper Performance (TGP): A Comprehensive Metric for Evaluating Modern Soccer Goalkeepers
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The Role of Sport Relationships in Positive Youth Development

Authors: Jim P. Arnold1 and William V. Massey1

1Department of Kinesiology, College of Health, Oregon State University

 

Corresponding Author:

Jim P. Arnold

[email protected]

Jim P. Arnold https://orcid.org/0009-0004-2282-1915
William V. Massey, Ph.D. https://orcid.org/0000-0002-4002-3720
We have no known conflicts of interest to disclose.

ABSTRACT 

Purpose. Research on positive youth development (PYD) through sport remains unclear and speculative (Whitley et al., 2019). It has been suggested that sport-based PYD can occur implicitly through positive relationships (Holt et al., 2017). The present pilot study examined the impact of changes in the coach-athlete relationship, peer cohesion, and parental involvement on PYD outcomes across a sport season in a sample of youth soccer participants (N = 41, Mage = 11.85, 61% boys).

Methods. Athletes responded to surveys rating their relationships with coaches, parents, and peers at two time points, and additionally reported their perceptions of developmental skills gained across the sport season. A difference score was calculated for each relationship variable to measure change across the season. Four developmental outcomes (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) were regressed on changes in relationship quality across the season, while controlling for age, race, and gender.

Results. Changes to the coach-athlete relationship (b= 0.482, p = 0.002) and parental involvement (b= 0.326, p = 0.022) were significant predictors of perceptions of social skill development (R2 = 0.454, F(5,34) = 5.664, p < 0.001), supporting a relationship-based model of PYD in sport. Significant age and gender differences in ratings of the coach-athlete relationship were also discovered.

Conclusions. The present study not only offers partial support to a Holt and colleagues’ (2017) theory of implicit PYD through sport but also highlights the need important developmental role of relationship building in in the sport context.

Applications in Sport. Organizations should prioritize positive sport relationships through education, training, and programming, as poor or absent relationships may undermine the envisioned benefits of sport. In particular, the present study highlights the need for positive parental involvement, which may require stakeholders to work with parents to define their role expectations.

KEYWORDS: youth sport, positive youth development, sport relationships, coaching, parental involvement

INTRODUCTION 

The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011).  The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:

  • Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.

At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected.  Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.

LITERATURE REVIEW

Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).

Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).

Coach-Athlete Relationship

Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.

Peer Cohesion

Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020).  Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.

Parental Involvement

While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.

Study Aims

In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.

METHODS 

Participants

Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (Mage = 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.

Measures

Coach-Athlete Relationship Questionnaire (CART-Q)

To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”

Youth Sport Environment Questionnaire (YSEQ)

Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.

Parental Involvement in Sport Questionnaire (PISQ)

The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”

Youth Experience Survey for Sport (YES-S)

Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.

Procedure

Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season.  Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.

The dataset contained 0.3% missingness, and results of an MCAR test were not significant (X2(1386) = 0.00, p = 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (MT2 – MT1) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (r = 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.

Table 1

Sample Characteristics and Descriptive Statistics

   CART-QYSEQPISQYES-S Social SkillsYES-S Cog. SkillsYES-S Goal SettingYES-S Initiative
Variablen%T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)
Age            
1037.35.61(1.24)*5.97(1.47)*4.25(2.01)*5.08(1.98)*2.39(0.18)2.91(0.45)3.58(0.52)3.67(0.58)4.25(0.58)4.58(0.52)
11922.05.46(1.73)6.36(0.39)4.74(1.52)5.53(0.83)3.02(0.60)3.13(0.52)4.00(0.60)3.69(1.05)4.00(0.85)4.50(0.45)
122048.86.10(0.40)5.96(0.85)5.10(0.75)5.30(0.91)*2.92(0.60)3.25(0.74)*4.17(0.75)3.53(1.16)3.93(0.90)4.25(0.59)
13922.05.71(1.04)*5.15(1.26)* †4.69(1.40)4.89(1.18)3.16(0.69)3.30(0.58)4.03(0.57)3.56(0.69)4.25(0.57)4.43(0.66)
Gender            
Boy2561.06.03(0.61)6.17(0.69)†4.91(1.03)*5.26(0.89)*2.92(0.62)*3.24(0.66)*4.12(0.66)3.72(0.85)4.11(0.71)4.43(0.41)
Girl1639.05.56(1.43)5.41(0.99)†4.81(1.42)5.22(1.25)3.01(0.62)3.17(0.62)3.96(0.89)3.35(1.19)3.90(0.94)4.27(0.76)
Race            
White2765.95.77(1.13)5.97(0.83)4.75(1.21)*5.24(1.03)*2.95(0.61)3.14(0.57)4.07(0.69)3.52(1.04)3.99(0.85)4.43(0.54)
Black12.4          
Asian49.85.50(1.38)5.41(1.85)4.77(1.85)*5.30(1.64)*2.74(0.90)3.29(0.90)4.00(0.35)3.94(0.43)3.94(0.43)3.94(0.66)
Hispanic49.86.27(0.45)5.86(1.12)5.50(0.89)5.55(0.74)2.99(0.57)3.41(0.83)4.50(0.41)4.25(0.54)4.69(0.47)4.63(0.32)
Other512.26.13(0.31)5.65(1.20)5.05(0.83)4.99(1.04)2.99(0.45)3.31(0.52)3.80(0.89)3.15(1.29)3.80(0.94)4.15(0.74)
Total41100.05.84(1.02)5.87(0.99)4.87(1.18)*5.24(1.03)*2.96(0.62)*3.21(0.64)*4.06(0.67)3.58(1.00)4.03(0.80)4.37(0.57)

Notes. n = 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points; †Difference is significant between groups.

RESULTS

The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (R2 = 0.454, F(5,34) = 5.664, p < 0.001).

Regression Results for Perceptions of Social Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 0.7741.268-1.8023.3500.546
Gender-0.129-0.1750.184-0.5500.1990.348
Age0.3690.2910.1100.0670.5150.012
Race-0.024-0.0080.042-0.0940.0780.858
DCART-Q0.4820.2500.0740.0990.4000.002
DPISQ0.3260.3820.1600.5800.7070.022

Notes. n = 41; R2= 0.454, F(5,34) = 5.664, p < 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

**When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (R2 = 0.317, F(4,35) = 4.063, p = 0.008).

Within this model, both changes to the coach-athlete relationships (b= 0.482, p = 0.002) and changes to parental involvement (b= 0.326, p = 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b = 0.369, p = 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (R2 = 0.251, F(5,34) = 2.275, p = 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403, p = 0.022), whereas changes to parental involvement was not (b= 0.158, p = 0.330).

Table 3

Regression Results for Perceptions of Cognitive Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 2.0482.221-2.4656.5610.363
Gender-0.155-0.3150.323-0.9720.3420.337
Age0.1430.1690.193-0.2240.5610.389
Race-0.066-0.0320.074-0.1820.1190.670
DCART-Q0.4030.3120.1300.0480.5760.022
DPISQ0.1580.2770.280-0.2920.8450.330

Notes. n = 41; R2= 0.251, F(5,34) = 2.275, p = 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

The models predicting goal setting skills (R2 = 0.183, F(5,34) = 1.528, p = 0.207) and initiative (R2 = 0.185, F(5,34) = 1.542, p = 0.203) were not statistically significant.

DISCUSSION 

The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.

While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).

The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.

Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.

Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.

Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research.  As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.

CONCLUSION 

The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.

APPLICATIONS IN SPORT

In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:

  • Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
  • Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).

Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).

REFERENCES 

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2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition. The Sport Psychologist, 6(2), 111-127. https://doi.org/10.1123/tsp.6.2.111

3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice. Search Institute Insights & Evidence 3(1), 1-13.

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Appendix A
Supplemental Materials

Table 4

Correlation Matrix of Study Variables

Variables1234567
1. Age       
2. CART-Q-0.34*      
3. PISQ0.150.23     
4. YSEQ-0.140.66**0.31*    
5. Social Skills0.140.62**0.35*0.47**   
6. Cognitive Skills-0.050.40*0.160.160.66**  
7. Goal Setting0.030.43**0.130.42**0.57**0.70** 
8. Initiative-0.100.53**0.180.47**0.51**0.40*0.70**

Notes. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships

Table 5

Regression Results for Perceptions of Goal Setting Skills Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept2.0531.862-1.7315.836 0.278
Gender-0.2280.271-0.7790.322-0.1400.405
Age0.1860.162-0.1430.5150.1960.259
Race0.0110.062-0.1150.1370.0280.863
DCART-Q0.2300.1090.0080.4510.3690.042
DPISQ0.1750.235-0.3020.6510.1240.462

Notes. R2= 0.183, p = 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

Table 6

Regression Results for Perceptions of Initiative Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept4.0001.3151.3286.671-0.1203.043
Gender-0.1380.191-0.5270.2500.062-0.723
Age0.0420.114-0.1910.2740.0350.365
Race0.0100.044-0.0790.0990.3890.221
DCART-Q0.1710.0770.0150.3270.0872.224
DPISQ0.0860.166-0.2510.423-0.1200.520

Notes. R2= 0.185, p = 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

2025-10-01T13:40:31-05:00March 18th, 2026|Leadership, Research, Sport Education, Sport Training, Sports Coaching, Sports Studies and Sports Psychology|Comments Off on The Role of Sport Relationships in Positive Youth Development
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Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer

Authors: Asher L. Flynn1, Joanne Spalding2, and Sellena Dixon3

1Department of Sport & Exercise Science, Lincoln Memorial University, Harrogate, TN, USA

2Department of Health Sciences, Georgia College & State University, Milledgeville, GA, USA

3Athletics Department, Lincoln Memorial University, Harrogate, TN, USA

 

Corresponding Author:

Asher L. Flynn, PhD, CSCS

6965 Cumberland Gap Parkway

[email protected]

423.869.6828

Asher L Flynn, PhD, CSCS is an Assistant Professor of Sport and Exercise Science at Lincoln Memorial University, TN. His research interests focus on fatigue and athlete monitoring in colligate athletes, and aspects of women’s soccer performance.

