Scraping the Surface: A Guide to Data Collection Using Web Scraping in Sports Medicine
Bibliographic record
Abstract
ABSTRACT: Pinkoski, AM, Ward, P, Kluzek, S, Arundale, AJH, and Bullock, GS. Scraping the surface: a guide to data collection using web scraping in sports medicine. J Strength Cond Res 39(12): e1473-e1479, 2025-Publicly obtained injury data, which can be combined with performance data for the team and individual, have become an attractive option in sports medicine research. Publicly obtained injury data are primarily collected through reading a website's source code and converting the results into a format that can be used for further analysis, a process known as web scraping. Despite its growing popularity and public domain in which data sources exist, studies that use these methods often do not disclose their methods of extraction and are subject to replicability issues. The adoption of Open Science practices through transparency of methods and sharing of code or data sets is one such manner to ensure reproducible and meaningful results across sports medicine research and applied settings. The purpose of this article was to (a) operationalize and describe data-scraping methods, (b) describe the strengths and weaknesses of data-scraping methods in an applied sports medicine setting, and (c) provide a practical example with adjoining data and code on how to use replicable and reliable data-scraping methods in applied sport settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.100 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".