Protecting Big Data in the Big Leagues: Trade Secrets in Professional Sports
Bibliographic record
Abstract
The protection of trade secrets within the professional sports industry became a hot-button issue in the summer of 2015, after news reports emerged revealing that officials from Major League Baseball’s St. Louis Cardinals were under federal investigation for having illegally accessed proprietary information belonging to their league rival, the Houston Astros. Indeed, professional sports teams in the United States and Canada often possess various forms of proprietary information or processes — ranging from scouting reports and statistical analyses to dietary regimens and psychological assessment techniques — giving them a potential competitive advantage over their rivals. Unfortunately, as with the rest of the economy at-large, little empirical data exists regarding either the types of proprietary information owned by these teams, or the measures that teams are taking to protect their trade secrets. Drawing upon freshly collected survey data, this article helps to fill this void in the literature by providing novel empirical evidence regarding the modern trade secret practices of the teams in the four major North American professional sports leagues. Based on the results of a first-of-its-kind survey conducted in the spring of 2016 of the general counsels of teams in the four major leagues, the article sheds light on both the types of information subjected to trade secret assertion by these firms, as well as the methods they are using to safeguard their data. In the process, the article examines the implications of these survey results for the professional sports industry, while also identifying potential new lines of inquiry for future trade secret research.
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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 | 0.059 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".