Decision Science

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? 

Continue reading →

Using Sequences for Analysis: Expected Goals Contribution and more

Thibaud Chatel Data Analysis, Decision Science Tags: , , 2 Comments

In a previous article, I presented a way to cut and slice a hockey game into Sequences. A Sequence extends from the moment a team gets control of the puck and starts moving forward, to the moment the team loses it for good. The objective was to measure the importance of every event happening between the beginning of a Sequence and its end, from a zone exit to any shot attempts, to a zone entry or any high-danger passes in between. If a Sequence includes one or several shot attempts, its value is the sum of the Expected Goals of all those attempts.

The natural follow-up was the creation of an Expected Goals Contribution metric for players.

The thinking behind it was to answer one of the two main questions we face in the daily use of analytics with coaches: What is the real contribution of each player? Overall, there are the well-known GAR or WAR type of metrics, but these are beyond the comprehension of many staffs as they are not tangible enough for a daily use.

Now, if we use Sequences where the team has possession of the puck, it means Expected Goals Contribution would only look at the offensive side of the game. Still, instead of looking separately at transition or shooting stats to evaluate a player, the objective is to sum all offensive efforts into one metric, weighting those efforts (zone exit, entry, etc.) according to their contribution to the Sequence. It also makes playmaking more apparent statistically.

In other words, it means sharing the total value of the Sequence (in terms of Expected Goals), between the players responsible. This is what we called Expected Goals Contribution.

Continue reading →

Introducing Offensive Sequences and The Hockey Decision Tree

Thibaud Chatel Data Analysis, Decision Science Tags: , 4 Comments

If you ever work for a hockey team as an analyst, you could be facing two very recurrent questions from the coaching staff. The first one is very practical: How can analytics help us work better and faster? The second one is: What is the real contribution of each player? Meaning beyond the usual on-ice “possession” stats like Corsi or Expected Goals and individual production metrics such as shots taken, scoring chances, expected goals created, zone exits, entries, or even high-danger passes (passes that end or go through the slot). But those events were not yet statistically linked to each other. Finding a way to provide answers to both questions was my goal for the last few months, and the solution was: I needed to split the game in “Sequences”.

Video coaches often break down game tape to highlight certain plays, such as a rush-based attack or a zone exit under pressure. I wanted to do the same and divide a game in as many parts as necessary, or “Sequences”. Roughly, every time the puck changes possession between teams, a new Sequence” begins. That’s about 250 Sequences per game.

Looking at this from the point of view of the team that owns the puck, offensive Sequences extend from the moment a team gets control of the puck and starts moving forward, to the moment she loses it for good, and it must include a shot attempt in the process to have a positive value. How does this work? Let’s say a player gets the puck back in your defensive zone, you try a zone exit but fail. Sequence starts over, there can only be one exit recorded in the Sequence. So he tries another zone exit and succeed, gets into the offensive zone, the team records a couple of shot attempts, loses the puck and if the other teams gets enough control of it to try a zone exit, it means the end of the Sequence.

How does this help? Well, the basic principle is to see the total value of a Sequence. We’re use Expected Goals as our measure of “value”. To do that, we add the Expected Goals of the shot attempts in the Sequence. For example, a Sequence with two shot attempts:

  • A high danger shot: 0.23 Expected Goals
  • A shot from the blue line: 0.01 Expected Goals
  • Total Sequence value: 0.23 + 0.01 = 0.24 Expected Goals

Sequences

Continue reading →

When Can You Trust Your Intuition: The problem with having played the game

petbugs Decision Science Tags: , , 3 Comments

A common retort that many in the hockey analytics community have gotten is: “have you ever played the game?”

The insinuation, of course, is that if you haven’t played hockey at a high level, let alone in the NHL, then you can’t possibly understand the game. Certainly not as well as those who have. And that when it comes to evaluating players or making decisions on how best to improve a hockey team, the former players and lifelong hockey men that populate the league’s front offices can always fall back on their instinct for the game in ways that no one else can.

But let’s talk about relying on your gut instincts to make decisions.

Continue reading →

It’s Time To Stop Talking About Analytics

petbugs Decision Science 1 Comment

Look, nobody knows what analytics actually is anyway, so why are we still talking about it? At its most basic, analytics is simply a tool. Much like a hammer is a tool.

Maybe too much like a hammer. As the old saying goes, when all you have is a hammer, everything looks like a nail. The same may be true for analytics. At least in some contexts. Yes, analytics is simply a way to draw meaning out of data, but just because you finally figured out how to apply gradient boosting to your ridge regression model doesn’t mean you should.

Once you think of analytics as a tool, a means to an end, then it’s much easier to see that it’s not just a tool, but an entire toolbox. And when you reach into that toolbox, the tool you take out should depend on what you want to accomplish.

Continue reading →