Joanne Spalding, PhD, is an Assistant Professor in Exercise Science at Georgia College & State University, GA. Her research interests include athlete monitoring and long term athlete development in female athletes.

Sellena Dixon, BS, is the graduate assistant for the women’s soccer team at Lincoln Memorial University, TN.

ABSTRACT 

The purpose of this research was to investigated the match demands of 24 female soccer players over one season (15 conference matches) playing in a 3-5-2 formation to determine the match demands and work rates of each position. In order to determine formation specific workload, positions were grouped as center-backs (CB), wingbacks (WB), midfielders (MF), and forwards (FW). Velocity bands used to compare distances and work rates were total distance (TD), 2-3 m/s, 3-4 m/s, 4-5 m/s, 5-6 m/s, and >6 m/s. Results revealed that MF covered significantly more total distance (TD; 10743 ±1520 m) and distance between 2-3 m/s (3444 ± 423 m) than all other positions (CB TD: 8549 ± 1106 m, 2-3: 2497 ± 276 m; WB TD: 8860 ± 1216 m, 2-3: 2324 ± 344 m; FW TD: 8069 ± 1286 m) and greater distance between 3-4 m/s (2515 ± 382m) compared to CB (1574 ± 214 m) and FW (1270 ± 281 m), but not WB (1814 ± 258 m). There were no significant differences between any of the higher velocity bands (>4 m/s). These results can be useful for the coaching staff as descriptive data for the expected distances covered and work rates of Division 2 Women’s soccer teams playing a 3-5-2 formation. These data can be used to determine practice plans in season and for off-season training plans, to prepare athletes for reporting for preseason in appropriate condition.

KEYWORDS: GPS, Sport Science, Athlete Monitoring, Fitness, NCAA

Abbreviations: CB: Center-back, WB: Wingback, MF: Midfield, FW: Forward, GPS: Global Positioning System, MD: Match Day, vVO2max: Velocity at VO2max

INTRODUCTION 

There is a growing interest in the physical demands of women’s soccer, but the majority of research has focused on the top levels of competition (International and Professional; 23). Although this information is important, it is likely to have limited use for lower levels of competition. It has been reported that “top class” females perform more high intensity running compared to “high-level” females (16, 17, 19). Currently, there is a lack of research investigating match demands at other levels of women’s soccer, especially at lower levels of collegiate soccer in the USA.

The majority of the literature investigating the demands of lower levels of women’s soccer has focused on the match demands of NCAA Division I (2, 5, 12, 20, 21), two articles focused on NCAA Division II athletes (9, 11), and one article focused on NCAA DIII (22), with no studies investigating the match demands of women’s NAIA soccer. Gentles et al (2018) observed that NCAA DII female players covered approximately 5480 m in 45 minutes of match play, while NCAA Division I players covered between 9486m and 9930 m in 90 minutes (20, 22). With little evidence exploring the activity profiles of the lower divisions of collegiate women’s soccer, further investigation is needed.

Many of the studies to date, at any level, have been conducted using only a few matches (3, 17), which, due to the high standard deviations (approximately 10 – 40%; 3, 11, 13, 20, 21-23), raise the question of whether a small number of matches truly reflects the average match demands. Previous studies have also shown differences in match demands based on position (20, 21). On average, defenders covered less distance than midfielders and attackers (forwards), and forwards covered more distance than midfielders (20, 21). Another complication when extrapolating information from current research is that the formation type may also alter the match demands (4, 6, 7). These limitations can lead to complications when inferring information from current research for use in a practical setting with different teams.

Factors that alter expected match demands, level of play and formation used, imply that it would be improper for the coaching staff to use data from a different level of play and unknown formation to determine the expected demands for their specific situation; that is, a high school coach should not be implementing a training plan based on data from college, professional, or international level teams. As such, the purpose of this research was to observe the match demands (distances and work rates) of an NCAA Division II women’s soccer team playing in a 3-5-2 formation, and to determine if there are significant differences in distances covered and/or work rates between positions.

METHODS 

Participants  

Retrospective data from 24 field players (age: 19.90 ± 1.56 years old; height: 163.43 ± 6.18 cm, weight: 59.35 ± 7.20 kg,) on the same team were included in this study. A total of 194 observations were included in the analysis (center back (CB), n = 49; wingback (WB), n = 36; midfield (MF), n = 61; and forward (FW), n = 48). This study was approved by the institutional review board of Lincoln Memorial University.

Procedures

Match data from an NCAA division II women’s soccer team playing a 3-5-2 formation were collected during a single competitive season. Only conference regular season matches, conference tournament, and national tournament matches were included, with 15 matches used for analysis.

Global Positioning System (GPS) devices (TITAN sports, Houston, TX, USA), sampling at 10 Hz, were used to track player movement duringcompetition. GPS units were activated at least 20-minutes prior to kick-off and were worn in a provided chest halter that secured the device between the shoulder blades under their game uniform. GPS devices were provided to all athletes (starters and substitutes) during the 10-minute window after the team warm-up and before the start of the match, and were collected again after the final whistle. All data from the duration of the match (substitute warm-up and half-time warm-up) were included in the analysis.

For positional analysis, distances accumulated by all athletes who played in a specific position for a match were summed and then divided by the number of positions in the formation. For example, in the forward position, if one athlete was substituted for a portion of the match, the total distance covered by all three athletes playing in the forward position was summed and then divided by two for the two forward positions in the 3-5-2 system. For the work rate analysis, meters covered per minute at each threshold (distances divided by total match time) were averaged for all players in that position (20). Due to the different substitution rules in college soccer, this analysis was deemed optimal to determine the distances and work rate of each position, instead of specific players. Variables of interest included total distance and distances covered in velocity thresholds; 2 – 3 m/s (7.2 – 10.8 km·h-1), 3 – 4 m/s (10.8 – 14.4 km·h-1), 4 – 5 m/s (14.4 – 18.0 km·h-1), 5 – 6 m/s (18 – 21.6 km·h-1), and >6 m/s (>21.6 km·h-1).

All matches analyzed were official NCAA matches consisting of two 45-minute halves with a 15-minute half-time period, with a maximum of two 10-minute extra-time periods with a 2-minute intermission, with extra time being stopped in the event of a goal if the competition was tied at the end of the normal 90-minute match.

Data Analyses

Two Repeated Measures ANOVA analyses with Bonferroni corrections were performed to determine whether there was a significant difference in distances and work rates for each position at each threshold (TD, 2 – 3 m/s, 3 – 4 m/s, 4 – 5 m/s, 5 – 6 m/s, and >6 m/s). Statistical analyses were performed using JASP (Version 0.16.2). The alpha level was set at 0.05.

RESULTS 

The first RM-ANOVA revealed significantly higher distances (F(3.37, 62.82) = 11.96, p < 0.001) covered in the MF position compared to all other positions for TD and 2 – 3 m/s. Midfielders also covered significantly more distances than CBs and FWs, but not WBs, at 3 – 4 m/s. The only other significant difference observed was between the WB and FW positions in TD covered. There were no significant differences between any other positions at any other threshold. Descriptive statistics are provided in Table 1.

Table 1

Mean distance covered by position in each velocity zone (meters).

 CBWBMFFW
TD8549 ± 1106 (6467 – 11066)8860 ± 1216# (5443 – 10854)10743 ± 1520* (7060 – 12864)8069 ± 1286# (6204 – 10537)
7.2 – 10.8 km·h-12497 ± 276 (1827 – 2972)2324 ± 344 (1389 – 2842)3444 ± 423* (2180 – 3916)2301 ± 400 (1668 – 2942)
10.8 – 14.4 km·h-11574 ± 214 ǂ (1191 – 1959)1814 ± 258 (1068 – 2218)2515 ± 382# ǂ (1771 – 3129)1270 ± 281# (759 – 1688)
14.4 – 18.0 km·h-1607 ± 113 (449 – 769)878 ± 135 (639 – 1124)1092 ± 211 (792 – 1453)587 ± 109 (395 – 734)
18.0 – 21.6 km·h-1246 ± 58 (155 – 342)404 ± 86 (252 – 552)381 ± 79 (241 – 533)272 ± 60 (149 – 357)
>21.6 km·h-196 ± 35 (45 – 175)200 ± 102 (68 – 426)120 ± 55 (68 – 295)180 ± 112 (45 – 530)

Note: Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward
* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range
ǂ = p < 0.05 compared to the other indicated positions in that velocity range

The RM-ANOVA performed to determine differences in work rates at each threshold revealed significantly higher work rates (F(3.47, 64.82) = 6.05, p < 0.001) over the entire match (TD) for the MF position compared to all other positions, and a significantly higher work rate from the MF position in the 3 – 4 m/s velocity range compared to the FW position. There were no other significant differences between any of the other positions at any other threshold. The results are presented in Table 2.

Table 2

Mean work rate by position in each velocity zone (meters per minute).

