A while back, I talked about a simple, flexible metric called Time-to-Shot:

TTS looks at a particular game state and simply calculates the time – in seconds – until the team in possession makes a shot (Time To Shot For, or TTSF) and the time until their opponents make a shot (Time To Shot Against, or TTSA). The lower this time, the more dangerous the situation for one team or the other, the higher the time, the safer.

I was uncomfortable about it then, and to an extent I still am, it certainly doesn’t have that New Analytics smell we’ve all come to expect. But as long as you don’t treat it as a source of absolute truth, and just a tool to give you a rough glance at tactical trends, teams and players, it’s an absolutely wonderful time-saver. So, I thought I’d show a couple more quick examples of ways to use it in anger.

Goal Kicks

Jared Young wrote a great piece on Analysis Evolved recently about goal kicks, pointing out that long goal kicks past the halfway line have negative expected value, and shorter kicks have positive EV. Jared does this by looking at the probability of scoring from the second or third possessions after the initial challenge for the ball from a goal kick to see what happens next. And what happens next is exactly TTS’s wheelhouse, let’s see if we can confirm his result.

This is a chart of the average TTSA minus TTSF (TTSD, as I like to call it) in various zones of the pitch when a goal kick is aimed there, taken from the big 5 European leagues from 2010/11 onward:

The first two-thirds of your half, as you can see, are positive values – you’re closer to a shot for than against. Most of the rest of the field is negative – putting the ball there means, on average, your opponent is going to get a shot away first. You should note that some of these buckets get quite small – the area around your own goal, obviously, as there’s only a tiny sliver of space in front of the box to pass to in that zone, and the very furthest zones (but by all means experiment trying to get it to that top-right corner). But that caveat aside, we can quickly check a result, and delve deeper with other metrics if we want to follow up.

Throw-ins

We can perform the same sort of calculation for anything we like, for example: where should you be aiming our throw-ins? These charts show throw-ins from various parts of the pitch (marked with a dotted border), with average TTSD values in each zone with more than 50 throws in the dataset:

What does this show? Long throws aren’t just a attacking routine you should be working on near the box, they actually seem to be a reasonable default. In basically every case, teams are better off (i.e. closer to their own shot or further away from an opponent’s shot) if they forget about progressing the ball down the wing off a throw in, and instead focus on getting the ball into the centre of the pitch.

Now, once again: TTS is just a bunch of numbers munged together as averages, and the zones above are pretty broad. You’re not going to take these numbers straight to the training pitch. But I do think it’s simple and flexible enough that we’ve quickly found something intriguing, and this result merits a deeper look into the data at some point. I will work on that soon, and see if we can confirm that the inferences we get out of TTS very quickly can be backed up more rigorously.