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Football data is hitting up against a new type of access problem. It used to be the case* that merely having access to the datapoints you were interested in was the hurdle. Nowadays you increasingly have that access, but the issue is finding what you want.

*To be totally accurate, there's still a chunk of professional football lacking good access; and a (diminishing) chunk of football problems lacking decent data to describe them.

Why this problem? First, the number of events being collected expanded. Then it expanded again, with opponents being bypassed and phases of play attached to everything. And now, every Tom, Dick, and Harry are packaging up off-ball runs. (And Peter, and various Pauls who don't have proper blog posts to re-find and link to).

Get Goalside never used to be a UI/UX blog. But the biz is insisting it become one.

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There are two easy approaches to this kind of situation. One is – as you mighht have guessed from the conceit of this post – letting somebody find or create anything they want through a series of menus. This seems to be pretty popular.

It's probably popular in part because the other main option is scary. It's to be very opinionated about what you put in front of people from the start, leaving everything else behind a basic keyword search that you know only 5% of users will even look for. Opinions. Who'd have 'em.

Turns out I've actually written about this before, last September, positing a potential Third Way:

Manual curation naturally limits what is put in front of an individual, relies on smart experts doing the curating, and has a tendency of keeping people in the status quo. So are algorithms the way? Is that how people will make sure that the right dozen metrics, out of a pool of thousands, get put in front of people?

But this was only thinking about half of the equation. Given that football data is in such flux, and football is so fickle, you could just make like Meccano and let people create their own statistics. This doesn't solve the UI/UX problem, of course, just shifts it to a different part of the process.

As with many things, though, it's not like this is a problem limited to the football biz. Navigating phone or computer settings is frequently horrible. Toolbars of Microsoft and Adobe products are well-known for being packed with features that few people use but which are each used by enough people to keep them there.

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To return to the well of analogies, we can always return to language. Data is a means of describing football just as the spoken word is; like how quaint and deprimentemente nublado are ways of describing England.

Google Translate, when you search for a word, not only gives you several translation options, but often gives you common synonyms for each of them in the input language underneath. For example, translating 'funny' to German gives you – flying in the face of English stereotypes – a wealth of options. The first is 'lustig' (which Google also relates to merry, cheerful, amusing), and then 'komisch' (comic, comical), 'witzig' (witty, humorous), 'spaßig' (droll, jocular).

Maybe this is an interface pattern to take inspiration from. 'You want line-breaking passes? You may also be interested in: progressive passes; Possession Value added; Buzzy Marketing Material.'

[Aside: at what point in a pass is a pass line-breaking? How deep past the line signifies the line being broken? If the pass delivery forces the recipient to remove the sting from the ball, during which time the line that was broken recovers, is that still a line-breaking pass? If defenders, anticipating the pass, pounce immediately on an isolated recipient, is that a line broken?]

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Truthfully, this is all much more boring than the advanced modelling that Get Goalside used to write about. But so is data engineering, and everybody and their dog are hiring for football data engineers these days.

Via this post from Nicola Graham, I stumbled on this product-market fit diagram by Chris Peploe. It splits into four types of fit: Problem, Workflow, Proof, Ecosystem. Are you solving a problem; does it fit into how people work; is there proof of the efficacy; does the wider ecosystem support adopting and integrating the product.

We can talk Problem, Ecosystem, and Proof some other time (although: publish more research), but the UX of a product – whether a mass of filters or some other UI system – is gonna be part of that Workflow fit test.

How do you make something easy to find?

There's a deliberate typo in this post. How would you go about finding it?