 CBWBMFFW
TD96 ± 8 (88 – 123)101 ± 7 (85 – 117)119 ± 20 * (88 – 167)101 ± 24 (72 – 156)
7.2 – 10.8 km·h-128 ± 2 (25 – 33)26 ± 3 (21 – 35)36 ± 3 (29 – 40)27 ± 5 (19 – 35)
10.8 – 14.4 km·h-118 ± 2 (14 – 21)21 ± 2 (18 – 26)27 ± 3 # (21 – 32)16 ± 4 # (8 – 21)
14.4 – 18.0 km·h-17 ± 1 (5 – 10)10 ± 1 (8 – 12)12 ± 3 (9 – 19)8 ± 3 (4 – 14)
18.0 – 21.6 km·h-13 ± 1 (2 – 4)5 ± 1 (4 – 6)4 ± 1 (2 – 7)4 ± 2 (2 – 9)
>21.6 km·h-11.3 ± 1 (1 – 3)2.3 ± 1 (1 – 4)1.3 ± 1 (1 – 3)3.4 ± 4 (1 – 13)

Note. Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward

* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range

DISCUSSION 

The purpose of this study was to examine the external demands of NCAA DII women’s college soccer playing in a 3-5-2 formation. The most interesting finding from these data was that the WB position was only significantly higher in total distance covered compared to the FW position (p = 0.044), but there were no other significant differences in distances or work rates compared to other positions. This was interesting, given that the WB position is commonly accepted as the most demanding position. Another interesting result was that the MF position had significantly higher distances at lower speeds (2-3 m/s, p < 0.001; 3-4 m/s, p < 0.001) only when compared to FW position. Other studies have reported that different positions have significantly different total distances covered in a match (1, 14). Abbot et al.(2018) reported that central midfielders covered the greatest distance (11,570 ± 469 m), followed by wide attackers (10,918 ± 353 m), wide defenders (10,747 ± 420 m), strikers (10,320 ± 420 m), and central defenders covering the least amount of distance (9,830 ± 428 m), while Lago-Peñas (2009) reported significant differences between nearly every position for each threshold, except for total distance (11.1 – 14 km·h-1, 14.1 – 19 km·h-1, 19.1 – 23 km·h-1, > 23 km·h-1). Both these studies observed high-level male soccer teams (U23 English Premier League, Professional Spanish Premier League) and as such they would not be expected to mimic the results of this study and highlight the importance of sex- and level-specific research.

In a more direct comparison with other women’s college soccer research, Sausaman et al(2019) and Alaxander et al (2014) reported significant differences in distances covered by position, whereas Corrales (2020) reported no differences, regardless of position. Sausaman et al (2019) reported that attackers covered more high-speed (> 15 km·h-1) and sprint (> 18 km·h-1) distances than midfielders and defenders, with no difference between midfielders and defenders. Alexander et al (2104) reported that central defenders covered the least amount of total distance (8041.2 ± 371.0 m), followed by central attacking midfielders (9236.1 ± 491.3 m), fullbacks (9306.2 ± 367.8 m), and wide midfielders (9500.4 ± 847.0 m), with central defensive midfielders (9947.4 ± 577.9 m) covering the greatest amount of total distance. Fullbacks (1321.5 ± 173.7 m) and wide midfielders (1208.2 ± 314.1 m) covered significantly more distance at high speed (> 15 km·h-1) compared to central defensive midfielders (847.7 ± 234.9 m), central attacking midfielders (747.64 ± 196.5 m), and central defenders (614.1 ± 98.9 m). The difference in results between these studies and the current investigation could be due to a different level of competition (NCAA Division 1), a possible difference in formation (not reported), or due to the current studies banding velocity zones (i.e. 4 -5 m/s, 5 – 6 m/s) instead of summing distances above thresholds (> 15 km·h-1).

CONCLUSION 

This present study provides information on the expected work and work rates of division 2 women’s soccer. Data analysis revealed minimal differences based on position, with the midfield position being the only position with significant differences and only at low intensity thresholds (TD, 2-3 m/s). All other positions and intensities were not statistically different, highlighting the possibility that training for these positions likely does not need to be modified to fit each position but rather each athlete.

APPLICATIONS IN SPORT

The primary application of this research is to allow the coaching staff to determine the appropriate fitness, conditioning, and practice workloads for their team with respect to their level of competition, formation, and style of play. Using typical tactical periodization plans for match-day preparation (Match day (MD) +1, MD -2, MD -1, etc.), position-specific workloads can be determined and monitored to ensure optimal loading during each practice session. This information can also be used to determine fitness testing requirements. Since there was a significant drop (43.7%) in distance covered and work rate (approximately 43% decrease) at intensities above 4 m/s (14.4 km·h-1) observed from this study, minimum criteria for aerobic fitness tests could be set at 15.5 km·h-1, which would allow players to do the majority of the work expected below their estimated lactate threshold (85% vVO2max; 8, 18)

In addition, these data can be used to determine the appropriate time requirements for different conditioning drills. For example, making a time criterion of 3:40 for an 800 m run would meet the Long Interval definition for an individual with a vVO2max of 15.5 km·h-1, but if a team had higher/lower requirements, adjusting time cut offs would be suggested (15). These data can also be used to create game-specific conditioning drills, such as creating an interval training exercise (100m active running, 100m recovery jog; Table 3) that would provide about 30 – 35% of game distances at the higher end of expected game work rates.

Table 3

Interval conditioning exercise.

Speed LevelRepsTimeWork RateAccumulated Distance
8.0 km·h-12245s89 m/min2200
12.0 km·h-11030s36 m/min1000
15.0 km·h-1524s20 m/min500
20.0 km·h-1218s8 m/min200
>21.6 km·h-11<16s4 m/min100

Note: Work Rate was calculated as the total distance covered in each velocity threshold divided by the total exercise time (24.5 min).

When retroactively analyzing team distances and work rates to create a training plan, it is important to note that since the majority of the total distance covered during a match is at low intensities (<3 m/s; 11), focusing on “running” the observed TD is likely unnecessary. Lower speed distances (<4 m/s) would be expected to be accumulated through daily technical drills and exercises. Higher speed distances (>4 m/s) could be accumulated in any manner chosen by the coach, such as a mixture of soccer technical drills and/or conditioning drills.

When using workload data in this manner, this would allow the coaching staff to create training plans that develop physical characteristics in a manner appropriate to the athletes level and expected match play requirements instead of arbitrarily spending time and effort developing a specific characteristic beyond projected usefulness. For example, since the majority of work is performed below 14.4 kph, spending the time and training effort for a vVO2max above 16 kph would be counterproductive. The extra focus could be better spent on improving other training targets to improve performance (technical, tactical, sprint, acceleration, change of direction)

ACKNOWLEDGMENTS

The authors declare no conflicts of interest, and no funding was received for this research.

REFERENCES 

1.   Abbott, W., Brickley, G., & Smeeton, N. J. (2018). Positional differences in GPS outputs and perceived exertion during soccer training games and competition. The Journal of Strength & Conditioning Research, 32(11), 3222-3231.

2.   Alexander, RP. (2014) Physical and Technical Demands of Women’s Collegiate Soccer (Publication No. 2421). [Doctoral dissertation, East Tennessee State University], Digital Commons.

3.   Andersson, H. Å., Randers, M. B., Heiner-Møller, A., Krustrup, P., & Mohr, M. (2010). Elite female soccer players perform more high-intensity running when playing in international games compared with domestic league games. The Journal of Strength & Conditioning Research, 24(4), 912-919.

4.   Aquino, R., Vieira, L. H. P., Carling, C., Martins, G. H., Alves, I. S., & Puggina, E. F. (2017). Effects of competitive standard, team formation and playing position on match running performance of Brazilian professional soccer players. International Journal of Performance Analysis in Sport, 17(5), 695-705.

5.   Bozzini, B. N., McFadden, B. A., Walker, A. J., & Arent, S. M. (2020). Varying demands and quality of play between in-conference and out-of-conference games in division i collegiate women’s soccer. The Journal of Strength & Conditioning Research, 34(12), 3364-3368.

6.   Bradley, P. S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di Mascio, M., Paul, D., Gomez Diaz, A., Peart D., & Krustrup, P. (2011). The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches. Journal of sports sciences, 29(8), 821-830.

7.   Carling, C., Espié, V., Le Gall, F., Bloomfield, J., & Jullien, H. (2010). Work-rate of substitutes in elite soccer: A preliminary study. Journal of science and medicine in sport, 13(2), 253-255.

8.   Cerezuela-Espejo, V., Courel-Ibáñez, J., Morán-Navarro, R., Martínez-Cava, A., & Pallarés, J. G. (2018). The relationship between lactate and ventilatory thresholds in runners: validity and reliability of exercise test performance parameters. Frontiers in physiology, 9, 1320.

9.   Choice, E. E., Tufano, J. J., Jagger, K. L., & Cochrane-Snyman, K. C. (2022). Match-play external load and internal load in NCAA division II women’s soccer. The Journal of Strength & Conditioning Research, 10-1519.

10. Corrales, I. (2020). The Physical Demands of Women’s Collegiate Soccer Matches Assessed Using GPS Devices [master’s thesis, California State University, Fullerton], https://scholarworks.calstate.edu/.

11. Gentles, J. A., Coniglio, C. L., Besemer, M. M., Morgan, J. M., & Mahnken, M. T. (2018). The demands of a women’s college soccer season. Sports, 6(1), 16.

