Before we kick off, I should clarify the title. These new numbers are of my creation and figured out in spreadsheets but using data exported from Football Manager 2022. They are not new columns appearing in the game itself.
The Argument
At one extreme end of the spectrum, some believe that performance statistics offer very little actionable insight and have no place in football. They feel the sport is primarily about heart, passion and desire. Computers and data science should remain the domain of academics, and the only numbers in football that matter are those on the scoreboard and on the referee’s watch. Years of experience and the “eye-test” conquer all. Instinct over analysis.
Those at the extreme other end are probably baseball fans and/or gambling enthusiasts. They love a statistical prediction and diving deep into the numbers. Often they don’t even need to watch a player play in a game of football in order to make a perceived complete judgement on him or her. A handful of graphs and a few lines of numbers and they truly feel they know everything worth learning about the athlete in question. Gut feeling and prior experience be damned, the spreadsheets and scatter plots tell them everything they need to know.
This article isn’t about that debate. Mainly because the argument is extraordinarily boring.
Like most things in life, I think there should be room for balance. Albert Einstein said it best when he said “Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted.” I dare say he wasn’t talking about Kevin de Bruyne’s progressive passes per 90 or Romelu Lukaku’s xG per shot, however.
If the numbers show that a player covers more grass and dribbles past more players than anyone else in his division, I want to go and watch him play. Those numbers alone probably can’t tell me if he’s Dan James or Ronaldinho. I can make my mind up when I see him in action. That said, how many times have you watched a football match and been wowed by an individual performance, only for it to turn out that the player just had the luckiest run of his life, and deeper analysis shows that while his peers don’t have the same YouTube highlight reel of four or five special moments, if you were managing his football club, you’d in fact get more consistent quality performances from his understudy who is sat on the bench. The understudy’s ability has been overlooked as no-one has thought to analyse what he does do, whenever he actually gets the chance to get minutes on the pitch.
Another useful application is when your team lacks the financial muscle to go out there and buy Lionel Messi (for example) when you want an agile and creative forward. Sure, you could buy another diminutive Argentinian who is left-footed and often also sports an exquisite ginger beard, but surely it would be more logical to try and find a transfer target who delivers similar performances on the pitch, be it delivering key passes, creating chances, completing successful dribbles or regularly getting into positions of high goal-scoring potential. Even if he isn’t quite at Leo’s level.
The numbers aren’t the whole story, but they sure as hell help.
Statistics in Football Manager
We all play Football Manager differently. From the casual gamer to the hardcore fanatic, there’s no strictly right or wrong way. As the game has developed over the past thirty years, most of us take what the game gives us, then add our own colour and narrative to ensure our saves and experience of the game is unique and above all fun. I’ll explain what I mean.
In the 1990s and early 2000s, most of us invented imaginary press conferences in our minds. Answering questions from the media about what’s next for our triumphant yet fictional distant future megabucks Millwall team who had just won back to back Champions League trophies. If you are fortunate to be young enough not to know, deep press interactions weren’t in the game back then. The irony of that example is that now press conferences form a substantial part of our FM interface and experience, no one wants to do them and most are delegated to the AI assistant manager!
Another example is that pre FM09 we had no 3D match engine. While to this day there are those who still swear by the classic 2D dots beyond while evaluating the movement in a new tactic (the scenario when I still use it), most rely on the 3D match engine at least in replays to see if that lovely shot was in fact a first-time volley or not. Was there a deflection or was that 30 yard drive sweetly struck? Before the 3D match engine, we visualised the goals going in and the wild celebrations on the pitch, all in our heads. Some people even ran up and down their parent’s hallway at 13 years old in their socks kicking a foam football while doing Peter Brackley’s voice out loud and pretending to be some of their favourite Champ Manager signings. That last one might have been me…
Fast forward to FM22. The introduction of the Data Hub mirrors recent developments in modern football and the rising popularity of statistical analysis. Proper data metrics in Football Manager are a relatively recent addition and still have their teething troubles. Anything new does. So enamoured by this new feature however, I wrote a trio of guides for Sports Interactive’s The Byline website on how managers can get the best out of the Data Hub in it’s current form. Click the link below to access my Tweet which links to all three. That was a shameless plug I know, but I had fun writing the guides and hope you find them useful too.
