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Record W4247065186 · doi:10.1142/9789814405461_0024

An Application of Expert Information to Win Betting on the Kentucky Derby, 1981-2005

2012· book-chapter· en· W4247065186 on OpenAlexaff
Roderick S. Bain, Donald B. Hausch, William T. Ziemba

Bibliographic record

VenueWORLD SCIENTIFIC eBooks · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of British Columbia
FundersUniversity of Pennsylvania
KeywordsOperations researchEngineeringData scienceComputer scienceForensic engineering

Abstract

fetched live from OpenAlex

AbstractThe Kentucky Derby features top three-year-old thoroughbred horses. Run at 1¼ miles, it is typically at least 1/8 mile longer than any of the horses has raced before. This extra distance, usually combined with a large field, makes the race a difficult test of stamina for horses this young. Bettors, because there is no direct evidence of whether a horse has the stamina to compete effectively at 1¼ miles, are also challenged. The informational content of one publicly available, pedigree-based measure of stamina, the Dosage Index, is used with simple peiformance measures to identify a semi-strong-form inefficiency, and to create a betting scheme based on the optimal capital growth model that merges these criteria with the public’s opinion. Statistically significant profits, net of transaction costs, could have been achieved during the period 1981 to 2005.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.034
GPT teacher head0.225
Teacher spread0.191 · 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 teacher head, not a consensus.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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