"Find The Corners!" Joseph, Armstrong, Ehibhatiomhan, Acquah set to fly?

Lou McKenzie investigates EFL strikers’ shot placement and nominates four for a goalscoring upswing in 24/25

Ali Maxwell — “Our latest guest writer is Lou McKenzie, aka @Louorns on Twitter. Having followed Lou for many years, his passion for EFL analysis shines through. He always has original ideas, and likes to use data to dig beneath surface level to ask questions and uncover EFL insight. His forthright views on the Championship - and his club Watford FC - are always worth reading. He contributes a lot to EFL discussion and it is a pleasure to host this interesting and original piece of work.”

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Lou McKenzie

Picture the scene…

It’s an EFL Dragons’ Den special. Sitting in front of me is Ali Maxwell, George Elek, David Prutton and Jobi McAnuff. I’m about to sell them my shot placement model and explain why they should take note of it.

First: the idea.

You’re at a game, or sitting in the pub with your mates, and a striker scores a lovely goal. You mutter the words, “He’s a great finisher”. You remember doing shooting drills as a child, hearing your coach shout, “FIND YOUR CORNERS!”.

What if I told you there was a way to quantify those shots? To measure – not just with your eyes – how often strikers are able to find those very corners?

Second: the method.

Using FotMob’s shot maps, I was able to break down the location and placement of every shot on target by a striker in the EFL, and put it into a spreadsheet. Unlike the app, however, I tinkered to include only shots taken from inside the box in open play, and I also removed headed efforts. Then I added certain parameters, such as ‘a minimum of 15 90s played in a season’ and ‘a minimum of 30 shots taken from inside the box’, in order to get a clearer outlook and a fairer comparison between each striker.

In all, I’m using shot data from the past two seasons to see if there’s a correlation between shot placement and goals scored.

Guess what? Of course there is! Those players who put plenty of shots on target and find the corner 20% or more of the time generally score at a high rate.

Finally: the usage.

My main goal in creating this is to collect as much information as possible from strikers inside and outside the EFL, looking at leagues such as the Scottish Premiership, Belgian Pro League, Austrian Bundesliga, Danish Super Liga and, of course, the top five leagues in Europe.

This is an attempt to discover which strikers would be able to translate their finishing ability from a Euro league into, for example, the Championship, or to see which strikers in the EFL can scale up and not be affected too greatly by the step up in quality of goalkeeper, should they move to a higher level. It could even help to predict which striker may have a strong season the following year, or an upturn in form based on the way he’s placing his shots.

No model is perfect, so it will always throw up some numbers that don’t align with the concept. It analyses shots on target (as well as those that hit the woodwork), and not those off target, although including shot volume means we remain focused on goal threat. Plus, this model is designed to look purely at finishing ability and not the entire profile of a player.

xG data is very useful, but I feel with this I can apply even more context — that ‘regression to the mean’ may not always apply to every striker equally. I believe there are a handful of strikers in each league who will consistently score goals as they rise to higher tiers, perhaps even performing at Premier League level.

When it comes to players outside the EFL who have joined Championship clubs this summer, my model says that QPR’s new striker, Žan Celar (signed from FC Lugano), and Cardiff’s Wilfried Kanga (who played for Standard Liege last season) should translate well to the league and score goals.

NB: Twenty First Group’s League Ranking model has the Belgian Pro League as slightly better quality than the Championship, with the Swiss Super League slightly worse, but all in the same ballpark.

Kanga (Cardiff) and Celar (QPR) should score goals for their new clubs

Now here comes the fun part.

I put the spreadsheet into action. I want to identify strikers that my model considers precise finishers to predict who will score, let’s say, 10 league goals minimum this season. I want to pick out one striker per league and put my neck on the line and say: these guys will smash it.