InCycle designed a model focusing on simplicity and explainability through a univariate approach.
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Benefits |
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Through automation, scaled model capability to handle a vast array of datasets with ease, maintaining straightforward interpretability. To achieve this, we used multiple datasets to train models and ensemble inference to reduce population bias. With archetype models prepared, we scored current college player data to identify top candidates.
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Besides championships, efficiently scout more players and accurately forecast pro success and trade value without dependency on traditional and subjective scouting. |
