Informing Fitness Testing Data Reporting and Visualization in Elite Soccer: A Survey Into Practitioner Preferences and Needs

Asimakidis, ND, Beato, M, Parmar, N, Weldon, A, Bishop, C, and Turner, AN. Informing fitness testing data reporting and visualization in elite soccer: A survey into practitioner preferences and needs. J Strength Cond Res XX(X): 000-000, 2025-This cross-sectional survey explored elite soccer practitioners' fitness testing reporting and visualization preferences. Overall, 145 soccer practitioners completed an online survey comprising 27 questions, with 3 sections: (a) background information, (b) preferences for fitness testing report content and insights, and (c) preferences and interpretation of data visualization formats. Fixed-response questions were analyzed using frequency analysis, while thematic analysis was applied to open-ended questions to generate key themes. Subjects predominantly valued tracking changes (91%) in performance over benchmark (61%) or position-specific (58%) comparisons. Although 44.8% prioritized individual performance, 42.1% emphasized both individual and team-level insights. Concerning the presentation of data, raw scores were the preferred format (62.1%), followed by standardized (37.2%) and composite scores (23.5%), depending on the context. Subjects highly valued interactive dashboards (91%), quadrant charts (87.6%), and traffic light systems (77.2%). Bar charts (77.2%) were the preferred visual format for interplayer comparisons, valued for their clarity, and reduced cognitive load, while line charts (51.7%) were favored for tracking performance over time, due to their simplicity and user familiarity. Radar charts (59.3%) were the preferred format for comparing individual performances to the wider team, valued for their ability to provide a holistic overview of strengths and weaknesses. Uncertainty indicators were endorsed by 52.3%, primarily for enhancing data interpretation and decision making, although concerns were raised about their ease of understanding and visual clutter. Practitioners can use this study's findings to inform user-friendly reporting strategies and meet diverse audience needs. Findings should also support practitioner training and the adoption of evolving industry practices.

Authors

Institutions

Publication Details

Journal
The Journal of Strength and Conditioning Research
Published
2026-03-13
DOI
https://doi.org/10.1519/jsc.0000000000005355
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Informing Fitness Testing Data Reporting and Visualization in Elite Soccer: A Survey Into Practitioner Preferences and Needs

Nimai Parmar, Anthony Weldon, Marco Beato, Nikolaos D. Asimakidis et al.
The Journal of Strength and Conditioning Research
Sports Performance and Training
article

Informing Fitness Testing Data Reporting and Visualization in Elite Soccer: A Survey Into Practitioner Preferences and Needs

Nimai Parmar, Anthony Weldon, Marco Beato, Nikolaos D. Asimakidis, Anthony Nicholas Turner, C. Bishop
article en

Abstract

Asimakidis, ND, Beato, M, Parmar, N, Weldon, A, Bishop, C, and Turner, AN. Informing fitness testing data reporting and visualization in elite soccer: A survey into practitioner preferences and needs. J Strength Cond Res XX(X): 000-000, 2025-This cross-sectional survey explored elite soccer practitioners' fitness testing reporting and visualization preferences. Overall, 145 soccer practitioners completed an online survey comprising 27 questions, with 3 sections: (a) background information, (b) preferences for fitness testing report content and insights, and (c) preferences and interpretation of data visualization formats. Fixed-response questions were analyzed using frequency analysis, while thematic analysis was applied to open-ended questions to generate key themes. Subjects predominantly valued tracking changes (91%) in performance over benchmark (61%) or position-specific (58%) comparisons. Although 44.8% prioritized individual performance, 42.1% emphasized both individual and team-level insights. Concerning the presentation of data, raw scores were the preferred format (62.1%), followed by standardized (37.2%) and composite scores (23.5%), depending on the context. Subjects highly valued interactive dashboards (91%), quadrant charts (87.6%), and traffic light systems (77.2%). Bar charts (77.2%) were the preferred visual format for interplayer comparisons, valued for their clarity, and reduced cognitive load, while line charts (51.7%) were favored for tracking performance over time, due to their simplicity and user familiarity. Radar charts (59.3%) were the preferred format for comparing individual performances to the wider team, valued for their ability to provide a holistic overview of strengths and weaknesses. Uncertainty indicators were endorsed by 52.3%, primarily for enhancing data interpretation and decision making, although concerns were raised about their ease of understanding and visual clutter. Practitioners can use this study's findings to inform user-friendly reporting strategies and meet diverse audience needs. Findings should also support practitioner training and the adoption of evolving industry practices.

The Journal of Strength and Conditioning Research
University of Verona (IT), University of Suffolk (GB), Birmingham City University (GB), Aston University (GB), Centre for Life (GB), Ipswich Hospital (GB), Manchester City Football Club (GB), Middlesex University London (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 21%
Sports Performance and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.