Benchmarking Physical Performance in Women’s Football

This post summarises our new research, supported by FIFA, that provides physical performance testing benchmarks across different levels of participation in women's football.

The physical demands of women’s football have increased dramatically in recent years, driven by the rapid professionalisation of the game and the growth of elite competitions worldwide. As I've discussed in recent Sports Science Trends videos, women's sport is evolving quickly, but a significant research gap remains. Until now, practitioners have lacked a comprehensive evidence base to benchmark the physical qualities of female players across performance levels.

Our new open-access paper in Sports Medicine, led by Heidi Compton (née Thornton) and co-authored by Ric Lovell, Dawn Scott, Tzlil Shushan, and myself, addresses this gap. By synthesising nearly two decades of research, we created normative benchmarks that practitioners can apply directly in performance profiling, athletic development, and talent identification.

Even more importantly, the project includes an interactive dashboard, enabling coaches and practitioners to explore the data and compare their players against reference values.

Why Benchmarking Matters

Benchmarking gives context to testing and monitoring data. Without reference values, it is difficult to interpret whether a player’s score represents a strength, a weakness, or something typical for their competition level.

In men’s football, decades of research and large-scale data sharing have created a relatively clear picture of expected performance standards. Women's football, like many women's sports, has historically suffered from reduced attention and investment, contributing to the gender data gap (Cowley et al., 2021; McQuilliam et al., 2022).

This work represents a step towards closing that gap by offering:

• Evidence-based reference points for player profiling

• Comparisons across competition tiers from amateur to international

• Applied tools that can directly inform programme design and talent pathways

How the Study Was Conducted

This was a systematic review and meta-analysis, pooling existing evidence to establish normative data for women’s football performance.

• Studies included: 288

• Athlete groups: 982

• Total players: 18,722

• Observations analysed: Over 32,000

Figure 1. Summary of the research included in the study and underpinning the interactive dashboard

We faced the challenge of comparing test outcomes across different competitive levels in the women's game. We used the Participation Classification Framework (McKay et al., 2021), which I've previously discussed on the blog here, as a starting point. However, the realities of women's football (such as very large disparities in financial investment) led us to modify the tier levels based on expert consultation and FIFA's global league rankings (see below).

Figure 2. Our Participation Classification Framework (modified from McKay et al., 2021)

The analysis focused on seven key physical performance qualities:

1. Cardiorespiratory fitness

2. Acceleration

3. Sprint ability

4. Change of direction ability

5. Maximal velocity

6. Strength

7. Power

   
   

Within each of these were a variety of contextual layers that required careful consideration. The physical qualities were therefore, broken down according to testing protocol, and then further based on the procedure, instrument and/or technology used for evaluation (see below) - all of which was extracted from each study.

By standardising data across tests and studies, we could compare results across competition levels and quantify the differences between tiers.

Key Findings

From over 18,000 athletes, we established normative benchmarks across all seven qualities for a variety of test protocols and technologies. These benchmarks (all available in the open-access full text in Sports Medicine) offer valuable insights for women’s football practitioners, allowing for more objective evaluations to support decision-making in the areas of athletic development, performance readiness and talent identification.

Figure 3. Differences across tier levels in YYIRL1 distance covered.

In terms of participation level differences, field-based assessments of intermittent aerobic capacity, including the Yo-Yo Intermittent Recovery Test Level 1 (YYIRL1) and the 30–15 Intermittent Fitness Test final velocity (VIFT), differentiated cardiorespiratory fitness across performance levels, with higher results observed in Tiers 4 and 5 compared with Tier 2.

In addition, sprint performance and lower limb power, assessed via squat and countermovement jumps, were greater in higher participant tiers (i.e. competition level), with the most pronounced differences observed between Tier 2 and Tiers 4 and 5.

Together, these results suggest that intermittent fitness, sprinting, and power may be the most critical physical qualities associated with progression in women’s football. It remains however, that longitudinal data is required to understand potential causal effects.

Making It Practical: The Dashboard

To bridge the gap between research and applied environments, the brilliant Heidi Compton (née Thornton) developed an interactive Shiny dashboard.

Practitioners can:

• Select a physical quality of interest

• View descriptive statistics across tiers and age groups

• Explore specific datapoints from the underlying research

• Use the tier-specific normative values for comparisons with their own squad or individual athletes.

