Data is a powerful tool in the world of football. It can be even more powerful when utilised within Football Manager. This makes using data in FM19 essential. FM is essentially a huge database of attributes and statistics, which can be manipulated and analysed to produce better results for your team. In this post I’m going to outline my tips for utilising this data to improve your save.

The King is dead. Long live the King.

My Data driven Moneyball experiment was coming along nicely. I was in the 24/25 season, my third at the club. After completing my transfer window I hit the long slog that is the Premier League season. I decided to holiday until January, confident in my tactics and my team, and check back in after I had hoovered and done the washing.

When I returned with my brew, I was surprised to find the loading bar flashing “Available Jobs” at me. Palace were struggling in 18th while I was off on my jollies, putting my feet up in the south of France.

This was the first sacking of my entire Football Manager career. Of course my first thought was to reload and take it game by game. Clearly my tactic needed tweaking or my assistant was playing the wrong players. However, that’s not what I did.

I had a quick look around to see what jobs were available. One stood out to me, so I applied. I attended an interview and the rest was history… I welcomed myself to Atalanta BC!

Set up your tactic

The first thing I would recommend doing is forming a tactic that you’re happy with and sticking to it. It’s important that you know the roles in your side will be constant, so forming a clear philosophy and style of play will help this process. I decided on a 4-1-2-3 vertical tiki taka I have been working on.

Pick your team

There is a great feature on the squad screen where you can look at attributes across the whole squad based on the role and position you want players to play. Using that feature, I assessed each player’s suitability in various roles and crafted my team accordingly.

Want help spotting players who can be retrained? This view is perfect. Once you have an idea of who can play where and what your squad will look like, it’s time to create a spreadsheet.

Set up a spreadsheet

In my opinion, spreadsheets are the perfect companion to FM if you want to crunch some FM data. My spreadsheet for Atalanta includes the ages and abilities of my squad, along with the attributes needed for the role they will be playing in. I’ve also used an average- a crude indicator of a players ability.

I used conditional formatting to make it clear where players are lacking in terms of what I expect from them. From here it’s easy to organise individual training to improve their attributes. I’ve also formatted the age and contract info to spot players who are in danger of becoming too old to move on.

From here you should have a good idea of which players are best for each role. This process allowed me to spot that one of my fringe CM’s could play DLP. I was previously about to ship him out! In addition, one of my AMRs could be retrained successfully as a DR. Keeping an eye on the training and squad views is really important to upkeep of your spreadsheet.

Results

Once I’d decided which player would play in each position, it was onto the games. We made a good start in the league but once the European games came back, the fatigue started to show.

With two games left in the league we’re currently 10th; there’s no chance of relegation or continental football.

We’re currently unbeaten in the Europa League since I took over, beating Betis, RB Salzburg, Marseille and Wolfsburg along the way. The final’s held in Bulgaria, against an Arsenal side who recently hammered Olympiacos 6-0 in their semi-final second leg. I’m thinking of streaming the final, or possibly posting it on the YouTube. Keep an eye out!

This sacking has helped me fall back in love with FM19, when I’d all but given up. Sometimes, being sacked can be exactly what you need. It’s not that I wasn’t enjoying my Moneyball experiment. I still have the save file and I may revisit it in the future.

This post focused more on attributes, but if you’re more interested in raw statistics then you can also check out my Moneyball series.