Publications

Stats & Stumps: Using Machine Learning to Predict T20I Matches with Player and Venue Data

Published in Wharton Sports Analytics Journal, 2025

Predicts Twenty20 International outcomes from venue-adjusted, player-level impact metrics. Regularized logistic regression reached 70.24% test accuracy on 1,029 matches.

Recommended citation: Sharma, A. (2025). "Stats & Stumps: Using Machine Learning to Predict T20I Matches with Player and Venue Data." Wharton Sports Analytics Journal.
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Modeling American Kestrel Decline Using Spatiotemporal Subsampling to Improve eBird Data Reliability

Published in bioRxiv (preprint), 2025

Debiases eBird citizen-science data with spatiotemporal subsampling, then uses a time-series model to test whether rising Cooper’s Hawk numbers help explain the American Kestrel decline.

Recommended citation: Sharma, A., Bednarz, J. C., & Kwon, J. (2025). "Modeling American Kestrel Decline Using Spatiotemporal Subsampling to Improve eBird Data Reliability." bioRxiv. doi:10.1101/2024.10.31.621376.
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Archith Sharma

Publications

Stats & Stumps: Using Machine Learning to Predict T20I Matches with Player and Venue Data

Modeling American Kestrel Decline Using Spatiotemporal Subsampling to Improve eBird Data Reliability