Month: January 2018

The 2018 NHL Trade Value Rankings

yolo_pinyato For Fun, NHL League-Wide Analysis 1 Comment

Most years, the NHL trade deadline is basically the equivalent of an annual Y2K party: Much Ado About Nothing. The issue comes from the underlying inertia the permeates most of the league’s landscape.

The best players almost never switch teams in their prime (Seriously, who was the last top 10 player to leave their current team? Marian Hossa?)

Even when a trade does get made, there’s often no rhyme or reason to how it plays out. Sometimes you trade your team’s top disgruntled forward and get Seth Jones. Sometimes you get Adam Larsson.

So, to give the league’s decision makers a little kick in the butt, I’ve put together a trade model that identifies the trade value of every regular NHL player and determines what would be a fair return in a trade.

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Goal Scorer Cluster Analysis

Alex Novet Data Analysis, NHL League-Wide Analysis, Player Evaluation, Playing Styles, Shot Quality 6 Comments

“They don’t ask how. They ask how many.”

-Hockey Proverb

“But seriously though… how?”

-Me

To state the obvious: goal-scoring is an essential skill for a hockey team. Players have made long careers by putting the puck in the net.

But how do players create goals? Skaters rely on all sorts of skills to score; some are fast, some have a huge shot, and some know how to be in the right place for an easy tap-in. But we don’t have a rigorous view of what those skills are, how they fit together, and which players rely on which ones.

In this piece, I take 100 of the top NHL goal-scorers and apply unsupervised learning techniques to group them into specific goal scoring types. The result is a classification that buckets the scorers into 5 categories: bombers, rushers, chance makers, chaos makers, and physical forces. These can help players understand how to apply their skill set to goalscoring. It can also help teams make sure that their system is putting their top players in a position to score.

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An Introduction To New Tracking Technology

prashiyer97 Community, Neutral Zone Analysis, Passing Data, Resources 4 Comments

The first significant breakthrough in hockey analytics occurred in the mid-2000’s when analysts discovered the importance of Corsi in describing and predicting future success. Since that time, we’ve seen the creation of expected goals, WAR models, and more. Many have cited that the next big breakthrough in hockey analytics will come once the NHL is able to provide tracking data. We’ve already seen some of the incredible applications of the MLB’s Statcast data and the NBA’s SportVu data. Unfortunately, the NHL has no immediate plans to publicly provide this data and as such, many analysts have decided to manually obtain the data.

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