Moneyball in the Era of Biometrics: Who Has Ownership Rights Over the Biometric Data of Professional Athletes?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it