12. Ishida, A., Travis, S. K., Draper, G., White, J. B., & Stone, M. H. (2022). Player position affects relationship between internal and external training loads during Division I collegiate female soccer season. The Journal of Strength & Conditioning Research, 36(2), 513-517.

13. Jagim, A. R., Murphy, J., Schaefer, A. Q., Askow, A. T., Luedke, J. A., Erickson, J. L., & Jones, M. T. (2020). Match demands of women’s collegiate soccer. Sports, 8(6), 87.

14. Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Dominguez, E. (2009). Analysis of work-rate in soccer according to playing positions. International Journal of Performance Analysis in Sport, 9(2), 218-227.

15. Laursen, P., & Buchheit, M. (2019). Science and application of high-intensity interval training. Human kinetics.

16. Martínez-Lagunas, V., Niessen, M., & Hartmann, U. (2014). Women’s football: Player characteristics and demands of the game. Journal of Sport and Health Science, 3(4), 258-272.

17. Mohr, M., Krustrup, P., Andersson, H., Kirkendal, D., & Bangsbo, J. (2008). Match activities of elite women soccer players at different performance levels. The Journal of Strength & Conditioning Research, 22(2), 341-349..

18. Parpa, K., & Michaelides, M. A. (2025). Ventilatory thresholds in professional female soccer players. International Journal of Sports Medicine, 46(02), 97-103.

19. Ramos, G. P., Nakamura, F. Y., Penna, E. M., Wilke, C. F., Pereira, L. A., Loturco, I., Capelli, L., Mahseredjian F., Silami-Garcia E., & Coimbra, C. C. (2019). Activity profiles in U17, U20, and senior women’s Brazilian national soccer teams during international competitions: are there meaningful differences?. The Journal of Strength & Conditioning Research, 33(12), 3414-3422.

20. Sausaman, R. W., Sams, M. L., Mizuguchi, S., DeWeese, B. H., & Stone, M. H. (2019). The physical demands of NCAA division I women’s college soccer. Journal of Functional Morphology and Kinesiology, 4(4), 73.

21. Vescovi, J. D. (2014). Motion characteristics of youth women soccer matches: female athletes in motion (FAiM) study. International journal of sports medicine, 35(02), 110-117.

22. Vescovi, J. D. (2015). Physical demands of regular season and playoff matches in professional women’s soccer: a pilot from the Female Athletes in Motion (FAiM) study. In International research in science and soccer II (pp. 95-106). Routledge.

23. Vescovi, J. D., & Falenchuk, O. (2019). Contextual factors on physical demands in professional women’s soccer: female athletes in motion study. European journal of sport science, 19(2), 141-146.

2025-09-30T16:08:05-05:00March 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Women and Sports|Comments Off on Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer
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Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues

Authors: Tyler B. Becker12, Ronald L. Gibbs, Jr2

1Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA

2Michigan State University Extension, Health and Nutrition Institute, Michigan State University, East Lansing, Michigan, USA

 

Corresponding Author:

Tyler B. Becker, PhD, CSCS

469 Wilson Road, Room 125

East Lansing, MI 48824

[email protected]

517-353-3338

Tyler B. Becker, PhD, CSCS is an Associate Professor of Nutritional Sciences at Michigan State University in East Lansing, MI. His research interests focus on sports nutrition practices and strategies in youth athletes and higher education andragogy.

Ronald L. Gibbs, Jr, PhD, MCHES is currently a Program Evaluation Specialist for Michigan State University in East Lansing, MI. His research interests focus on coach and athlete education, long-term athlete development (LTAD), psychosocial aspects of sports and physical activity, adolescent nutrition and physical activity behavior change through sport participation, sports performance, and reducing childhood obesity.

ABSTRACT 

Youth sports injuries are quite common in sport and have several negative consequences, including healthcare costs, loss of playing time, and producing mental stress. Nutritional strategies have been suggested to improve recovery from sports-related injuries. The Peak Health and Performance (PHP) youth-sports curriculum was developed to use sport to promote healthy eating behaviors. Six additional lessons on nutrition for recovery from injury, energy management, and gastrointestinal issues have been added as addendums to PHP. Lesson A discusses the importance of key nutrients (eg., protein, complex carbohydrates, unsaturated fatty acids, water) for promoting tissue healing following an injury. Lesson B describes several micronutrients and the possible role of nitrates for aiding in injury recovery. Lesson C discusses the implications of low energy availability, including how to identify and prevent it. In Lesson D, several nutritional strategies for addressing mild traumatic brain injuries are explored. Lesson E discusses the importance of sleep for injury recovery and describes nutritional strategies for improving sleep quality. The final lesson (Lesson F) describes various gastrointestinal issues encountered in sport and how to prevent them. Future research will examine youth athlete knowledge of nutritional strategies for recovering from a sports-related injury following these lessons.

KEYWORDS: adolescent sports; sports nutrition; injury management

INTRODUCTION 

Sports-related injuries are a significant concern among adolescent athletes, with prevalence rates ranging from 34.1% to 65% (2). Certain groups, including female athletes, obese athletes, and those participating in contact sports, are at particularly high risk.  In the US, the rate of injuries in sports, recreation, and leisure activities is 117.1 per 1000 children and adolescents aged 12-17 years of age (57). These injuries impose substantial financial burdens; for example, over a 5-year period in Florida, inpatient care costs for pediatric sports injuries totaled $24.55 million, while emergency care expenses reached $87 million (61). Beyond the economic impact, sports-related injuries also incur both physical and mental challenges to the athlete, including lost playing time, with female athletes averaging 10 days of missed competition per injury (5). This contributes to social isolation and depressive symptoms in adolescent athletes during recovery (63). In addition, gastrointestinal (GI) problems, including diarrhea, vomiting, and abdominal injuries are common place among athletes (12,73), and can contribute to decreases in performance and a loss of playing time (39). Given these multifaceted challenges, there is a critical need to optimize injury prevention and rehabilitation strategies to support young athletes’ physical and psychological well-being.

Proper nutritional intake plays a critical role in injury prevention and rehabilitation among youth athletes (3). In addition to supporting overall health and well-being, adequate nutrition is essential during adolescence–a period marked by rapid growth and development–and contributes to athletic performance and post-workout recovery (15, 66). Despite its importance, many adolescent athletes demonstrate a lack of knowledge regarding both general and sport-specific nutritional practices (6).  A recent systematic review by Hulland et al. (2023) revealed that adolescent athletes are more familiar with general than sport-specific dietary strategies (32), while Gibbs and Becker (2025) found that both male and female adolescent athletes scored below 50% on assessments covering both areas (24). These findings underscore a significant gap in nutritional literacy among youth athletes, indicating the need for targeted education to optimize their health development and athletic outcomes.

Several nutritional strategies have emerged highlighting the importance of it for injury rehabilitation primarily in adult athletes (26, 54, 65). For example, kilocaloric and protein needs often increase following injury due to a need for recovery and maintenance of lean body mass resulting from disuse (58, 65). However, the application of nutritional strategies for recovering from injury for youth athletes remains understudied. Alcock et al. (2024) offered a comprehensive overview of injury rehabilitation strategies for youth, indicating practical applications; however there remains a critical gap in understanding how adolescent athletes perceive and apply nutrition during recovery (3). To date, no research has directly examined youth athletes’ knowledge of nutrition for injury rehabilitation, but existing evidence suggests they are likely deficient in this area as well (46). Research suggests that poor food literacy and nutrition knowledge could theoretically contribute to increased injury risk (3, 15). This reinforces the urgency of developing age-appropriate interventions that address both performance and recovery nutrition, particularly in the context of injury.

The Peak Health and Performance (PHP) curriculum was designed from a collaboration by faculty and staff at Michigan State University, Division of Sports and Cardiovascular Nutrition, College of Osteopathic Medicine, East Lansing, MI and Spartan Performance Training, East Lansing, MI (25). This curriculum incorporates various sports nutrition best practices from several areas of literature providing sports nutrition recommendations (17, 66, 69). Fruit and vegetable intake significantly increased in 290 children and adolescents who completed the PHP curriculum (4). Due to the success of the program in modifying nutrition behaviors, additional lessons were created to educate youth on nutritional strategies for injury recovery, energy management, and managing GI issues. These topics include nutritional strategies for musculoskeletal and mild traumatic brain injury (mTBI) recovery, and other nutritional considerations around injury risk and recovery including sleep, low energy availability (LEA), and GI issues. This manuscript describes the rationality and creation of these addendum lessons for the PHP curriculum.

LESSON CONTENT

The original PHP curriculum consists of six lessons labeled as: Lesson 1- Nutrition Basics; Lesson 2- Athletes Performance Plates; Lesson 3- Timing of Intake; Lesson 4- Hydration, Energy Drinks, and Sugary Beverages; Lesson 5- Convenience Foods; and Lesson 6- More Than a Game (25). Further information on PHP learning objectives and topics inclusion can be found in Gibbs & Becker (25). The new additional lessons and their learning objectives can be found in Table 1.  These additional lessons are meant to serve as their own lesson series, a single lesson session, or as supplemental lessons to the original PHP lessons.

