Events

The introduction of player and puck tracking in the NHL has the potential to transform our understanding of hockey. The shift from reliance on records of discrete events (shots, passes, etc.) to full spatiotemporal tracking data opens new avenues for analysis, such as evaluating off-puck behavior or better isolating individual player contributions. However, it is nontrivial to go from this rich new dataset to specific, actionable insights. Many of the core challenges are not unique to sports data but instead are shared across disciplines that work with complex spatiotemporal data, such as systems neuroscience. While the subject matter may seem unrelated, the fundamental question is how to extract meaningful insights from information-rich but complicated and imperfect datasets. Thus, the same principles used to approach data analysis in scientific fields can also be applied to sports data.