The world doesn’t need another hot take on whether analytics are good, or useful, or the temperature at which stats people keep their offices. I honestly stopped reading the media pieces on the topic after the Rory Smith debacle a week or two ago, but I do think there is room to discuss ways to make analytics more accessible and interesting to a larger audience. Academia deals with this quite a bit, especially in disciplines like Political Science where what we study has consistent relevance to the media, so I wanted to share some of the strategies on how to make often dense statistical research more accessible to media and practitioners.1
- DON’T assume anyone knows anything about math.
- Most people haven’t taken a math class since college, and even then they didn’t like it. Not only should you not get lost in the math, you should avoid it entirely. Have it in reserve if they ask for more details, but be prepared for people’s eyes to glaze over when you start.
- DON’T assume anyone cares about math
- People don’t care about the method. They care about what they can learn.
- DON’T use jargon.
- Many of the metrics we use have technical sounding names or abbreviations. Maybe the measure is clear and interesting, but when you lead with the technical part people will lose interest. Similarly, instead of saying “R2” talk about “correct predictions.” Present confidence intervals (don’t call them that) instead of p-values, and if possible show me, don’t tell me. Model fit can be intuitive when shown on a graph, but it can be daunting when it’s explained.
- DO start with a question
- What do people care about? Is there a story in the news that you can shed light on with analytics? What can we learn from your method? Analytics that answer a question people care about are more likely to be embraced by media/practitioners than analytics than those that simply present a measure.
- DO focus on what we learn, rather than how you did it
- What new insights does your method give us? What did we not know before that we do now? How does your analysis teach us something about soccer that we didn’t know before?
- DO explain why people should care about what you did.
- In academia, we call this the “so what?” question, and it’s the most important part of this whole process. You did a bunch of math, so what? Why does this matter to a larger audience? Why should people care about what you did here?
- DO focus on clear, concise presentation of results.
- I know of several high profile studies in political science that only got attention because they had nice infographics attached to them. It could be a clever, clear, infographic, or an interactive tool of some sort that people can play with. If you’re not good with graphics, it could be a table. Or a short paragraph, or anything that is clear, attention getting, and concise. Save description for those who want it.
- DO be ready for people to not accept your conclusions.
- Confirmation bias is a real thing, and it’s difficult to overcome. Analytics that confirm what people already believe are much easier to accept than those that aren’t. And beyond that, some people just don’t like stats – you’ll never convert them and it’s not worth trying.
That’s all I’ve got – I’ve tried to distill a couple dozen articles about outreach in academia to a few bullet points. I’m confident that if soccer analytics folks focus on these things, and are patient enough, things are going to change. They changed in politics pretty quickly, there’s no reason the same thing won’t happen in soccer.
- I don’t have a lot of experience with mass media and soccer, but I have had some folks in the industry reach out to me privately. In my political life, I’ve been quoted multiple times in USA Today, The New York Times, have had op-eds placed in Washington Post and USA Today, and have had my research featured in The Huffington Post. I’ve also been on Voice of America, Al-Jazeera America, and am a regular guest on the News and Views radio show out of Minnesota so I know a fair amount about public outreach for arcane topics. ↩