👉 Explore the dashboard here

For example, the figure below - taken directly from the dashboard - illustrates VIFT across participation tiers. As I've previously discussed, it's important we reveal not conceal our data in sports science, and this bubble plot shows both averages and the underlying datapoints, enabling practitioners to see now just the central trend but also the spread of results.

In addition, you can hover to view the original study citation, the individual effect estimates included in the meta-analysis, and the relative weighting of each datapoint.

Figure 4. Meta-regression bubble plot below depicts the modifying effect of performance tier on the model's estimate. Error bars represent the 90% confidence limits of the estimate (middle bar), and the further limit represents 90% prediction limits.

We also illustrated applications of benchmarking using z-scores, t-scores, and STEN scores, demonstrating how practitioners can contextualise individual or squad data against population norms.

Figure 5. Example applications of meta-analysis data for benchmarking purposes in applied settings; A benchmarking scores presented at a team level with a standardised ten score (STEN); B benchmarking scores presented at an individual level with Z-scores. YYIRL1 Yo-Yo Intermittent Recovery Test Level 1, CMJ countermovement jump, 1RM one repetition maximum

Why This Matters for Women’s Football

The meta-regression results, accessible through the web-based dashboard, provide an interactive platform for practitioners to compare their athletes not only against broader cohorts of women's football but also tier-specific benchmarks.

This tool enhances the practical utility of our findings by making them readily applicable in day-to-day training environments. In particular, these benchmarks can support:

• Athlete profiling: Identifying strengths and weaknesses relative to competition standards

• Training design: Prioritising physical qualities most associated with progression to higher tiers

• Talent identification: Offering objective data to support pathway decisions

• Communication: Providing simple visuals to engage players, coaches, and stakeholders

What’s Next?

While this is a major step forward, future research should aim to:

• Expand datasets with position-specific benchmarks

• Incorporate female health considerations (e.g. hormonal influences, injury risk)

• Integrate technical and tactical metrics alongside physical benchmarks

• Include consistent reporting of mean age, competition details (i.e. country, league name, and division), position-specific data, stage of season, and more detailed test protocols and conditions.

• Encourage data-sharing collaborations to build even larger, more representative datasets

Our research highlighted areas where evidence remains limited, such as position-specific demands, contextual influences (e.g. match congestion, menstrual cycle), and longitudinal development data.

Final Thoughts

Women’s football is evolving rapidly, and so too must the science that underpins it. This benchmarking study and interactive dashboard provide practitioners with tools to evaluate, train, and develop players with greater precision. The findings highlight the role of sprinting ability, lower-limb power, and intermittent aerobic capacity in differentiating athletes across the performance scale.

Of course, variability in testing protocols and classification systems presents limitations to this type of research. But this only underscores the need for more standardised performance assessments to support a globally consistent development pathway. As the women’s game continues to evolve, so too will its physical demands, making it essential to grow both the quantity and quality of research in this space. Our hope is that the recommendations in this paper, especially around consistent reporting standards, become commonplace.

While this blog provides a summary, there is much more detail in the full paper. I encourage you to read the open-access article and explore the interactive dashboard to dive deeper into the data and apply it within your own environment.

This project was a huge undertaking, and I want to thank my fellow authors for their collaboration. In particular, Heidi Compton and Tzlil Shushan deserve special credit for their incredible effort: from the epic analysis you see in the paper to personally contacting nearly 200 research authors to clarify study details!

We are also grateful to Belinda Wilson and the FIFA Women’s Health & Performance Programme for their support and partnership, without which this project would not have been possible. Keep an eye on my channels for more information about the wider FIFA Female Health Project.

I believe this paper helps fill a major gap in women’s football research. My hope is that practitioners will use both the publication and the interactive dashboard to strengthen the evaluation, preparation, and development of female players worldwide.

👉 Read the full text here

👉 Explore the interactive dashboard here

Compton H, Lovell R, Scott D, Clubb J, Shushan T. Benchmarking the Physical Performance Qualities in Women’s Football: A Systematic Review and Meta-analysis Across the Performance Scale. Sports Medicine. 2025. https://doi.org/10.1007/s40279-025-02251-0