SportCon

As sports leagues add additional real-time player tracking technologies (e.g. Statcast, NBA player tracking, NFL Next Gen Stats), the amount of data available to analyze to understand (and potentially predict) player performance is growing at an incredible rate. With this new data, a new approach is needed to be able to make sense of this data. Many industries employ machine learning to use their wealth of data to make predictions (and understand the past). Traditionally, machine learning required specialized programing and statistical skills. Even with these skills, the rapid pace of change in the machine learning community makes it difficult for individuals to remain up-to-date with the latest techniques. In this talk, we will show how DataRobot can help everyone turn this new data into useful predictions. Then, as an example, we will use DataRobot to conduct a machine learning analysis of pitchFX data to understand catcher framing and how a tool like DataRobot allows us to easily try and compare multiple different approaches to the same problem.