{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Science Tech Brief By HackerNoon","title":"1.89 Seasons: A Baseball Experiment About Hiring and Human Judgment","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/bf0269d8\"></iframe>","width":"100%","height":180,"duration":719,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/189-seasons-a-baseball-experiment-about-hiring-and-human-judgment.\nA 69-season baseball pilot tests when an individual’s own record becomes more predictive than their reference class, with careful caveats for hiring.\nCheck more stories related to data-science at: https://hackernoon.com/c/data-science.\n            You can also check exclusive content about #predictive-modeling, #sports-analytics, #referential-evaluation, #lahman-baseball-database, #reference-class-prediction, #empirical-bayes, #irep, #resume-screening,  and more.\nThis story was written by: @elodieaishwarya. Learn more about this writer by checking @elodieaishwarya's about page,\n            and for more stories, please visit hackernoon.com.\nThe article uses public baseball data to test when an individual’s prior record beats reference-class prediction. It argues that hiring may need a similar shift away from static résumé categories, while clearly stating that the baseball number does not directly transfer to hiring.","thumbnail_url":"https://img.transistorcdn.com/8VxAgS1Ll3FJEERcAdhFdqqXJMnE7OfD2RUvrjauLt0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjY4LzE2ODM1/ODI1ODUtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}