Testing the effectiveness of Semi - Predictive Markets: Are fight fans smarter than expert bookies?
Bibliographic record
Abstract
Crowd wisdom has manifested itself in several successful business applications, most notably predictive markets. Notwithstanding there have been few objective long term measures of its underlying principles, something this study aimed to rectify. through the mechanism of predictive sports markets on the basis of fan (i.e., the crowd) prediction participation of UFC fight outcomes as compared to the fight outcome predications made by bookmakers (i.e., the experts). For the purpose of this study, we obtained the results of predictions from both bookies and fans for three years of Pay-Per-View events. We found that 85.7% of event outcomes were accurately predicted by the crowds (fans), compared to only 67.6% by the experts (bookies). Our prima facia results suggest that crowds can provide more accurate predictions than bookies on a binary level (Win – Loss). However, the scope of this study was limited by access to primary UFC fan voting data and the smallness of the data set.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".