Data Reporting: Delivering Insights Effectively to Coaches and Athletes

Prerequisites: This article assumes familiarity with external and internal load concepts and basic GPS/tracking system principles. If any of these topics are new to you, start with:

External and Internal Load: Concepts and Monitoring

Understanding GPS, Accelerometers, and Tracking Systems

Learning Objectives

Explain audience-centered visualization design principles, including universal design and pre-attentive attributes.
Select appropriate visualization formats for different data types and messages, and represent uncertainty honestly.
Distinguish tailored reporting strategies and delivery timing for different stakeholders (coaches, athletes, medical staff).
Recognize principles and ethical pitfalls of data storytelling, including cherry-picking.
Apply trust-building and communication strategies as prerequisites for effective information delivery.

Why Data Reporting Matters

Sport scientists collect vast amounts of data. GPS tracking generates thousands of data points per session. Wellbeing questionnaires arrive daily. Force plate assessments produce time-series curves. The challenge is not collection — it is converting this data into decisions that improve athlete performance and availability.

Coaches do not operate from perfect memory. When qualified football coaches were asked to recall critical events from a match, their overall accuracy reached only 59.2% (Laird & Waters, 2008). Shooting events were recalled at 95%, but possession-related events dropped to 32.5%. The recall gap widens further when considering interpretation rather than simple factual memory. When coaches viewed the same 20-minute video clip and answered identical questions about tactical formations and key players, inter-coach agreement was extremely low, with Fleiss-Kappa values ranging from -.036 to .236 (Furley et al., 2024). The same footage produced over 30 different tactical themes across a sample of 15 coaches.

These findings do not diminish coaching expertise. They demonstrate that systematic data reporting is an essential complement to experiential knowledge — not a replacement for it. The sport scientist’s role is to support decision-making, not to make decisions on behalf of coaches (Brewer, 2022). Key performance decisions emerge from expert discussions around data, with the data providing a shared reference point rather than a final answer.

Data reporting is the structured process of communicating collected and analysed data to stakeholders. Its quality determines whether monitoring efforts produce actionable insights or merely occupy storage space.

Audience First — Universal Design and Stakeholder Tailoring

The most common failure in data reporting is designing for yourself rather than the recipient. A performance team includes individuals with diverse backgrounds, technical capabilities, and time constraints. The sport scientist who understands z-score transformations must present information in ways that a head coach — who may have two minutes between sessions — can interpret immediately.

Universal design offers seven principles originally developed for physical environments that transfer directly to data visualization: equitable use, flexibility in use, simple and intuitive use, perceptible information, tolerance for error, low physical effort, and appropriate size and space (Bosch & Tran, 2022). Applied to reporting, these principles mean that a wellbeing dashboard should be readable by anyone on the performance team without requiring a statistics background.

Every report represents an opportunity for education. When stakeholders interact regularly with well-designed visualizations, their data literacy — the ability to read, understand, and critically evaluate data — improves over time. Rather than simplifying to the point of losing meaning, the goal is progressive complexity: start accessible, build understanding gradually.

Different stakeholders require different information from the same dataset. Coaches focus on session planning and match preparation. Medical staff prioritise injury risk and return-to-play monitoring. Athletes benefit from individual trend data that connects their daily behaviours to performance outcomes. A survey of 206 football practitioners worldwide confirmed this diversity in practice: spreadsheets remained the most common tool for data cleaning (76%) and reporting (62%), while summary tables (92%) were the most frequent content format (Dello Iacono et al., 2025). The persistence of spreadsheets across elite environments underscores that tools matter less than communication clarity.

A professional practice framework for applied performance analysis (PA) identifies nine components of effective practice, with reporting to key stakeholders and facilitation of feedback to athletes positioned as distinct activities requiring different skills (Martin et al., 2021). Reporting is not merely producing a document — it involves selecting information, matching format to audience, and timing delivery for maximum impact. The guiding principle is “simple but powerful”: focus on three to four key bullet points that highlight the most important information (Le Meur, 2022).

The Science of Visualization — Pre-Attentive Attributes and Chart Selection

Effective data visualization exploits how the human visual system processes information. Before conscious awareness engages, the brain performs rapid pre-attentive processing — recognising patterns in under 200 milliseconds. Four visual properties drive this process: colour, form, spatial positioning, and movement (Bosch & Tran, 2022). A red dot among grey dots is detected instantly. A steeper line slope communicates faster change without requiring numerical reading.

