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Beyond Moneyball: Applying Learnings from Sports Analytics

The world of sports analytics took off in the late 90s and hasn’t stopped. But what propelled it early on, and what creates the advantages certain organizations and players have today, is not what most data scientists are focused on. In fact, most resources spent on data science in the sports industry are being used with staggering low inefficiency and ROI. Taking a broader look at the lessons from the sports analytics movement, it’s apparent how much data science, in almost all industries and fields, can be improved by basic changes to approach and thinking. From entrepreneurs to fortune 500 companies, the greatest advantages moving forward will be shaped not by marginal improvements to gradient boosting and black box models, but by more optimally targeting problems long overlooked and misunderstood.

About our Speaker

Our speaker, Trevor Thoma, graduated from UCLA with a degree in statistics before becoming an analyst in the analytics department of the Orlando Magic basketball team. While there, he developed a metric to improve upon the predictive accuracy of the most advanced plus minus metric in the NBA: RPM. Afterward he worked in golf analytics, using data science as well as motor learning and biomechanics, to improve professional golfers’ performance in practice and on the course.

Later Event: February 18
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