Month: January 2014

Overemphasizing Context – A mistake just as poor as explaining context in the first place.

garik16 Exploring Context, NHL League-Wide Analysis, Theory Tags: , , , , , 4 Comments
AMac Context

The only context that can explain Andre MacDonald’s performance is if he’s actually wearing these chains under his uniform.

Eric Tulsky frequently points out on twitter that common critiques of analytics people (whether it be hockey or any other sports analytics) tend to act as if those involved with analytics are kind of stupid and have ignored the obvious.  For example, people tend to respond to arguments involving corsi and possession by bringing up the obvious subject of context – “Sure he has a bad corsi, but he gets tough minutes!”  And the general response of course is, yes we have, and we wouldn’t be making these assertions had we not done so.   Hockey Analytics has come up with a multitude of statistics to measure context – Behind The Net alone has 3 metrics for quality of competition and 3 metrics for quality of teammates, plus a measure of zone starts – HA has multiple different measures for the same thing and so does now Extra Skater (with Time on Ice QualComp and QualTeam).

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Consistency in the NHL: How much does consistency vary in the NHL relative to performance

Garret Hohl NHL League-Wide Analysis 4 Comments

Photo Cred: John Woods (The Canadian Press)

INTRODUCTION:

It is not unusual to hear fans or media claim lack of consistency in a team’s performance as the main culprit to a team’s failing record, rather than the alternative narrative in a team just not being as good on average.

Fortunately there is a way to test this hypothesis in mathematics, specifically statistics.

Corsi is one of the strongest gauges in assessing a team’s success due to Corsi’s strong relationship with scoring chances and puck possession, even within a single game sample spacing. This evaluator is even stronger when restricting to “score-close” minutes to limit score effects.

How well a team performs game-to-game on average can simply be evaluated using the average, or mean, of a team’s Corsi differential for all of their games. Consistency can also be evaluated mathematically using standard deviation, a measurement in the magnitude of dispersion from the mean value.

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