The ability for us amateur analysts to access Opta data through front end publishers such as StatsZone and Squawka has probably been the single biggest reason for whatever advances have been made in the field of football analytics over the last year or two.
But, I discover that having access to this data now causes me problems. I find that I now always have a desire to dig ever deeper into the available data; I have a need to go further and further into the Rabbit Hole. Believe me, it’s a bloody frustrating place to exist in.
So why am I going all philosophical on my readers?
Key Passes metric
This was sparked by the excellent piece published yesterday by Adam Bate on Mesut Özil where he uses metrics such as Key Passes and Key Passes per90.
As Ted Knutson remarked on Twitter, it is great to see the emergence of metrics such as Key Passes being used in the mainstream media and it’s a clear sign that analytics is slowly making its way into football parlance. The use of this metric helps put an objective measurement around the creativity of footballers.
Before the Key Pass metric came into existence, reporters would have had no option but to gush in subjective terms about the “wand of a right foot that player x possesses” or at best they perhaps could have cited one or two specific examples of great passes.
Thanks to the Opta recorded measure of Key Passes it is accepted that it now possible to move away from relying solely on subjective measurements and be able to construct a synopsis of a player on undisputed facts. This synopsis can then have the “soft factors” added into it by the writer to complete the picture of a creative player.
So what’s the problem?
I’m not totally happy with the use of Key Passes being used to measure the creativity of a player, because I know we can do better.
Now don’t get me wrong, I’m much happier with using this measure than not having any objective measures available to us at all, but the drawback when using Key Passes is the implicit assumption that all Key Passes are the same. I say this as the data providers (edit – just to clarify I mean in terms of the standard metrics that are published) do not distinguish between the relative effectiveness of different key passes, it therefore follows that users of this stat just cannot distinguish between different types of Key Passes.
Coutinho v Sissoko
I’ll use the example of the Key Passes created by Liverpool’s Philippe Coutinho and Newcastle’s Moussa Sissoko over the first 3 games of this current Premier League season to demonstrate my point.
Firstly, how did the two players fare in terms of Key Passes so far this season?
The above images are taken from whoscored.com
We can see that both players have an average of 2 Key Passes per game, or 6 Key Passes in total.
The Analytics Skeptic
Now picture a journalist who was previously an analytics naysayer deciding to finally embrace analytics. He goes along to the whoscored site and uses the best data (Opta supplied) that is readily available to him and states that, on the objectively measured data, Sissoko and Coutinho were equally creative over the opening 3 games of the season.
Our journalist has taken the stats at face value and penned his piece along those lines. However, within 10 minutes of his piece being published he gets bombarded with comments by readers asking what planet he lives on if he is suggesting that, even over those 3 games, Sissokho was as creative as Coutinho.
Let’s go back to the sentiments I expressed at the top of this piece.
I now appreciate that it is wrong to stop at the top level of detail, Key Passes in this example. It’s just a pity that no one told our journalist friend this as he has now decided that he’s never going to look at an Opta statistic again, and that he was right all along not to trust those pesky stats.
More detailed data is available, so why don’t we use it instead of being content with the top level metric of Key Pass?
The obvious answer for why we tend not to use the whole depth of data that is available to us is that it is so bloody time consuming to digest and interpret. Thankfully, at this stage of the season with just 3 games played that task is just not quite so onerous.
Let’s now look in detail at the 6 Key Passes that the two players played over their first 3 games, starting with Sissoko.
Apparently Sissoko created 6 chances during Newcastle’s opening 3 games. I would contend that he actually made 1 very good chance, 1 poor chance and the other 4 are falsely labelled as chances as they all must be 40 yards or more out from goal.
So what about Liverpool’s Brazilian playmaker, Coutinho?
Coutinho’s 6 Key Passes really look like Key Passes. I would argue that only one of those passes led to what could be classified as a “poor shot”. The other Key Passes led to very high quality shooting opportunities for Liverpool.
This piece isn’t intended as a slight on Sissoko, and indeed I don’t think that too many would have argued that the Frenchman was as creative as Coutinho. However, when looking at the bare Key Pass numbers it appeared that the two players had the same creative impact over their first 3 games. When delving a little deeper into the data available to us we see that this simply wasn’t the case.
So what’s the point of this?
I guess I have 3 points:
1 – There just aren’t enough hours in the day to be able to process and effectively use all of the data that is now available to us in one form or another.
2 – On a macro level, even being armed with the data is not enough. The data needs a lot of TLC to be able to be used efficiently and to release the secrets that it undoubtedly holds.
3 – On a much narrower micro level, it is obvious not all Key Passes or chances created are the same. Now that the term “Key Passes” is slowly appearing in the mainstream media is it time to go a little further into the Rabbit Hole and attempt to assign some quality measure to the raw, but important statistic of Key Pass? I feel it is, and the above extreme example illustrates why.
And remember, all Key Passes are equal, but some Key Passes are more equal than others.