Table 1.
Additional Lessons for the Peak Health and Performance Curriculum: Learning Objectives
LessonLearning Objectives
A: Macronutrients for Injury Rehabilitation • Explain the four phases of an injury
• Understand the importance of consuming enough calories following an injury
• Explain why protein is needed during the health process and recall amounts needed
• Explain the role that carbohydrates have during the healing process
• Describe what role unsaturated fatty acids, such as omega-3s, have while healing an injury.
• Understand the importance of water during the healing process
B: Micronutrients for Injury Rehabilitation• Explain the importance of choosing food sources of vitamins and minerals over dietary supplements
• List and understand the roles that vitamins A, C, D, and E have in injury healing
• Identify good food sources of vitamins A, C, D, and E
• List and understand the roles that calcium, zinc, and iron have in injury healing
• Identify good food sources of calcium, zine, and iron
• Explain why foods high in nitrates may promote injury healing and identify good food sources of them
C: Low Energy Availability• Explain what low energy availability is
• Identify what causes low energy availability
• Understand how low energy availability negatively impacts performance and recovery
• Explain how low energy availability may lead to other negative health outcomes
• Recognize the symptoms of low energy availability
• Describe prevention and treatment strategies for low energy availability
D: Nutrition for Head Injuries• Explain what happens during a head injury in sport
• List the different phases of concussion recovery
• Explain the benefits of creatine, magnesium, and flavonoids for head injury recovery
• Identify good food sources of creatine, magnesium, and flavonoids
• Identify other nutritional considerations to have when recovering from a head injury
E: Nutrition and Sleep for Injury Reduction and Recovery• Explain why sleep is important for performance and reducing and healing injuries
• Identify how much sleep an athlete should be getting each night
• Explain the benefits of melatonin and serotonin rich foods for improving sleep quality
• Identify other nutrients of interest that are related to sleep quality
• Identify foods to avoid prior to sleep
• List strategies to set up an ideal bedtime routine
F: Gastrointestinal Issues and Sport• Understand how vomiting and nausea symptoms may appear during practice and sport
• Provide strategies to reduce vomiting and nausea symptoms during practice and sport
• Explain how diarrhea can happen during practice and sport
• Identify strategies to prevent diarrhea during practice and sport
• Explain how probiotics and prebiotics are important for gut health

Each of these six lessons will be discussed in detail in the next section. These supplemental lessons were created in a manner to instruct participants to refer back to the original lessons for further information.

Lesson A: Macronutrients for Injury Prevention

This lesson begins by describing how musculoskeletal injuries heal and the importance of proper caloric intake and macronutrients during recovery from sports-related injuries. Each macronutrient is then highlighted to show its main role in providing both energy and nutritional needs to promote recovery. Macronutrient roles and responsibilities are described in detail in PHP Lesson 1 of the original curriculum (25).

Caloric Intake: The following section of the lesson describes the importance of meeting kilocalorie (kcal) needs to help heal an injury. Research on adult athletes suggest increasing kcal consumption by 10-15% during injury and recovery (58). Additionally, to offset sarcopenia in adults resulting from injury and disuse, energy intake should be between 25-40 kcal/kg of bodyweight per day (54). Independent of injury status, growth and development demands of children aged 9 and up typically require 60-65 kcal/kg of bodyweight per day (21). Taking energy needs during injury into account, coupled with normal demands for growth and development (21), an injured adolescent would need slightly more than the recommended 60-65 kcal/kg of bodyweight per day.

Protein: Following injury, protein requirements are significantly elevated to offset bodily stress incurred from the injury (65). Additionally, protein intake helps offset muscle atrophy due to disuse (47). Protein requirements for adult athletes and recreationally active adults is between 1.2 to 2.0 g/kg of bodyweight per day (69), with protein recommendations for adolescent athletes being in a similar range (15, 41). Following injury, it is suggested to increase daily intake of protein to 2.0 to 3.0 g/kg of bodyweight in athletic adults (65), which likely suffices for protein requirements for adolescent athletes.

Carbohydrates: Carbohydrates can provide a  source of energy while healing through an injury (65), and aid in muscle adaptations and recovery (69). Due to a decrease in the amount of high-intensity exercise that can be performed while injured, carbohydrate needs are not as large as what is needed in an uninjured athlete (65). Thus, to meet demand while recovering from an injury, up to 60% of daily kcals should come from carbohydrates (65), with an emphasis on complex carbohydrates.  Additionally, fatty acids are important in the recovery process as they synthesize several hormones and aid in the absorption of several vitamins (3,  27). Unsaturated fatty acids, such as omega-3 fatty acids may reduce inflammation, thereby making their need instrumental during the recovery process (27). It is recommended to consume good sources of omega-3 fatty acids including fatty fish, walnuts, flaxseed, and avocado, which this lessons includes as suggested food sources (27, 65).

Fluid Intake: Hydration for performance is covered in Lesson 4 of the original PHP, but in this lesson, it is explored in more detail pertaining to injury risk and recovery. Over half of US children are inadequately hydrated (37), and being in this state can increase risk of injury and prolong recovery (10). Muscles on average are 75% water with bones comprising 25% of it, suggesting that a lowered consumption of it could further exacerbate healing of injuries to these structures (27). Males aged 9 to 13 years need at least 8 cups of fluid per day, while females of the same age need at least 7 cups per day (34). Adolescent males aged 14 to 18 years of age, need at least 11 cups of fluid per day, while females of the same age need 8 cups. Thus, it could be hypothesized that an injured youth athlete should strive to meet and exceed these recommendations for fluid consumption.

Lesson B: Micronutrients for Injury Rehabilitation

Lesson B highlights the importance of specific micronutrients that provide a key role in injury rehabilitation (3, 26). Consuming adequate nutrients, including micronutrients, from whole food sources, is a major goal of the PHP curriculum (21). This lesson begins with a discussion on the concerns with the use of dietary supplements to meet micronutrient recommendations such as issues with regulation (20), and possible contamination (40). Each section of the lesson describes how the micronutrient of interest is implicated in the recovery process, how much is needed, other important functions it provides in the body, and suggested foods that are good sources for the micronutrient of interest.

Vitamin D and Calcium: As summarized in Alcock et al. (2024) micronutrients of interest for bone injury rehabilitation include vitamin D and calcium (3). Calcium is needed to increase bone mineral density and bone remodeling such as when following an injury (27). Vitamin D is needed for calcium absorption and maintenance. Children and adolescents between 9 and 18 years old, need 1,300 mg of calcium every day (23). Adolescents between 14- and 18-years old need at least 15 mcg (600 IUs) of vitamin D daily. Food sources of calcium listed in the lesson include milk, yogurt, salmon, fortified fruit juice, and collard greens (27). Good food sources of vitamin D suggested in the lesson includes salmon, fortified milk, tuna, and cashews.

Zinc and Iron: Other micronutrients of interest for muscle injury also include zinc and iron (3). Zinc and iron are both trace minerals that have several important functions in the human body (27). Zinc is involved in hundreds of functions in the body, such as involvement in DNA synthesis and wound healing, and immune system function (27). Zinc is needed for protein synthesis and iron is needed for the transport of oxygen to several tissues in the body which would increase healing (27). Youth aged 9 to 13 years, need 8 mg of zinc per day (23). Male adolescents aged 14-18 years of age need 11 mg of zinc per day, while females of the same age require 9 mg each day.  Children aged 9-13 years of age need 8 mg of iron per day (23). Males aged 14-18 years of age need 11 mg of iron per day, and females of the same age need 15 mg per day. Good sources of zinc include dark meat, legumes, shrimp, and nuts (27). Good food sources of iron includes dark meat, and also spinach and cashews.

Vitamins A, C, and E: Vitamin C plays a pivotal role in the synthesis of collagen (3). Similar to vitamin C, vitamin A aids in collagen formation, specifically the laying down of new collagen (65). Vitamin E can reduce muscle breakdown and promote muscle repair (27). Each of these vitamins can reduce oxidative stress and inflammation and improve tissue healing (27). Children aged 9 to 13 years old need 1,200 mg of vitamin C every day, and adolescents aged 14 to 18 years old, need 1,800 mg per day (23). Good food sources of vitamin C include kiwis, green peppers, strawberries, and cantaloupe (27). Youth aged 9-13 years need 600 mcg of retinol activity equivalents (vitamin A) per day, while adolescents aged 14-18 years need 600 mcg of retinol activity equivalents each day (23). Youth aged 9-13 years of age need 11 mg of vitamin E per day, while adolescents over the age of 14 need 15 mg per day (23). Dietary sources of vitamin A include sweet potatoes, pumpkins, spinach, and squash, while good sources of vitamin E include sunflower seeds, apricots, avocados, and almonds (65).

Although not a micronutrient, eating foods high in nitrates, like beets, could theoretically help heal an injury (76). About 20% of the nitrates consumed in food is converted to nitrite by bacteria found in the oral cavity (76). In turn, the stomach transforms this nitrite into nitrous oxide which can cause vasodilation. Thus, more oxygen and nutrients are transported to the injured area, supporting the healing process. A recent systematic review examined nine studies and concluded that short-term consumption of beetroot may accelerate the recovery of muscle soreness and various functional markers due to its antioxidant and inflammatory properties likely exerted by its nitrate content and several phenolic compounds (60). Therefore, it could be assumed that consuming foods high in nitrates and phenolic compounds could expedite the injury healing process. Aside from beets, good food sources of nitrates include spinach, radishes, celery, and rhubarb (36).

Lesson C: Low Energy Availability

Energy availability is the amount of energy available after energy expenditure, that is used for bodily functions (9). Thus, LEA is the state of inadequate energy intake relative to energy expenditure (9) and the prevalence for LEA in athletes ranges from 22% to 58% in a given sport (44). LEA can lead to several negative impacts on performance including decreased muscular strength, decreased endurance performance, and decreased responses to training responses and adaptations (50, 70). Additionally, there is an increased injury risk with LEA (29,  56).