Much like the 2D match engine limitation or the limited press interactions in the early days, it’s on us as players if we wish to use our imaginations and creativity to fill in the gaps that a computer game can’t give us, no matter how immersive the football simulation becomes. To take the leap from just hammering the space bar to continue, to really bringing it to life. For some, it’s creating entertaining video content and engaging with their followers, bringing them into their save universe. For others, it’s writing epic fictional and fantastical stories involving managers who are drug barons and revolutionaries. For me, it’s exporting the numbers FM gives me access to and diving a little deeper into the statistics to help inform my squad-building strategy and transfer planning.
I may no longer be running up and down the hall in my socks pretending I’m Pierluigi Casiraghi, but I’m still finding ways to expand my football management simulation experience, and enjoying myself too. If you’ve made it this far into the article, maybe you’ll enjoy it too.
New metrics
I’ve recently been getting into baseball sabermetrics. Now whether or not you follow baseball is irrelevant as the only reason I mention it is that something that baseball does really well but football often does not is combine various performance statistics to create a singular number that indicates something fundamental about that player’s performances that is quicker to read than a sheet full of individual figures. It is also easier to digest than a long read article from a sports journalist about why a particular player is useful to a specific team.
A common example of this is ‘OBP’ or ‘on-base percentage.’ This isn’t just the number of times a player got on base, but rather a calculation that considers a number of actions. Hits, walks, hit by pitch etc. Putting the specifics aside, the point is that it’s all boiled down into a single number that allows you to more easily compare multiple players side by side on a major element of their game.
For fun, I decided to create a few new combined football metrics of my own, for use when comparing players in Football Manager. I know that these may not be truly unique ideas and some of these may even have existing names, but bear in mind I’m having fun playing a computer game, not applying to be the head of Opta or brokering the next deal between the Premier League and Oracle, so chill out.
I created a calculator where in Football Manager you set up a Player Search for a transfer target, then export the results into my Excel spreadsheet and it does all of the calculations of the new metrics I’m about to tell you about automatically, more or less. There’s a bit of manual editing, but the specifics can come later in a second blog post. I’ve adopted variations of this approach over the last couple of years but recently it’s becoming popular enough to write about.
All of these calculations are adjusted in various ways to ensure the numbers are in a relatively comparable range between metrics.
Here we go.
Striking Efficiency (SE) – A player’s non-penalty goals per 90 minutes divided by his total non-penalty shots plus his non-penalty xG (Expected Goals) per shot. Basically how efficient a player is at finding the back of the net in comparison to how many shots he attempts, but removing penalties from the calculation and including an element of how good his movement and anticipation is (higher npxG means he got into more positions of higher goal-scoring potential in open-play).
Creative Efficiency (CE) – A player’s chances created per 90 divided by his completed passes per 90. This is how many times per x amount of passes does the player play a pass where the receiver takes a shot at goal from a position of 0.15xG (a “half-chance” in FM terms) or higher. A way of understanding if the player is a regular playmaker.
Defensive Interferences (DI) – A player’s combined interceptions, clearances and successful tackles per 90. I’d like to include blocks in this calculation, but unfortunately getting the blocks statistic into the search and squad views isn’t possible in 22. Basically how effective a stopper the player is at denying opposition chances.
Overall Attacking Contribution (OAC) – A player’s combined key passes, chances created, successful dribbles, shots on target, goals scored and non-penalty xG per 90, plus his non-penalty xG per shot and an adjusted average rating. A combined number denoting overall offensive effectiveness.
Overall Defensive Contribution (ODC) – A player’s combined interceptions, clearances, successful tackles and headers won per 90, plus an adjusted average rating. A combined number denoting overall defensive effectiveness.
OAC + ODC – Like it sounds. A combination of both of the above metrics to determine a player’s “all-round” effectiveness. Probably best to use for comparing central midfielders or full-backs who you may require to be active and competent at both ends of the pitch, and not solely in just the attacking or defensive discipline or phase.
