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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Sports Analytics and Performance
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,185 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,185 works in the cohort · of 4,299,418page 4 of 24

Labels cover 3 of 1,185 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,185 of 1,185 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
A study of the powerplay in one-day cricket
Rajitha M. Silva, Ananda B. W. Manage, Tim B. Swartz
2015· article· en· European Journal of Operational Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Semi-Automated Gameplay Analysis by Machine Learning
Finnegan Southey, Gang Xiao, Robert C. Holte, Mark Trommelen, John W. Buchanan
2005· article· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Sports Data Mining for Cricket Match Prediction
Antony Anuraj, Gurtej S. Boparai, Carson K. Leung, Evan W.R. Madill, Darshan A. Pandhi, Ayush Dilipkumar Patel +1 more
2023· book-chapter· en· Lecture notes in networks and systems· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
FIFA ranking: Evaluation and path forward
Leszek Szczeciński, Iris-Ioana Roatis
2022· article· en· Journal of Sports Analytics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
The “Hot Hand” Myth in Professional Basketball
Jonathan J. Koehler, Caryn A. Conley
2003· article· en· Journal of Sport and Exercise Psychology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
Rao-Blackwellizing field goal percentage
Daniel Daly‐Grafstein, Luke Bornn
2019· article· en· Journal of Quantitative Analysis in Sports· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
16
citations
affaboutunlabeled
Preferred Scenarios in the Sport of Curling
Keith A. Willoughby, Kent J. Kostuk
2004· article· en· INFORMS Journal on Applied Analytics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
15
citations
afffundunlabeled
Resource estimation in T20 cricket
Harsha Perera, Tim B. Swartz
2012· article· en· IMA Journal of Management Mathematics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
CyberAbuse in sport: beware and be aware!
Emma Kavanagh, Margo Mountjoy
2024· editorial· en· British Journal of Sports Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Strike Three: Umpires' Demand for Discrimination
Christopher Parsons, Johan Sulaeman, Michael Yates, Daniel S. Hamermesh
2007· report· en· National Bureau of Economic Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
13
citations

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