The next section of this lesson discusses how LEA can negatively impact the growth and development of a child or adolescent, potentially resulting in poor bone health, delayed puberty, short stature, and menstrual irregularities (15). It also highlights several signs and symptoms felt by an athlete that could indicate LEA (9, 70). 

LEA, with or without the presence of an eating disorder, is a characteristic of the Female Athlete Triad, which is a condition that also includes decreased bone mineral density, and menstrual dysfunction (53, 59). The concept of Relative energy deficiency in sport (RED-S) expands upon the Female Athlete Triad by recognizing a broader range of health consequences including disruptions to the endocrine system, immune system, and cardiovascular health (9). Raising awareness of these signs and symptoms is essential, especially given that knowledge of LEA remains low among both athletes and coaches (44). The lesson concludes with evidence-based strategies to prevent LEA, as well as treatment options to address its underlying causes (9).

Lesson D: Nutrition for Head Injuries

This lesson discusses various nutritional considerations to assist in the healing process for someone who has had a concussion, or other types of mTBI (22, 62). Current concussion rates in youth sports are 4.17 cases per 10,000 athlete exposures (38). There are several nutritional aspects that may support brain health among those recovering from mTBIs (22, 62). Although several macronutrients are considered nutrients of interest during this process (22, 62), this lesson discusses other nutrients and micronutrients (aside from those discussed in previous lessons) that may have a place while recovering from a mTBI, including creatine, magnesium, and flavonoids.

Creatine: Creatine is a compound that is formed in protein metabolism and works to recycle adenosine triphosphate (ATP) for energy metabolism (42). It has been shown that creatine content in the brain is diminished after a mTBI, and increasing its intake could maintain ATP levels in the brain (1, 65). This could help offset injury sustained from the mTBI, such as decreasing protease activation that degrades axon structures (1). Good food sources of creatine listed in this lesson includes lean red meats, fatty fish, pork, and wild game (72). 

Magnesium: Magnesium is a trace mineral that has several functions within the body (27). In the brain, magnesium is involved in efficient nerve signaling and maintaining the blood brain barrier (45). Following a mTBI, magnesium levels decrease in the brain (67), and low magnesium levels have been associated with neuroinflammation and neurodegeneration, including several diseases such as Alzheimer’s and Parkinson’s diseases (67). Research has revealed that magnesium supplementation can reduce concussion symptoms in adolescents following injury (67). Youth aged 9 to 13 years of age need 240 mg of magnesium per day (23). Older adolescent, males aged 14-18 years of age need 410 mg or magnesium per day while their female counterparts need 360 mg per day. Good food sources of magnesium include almonds, cashews, peanut butter, and spinach (27).

Flavonoids: Lastly, flavonoids are phytochemicals found in many fruits and vegetables, that have anti-inflammatory and antioxidant effects, which may reduce swelling after a mTBI (28). Blueberries contain high amounts of flavonoids including anthocyanins, which contribute to the blueberry’s dark color (11). Anthocyanins could lower brain inflammation and stress caused by mTBI (30). Laboratory studies have shown beneficial effects from blueberry supplementation on various cognitive performance outcomes and symptoms following a mTBI (43, 68). Therefore, consuming foods high in flavonoids, including blueberries, could offer a benefit for healing from a head injury.

This lesson concludes with additional nutritional considerations for those recovering from a mTBI. For example, it is suggested to eliminate the consumption of caffeine following a mTBI (65). Other suggestions include taking note of any foods or drinks that cause vomiting or feelings of nausea, and reducing their consumption for a period of time while mTBI symptoms decrease (72).

Lesson E: Nutrition and Sleep for Injury Reduction and Recovery

This lesson highlights the importance of sleep for performance and injury recovery (19, 49). Youth athletes not getting enough sleep are 1.7 times more likely to get injured (52). School-aged children need 9-11 hours of sleep each night, while teenagers need 8-10 hours of sleep per night (31). It is likely an injured athlete should aim for the upper amount of sleep needed per day. Currently, adolescents aged 13-18 years of age are getting on average 7.7 hours of sleep per night, slightly less than the minimum amount needed (48). Several nutrients have been identified that can naturally aid in hormone regulation associated with sleep (55).

Melatonin: Melatonin is a hormone secreted by the pineal gland that is involved in circadian rhythm and increases total sleep time and may reduce time to fall asleep (13, 55). It is found naturally in several foods including tart cherries (18, 51). In addition, tart cherries include other constituents that have anti-inflammatory and antioxidant effects, which may aid in sleep and recovery (8). Other foods with a high melatonin content include milk, pineapples, oranges, and bananas (18, 55).

Serotonin: Serotonin is another hormone involved in sleep by synthesizing hypogenic substances that influence sleep quality (7, 55). Kiwi fruits are a good source of serotonin and contain several minerals, dietary fiber, and phytochemicals that also may aid in sleep (18, 55).

This section of the lesson also includes other nutritional considerations for quality sleep. For example, some foods that contain caffeine, can make it difficult to fall asleep and the recommendation is to reduce or eliminate its intake closer to bedtime (33). This lesson concludes with tips on how to establish an effective sleep routine such as minimizing screen time before it (33).

Lesson F: Gastrointestinal Issues and Sport

This lesson addresses common GI issues encountered in sport and concludes with practical applications for maintaining gut health.

Nausea and Vomiting: Nausea and vomiting are frequent complaints among athletes across various disciplines (77). These symptoms may result from elevated levels of norepinephrine reducing splanchnic blood flow to the gut, delayed gastric emptying, or increased production of gastric bile acids (77). This lesson outlines several risk factors that may contribute to these symptoms along with simple strategies to help prevent them.

Diarrhea: Diarrhea is a common condition experienced by athletes, particularly among endurance athletes (77). Proposed mechanisms include the secretion of vasoactive intestinal peptide which relaxes smooth muscle in the digestive system (35), and changes in gut motility (77). Many of the risk factors associated with diarrhea overlap with those linked to nausea and vomiting. This section concludes with evidence-informed approaches for minimizing the risk of diarrhea during training and competition.

Heartburn: Heartburn is another GI issue sometimes encountered by athletes during exercise and sport and can be caused by increased abdominal pressure, changes in posture, and changes in exercise intensity (74). Additionally, consuming large meals prior to exercise, not being properly hydrated, and having high levels of stress or anxiety can also trigger heartburn. Chronic heartburn could be caused by gastroesophageal reflux disease or GERD (74). This section provides strategies to prevent heartburn during practice or a game, with an emphasis on taking note of such foods that sometimes cause heartburn in an individual.

This lesson concludes by discussing several strategies to maintain gut health and gut microbiota which may impact immunological function and thus injury risk and recovery from them (75). Rationale for its inclusion within this lesson is from the US Olympic & Paralympic Committee sports nutrition handout on nutrients for GI injury (71). Consuming foods high in probiotics may maintain digestion and absorption while also preventing several GI issues described in this lesson (71). Prebiotic fibers are a type of fermentable fiber that stimulates intestinal bacteria growth and activity (64). In addition, prebiotic fiber consumption is associated with several other benefits including increasing the absorption of calcium, improving cognitive health, and reducing risk of some diseases (14). Therefore, it is important to incorporate prebiotic fibers into one’s diet.

CONCLUSIONS

Nutrition is a cornerstone of health and performance for adolescent athletes not only supporting their growth and development but also their ability to train, compete, and recover effectively (15). Integrating sound nutrition practices into youth athlete development programs is essential for promoting lifelong well-being and optimal athletic potential (16). In addition to enhancing performance, proper nutrition can play a key role in preventing injuries and accelerating recovery when injuries occur (3). To emphasize these critical areas, several new lesson have been added as targeted addendums to the PHP curriculum (25). When combined with the original PHP content, these additions aim to strengthen both general and sport-specific nutrition behaviors, equipping young athletes with the knowledge and habits needed to thrive on and off the field.

Following an injury, it is important to consume adequate kcals from protein, carbohydrates, and unsaturated fatty acids, along with being properly hydrated to facilitate recovery (3). Emphasizing certain micronutrients from food may also improve recovery from injury (3). Additionally, nutritional support is needed for athletes recovering from an mTBI (65). LEA is a common problem in youth sports and understanding its consequences and how to prevent it are important for reducing injury risk (9). Getting adequate sleep is important not only for athletic performance, but also injury prevention and healing from an injury (19, 49). Although not a direct injury caused by sport, GI issues can occur during it, and can be prevented using evidence-based nutritional strategies (77). Next steps are to examine adolescent knowledge of nutritional best practices for recovering from sports-induced injuries.

APPLICATIONS IN SPORT
These supplemental lessons are to serve as adjunct lessons to the PHP curriculum and to provide youth athletes with knowledge on injury management and other sports nutrition topics not otherwise discussed in athletic circles. Additionally, the hope is to encourage further research in this understudied area and add to the growing body of literature examining nutrition practices for injury management in youth athletes.

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2025-09-25T16:05:07-05:00February 18th, 2026|Research, Sport Education, Sport Training, Sports Medicine, Sports Nutrition|Comments Off on Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues
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Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season

Authors: Joanne Spalding1, Jacob L. Grazer2

1Department of Health & Human Performance, Georgia College & State University, Milledgeville, GA, USA

2Department of Exercise Science & Sports Management, Kennesaw State University, Kennesaw, GA, USA

 

Corresponding Author:

Joanne Spalding, PhD, CSCS, CPSS, ACSM-EP

231 West Hancock Street

Milledgeville, GA 31061

[email protected]

478-445-2135

Joanne Spalding, PhD is Assistant Professor in Exercise Science at Georgia College and State University. Her research interests include long term athletic development and monitoring at the club, high school, and college level with an emphasis on neuromuscular fatigue.

Jacob L. Grazer, PhD, CSCS, USAW-1, ACSM-EP is currently a faculty member in the Department of Exercise Science and Sport Management at Kennesaw State University. Jacob’s research interests include: accentuated eccentric loading, countermovement jump analysis, and sport technology.

ABSTRACT 

Purpose:  There has been a growing interest in American women’s college soccer with the majority of research focused on the NCAA Division I level.  With 250+ Division II programs competing in 2024, there is an underrepresentation of this group in the literature. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

Methods:  Twenty-two Division II female soccer field players from one DII college team were monitored over an 18-match season wearing GPS devices, a technology that is more commonly used at prevalent at Division I universities. Field players were divided into one of four positions: (1) Center Back Defenders; (2) Outside Back Defenders; (3) Midfielders; and (4) Forwards. The GPS devices provided data on: (a) total distance traveled; (b) total distance traveled per minute; (c) high-speed running distance traveled per minute; and (d) sprint distance traveled per minute. Due to NCAA Division II substitution rules, the focus in this study is the relative variables calculated as distance traveled per minute.

Results: Descriptive statistics for each GPS measurement are calculated based on the position played.  Then, Analysis of Variance (ANOVA) is used to identify any differences in player workload physical demands based on the position played. For distance traveled during a match, Center Back Defenders traveled the least distance compared to other positions. Similarly, for high-speed running distance, Center Back Defenders traveled the least distance compared to other positions. Finally, for Sprint Distance per minute, both Forwards and Outside Back Defenders traveled more distance compared to Center Back Defenders and Midfielders.

Conclusions: Findings suggest that the physical demands of Division II women’s soccer differ by position. Center Back Defenders tend to ‘stay at home’ and defend the goal while traveling less distance and sprinting less often. Forwards and Outside Back Defenders tend to spend more time sprinting compared to other players.

Applications in Sport: Coaches and practitioners can use this data when designing training programs to ensure their athletes are well-prepared for the differing physical demands of their sport based on the position played.

KEYWORDS: college soccer, GPS, match demands, women’s soccer, physical match performance

INTRODUCTION 

Over the past several years, there has been an increase in the number of peer-reviewed research articles focusing on the physical demands of women’s soccer. The majority of the research that has been conducted has focused on athletes who compete at the professional and international levels (E. Choice et al., 2022). Recently, there has been a growing interest in American college women’s soccer, with the majority of this research focused on National Collegiate Athletic Association (NCAA) Division I level. This can likely be attributed to the fact that wearable technology has become more prevalent at the collegiate Division I level making it easier to collect information regarding the physical demands experienced by female collegiate athletes. However, with 250+ Division II programs competing in the 2024 season, this significant population has been under-represented in research efforts to examine the physical demands on the athlete when competing.  As such, further analysis is needed to better understand the physical demands on athletes participating in the various levels of collegiate women’s soccer. 

Soccer is a high intensity, intermittent sport that requires running, jogging, walking, repeated sprints, jumping and change of direction (Al-Hazzaa et al., 2001; Bloomfield et al., n.d.; Wisløff et al., 2004). Matches consist of two 45-minute halves with a 15-minute halftime period. For international competition, the Fédération Internationale de Football Association (better known as FIFA) sanctions allow for five substitutions per game with a typical squad size of 26 players. However, NCAA soccer permits an unlimited number of players to be on the official roster during the regular season. Substitutions rules for the NCAA also differ from FIFA in that the number of substitutions is unlimited per game, and players are allowed to re-enter the match once in the second half. This difference is notable because it may influence the physical demands placed on players during match play.

Current literature has explored match play data at various playing levels and reports that there are differences in match-play demands at different levels in women’s soccer (E. Choice et al., 2022). A perspective review by Vescovi et al (2021) reported that there is a linear increase in total distance covered across playing levels ranging from youth (~7,500m), collegiate (~9,500m), to professional levels (~10,200m). A systematic review of women’s soccer also identified that playing demands increase between playing levels, and that there are positional differences in terms of total distance covered, high-speed running distance, and sprint efforts where midfielders covered greater total distances and sprint efforts compared to central defenders (Alexander, 2014; E. E. Choice et al., 2023).

Challenges arise when comparing research due to variations of inclusion criteria and determination of velocity thresholds. In addition to inclusion criteria varying, values are typically reported in total distances rather than relative to time spent playing on the field. As highlighted previously, the unique NCAA substitution rules make it challenging to fully grasp the physical demands that are placed on the athletes since it is uncommon for someone to play a full 90-minute match in women’s collegiate soccer. Due to the potential influence of substitution rules on physical demand, more research is needed to gain a greater understanding of the relative demands with respect to playing time. 

Very few studies have investigated the match demands of NCAA DII women’s soccer (E. E. Choice et al., 2023; Gentles et al., 2018). Gentles et al. (2018) assessed and compared accelerometry data to data collected using GPS signal. Although total distance and distance covered in specific velocity zones were reported, statistical analyses were not made comparing playing positions. Whereas Choice et al., (2023) investigated the external and internal load of players, including positional and time-specific differences.  

Due to the lack of research in women’s soccer in general, and specifically at the lower playing levels, there is a need for more research. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

SOCCER TEAM ROLES AND ROLE EXPECTATIONS

Given this study examines the possible differences in physical demands on soccer athletes based on the position played, it is advantageous to clearly outline each position and the expectation of athletes who play that position. Each position carries distinct responsibilities within a team’s formation and can change based on the formation. This study focused on a team that utilized the 4-3-3 formation where positions are separated into center backs, outside backs (right and left), midfielders (center and attacking), and Forwards (central striker and right/left wing) (see Figure 1).

Figure 1 – Visual Presentation of 4-3-3 Soccer Formation

Center Back Defenders

Center back defenders are primarily responsible for defensive organization and central coverage. When in a defensive scenario, center backs are responsible for defending the opposing forwards, intercepting through balls, contesting aerial duels and tackling to gain possession. In possession, center backs initiate playing out from the back, with passes to midfielders or out to forwards, and or switching play across the back line.

Outside Back Defenders

Outside backs (right and left) provide width for the back line and help with transitional play from the defensive half to the attacking half. Out of possession, outside backs typically deal with the opposing forward or winger depending on the opposition’s formation. Similar to center backs, outside backs with attempt to win aerial duels and tackles. In possession, outside backs will look to support the team by overlapping the forward, dribbling the ball up the field, delivering crosses and well as helping maintain possession in the attacking half. 

Midfielders

The center midfielders are composed of two defensive midfielders and one attacking midfielder. The defensive midfielders attempt to shield the back line, break up opposition attacks, and in possession help to maintain and build possession from the defensive to attacking half. The attacking midfielder helps the forward and midfielders with defensive duties and in possession looks to support the forwards, maintain possession as well provide goals and assists. 

Forwards

Forwards help to provide width and are important for stretching the oppositions defense. They will primarily defend the oppositions outside back and recover into midfield positions when out of possession. In possession they will aim to beat defenders one-on-one, deliver crosses and create shooting opportunities. When a player is in the central striker position, they are the focal point of the attacking line and are primarily there to convert scoring opportunities by finishing inside and around the 18-yard box. In possession, the central striker will look to hold the ball up allowing midfielders and other forwards to join the play. They will attempt to make runs to disrupt the defensive lines and apply pressure to the opposition center backs. 

METHODS 

Participants  

A total of 22 NCAA Division II female soccer players participated in this study. Participants were athletes from a team already using a GPS athlete monitoring system. Players were assigned 1 of 4 positions by the team’s coaching staff (Sporis et al., 2009).

  1. Center Backs (CB) (n = 4)
  2. Outside Backs (OB) (n = 5)
  3. Midfielders (MID) (n=6)
  4. Forwards (FWD) (n= 7). 

Procedures  

A total of 18 regular season matches were included in this analysis from a single competitive season. Global Positional System (GPS) devices (TITAN Sports, Titan2, Houston, TX, USA) sampling at 10 Hz were used to track player movement during competition.  Data included 234 observations (n= 79 FWD, n= 70 MID, n= 50 OB, n= 35 CB). The team used for this study played 1-4-3-3 formation (please note 1 = goalkeeper) during all matches included in the analyses. All matches were official NCAA matches with two 45-minute halves and a 15-minute halftime period. All players were informed of the risks and benefits of this study and voluntarily signed an informed consent. This study was approved by the Institutional Review Board.  

Variables for Analysis

Data was collected on the following variables to better understand the physical demands on each player based on position played (see Andersson, 2010).

  • Total Distance – The total distance a player covers during a game. This value is needed to determine the distance covered related to minutes played.
  • Relative Total Distance (TDREL) – The distance covered per unit of time, expressed as meters per minute (m/min).
  • Relative High-Speed Running Distance (HSRREL) – Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.
  • Relative Sprint Distance (SDREL) – Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour.

Using the Global Navigation Satellite System to Measure Match Load  

GPS units were worn in a harness that secured the device between the scapulae (i.e., shoulder blades). The units were turned on 10 minutes before being placed in the harness to ensure sufficient GPS signal was attained per manufacturer’s instructions. Due to the substitution rules in NCAA college soccer, calculating the variables per minutes played was deemed the optimal solution for comparing workload across playing position.

