Jun 9, 2019
New York Islanders
Posts per Day
<div class="quote"><div class="quote_t">Quoting: <b>PleaseBanMeForMyOwnGood</b></div><div>So GAR is usefull but it also has some glaring weaknesses and that really becomes apparent when you start digging into the Islanders as an example.
The following quote was taken from this article <a href="https://www.pensionplanpuppets.com/2017/6/16/15774850/advanced-stats-102-what-is-gar-dawson-sprigings-nhl" rel="nofollow noreferrer noopener" target="_blank">https://www.pensionplanpuppets.com/2017/6/16/15774850/advanced-stats-102-what-is-gar-dawson-sprigings-nhl</a>
"While I think the stat is tremendously useful, there are a few things you have to keep in mind when using it. For one, GAR values are estimates - because it uses regression techniques in some places, there is inherent uncertainty in the values output by the system. Those error bars are hidden from view - we don’t really get to see them, and as a result, you have to be careful not to make conclusions based on GAR values that are relatively close to one another.
Along these same lines, sometimes it spits out counterintuitive values, and it’s hard to see exactly why. The complexity of the model means there’s no longer easy mappings from things we consider ‘inputs’ to player value (points, possession ability, etc.) to the GAR output. They’re obviously correlated, but there are now contextual factors (teammates, competition, score usage) included that make the mapping from input to output more opaque. In that way, the model is perhaps more opaque than one would like. However, this is no different from the heavily accepted WAR stats used in baseball. You can break them down into their core components, but it takes a fair bit of effort.
When I asked Sprigings what he thought the biggest weakness of the stat is, he mentions a more conceptual issue, noting that the stat straddles the line between being a measure of ‘true talent’ as opposed to ‘the value a player provided’. Parts of the even strength offense and defense are more a measure of ‘true talent’ but the rest tends to be a measure of what happened. Sprigings brought up an example where assists per 60 minutes are used as one of the inputs to assess even strength offense. That is a measure of what happened. However, Sprigings feels it would make more sense to use something like expected assists, which is a more apt measure of talent.
GAR, in my opinion, also struggles a little bit in divvying up credit between teammates. Sprigings uses robust mathematical techniques to try and separate the effects from teammates, but it is a non-trivial problem, and even the most robust method may struggle if players spend all of their time on ice together and have very little time apart. This can be more pronounced in situations where one of the players doesn’t have a lot of historical data to go off of (for example, rookies)."
What tells me, is that GAR kind of correlates results without showing what made those results which would then ignore certain red flags about a players season infavour of actual results. So Rielly for example he's very obviously more talented than Pulock but the defensive numbers of the leafs were worse and GAR is based too much on end results rather than what made up the results.
So with all that in mind, and diving into the on ice play metrics of different players, you are 1000% looking for confirmation bias by ignoring every single red flag in the Islanders play and only accepting stats that make you feel more secure in your opinion. Which is exactly what confirmation bias is.</div></div>
That’s one stat down, now do WAR and SPAR! Cmon buddy it seems like you have all day!