Gestalt principles describe how humans infer organisation and relationships in visual information: proximity (nearby elements are grouped), similarity (same-coloured elements belong together), continuity (the eye follows smooth paths), and closure (the brain fills incomplete shapes). Sport scientists apply these principles when colour-coding position groups or aligning temporal data along consistent axes.

Choosing the right chart type depends on the data and the message:

Visualization	Best For	Caution
Line graph	Time-series trends	Missing data creates false linear interpolation.
Point plot	Individual variability within a group	Can become cluttered with large squads.
Violin plot	Distribution shape and range	Requires larger samples to be meaningful.
Radar chart	Multi-metric athlete profiles	All variables must share appropriate scaling.
Heat map	Spatial patterns, multi-variable team comparison	Colour saturation can obscure small differences.
Bar graph	Cumulative totals	Inappropriate for group means (a mean is not accumulation).

A critical principle: always show individual data points rather than team averages alone. A team average of zero on a z-score scale may hide half the squad scoring very high and half very low — representing no individual accurately. Within-individual comparison using z-score transformation (z = (x − μ) / σ) places every athlete on the same scale relative to their own baseline, revealing meaningful personal deviations that raw scores may obscure (Bosch & Tran, 2022).

Colour requires deliberate restraint. The traffic light system (red-yellow-green) is widespread in sport science but carries limitations. Colour-blind users cannot distinguish these hues. Cutpoint-based colouring creates artificial boundaries between very similar values — a z-score of -1.99 appears green while -2.01 appears red, despite negligible practical difference. For continuous data, colour gradients communicate proximity between values more honestly than discrete categories.

Communicating Uncertainty Honestly

Every measurement carries uncertainty. Skinfold-derived body composition estimates differ from DXA-derived values. GPS velocity data has known measurement error at high speeds. Wellbeing questionnaires reflect momentary states influenced by sleep, mood, and social context. Ignoring these limitations creates a false sense of precision that can lead to poor decisions.

Scientific integrity demands that sport scientists communicate what data does not mean with the same clarity as what it does mean (Bosch & Tran, 2022). The principle is straightforward: never convey non-existent precision.

Sources of uncertainty in sport science data include measurement error inherent to the device, statistical estimation from modelling, approximation from incomplete data, and biological variability within and between individuals. Practical approaches to visualising uncertainty include:

Error bars or confidence intervals showing the range within which the true value likely falls.
Individual data points displayed alongside summary statistics, revealing distribution shape.
Transparency encoding where higher opacity represents greater certainty.
Multiple information sources integrated to provide converging or diverging evidence.

The Least Significant Change (LSC) represents the minimum change magnitude needed to exceed measurement error with statistical confidence. When a body composition assessment shows a 1.2% decrease in body fat but the LSC for that method is 2.0%, the observed change cannot be distinguished from measurement noise. Reporting such a change without this context misleads decision-makers.

Pre-post test reporting follows three rules: show every athlete’s individual change rather than group averages alone, specify whether the change exceeds measurement error, and use raw values rather than percentage change — small denominators exaggerate percentage shifts (Bosch & Tran, 2022). These practices embed uncertainty communication as a standard reporting feature rather than an afterthought.

Data Storytelling — Power and Pitfalls of Narrative

Raw numbers rarely drive action. A table showing 47 training load values across a squad tells a sport scientist something meaningful, but a narrative connecting those values to a tactical shift, a fixture congestion period, and a subsequent injury cluster communicates urgency. Data-driven storytelling organises data-supported facts into a narrative structure that creates meaningful connections between information and decisions.

The five-part dramatic structure known as Freytag’s Triangle provides a template: introduction establishes context, rising action builds tension through data trends, climax identifies the critical finding, falling action explores implications, and resolution proposes a course of action (Bosch & Tran, 2022). A training load report might introduce with the fixture schedule, build through accumulated weekly loads, reach a climax at a load spike three weeks before an injury cluster, explore possible contributing factors, and resolve with adjusted periodisation recommendations.

The danger lies in reversing this process. Cherry-picking occurs when a practitioner selects only the data that supports a predetermined conclusion while ignoring or minimising contradictory evidence. Seven guidelines protect against this pitfall: never define the narrative before analysing the data, be cautious of confirmatory analysis supporting decisions already made, conduct peer review and critique, make underlying data transparent and reproducible, acknowledge alternative interpretations, explicitly identify limitations and unknowns, and continuously update visualizations based on recipient feedback (Bosch & Tran, 2022).

