Playing ball: evaluation, valuation, and accountants in Major League Baseball
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how professional sport clubs value their players and the roles of accounting and accountants in the process. Additionally, it highlights the distinction between financialization and assetization. Design/methodology/approach Drawing on extensive qualitative data, notably 47 semi-structured interviews, professional baseball in North America was used as the empirical context. Findings Major League Baseball clubs have developed tools to evaluate and value players and their contracts. One of these tools, player asset value, is a financialized valuation that contribute to reconceive players as assets. Yet, assetization – the process of turning things into assets – entails more than financialization. It is mostly a mode of governance, conditioned by real actions. Moreover, clubs’ accounting executives are mostly estranged from the financialization–assetization process. Originality/value This paper contributes to the emerging literature on accounting in the sport business. It is an industry where different value conceptions interplay. Clubs’ success depends largely on players performing on the field, and financial decisions on players are crucial. How accounting is involved in this industry and what matters from an accounting perspective are themes mostly overlooked.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".