Moneyball in the Era of Biometrics: Who Has Ownership Rights Over the Biometric Data of Professional Athletes?
Bibliographic record
Abstract
The 2003 release of Michael Lewis’s book, Moneyball, brought into the mainstream a new paradigm for professional sports management: the use of statistical analysis to identify currently undervalued athletes in an effort to gain a competitive advantage. This pressure to accurately value athletes has led, in part, to the widespread collection of professional athletes’ biometric data. While biometric data can create many benefits, its misuse can lead to detrimental outcomes for the athletes, including inequitable contract negotiations, loss of potential revenue from monetization of said data, and a loss of privacy. Thus, this paper seeks to determine who holds the ownership rights over biometric data collected from professional athletes. I argue that the question of ownership is unanswered by the collective bargaining agreements and standard player contracts for professional sports leagues in North America, as well as by the Personal Health Information Protection Act and the Personal Information Protection and Electronic Documents Act. I turn to the precedent set by the Supreme Court of Canada regarding ownership of patient medical records to conclude that ownership rights over the biometric data belong to the party collecting such data, and not the athletes themselves. Nevertheless, the collective bargaining agreements and relevant legislation afford athletes some protections against the misuse of their biometric data.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".