Prevent Defense: Trade Secret Protection in Professional Sports
Bibliographic record
Abstract
Data has never been more important in professional sports. On the field, players obsess over advanced statistics and the use of analytics. Off the field, front offices use newfound statistics and modeling to gain a competitive edge in setting starting lineups, drafting players, and structuring contract extensions. Stadium scoreboards and baseball cards now prominently display statistics that were an afterthought a generation ago. Thus, with this growth in analytics comes a need for teams and leagues to protect their information. Trade secret disputes are nothing new—companies have always taken efforts to protect their secrets and limit unfair competition. But, with the growth of data in front offices, this trend appears likely to enter the sports arena. Recent litigation between the New York Knicks and the Toronto Raptors highlights the issues teams and sports leagues may face in protecting their data and in determining which legal arena should hear the dispute. Further, with the growth of sports gambling, teams and leagues have additional incentives to protect their information from outsiders and competitors. This Article analyzes the current framework under the law and governing documents of the “Big 4” leagues: Major League Baseball (MLB), the National Basketball Association (NBA), the National Football League (NFL), and the National Hockey League (NHL). Additionally, this Article analyzes the issues presented in the ongoing legal saga between the Knicks and the Raptors before proposing solutions for teams and leagues to consider in order to avoid these issues, and when they inevitably occur, how to limit their exposure in the public arena.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".