Introduction
Last year in the FM23 cycle, by an absolutely absurd margin, my most popular post of the edition was What does “good” look like in FM23?
I’ll borrow the context and many of my own words from that post for this year, but all of the numbers in the image at the end are the result of hours and hours of fresh research; compiling and comparing statistics pulled only from Football Manager 2024. Some of the individual statistics are calculated completely differently in the FM24 engine, so a complete rework from scratch was required.
In this article, I consider the idea that performance statistics in football can be illuminating at best, misleading at worst and potentially meaningless in the middle. I personally find them fascinating and incredibly useful, but only when discussed and applied in ways that make logical sense.
What is this all about?
Baselines and benchmarks are different things, but effectively what we are interested in here is finding a starting point for comparing statistical performances in Football Manager. Or in other words, defining what good looks like. Generally speaking, we would all agree that 15 for the Finishing attribute is good, but what is objectively a “good” number for Shots on Target per 90 for a striker? 1.2 a game? 10? A Tackling attribute of 17 for a central defender we would all agree is excellent, but is 1.8 Tackles Completed per 90 any good? How could you possibly know? This is what I’m aiming to answer.
Now, I must stress that FM calculates performance statistics differently from real life football. This is because although Football Manager is a wonderful football simulator, it’s a computer game, believe it or not. Various designers and senior figures within Sports Interactive over the years have commented on this multiple times, not just on the SI forums but on various podcasts and in numerous interviews.
In this article I aim to answer questions like “4.2 dribbles per 90 for a winger, is that good?” and many others like it, by providing tables suggesting what good looks like across all of the key performance metrics in FM24 for each position on the pitch.
I have done this by taking pages and pages of performance data exported from multiple end of season save files in FM24 and calculated Good, Ok and Poor performance parameters for each metric related to each segment of the pitch, like central defence, wide midfield etc. This is slightly different to the naming used last year, as I feel this is more useful.
Disclaimer – I understand that sometimes you want different things from the norm for your players in their chosen positions depending on your tactical system. i.e. Where I’ve made the assumption that a “good” full-back will have a high number of open-play crosses completed per 90, I appreciate that your personal tactic may play with Inverted Full Backs who drift inside or No-Nonsense Full Backs who sit back and don’t contribute offensively. Similarly, you may want your Goalkeeper to push out and sweep, making interceptions and then delivering accurate passes upfield. Other FMers may want their goalkeeper to shot-stop and play risk-free passes. I can’t account for every tactical choice and role preference, but the broad strokes of this are intended to be useful.
Also, there is the consideration that a high number of tackles per 90 (for example) doesn’t always denote a “better” defender than one with a low number of tackles per 90. Perhaps only that the first defender plays for a lesser team who have to defend more. Until we include possession adjusted figures for defensive metrics (a future project?), this analysis will always be subjective. Even then, it still will be.
The numbers can tell any story you want them to. Use them as you desire. I’m just doing the hours of initial mathematics, so you don’t have to.
For each position/role I’ve created a table. I haven’t covered every role in the game (as that would be bonkers) but I’ve split the roles out as logically as I can.
How did I do it?
I explored three end of season save files from different save universes. In each one, I ran a Player Search for players who had played a minimum of 2,000 minutes and played for sides in the “big five” European leagues – the top tier in England, France, Germany, Italy and Spain.
To capture the right kind of players in the results, I ensured that players were natural in the required position. To fulfil the DNA required to match descriptions like “Midfield – Destroyer” or “Wide Attacker – Provider” I included a filter for a couple of key attributes to ensure the results held the closest match to the profiles of players who fit the description. Like a midfield destroyer had to be high in Aggression and Tackling. A wide attacking provider high in Dribbling, Vision and Crossing, for example.
I then took a cut of ten players from the top, middle and bottom of the pool of results for each of the statistics in question, then calculated an average for each. Once I had an average Good, Ok and Poor number for every field, I then repeated the same work for the other two save files, and averaged those results to ensure it was a fair and even spread, mitigating anything unique to a specific save universe.
Note to skin creators
Last year, a number of custom FM skin creators chose to use these numbers and implement them directly into their player profiles on their custom skins. Please feel free to do this again, just please name the profile section ‘FM Stag Stats’ or otherwise visibly credit the work. Please tag me in any tweets announcing the skin too, @FM_Stag. I love to see how creatively these pieces of work can be utilised. Thanks!
Show me the money metrics
After many, many hours of collating numbers, here are the results.
The most practical applications of this are when scouting targets, evaluating your own players or comparing the two.
Example – I want a ball-playing centre-back in the summer window and I’ve found one who plays for a team similar in level to ours but in a different country. He has 1.8 tackles, 0.77 clearances and 5.1 progressive passes, all per 90. Is that good? Well a quick glance at the table above tells me that he is nicely positioned in the upper category of “ok” towards “good” for a ball-playing centre-back, based on the numbers. A good find, who could be worthy of a closer scout, as the numbers will never tell you key information like how injury prone a player is, if he likes playing in big matches and how his personality will fit into your team dynamic.
Example 2 – I’d like a goalscoring striker, a proper number nine, and the target I have in mind has scored goals and has a nice attribute spread. Looking good so far. When inspecting the statistics, he has 0.17 non-penalty xG per 90 and a shot conversion rate of 16%. Is that good? Time to look at the table again. This is actually relatively poor. So despite his strong attributes and number of goals, it looks like he doesn’t get into dangerous positions as often as he could, and tends to squander a high number of chances. He’s probably not your man.
I could go on, but I’m sure you get the idea of the kinds of question these numbers can help answer for you.
Enjoy
For those of you who like data analytics or dare I say it, the “moneyball” approach in Football Manager, hopefully the table will help guide your decision-making when faced with endless columns of seemingly baseless performance statistics.
This work analysed elite players in top European leagues, but you can and absolutely should benchmark your players against these numbers while playing at any level with any tactical approach, just remember the context above. Numbers aren’t everything in football, but they can be a great help.
How will you use it? Let me know on X.
Thanks for reading.
FM Stag