Behind the Numbers: Why statistic-folks are sometimes assholes, UNjustifiably

Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net.

Here we go. Here is part two, to what really should have been part one in hindsight prior to this piece, which would have saved me some of the backlash on Twitter, as the point was frequently misunderstood. (And while we’re dealing with hindsight, the title was part of the misunderstanding as well.)

As I mentioned before, there are jerks everywhere, both in non-analytically and analytically inclined individuals. I’m a believer in the old 10-80-10 saying, that in every group about 10% of people will be jerks, 10% will be above-and-beyond good people, and the rest are just regular people.

My previous article wasn’t discussing the 10%. Jerks being jerks is not justifiable. Arrogance is not the right approach when talking to others, end of discussion.

What my previous article was on was about how sometimes those using statistical arguments can be wrongfully accused of talking down or being arrogant when that is not what they are doing. The justification I was giving was that they were actually not in fact being jerks.

However, sometimes the remaining 90% can be human and do wrong, as myself have done in my own time as well. And again, the previous article was not about those situations, despite some falsely thinking that (probably from reading the title, which is my fault).

This one, though, is.

While the last article discussed when those using statistical based arguments can be falsely viewed as arrogant, this article is about how the faults our own demographic makes often. Those who are statistically inclined can have discussions with being negative, or falsely accused as such, less often.

A few people after part one pointed out these things, which helps me feel I’m on the right track.

First of all, statistical evidence-based arguments are founded upon the scientific method. The scientific method asks us to be dismissive of our own emotions, which in turn causes us to sometimes be dismissive of others’.

Emotions are a large part of discussions, debates, and who we are. We all have emotions and they are worthy of our respect. It is wrong to dismiss someone’s emotional response because there is evidence that they are factually wrong, as it is wrong to believe someone who uses scientific evidence is working free from their own emotional responses.

Also, as I noted in our previous discussion, the scientific model is stating what the current best evidence indicates to be the answer. The key caveat is in the word “current”.

As we learn more about our game, things will change. While at this point any further discovery likely only adjusts what we know — rather than flip the Earth in its rotation — this is still important to not only keep in mind, but also acknowledge when you make statements and proclamations based off of statistical evidence.

The game may change too, which adjusts the environment that these observations accumulate from, and in turn our observations.

The unknown plays such an integral role in analysis, and that extends beyond simply our currently knowledge on the game.

One of my first “popular” tweets on Twitter was on how hockey analytics is about probability and not destiny. Exceptions to rules may be extremely rare to the point it is unwise to bank on them, but they may still exist. Outlier analysis is an important part of research.

How quickly stat-folks point this out when the statistical darling loses in a best of seven series, yet how slow are we to admit this when it comes to our own interpretations of data.

This segues nicely into the third part, and that’s to do with interpretation and subjectivity.

Numbers are objective. In hockey analysis they explicitly define the quantity of events that occur. However, our own interpretation of these numbers can have some subjectivity. This, in part, adds credence to the old “lies, damn lies, and statistics”.

The evidence may point to something, but an individual’s interpretation of the evidence may lead them the wrong way. No argument is ever had in a vacuum, devoid of personal bias.

And finally, there is the venue or forum of discussion.This article, Twitter, Facebook, Reddit, discussion boards, and comment sections are all void of emotional context. A person’s body language, their tone, and the way they acknowledge someone who they have a disagreement makes understanding all the more difficult.

In short, if you could summarize these two pieces, it would simply be a few things:

  1. Good, normal, and bad people can and will exist in all groups, classes, and cultures.
  2. Being in any group, class, or culture does not allow one to justify themselves as being a jerk. Arrogance is not a positive trait.
  3. People who use statistics as evidence to their arguments are not necessarily more likely to be a jerk than others, and this misconception can extend from misunderstandings between two different demographics.
  4. Both sides can take steps to reduce these misunderstandings.

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