Cowritten by Brendan Kumagai, Mikael Nahabedian, Thibaud Châtel, and Tyrel Stokes

This is part 2 of a two part series introducing our Bayesian space-time model for evaluating offensive sequences and player actions. In part 1, we outlined our methodology to build the model and explained the reasoning behind the key metric, Possession Added Value (PAV), derived from it. In this second part, we will illustrate how our model and the PAV metric can be used for team and individual player analysis. Read Part 1 here.

Introduction

Since the Big Data Cup in March, our team has continued to improve the model to better estimate Possession Added Value of offensive sequences. With some extra time on our hands we cleaned up a few coding bugs and made two changes to the underlying models themselves. First, we have explicitly separated failed and completed passes. Second, we drastically improved our models which predict the location of the next event.  With these changes we are able to more realistically simulate play sequences and more accurately value passing and as a result the findings presented below might differ a bit from our Big Data Cup paper.