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.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 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 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".