Real ChatGPT answer · index seedKEOEKLmjrfQcompleteSports and outdoors · sports bettinginformational
Are profitable betting systems based on historical data actually reliable for predicting March Madness upsets, or is luck still the dominant factor?
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The searches the AI actually ran for this prompt.
SEED QUERY
Are profitable betting systems based on historical data actually reliable for predicting March Madness upsets, or is luck still the dominant factor?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
NCAA tournament upset prediction model historical data betting market efficiency March Madness academic research calibration Google ↗
sports betting profitability backtesting overfitting historical data probability calibration paper Google ↗
- March Madness prediction: Different machine learning approaches with non‐box score statistics - Kim - 2023 - Managerial and Decision Economics · onlinelibrary.wiley.com
- Identifying NCAA tournament upsets using Balance Optimiza... · doi.org
- Efficiency in the madness? examining the betting market for the ncaa men’s basketball tournament · verso.uidaho.edu
- Machine learning for sports betting: Should model selection be based on accuracy or calibration? · sciencedirect.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
NCAA tournament upset prediction model historical data betting market efficiency March Madness academic research calibration OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
sports betting profitability backtesting overfitting historical data probability calibration paper OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |