Month: July 2021

Data May Not Drive Play, But It Should Drive Decisions

petbugs Decision Science Tags: , , , , 1 Comment

It’s not easy creating a data-driven decision-making culture in any organization, let alone one as bound in tradition and lore as the NHL, where hockey men are imbued with mythical powers of observation and judgement just by virtue of having played the game. And yet, the NHL is clearly moving in that direction. It may be at a glacier-like pace, but I suppose that makes sense, what with the ice and all. Despite some early stumbles, it’s probably safe to say that it is only a matter of time before data-driven decisions are the norm rather than the exception. Whether that happens while we still have glaciers is another matter.

But even when there is a managerial will and top-down direction to move toward a data-driven culture, it is often difficult to introduce data analysis into the existing decision-making process of an organization. It’s not just deep structural changes that are necessary, but also staff will need a robust change management process. It’s hard enough to get people to accept change, but a new culture requires that they go beyond acceptance and embrace it as a new way of doing things. This is a difficult process in any organization. However, it is made more difficult in the hockey world where many in positions of authority are in those roles precisely because they “played the game” and understand the traditional way of doing things.

But what if you could start from scratch and build something from the ground up? 

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Bayesian Space-Time Models for Expected Possession added Value – Part 1 of 2

tyrelstokes Bayesian Tags: , , , Leave a comment

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

Introduction

This is part one of a two part series introducing our bayesian space-time model for evaluating offensive sequences and player actions. In this part we describe the model and the key metrics derived from it. In part 2 we will show how the model can inform and integrate with player and team evaluations.

Hockey is a game of making the best possible decision in the shortest amount of time. Players need to react quickly to form a chain of plays to create valuable scoring chances. Our goal is to quantify the value of player actions as a function primarily of space and time in the offensive zone. This is to recognize that the puck location and the threat-level it poses to the opposition is a key driver of what options will be available to the puck carrier and – as a result – what is likely to happen next. Ultimately, we want to credit players that are able to make high quality decisions and difficult plays which advance the puck into more valuable locations on the ice.

To this end, we primarily build off of 3 previous papers. First, we use the conceptual framework of understanding play sequences in hockey (Châtel, 2020) from our team member Thibaud Châtel. Second, we adapt the multi-resolutional Expected Possession Value modelling framework pioneered by Cervone et al. (2016) in basketball to work with the detailed play-by-play data generously provided by Stathletes as part of the Big Data Cup hackathon. Finally, using this infrastructure we propose a metric called the Possession Added Value (PAV) based on Karun Singh’s Expected Threat Model in soccer (Singh, 2018) which has previously been adapted to hockey (Yu et al., 2020).

Due to the timeframe of the competition and the complexity of our proposed model, we decided to narrow our scope down to offensive even strength sequences that begin with an entry and end with either a shot or a whistle. By only considering offensive sequences the model as it stands can only properly evaluate the actions of the offensive team.

Before we dig into our methodology, here is an example of what our end product will look like in Figure 1 below. We assign each event in this entry-to-exit/whistle sequence with our Possession Added Value (PAV) metric, which can be thought of as the increase in probability that we score in the sequence by performing the observed action. For example, the pass by Landon McCallum from the top of the left circle into the slot adds 0.0677 goals to the expected value of the possession.

Figure 1: An offensive zone sequence with our Possession Added Value (PAV) metric

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