RESULTS 

Three separate one-way ANOVA analyses with Bonferroni corrections were conducted to determine if there were differences between positions for Total Distance (TDREL), High Speed Running Distance (HSRREL) and Sprint Distance (SDREL). Statistical analyses were performed in SPSS (Version 27.0.1). Alpha level was set at p <0.05. Eta-squared effect sizes were calculated to determine magnitude of differences for each variable. For pairwise comparisons, Cohen’s d effect sizes were calculated to determine magnitude of difference between playing positions (Hopkins, 2002).  Descriptive statistics for match physical performance for this specific team are summarized in Table 1.

Table 1 – Match Performance Data

Variables Midfielders (n=70) Forwards (n=79) Outside Back Defenders (n=50) Center Back Defenders (n=35) Total (n=234) 
Minutes Played per Match 51.6 ± 13.6 52.8 ± 15.1 52.1 ±13.9 68.0 ±19.9 57.1 ± 15.6 
Total Distance (m) 5,643.9 ± 1,435.0 5,507.9 ± 1,214.1 5,506.3 ±1,373.8 6365.1 ± 1884.9  Greatest total distance covered due to greatest minutes played5,855.8 ±1,476.8 
Relative Total Distance (m/min) The distance covered per unit of time, usually expressed as meters per minute (m/min). 110.4 ± 14.2 Significantly greater than Center Back Defender106.8 ± 14.4   Significantly greater than Center Back Defender106.8 ±12.6  Significantly greater than Center Back Defender93.8 ± 11.6 105.9 ± 14.5 
Relative High Speed Running Distance (m/min)  Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.  10.06 ± 3.45  Significantly greater than Center Back Defender12.96 ±4.01 Significantly greater than Center Back Defender 13.18 ± 4.21 Significantly greater than Center Back Defender7.53 ± 2.90   11.33 ± 4.26 
Relative Sprint Distance (m/min)  Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour3.45 ± 1.98  6.06 ± 2.64 Significantly greater than Center Back Defender  Significantly different than Midfielders5.53 ± 1.85 Significantly greater than Center Back Defender  Significantly different than Midfielders3.05 ± 1.92  4.72 ± 2.53 

 Relative Total Distance (TDREL)

A one-way ANOVA revealed a significant difference in positions for TDREL, F(3, 230) = 11.9, p = <0.001. An effect size (η2=0.135) indicated a medium effect. Post Hoc analysis indicated that there were mean differences between FWD and CB (p = <0.001), MID and CB (p = <0.001), and OB and CB (p = <0.001).  

Relative High-Speed Running Distance (HSRREL)

There were statistically significant difference for HSRREL, F(3, 230) = 23.73 p < 0.001, with a large effect (η2=0.23). Post Hoc analysis showed mean differences between FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), MID and CB (p = <0.008) and OB and CB (p = <0.001) as seen in Figure 2. 

Relative Sprint Distance (SDREL)

Finally, there were statistically significant differences for positions in SDREL, F=(3,230), 25.547, p <0.001 with a large effect η2=0.25 Post Hoc analysis revealed mean differences for FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), OB and CB (p = <0.001). 

DISCUSSION 

The purpose of this study is to determine the physical demands for NCAA Division II Women’s Soccer and to assess positional differences and physical demands relative to minutes played.  The study’s results include the average demands during match play of each field position as previously shown in Table 1. The main findings of this study indicate that center backs accumulate the greatest total distance, however, this is mainly attributable to extended playing time as they frequently remained on the field 15-17 minutes longer than other positions. When normalized for minutes outside backs, midfielders and forwards covered greater total distance and high-speed running distance than center backs. In addition, outside backs and forwards demonstrated greater sprint distance per minute compared to both center backs and midfielders. 

The average total distance covered for players included in analyses was 5,855 ± 1,476 meters, which differs from prior findings by Choice et al. (2023) who saw starters cover 9,463 ± 2,591 meters. It should be noted that the analysis by Choice et al. (2023) only included players who participated in over 50% of match play. Findings from this study are similar to results reported by Gentles et al. (2018) who reported findings of 5,480 ± 2,350 meters and average minutes played of 45.32 ± 26.01 which are similar to minutes played in this study.  The maximum distance covered by an individual in this study was 9,810 meters, which is much lower than distance covered by the sample in Gentles (13,850 meters) and Choice (12,054 meters), but similar to distances reported in Division I athletes, 9,786 meters (Sausaman, 2019). Both maximum and average distance covered varied across levels may indicate that more research is needed across differing playing levels. Also, from a practical point of view, this should help sport practitioners implement appropriate programs that develop players for both maximum and mean distances, regardless of minutes played.  

To our knowledge, this is the first study to report TDREL (105 ± 14.5 m/min), HSRREL (11.3 ± 4.2 m/min) and SDREL (4.72 ± 2.53) in NCAA Division II athletes. Gentles (2018) reported total distance accumulated at velocity zones 15.00 – 19.99 kph and 20.00-24.99 kph but not relative distances. Due to the substitution rules in NCAA College soccer, there is a high variance in match-to-match playing variables. Other studies have only included players who have played over 50% of the match or the entire match (E. E. Choice et al., 2023), whereas other research only included participants who completed the match in its entirety (Alexander, 2014; Sausaman et al., 2019).  If training programs are only based on average totals, then the whole picture may not be clear when designing training programs for those not participating in 90 minutes. Therefore, reporting data per minute may be more beneficial as reserves need to be trained throughout the season to compensate for minutes not played.  

There was a difference in positional demands for HSRREL, with FWDs covering higher distances than MIDs and CBs. Whereas OBs covered more HSRREL than CBs and MIDs, and MIDs only covered more distance than CBs. Although there was no statistical difference between OBs and FWDs, OBs covered slightly more distance per minute. Although the lack of statistical difference between FWDs and OBs may be due to formation and playing style. In the 4-3-3 OB and FWDs may have similar positional demands as OBs are often part of the attack by providing width higher up the field. Formation on the field was not controlled for in this study so more research is warranted to understand positional differences across a variety of formation styles. 

Results also showed that FWDs covered more SDREL than MIDs and CBs, and OBs covered more than CBs. Although variables are different within this study, results show similar trend within the literature, that forwards cover more HSR distance and sprint distance than midfielders (Sausaman et al., 2019; J. Vescovi, 2013). It should also be noted that CB and OB positions are typically grouped together as a singular position group (commonly reported as defender) (Stolen et al., 2005; J. D. Vescovi, 2012).

This study provides further evidence, particularly in women’s soccer literature, that there is a need to differentiate between central and outside defenders when evaluating physical demands during competition due to the different physical demands during competition (Alexander, 2014; Harkness-Armstrong et al., 2022). It is also important to note that in NCAA Division I studies, players covered more high-speed running and sprint distance on average compared to NCAA division II athletes. This indicates that the Division I game may be played at a faster pace, especially in those critical moments of the game (defensive recovery run, sprinting to goal etc.) As this study reports HSR and sprint distance per minute, it prevents the authors from making additional conclusions or comparisons.  

There are limitations to the study that may have impacted the results.  The data for this study was collected for one team playing one season of competition.  The level of soccer players at all NCAA level can vary greatly across teams and conferences.  Therefore, further research is needed to confirm the current findings. Differences in match demands as well as positional demands may be a result of opposition level, formations, match status, and match location.  As such, additional research is needed to assess these factors to determine their possible influence on the physical demands of Division II women’s soccer.  

CONCLUSION 

This study examined the physical demands of women’s soccer at the NCAA Division II level and analyzed differences among position groups. Unlike previous research, this study assessed Relative total distance (TDREL), relative high-speed running distance (HSRREL), and relative sprint distance (SDREL), providing average values for all players and specific positions to describe match demands. The findings highlight differences in physical demands based on position played. The findings from this study emphasize the importance of evaluating workload relative to minutes played and recognizing the unique demands of each position at the NCAA Division II level. Such insights provide a framework for tailoring training, conditioning and substitution strategies that reflect position-specific requirements rather than relying on team-wide averages.  Future research should continue to examine how positional workloads vary across divisions and competitive levels, as well as how these demands interact with injury risk, recovery and long-term players development. 

APPLICATIONS IN SPORT

These findings provide an insight into general and positional demands of women’s soccer at the NCAA Division II level. These results are particularly valuable to coaches who may strategically utilize the NCAA substitution rules to manage player workloads. Caution should be exercised when applying published findings from other levels of play and relying on total averages as positional differences can substantially influence match demands.

The results emphasize the importance of aligning athlete’s physical attributes with the requirements of their playing positions. Forwards and outsides backs perform the highest sprinting and high-speed running demands per minute and may require players that have higher acceleration and sprint capacities. On the other hand, players that have less high-speed and sprinting demands, but positional awareness may be better suited at center back. 

This may have implications for recruitment. Coaches may want to target athletes with performance profiles that align with the positional demands, rather than relying on general fitness. In regard to player development, training and conditioning programs should be tailored to the requirements of each position moving beyond a one-size-fits-all approach. For example, midfielders may benefit from training designed to sustain higher per-minute workloads, while forwards and outside backs should focus on repeated spring ability as well as increasing top speed. 

Finally, these results may extend to tactical decision making and injury prevention. Coaches may rotate outside backs and forwards more frequently to preserve sprint performance later in matches. These findings provide sport practitioners with evidence-based guidance to optimize recruitment, training and match management in NCAA division II soccer. 


REFERENCES 

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2025-09-25T15:33:55-05:00February 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Sports Health & Fitness, Women and Sports|Comments Off on Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season
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