MétaCan
Menu
Back to cohort
Record W117814413

Baseball's Contraction Pains. (Articles)

2003· article· en· W117814413 on OpenAlexvenueaboutno aff
Paul D. Staudohar, Franklin Lowenthal

Bibliographic record

VenueNine · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueGrievanceNegotiationArbitrationPoliticsFranchiseAbandonment (legal)Political scienceBusinessLawMarketing
DOInot available

Abstract

fetched live from OpenAlex

On November 6, 2001, owners of Major League baseball teams voted 28-2 in favor of eliminating two teams by start of 2002 season. The vote generated a welter of controversy, nearly all in opposition to plan. Although legal roadblocks quashed timely implementation of proposal, contraction remains a lively future option. This paper examines key economic, legal, and political aspects of contraction. What teams are most likely to be affected, and how would remaining teams gain? Will a bill in Congress remove a part of baseball's antitrust exemption, and what is effect of court decisions in Minnesota and Florida? What is impact of an arbitration decision on a grievance filed by Major League Baseball Players Association? Also of interest are outcomes that may result from contraction. How, for example, will players whose jobs have been lost be made available to other teams? What influence does contraction have on negotiations for a new collective bargaining agreement between owners and players? BACKGROUND Over years Major League Baseball (MLB) has had numerous franchise sales, relocations, and additions through expansions. (1) Four new franchises were added by MLB in 1990S. Since 1901, however, when American League was formed, there has not been a franchise abandonment. (2) In contrast fourteen NBA franchises were dissolved from 1946 to 1959, NFL lost thirty-eight franchises from 1920 to 1959, and NHL shed six franchises from 1917 to 1979. (3) Thus, for over a hundred years in baseball, and from two to four decades in other professional team sports, no franchises have been eliminated. Instead number of sports franchises has expanded significantly in recent years, perhaps by too much. Should MLB eliminate teams, it would not be surprising if other overexpanded sports leagues were to do same. This would end a long period of growth, and emphasis would shift from quantity to quality and greater financial stability. When MLB commissioner Bud Selig announced owners' vote, he did not specify two teams, saying only that the teams to be contracted have a long record of failing to generate enough revenues to operate a viable major-league franchise. (4) Because owners who voted against contraction were Jeffrey Loria of Montreal Expos and Carl Pohiad of Minnesota Twins, speculation focused on their teams as ones to be eliminated. As events unfolded other teams were identified as candidates for elimination, apparently for subsequent years, with a likely total of four subtracted teams over a two- to three-year period. ECONOMIC RATIONALE As shown in Table 1, Montreal and Minnesota were franchises with lowest estimated values in 2001. Other low-ranked franchises that are considered candidates for elimination include Tampa Bay Devil Rays, Kansas City Royals, and Florida Marlins. As indicated in table, each of these teams lost money in 2001, and amount of losses far exceeded league average. Whether to eliminate a franchise can be viewed as a capital budgeting decision based on models that include time value of money. Using net present method it can be shown that contraction is a sound course of action for owners functioning as a league or single business entity. The financial impact of contraction will be different for each of owners of remaining twenty-nine (or twenty-eight) teams; exact impact on each is impossible to predict since it is unknown how revenue sharing will be revised among surviving teams once contraction occurs. Since we cannot ascertain future cash flows for each of these teams, it is impossible to use a capital budgeting model to formulate a decision for each of these teams. Instead we will consider remaining twenty-nine (or twenty-eight) teams to constitute a single business entity, and we will analyze contraction decision as one that is made by this business entity (for example, we could consider MLB to be a partnership with twenty-eight partners buying out other two partners). …

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.095
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0950.030

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.

Opus teacher head0.012
GPT teacher head0.198
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueNineSame topicAmerican Sports and LiteratureFrench-language works237,207