Testing the effectiveness of Semi - Predictive Markets: Are fight fans smarter than expert bookies?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it