Football Injury Burden
The cost of being absent: an independent analysis of 15,603 recorded football absences across five European leagues. A bilingual interactive study separates frequency, duration, match loss and recording sensitivity, with a literature-backed discussion and a public reproducibility release.
Original figures from the reproducible analysis
What is implemented today
- Explore frequency, median duration and match-loss burden with exact subset summaries.
- Compare original records with a transparent repeated-interval sensitivity scenario.
- Inspect the discussion, verified references, source attribution and data-license boundaries.
- Reproduce the analysis and static site through GitHub and public Kaggle materials.
Context
In rehabilitation, a frequent short absence and a rare long absence imply different resource-planning questions. Public football records offer a useful view of consequences, provided their observation unit and limits remain visible.
Problem
A row is not necessarily an independent clinical injury. Club-context repetition, inclusive date counting, vague labels and missing exposure can make precise-looking summaries misleading.
My role
Independent analysis, data-quality and scientific audit, original visualization, targeted literature synthesis, reproducibility packaging and bilingual web publication. Original data are credited to Sanan Muzaffarov.
What I built
A checksum-verified Python pipeline, a two-view sensitivity audit and an interactive burden matrix with league/season filters. Subset medians are calculated from the underlying records. The public release contains aggregates, code, citations and a Kaggle review notebook, without republishing individual medical histories.
Data or evidence used
Sanan Muzaffarov, European Football Injuries (2020–2025), Kaggle v2, CC BY-SA 4.0. Derived tables and figures retain that license and transformation notices. Original code is MIT. The discussion cites surveillance consensus statements, prospective football studies and public-record validation research.
Limitations
Player names are not verified identifiers; source collection and recorded return dates are unverified. Candidate collapsing reduces summed days by 5.5%, but does not adjudicate unique clinical episodes. Missing healthy-player exposure and eligibility prevent incidence and availability percentages. No causal effect or clinical prognosis is claimed.
Next validation step
Verify stable player IDs, transfer intervals, medical endpoint meaning and eligible observation windows before exposure integration or time-to-return modeling. More modeling would not repair a missing denominator.
Source data and published analysis
- Sanan Muzaffarov. European Football Injuries (2020–2025), Kaggle version 2. CC BY-SA 4.0. ↗
Original source, separately credited from my analysis and derived figures.
- Football Injury Burden: discussion, targeted literature search and verified DOI bibliography. ↗
A critical discussion connecting surveillance definitions, prospective findings and public-record limitations.