Ok so the first thing I would like to point out is, using data analysis in Football Manager does work. It does. However, for this particular series, a fatal error was made early on in plumping for a high reputation Premier League side to use as our base for an experiment involving high-ranking championship players.
I have been sacked in December, currently 10th in the league. Any promoted side in this position would be lauded, certainly any side using primarily homegrown players having made a summer profit of around £150m would be delighted.
But not Leicester, and thus, I am unemployed.
Of course, I should have just added myself in as a manager at a Championship club, rather than selecting a managerless team. Hindsight is a wonderful thing, and despite some nice results along the way, particularly away from home, the save has ended.
To be fair, there are some shocking results in there too, mostly home defeats to bottom half teams, but it still is a harsh sacking imo.
Still, here we are, what’s done is done. This save, of course, was more about seeing the various ways data analysis can be done to identify footballers that a specific system, judging on their ability at playing a certain way.
The above image shows what Jake Cooper does in a team, he heads balls away (and scores with his head too). We knew this is what he can do, the data corroborated it, and he did it consistently. By analysing the data, we could see which players suited the exact player roles we wanted, and these players did their job to full effect.
But it wasn’t enough. I should point out that I ran a couple of simulations before managing the team myself – the longest the manager lasted in any situation was January (highlighting that this was the wrong team for this experiment, sue me).
When the new edition of the game hits our Steam libraries in November, we will revisit this series idea with a more logical team choice, and really fly the flag for data analytics.