This distinction between “knowing more” and “knowing better” applies directly to reporting practice (O’Sullivan et al., 2023). Accumulating metrics does not automatically improve decisions. The same KPI can produce spurious correlations or mask endogeneity — where the outcome variable influences the predictor rather than the reverse (Morgulev & Lebed, 2024). A report showing that teams with more fast breaks win more games may confuse cause and effect: leading teams may face opposition pressing that creates fast-break opportunities, rather than fast breaks causing wins. Responsible storytelling acknowledges these complexities rather than flattening them into simple causal claims.

Timing and Format — When and How to Deliver

Data that arrives too late loses its value. The appropriate delivery window depends on the data type and the decision it supports.

Daily athlete measures (wellbeing questionnaires, RPE, workload summaries) should reach athletes before they leave the facility that day. Ideally delivery approaches real-time; at minimum, information must be available before the next training session. Athletes who never see their data used lose trust in the monitoring process (Bosch & Tran, 2022).

Periodic monitoring (force plate assessments, isokinetic testing, sprint profiles) requires immediate feedback contextualised within the athlete’s personal norms and trend history. Failing to evaluate and communicate results promptly undermines the perceived value of data collection.

Match-related data can be provided more slowly because single-match samples contain substantial noise. These data gain meaning only when presented within a larger reference frame showing trends across multiple matches rather than isolated values.

Automation supports consistency. Automated reporting pipelines reduce manual entry errors and ensure visual consistency across reports — a frequently overlooked benefit that builds recipient familiarity with report layouts (Bosch & Tran, 2022). Among elite football practitioners, 76–79% of those in top-tier leagues reported using real-time data for in-session training modifications, compared to approximately 54% in lower tiers (Dello Iacono et al., 2025). This gap likely reflects both technological access and established workflows rather than differences in data value.

Format should match context. Information recalled from infographics is 6.5 times higher than from text alone (Le Meur, 2022). A weekly load summary delivered as a wall-mounted infographic in the changing room serves different communication goals than the same data presented in a spreadsheet during a Monday morning staff meeting. Both may be necessary for different audiences and decision timeframes.

Trust and Relationships — What Comes Before the Report

The most technically sophisticated report fails if its recipient does not trust the person delivering it. Information dissemination is fundamentally about psychology — understanding how stakeholders think, behave, and what solutions will genuinely help them (Le Meur, 2022).

Coaches acquire knowledge primarily through peer discussion (42%), books (12%), and observing other coaches (11%). Academic journals rank at approximately 2% (Le Meur, 2022). Scientific literature was identified as the least preferred evidence source regardless of department, context, or competition level (Dello Iacono et al., 2025). This does not reflect anti-intellectualism — it reflects a preference for procedural knowledge delivered through social interaction rather than formal academic channels.

The delivery of data-based feedback also carries interpersonal risks. Video-based performance feedback sessions can reproduce asymmetric power dynamics where the analyst or coach uses expert power and informational power to impose interpretations, constraining athletes to respond rather than engage (Groom et al., 2012). One-directional delivery risks producing resistance and non-learning rather than behaviour change. The alternative is dialogue-based feedback where data serves as a shared reference point for collaborative interpretation. The role of the applied performance analysis practitioner extends beyond delivering knowledge — it involves designing learning opportunities that enable stakeholders to understand, interpret, and act on information independently (Martin et al., 2021).

Effective performance analysts operate as value co-creators within the performance ecosystem — functioning as curators, translators, influencers, and educators (Martin et al., 2023). Embeddedness within the organisation enables the contextual intelligence, relationship building, and credibility required to translate data into accepted knowledge. This cannot be achieved from a distance or through reports alone.

The quality of staff communication relates directly to injury burden and athlete availability (Pillitteri et al., 2024). When multiple staff members deliver conflicting interpretations to coaches, or when monitoring intervention recommendations lack centralised coordination, confusion rather than clarity results. Intervention decisions based on monitoring data should flow through a single designated person to maintain consistency (Brewer, 2022).

Trust-building is a long-term investment requiring specific behaviours: understanding and respecting others’ work, learning from their expertise, being perceived as a resource rather than a constraint, acknowledging errors openly, and maintaining emotional stability under pressure (Le Meur, 2022). Curiosity and communication are the essential qualities for sport scientists operating successfully in applied environments (Brewer, 2022). When entering a new environment, the first week represents a critical window for establishing expectations — prioritising learning from others’ expertise and experience before asserting one’s own knowledge accelerates trust development (Marsh et al., 2023). In complex and ambiguous situations, soft influence tactics — rational persuasion, consultation, and inspirational appeals — prove more effective than hard tactics such as direct requests or legitimation (Marsh et al., 2023). Securing coach commitment through one-on-one dialogue is more powerful than one-directional presentations.