I think that the above makes the best out of the numbers we currently have access to in-game, and in my opinion using them to compare player performances is more useful than just a number of stars from a scout report or looking at the average rating or attributes alone.
Caveats
The same caveats need to be expressed for any statistic used anywhere for anything. I don’t believe that any singular number tells the whole story. Now to pre-empt any feedback regarding the usefulness of statistics, I believe they provide a guide. They can identify players you may never normally consider, sometimes highlighting diamonds in the rough. For me, the numbers are an invitation to inspect further and look at the minutiae of the individual, the team he is playing for and the league he is playing in. It’s all about context.
Daft and obvious example – A player scores 100 goals in a single season in the Malaysian Super League so he appears highly on our spreadsheet for striking efficiency. Does that number alone mean he is going to do the exact same when you sign him for your Manchester United side in the Premier League? Of course not. The gulf in class will probably swallow him up. FM Old Timer recently flagged some great work he found on Twitter by Tony El Habr which brings to life the relative increase in ‘difficulty level’ between many different leagues. It illustrates the point effectively.
A slightly less obvious example – An Elfsborg central midfielder has a higher Creative Efficiency (CE) rating than a superstar playmaker in your squad. You are managing Barcelona. Should you bin Pedri and bring in Simon Olsson, safe in the knowledge that he’ll smash those chance creation numbers again the following season? Probably not. He may do, but it’s not an exact science. Much like any advice or observation, the numbers are contextual to the team and quality of opposition where the player is plying his trade. Olsson will need a more in-depth look before you can decide if he’s truly capable of the step up in class.
An even less obvious example – You are managing Blackburn Rovers and a fringe player at Chelsea appears in the results and has a more impressive overall attacking contribution (OAC) score than all of your existing strikers. His wages aren’t a problem and he seems keen to join. Reputationally he’d be quite the coup. The numbers have done their job highlighting an appropriate transfer target, right? Not always. You run a scout report and realise that he’s injury prone and hates big matches. So yeah he has impressive numbers but in the context of your wider decision-making, he’s perhaps not the right signing for you.
In short, it’s an alternative lens to look at players through. Not the entire picture.
Workarounds
Now some clever soul could list every team in every league in the game on a hidden tab in the spreadsheet along with their respective reputational values and factor these in as weightings in the calculations I’ve put together, but I’m not that clever. There’s no easy way of grabbing these details from the game either and the amount of data would be absurd.
There are a few things you can do though. When running your initial Player Search (which in turn forms the data that is pasted into Excel) tick the box to ensure the results only contain players who are at least a little interested in joining your club. Perhaps add a search filter showing only players with “Very Good” reputation or above if managing a subjectively “big” team. Maybe only include players who have clocked up over 1000 minutes this season, so your sample size isn’t too small. Do whatever works to ensure that the initial list of players in the results are loosely relevant in your search for a signing. There is no point in Neymar appearing in the data if you are managing Ebbsfleet United and looking for a deadline day loan signing. Use your filters wisely.
Secondly, salary is often a great signifier of the level a player is at. The sweet spot is finding a player who is performing like an elite player, playing at a comparable level, but on a salary that makes him obtainable and representative of good value for money for a club of your stature.
That’s where the rest of my calculations come into play. For each of the new metrics (SE, CE, DI, OAC and ODC) I have a column that calculates this performance rating against the player’s basic salary. Now again, the caveats are important. Players may have small basic salary contracts but with huge additional bonuses. Or players may feel they have outgrown their current club and ask for a monumentally huge wage-rise when you get a bid accepted and go to sign them. But let’s be realistic. Negotiations are going to be less painful with a high-performing player who currently gets paid £10k a week than with the one who takes home £250k.
In essence, alongside using the calculator to identify high performing players, you can pivot to considering these targets including the context of their potential salary cost.
Example – In a test Barcelona save where the manager is looking for a new striker, the calculator example above ranks Erling Haaland as the striker from my Player Search results with the highest SE (striker efficiency) rating. Surprise surprise.
