Want to Make Analytics More Accessible? More Context, Less Acronyms!

One of my more popular posts in the past was my discussion of how to make analytics more accessible, so I wanted to follow up on that with some more thoughts on how to make analytics more accessible to a larger audience. These are from my own experience with Analytics TwitterTM , especially as I’ve tried to look into other sports for inspiration on different projects or different ways to do things.

  1. Use fewer acronyms, more wordsI know Twitter’s character lends itself to acronyms, and it’s difficult to be precise in 140 characters (minus someone’s username and 25 characters for an embedded image), but the acronyms imply a level of familiarity among your readers that keep analytics conversations at a level only a limited audience can understand. Here’s an example:

    To be clear, Footy in the Clouds is a great Twitter account with lots of great info for the soccer stats community that you all should follow, and I’m not singling him out here. Plenty of people do this sort of thing, and it’s a good way of communicating a lot of information in a short space. But this only works for people who understand what you’re already saying. Anyone outside the circle won’t get it, and if the goal is to expand the analytics discussion then we need to be more transparent with what we’re saying.1

  2. Offer context for any statistics you present

PDO’s a great example here: I know I’ve read about PDO in the past, and I vaguely remember that every team basically regresses to 1000 in the long-run, and I know that some analysts argue anything over 1000 is lucky while anything under 1000 is unlucky. I’ve never assessed those claims, and to be honest I can’t remember exactly what the measure is. I participate in Soccer Analytics TwitterTM regularly and read much of what people post, and I *still* don’t fully understand the measure.

Tell me what the measure is, tell me what the average is and whether a team is above or below it, tell me whether this is due to some inherent skill or whether we’d expect it to regress to the mean at some point. Numbers are useless without context, so if you want a broader audience make sure that the audience knows these things and can make proper use of the numbers.

A great example of this is Mike Goodman’s most recent ESPN column, an excerpt of which I’ll post here:

German teams tackle more, intercept more and generally contest their opponents more aggressively further up the field. In Germany, if an attack has progressed to the point where a player might consider shooting, they’ve already accomplished a lot of the hard work. In England, a player at a similar point is more likely to have the defense still set in front of him. It might be easier to get into the final third in England, but it’s harder to get a shot on target once you do. This might also explain why English teams play more passes in the final third (117 per game), than their German counterparts (97)

Mike Goodman does this better than anyone – finds numbers, thinks about what they mean, and writes them up in a sophisticated, yet transparent way. This is why he’s one of the best writers out there and is so widely respected. We should all try to emulate him, whether we’re trying to expand the audience of analytics work or not.

Don’t assume people know what you’re saying, and don’t assume that your point is self-evident. Maybe this is why we need more long-form blogs to supplement the Twitter conversation – it’s hard to explain and offer context in 140 characters, but it’s crucial if we want to expand the reach of analytics work.






  1. There is a legitimate discussion to be had about audiences here: I get into this with Neil Charles and Simon Gleave on a semi-regular basis and there is no right answer. It’s all about what individuals want from their work – if one wants to appeal to the niche high-level analytics audience, that’s ok with me. If one wants a larger audience, then that requires a broader communication style.

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