This is the latest in a series of posts looking at which team level metrics are repeatable from season-to-season in the Premiership. The following three paragraphs are generic for the series, feel free to skip.
As I’ve said before, it’s all well and good knowing that team ‘x’ took 20 shots in the first half against team ‘y’, but unless we know whether the number of shots a team takes is repeatable over time then trying to put that number into context is essentially useless (a point that is made far more eloquently by Richard Whittall in this column).
Ultimately, determining how repeatable a metric is allows it to be broken down into a ‘skill’ component – something that teams can control – and a ‘luck’ component – something teams have no control over. (For the story so far see I’ve placed a summary table at the bottom of this post, which I’ll continue to update in the future). Whilst those that are dominated by luck are wonderful insofar as it’s funny to watch the media play out narratives that can be explained very simply by regression towards the mean over time, those that are dominated by skill tell us something useful about the team posting the numbers, which will be repeated season after season, and thus are the metrics we should truly be interested in.
The theory here in this series is pretty simple. I take a big group of teams and compare how well the value they record for a given metric in one season correlates to the same metric the following season, and determining the correlation coefficient (R value) of a plot with year ‘n’ on the x axis and year ‘n+1’ on the y axis allows the breakdown of a metric into skill and luck components to be established. The sample comprises of the 204 pairs of ‘back-to-back’ team Premiership seasons that have occurred since the beginning of the ’00-01 Premiership season (17 non-relegated teams per season x 12 back-to-back seasons).
This time I’m focussing on the total shots a team takes, both on target and off. I’ve demonstrated before that total shots are pretty well correlated to the points a team will score over the course of a season, important in determining a teams final league position, that they are repeatable from early on in the season, and during the early part of the season that they the best job of all of what I call the ‘simple-advanced’ metrics (those that are freely available and don’t require much processing to calculate) of predicting the number of points a team will end the season with.
So, lets split total shots into four categories – for, against, differential, and ratio, and see how they stack up versus the metrics I’ve already looked at – a summary of which may be found at the bottom of this post.
First up, this is the total shots taken by a given team in year ‘n’, and year ‘n+1’.
There’s undoubtedly a decent correlation there – the breakdown is 80% skill and 20% luck, slightly better than we saw for goals scored (which had a 75/25 split).
Next, lets take a look at total shots against.
So this is interesting – to the point I actually had to go back and check whether I’d input the correct data. This is the first time in the series where the defensive metric has been more skill-based than the corresponding attacking metric (see the table below). I actually don’t have a theory as to why this is – if anyone does I’d be interested to hear it. In this case the breakdown here is 82% skill, 18% luck.
Thirdly lets look at total shot differential over the course of a season.
It doesn’t really surprise me that the correlation improves here – by adding data at both ends of the pitch we’re getting a better idea of how good a team really is. in this case we’re up to 86% skill/14% luck.
Finally we’ll move on to total shots ratio, defined as total shots for/(total shots for + total shots against).
As with goals we see a slight improvement when moving from differential to a ratio, but the skill luck split remains at 86/14 (I have no intention of getting into decimals on this).
In summary – total shots are an excellent indicator of how good a team is. The fact that they stabilise early on in the season is really useful too. TSR undoubtedly has it’s flaws, but it is truly amazing to me that something so simple does so damn well at predicting the Premiership knowing only a teams TSR from last season with no adjustment for score effects, personnel changes, or shot quality.
Finally, below is a table summarising this series so far, with each metric broken down into its skill and luck components. Skill and luck are defined therein in the context of ‘the repeatability of metric ‘x’ is ‘y%’ skill driven, and ‘z%’ luck driven at the team level over the course of a Premiership season. Click on the names of any of the metrics to be taken to the post with the relevant plots posted.
|Metric||% skill||% luck|
|Total shots ratio||86||14|
|Total shots differential||86||14|
|Total shots against||82||18|
|Total shots for||80||20|
|% of total shots that are on target (%TSOT) for||53||47|
|%TSOT for + %TSOT against||52||48|
|PDO (penalties excluded) (1)||46||54|
|% of total shots that are on target (%TSOT) against||44||56|
|sh% on shots from inside the box (2)||37||63|
|sh% (penalties excluded) (1)||36||64|
|sv% (penalties excluded) (1)||32||68|
|sv% on shots from inside the box (2)||24||76|
|sv% on shots from outside the box (2)||23||77|
|Penalties awarded differential
(penalties awarded for minus penalties awarded against) (1)
|Having penalties awarded against (1)||9||91|
(penalty goals for minus penalty goals against) (1)
|sh% on shots from outside the box (2)||8||92|
|Being awarded penalties (1)||4||96|
|Penalty goals conceded (1)||3||97|
|Penalty goals scored (1)||<1||>99|