Stats for Coaches and Journalists: Thoughts and a Draft Syllabus

I’ve mentioned this before, but my day job is professor of political science, and specifically I teach courses about statistical methods and research design (among other classes). With the latest round of “Analytics, LOL” foolishness from the media on Twitter, I thought I would do something productive and create a syllabus for a hypothetical course on soccer analytics for coaches and journalists.

I wanted to share a few thoughts about this idea, and have an open question for anyone who reads this (which I’ll tweet as well). The target audience is people who have some interest in learning about analytics, their uses, and some basics with a goal of being able to speak to analytics types/read blog posts written with analytics. I had a Twitter exchange with @unfitforpurpose about this, and it may be worth re-thinking without the assumption that people are interested in learning the material.

I’m assuming zero knowledge on the part of the audience members. Gab Marcotti tweeted something about many people not knowing what a standard deviation is, which I think is potentially even overstating both the lack of math knowledge and math awareness of the audience. I also think focusing on the math is problematic: at their core, analytics aren’t about math, they’re about using tools to answer a question. I’m a big believer that measurement for the sake of measurement (or math for the sake of math) is a waste of time. To really appreciate and understand analytics, you need to start with simple concepts like hypotheses, measurement, and operationalizing variables. Even in the analytics community, we often forget that measures of uncertainty only come after proper model building.

I broke the course down into four sections:

  1. “What is science?”
  2. “Case Study and Small Sample Research”
  3. “Stats and Large Sample Research”
  4. “Data visualization techniques”

I think people were picturing an hour long seminar on how to do analytics, and I don’t think that’s the best way to do it. This isn’t a semester’s worth of learning, but I think it’s at least four 2 hour sessions to get a basic understanding of what we’re doing, although one could cut data viz out if the goal was just to understand rather than to produce, leaving us with three 2 hour sessions. Longer might be better, but if the goal is basic understanding then I think this would be enough.

However, I’m curious if other people think this could be broken down into a 1 hour session? What would you include? I don’t see it, but I’m thinking about ways it could be done and am curious for suggestions.  Here’s the syllabus I wrote, and  I may flesh it out even further with readings or videos if people are interested. Let me know what you think.

 

 






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