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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. To avoid leakage during cross-validation, we grouped all plays from the same game into the same fold. For more information on how to use GluonTS SBP, see the following demo notebook.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. Each season consists of around 17,000 plays.

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Deployment of Data and ML Pipelines for the Most Chaotic Industry: The Stirred Rivers of Crypto

The MLOps Blog

And that includes data. Given that the whole theory of machine learning assumes today will behave at least somewhat like yesterday, what can algorithms and models do for you in such a chaotic context ? Quick shout out to the amazing data engineering team at CTF Capital, they really poured their hearts and brains into this!

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