The Qualifications, Demographics, and Characteristics of a Major League Baseball General Manager
Bibliographic record
Abstract
The 2008 World Series between the Philadelphia Phillies and Tampa Bay Rays was a study in contrast for the two teams’ front offices. The eventual champion Phillies were led by General Manager (GM) Pat Gillick, who had 45 years of experience in Major League Baseball (MLB) front offices. Gillick, 71, had previously been General Manager of the Toronto Blue Jays, Baltimore Orioles and Seattle Mariners after having broken into the industry in the scouting departments of the Houston Colt .45s and Astros and New York Yankees. The Phillies victory was Gillick’s third World Series title as a GM, having guided the Blue Jays to championships in 1992 and 1993. In his 27 years a GM, Gillick’s teams made the playoffs 11 times. On the other hand, the Rays GM was 31-year old Andrew Friedman, who was only in his fifth year in MLB. Like Gillick, Friedman played college baseball. However, Friedman never made it to the minor leagues like Gillick. Instead, Friedman, who earned a B.S. in management with a concentration in Finance from Tulane University, worked on Wall Street, first for Bear Stearns then for MidMark Capital. Friedman got into baseball after he had a chance to meet Rays principal owner Stuart Sternberg, a fellow New Yorker who made his fortune on Wall Street. The two unique paths of Gillick and Friedman exemplify the increasingly divergent paths MLB GMs have taken to their positions. In any case, the obligations of a MLB GM are difficult and wide-ranging. The first part of this article will examine some of the duties of a GM, including representing the organization at league meetings, preparing for amateur player drafts, negotiating with agents, representing the club during salary arbitration, dealing with the media, managing the club’s payroll, ensuring compliance with MLB rules and the collective bargaining agreement and of course creating and developing the clubs roster. The second part of the article will examine the characteristics and experiences of MLB GMs including playing experience, coaching experience, education, age, gender, race, family ties and career path. In addition, the article will provide a longitudinal study, showing how these traits have changed over 20 years, comparing GMs from 1989, 1999 and 2009.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".