Key Takeaways

Coaches recall only approximately 59% of match events accurately, and inter-coach agreement on identical video data is extremely low (Kappa -.036 to .236), making systematic data reporting an essential complement to intuition.
Effective visualization begins with understanding the audience’s background, capability, and context, following universal design principles — every report is an opportunity for education and progressive data literacy development.
Choose visualization types matching data characteristics and messages, and always show individual data points rather than team averages alone — a team mean may represent no individual accurately.
Include uncertainty as a standard feature in visualizations and specify whether changes exceed measurement error (LSC) — never convey non-existent precision.
Data storytelling is powerful for communication, but never define the narrative before analysis, acknowledge alternative interpretations, and state limitations openly to prevent cherry-picking.
Delivery timing varies by data type: daily measures before the athlete leaves that day, periodic monitoring immediately with personal trend context, and match data within a multi-match reference frame.
Report effectiveness depends more on trust relationships with stakeholders than on technical quality — information dissemination is fundamentally about psychology, requiring embeddedness, curiosity, and sustained communication investment.

References

Bosch, T. A., & Tran, J. (2022). Data delivery and reporting. In D. N. French & L. Torres Ronda (Eds.), NSCA’s Essentials of Sport Science. Human Kinetics.
Brewer, C. (2022). Performance interventions and operationalizing data. In D. N. French & L. Torres Ronda (Eds.), NSCA’s Essentials of Sport Science. Human Kinetics.
Dello Iacono, A., Datson, N., Clubb, J., Lacome, M., Sullivan, A., & Shushan, T. (2025). Data analytics practices and reporting strategies in senior football: insights into athlete health and performance from over 200 practitioners worldwide. Science and Medicine in Football, 10(1), 80-95. https://doi.org/10.1080/24733938.2025.2476478
Furley, P., Mehta, S., Raabe, D., & Memmert, D. (2024). Objectivity of match analysis in football: Testing the level of agreement between coaches’ interpretations of video data. International Journal of Sports Science & Coaching, 20(1), 45-55. https://doi.org/10.1177/17479541241278603
Groom, R., Cushion, C. J., & Nelson, L. J. (2012). Analysing coach–athlete ‘talk in interaction’ within the delivery of video-based performance feedback in elite youth soccer. Qualitative Research in Sport, Exercise and Health, 4(3), 439-458. https://doi.org/10.1080/2159676x.2012.693525
Laird, P., & Waters, L. (2008). Eyewitness recollection of sport coaches. International Journal of Performance Analysis in Sport, 8(1), 76-84. https://doi.org/10.1080/24748668.2008.11868424
Le Meur, Y. (2022). Information dissemination. In D. N. French & L. Torres Ronda (Eds.), NSCA’s Essentials of Sport Science. Human Kinetics.
Marsh, J., Cosgrave, D., Guyett, S., Caffrey, P., & McGregor, P. (2023). Coach and staff integration. In A. Calder & A. Centofanti (Eds.), Peak performance for soccer: The elite coaching and training manual. Routledge.
Martin, D., O’Donoghue, P. G., Bradley, J., & McGrath, D. (2021). Developing a framework for professional practice in applied performance analysis. International Journal of Performance Analysis in Sport, 21(6), 845-888. https://doi.org/10.1080/24748668.2021.1951490
Martin, D., O’Donoghue, P. G., Bradley, J., Robertson, S., & McGrath, D. (2023). Identifying the characteristics, constraints, and enablers to creating value in applied performance analysis. International Journal of Sports Science & Coaching, 19(2), 832-846. https://doi.org/10.1177/17749541231180243
Morgulev, E., & Lebed, F. (2024). Beyond key performance indicators. German Journal of Exercise and Sport Research, 54(3), 335-340. https://doi.org/10.1007/s12662-024-00944-8
O’Sullivan, M., Vaughan, J., & Woods, C. T. (2023). Not just to know more, but to also know better: How data analysis-synthesis can be woven into sport science practiced as an art of inquiry. Sport, Education and Society, 29(9), 1114-1132. https://doi.org/10.1080/13573322.2023.2261970
Pillitteri, G., Clemente, F. M., Sarmento, H., Figuereido, A., Rossi, A., Bongiovanni, T., Puleo, G., Petrucci, M., Foster, C., Battaglia, G., & Bianco, A. (2024). Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals. International Journal of Sports Science & Coaching, 20(1), 388-406. https://doi.org/10.1177/17479541241289080

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