If you open the above screenshot and look closely, he has an SE of 0.975, topping the list. He has a ‘cost per 1SE’ of £189,763.80. These numbers are meaningless on their own, and are merely calculations that become useful when comparing Haaland to another player.
Two rows down the list you have Luis Súarez (p.s. it’s the ex-Watford one at Granada, not the ex-Liverpool mischief-maker). Súarez has an SE of 0.847. Therefore he is performing at (*opens Percentage Calculator.net on browser and uses it quickly*) 86.9% of Haaland’s level (0.847 vs 0.975), but because Súarez doesn’t command the same elite wages as the Norwegian, his cost per 1SE is just £23,311.15. Basically, Súarez gives you 86.9% as much statistical attacking quality as Haaland, but at 12.3% of the cost. Easy, right?
Now as manager of Barcelona I’d still prefer to sign Haaland because 1) he is literally performing better than Súarez (0.975 vs 0.847, remember?), 2) he is an ultra-high reputation player that will boost shirt sales and represent a big statement of intent etc and 3) Barcelona’s real-life financial troubles aside, I’m at one of the few clubs on Earth who could attract and afford one of the best strikers on the planet so why shouldn’t I?
That said, imagine I was managing Valencia or Sevilla and ran this same search. Getting a striker who gives me 87% of Haaland’s attacking contributions at 12% of the cost? Count me in.
Lastly, you’ll see from the sheet there’s another metric right at the end for ‘value for money,’ or VFM. Remember earlier when I mentioned OAC + ODC combined? VFM is just the cost calculation for this overall combined contribution rating.
Other applications
The above example was just one Player Search for one position and a comparison between two players using two metrics, striking efficiency (SE) and cost per 1SE. There are many other possible use cases.
Why not run a Player Search showing only your own first team players, export them and bring them into the spreadsheet then see who is delivering appropriate bang for your buck in terms of performances vs salary cost?
Are you having to sell one of your highly paid defenders due to budget constraints? Why not look for a player who plays in the same position and delivers around the same number of Defensive Interferences (DI) as your outgoing player, but at a significantly lower salary cost (a lower cost per DI)?
If you’ve ever seen the movie Moneyball (if you are reading this article in the first place, I imagine that you have. If not, do so. It’s great), this is like when the bold Billy Beane tells his bewildered scouts he wants to replace one outgoing player who he simply couldn’t retain with three misfits who combined deliver the same performance metrics as the outgoing first-teamer, but at significantly lower cost.
It’s not always a case of just upgrading or downgrading your players based on their performance statistics when you are forced to sell or when a player won’t renew his deal and leaves. Sometimes all it takes is a dive into the numbers to identify potential targets you may never have otherwise considered who may give you exactly what you need at that time.
In conclusion
If there is sufficient interest, I’ll post my calculator tool spreadsheet and make it downloadable along with a guide on how to use it for yourself. I wanted to write this article first to explain my thinking behind creating the new metrics, the joy I get from them and to find out if it’s of interest to you too.
Along with the aforementioned FM Old Timer, thanks goes to FM Tahiti for his regular input as a fellow analytics advocate. Shout out to Jack The Cult of FM too, who’s recent series highlighted to me the value in looking at ‘team goals scored/conceded per 90′ as a better metric to compare players’ intangible influence on matches in a more illuminating way than just looking at ‘games won %.’
There are tons of other FM folk I’d thank too who are also interested in analytics, but it’s not the bloody Oscars.
Finally, if your gut response is “I just look at the star ratings or the attributes instead of all this nonsense, you number-crunching lunatic,” then that’s ok. Even if you prefer to just hit up the wonderkid lists and buy those with the highest PA. Even if you go rogue and use the in-game editor to give your Scunthorpe team Mbappe, Camavinga and Foden and quit and reload your save every time you concede a goal. You do you.
This is just about an alternative way of looking at things that makes me enjoy FM more as a result. I am hoping that you might do too.
Thanks for reading.
FM Stag