Football Analytics – Part Two: Shots

*If you haven’t read Part One, you can find it here.*

Football analytics tries to do many things, the principal among them evaluating the strength of a team and predicting future results. When looking at team statistics in football, there are two main ideas to consider; their repeatability and their predictive ability.

Repeatability basically means that if your team does something in one game, how likely is it that they can continue to do this over future games? If something is not repeatable, it has little predictive value.

Predictive ability is how strongly correlated the statistic or metric is to winning matches, often compared to points per game, points or future goal ratio (the proportion of goals a team scores in its games; above 0.5 means they score more goals than the opposition).

“At the end of the day, the only stat that matter is the scoreline.”

So a wise man on the television once told you, or maybe a stranger on Twitter. But as this exchange tried to show, there’s far more to football that the result. A side can play poorly and win, but if they were to continue to play poorly, results would likely catch up with them, meaning that they weren’t as good as the initial result would appear to make them. Results are a bad way of evaluating how good a team is and predicting their future performance in the longer-term.

Why?

There are so few goals in a game compared to points in a tennis match or balls bowled in a cricket match. This means that goals are really important whenever they’re scored in comparison to other sports, meaning that if a weaker team can score against a stronger one, they’ve got a much better chance of winning the match than if an unseeded player managed to take a game or set off Djokovic. This means shocks in football are relatively common, which is part of the attraction of the sport. However, it also means that individual results are less driven by skill and more by luck than in other sports. Hence why individual results are a bad way to rate teams, not to be mention the tiny sample size that goes into forming opinions off them. Of course, over time, better sides will score more and concede fewer goals than worse sides, but the same problems still remain, as goals are often not the most repeatable of statistics. 

As this graph from the brilliant @11tegen11 shows, goal ratio and a side’s points per game are relatively poor predictors of future performance.

correlation with goal ratio and ppg

So we have to dig deeper to get a better metric than one that uses results and goals, and the logical next step is shots.

The first metric I want to discuss is called TSR. TSR stands for ‘total shots ratio’, and it’s really easy to work out. Let’s say that team ‘X’ has taken 20 shots this season, and has had 30 shots against them. In their matches, there have been 50 shots (20+30), of which team ‘X’ have taken 20 of them. Simply divide the number of shots taken by team ‘X’ (20) by the total number of shots in their matches (50), and you’ll get that team’s TSR, the proportion of shots they take over the course of a few games/season etc (0.4).

Obviously over the course of just one game, having more shots than the opposition doesn’t mean you’ll win the game, but if such shot dominance can be extended over a long period of time, impressive results should follow (assuming you’re not going full-on Coutinho every game and shooting from 25 yards).

Why do we like shots, or more specifically, TSR? 

Shots inherently have a larger sample size than goals, and this, combined the naturally streaky nature of finishing, makes them more representative of a side’s quality. It’s also really easy to find their stats. Every match report, be it on BBC or Sky Sports, will have simple shot statistics that can be easily collected.

It’s more repeatable than goals (thanks to @JamesWGrayson, whose article can be found here).

goal ratio repeatability

The titles on these axes are incorrect, it should read 'TSR' instead of 'Total shots for'.

The titles on the axes on the second graph are incorrect, it should read ‘TSR’ instead of ‘Total shots for’.

It’s a better predictor of future performance than goals (again, thanks to @11tegen11).

TSR predictability

For the sake of brevity I won’t try to explain why this is the case in this blog post, as there’s another very similar metric that is also widely used to be explained. It’s called SOTR, or ‘Shots on Target Ratio’, and it’s calculated in the same way TSR is, except that it only includes shots on target in its calculations. The theory behind this is that better quality chances are more likely to be hit on target, so if you have more shots on target than your opposition, you’re likely to be creating better quality chances, scoring more goals, and getting better results. It’s not so surprising that it correlates with future points better than TSR, although only slightly.

SOTR predictability

Quick summary of the ideas I’ve tried to cover here:

Repeatability

Predictability

TSR = total shots for/(total shots for + total shots against)

SOTR = total shots on target for/(total shots on target for + total shots on target against) 

Thanks for reading, and I hope I’ve been able to help you understand some parts of  football analytics a bit better. If you’ve got any questions, feel free to drop me a DM @OneShortCorner.

 

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