It’s tough to break with tradition. To start something over in a different way to the way it’s always been done. Since the first versions of Championship Manager began to appear, the players in game have been graded in a Top Trumps kind of way; graded on the attributes that make a footballer. Not that long ago, these attributes moved from an Alphabetical Listing to a Categorised Listing. Attributes were noted as Technical, Mental or Physical Qualities. Managers would then scour the database to find the best of the best, judging a player’s strengths on the rating they were given for the attributes appropriate to their position/role. However, as FM has become a Data-based goldmine, reflecting the modern game, some managers have searched for that added layer of realism and forgone the attribute ratings. This year, I have joined them and turned my back on the way I have played FM since that first day I loaded the game up all those years ago. This year, I’m breaking tradition and embracing the battle of Attributes v Statistics.

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Attributes & Ratings out of 20

Much has changed in the way that the Attribute ratings have been gathered, corroborated and inputted into the FM Database. While the Collier brothers’ ratings came from their judgements using whatever information was to hand, now we have see teams of researchers come together and work collectively to make the judgements of a players starting ratings and potential growth/decline over the course of their career. While the days of Mark Kerr, Taribo West and Tonton Zola Moukoko may be long in the past, FM’s attribute ratings can still shine a light on, what turns out to be, a false dawn or a star in the making. No one can predict the future, and FM is no different.

When assessing the qualities of a player, we look to lists of strengths and weaknesses, similar to our Scout Reports in game. In a modern sense, we use statistics and data to fill in the blanks and flesh out the details further. While Leo Messi may have a Long Throw attribute in FM, I’m not sure this weakness would have the data to necessarily stand behind the attribute awarded in game. While this may not be one of the reasons I and many other have turned our backs on attributes in FM23, it is not realistic to expect to see an attribute quantified when the data to back it up does not necessarily exist.

Attribute Progression

The ability for attributes to increase and decrease season-by-season is one of the most admirable aspects of the life-cycle of a player in game. The same with Staff Attributes. Education for coaches, Training and exposure to regular First-Team for players, all play their part in boosting the ratings of players, though when the physical attributes begin to decline as a player ages, we feel the realism of the game yet again. Nonetheless, the growth and decline features can make FMers feel that elements of subjectivity still exist in game. Only natural when humans are involved, but nonetheless, many may question the qualities of certain players and clubs in game, and rightly so.

An area that many find issue with in FM regarding attributes is in the area of retraining. A player may come across your radar who you determine has the skills to play in a different role/position than the Natural & Accomplished roles and position you can see from their Profile Screen. However, in the background where the Hidden Attributes lurk, a player’s versatility is also rated between 1-20. While Hidden Attributes can be “revealed” by your Coaches & Scouts in their reports, we do not get an accurate understanding of a player’s versatility and ability to adapt and be retrained in their new position and role. Realistic or not, this has the potential for any player of the game to become frustrated at what appears, at the surface level, to be something where no issue exists. Thus, the discussion between attributes v statistics begins at this point.

Attributes v Statistics

While choosing an FM save where you know no players, and as such cannot question their in-game attributes, a bias will develop over time as you make informed decisions of their qualities based on their attribute scores. This is fine and reasonable, and something the makers of the game want you to do as you play through the game. But if these numbers are to be used by you, then so too are the ever growing repertoire of statistics and metrics which are used in the game. While more time consuming for some players, and daunting for so many others who do not understand what is meant by others, the numbers here do not lie, and as such act as an untapped gold mine of information that when used right, can leave you looking and feeling like Billy Beane/Brad Pitt in Moneyball.

Using real life metrics and comparing them to those gathered in FM is not straightforward. Given some metrics are calculated differently in-game than those IRL, with many community members so Data-Literate and willing to provide tools and guide for us to understand what we see in front of us. We can suddenly arrive in a place where the attribute screen matters less and less. Attributes show us strengths, but statistics and data show us how a player uses their strengths in real scenarios. For example, a player with a high attribute rating for Passing may attempt many passes in game. The data may show that they have a low completion rate. When other attributes are factored in, we may begin to understand why this is happening.

Statistics in the Match Engine

In-game, the Match Engine will consider so many factors in the calculations that a high passer rating may be let down by several other factors – poor technique, match day conditions, the abilities of teammates, tactical instructions, etc. Many of us may be confused when their Match Ratings are low given their attribute ratings, and this is where we need the data to really understand what is happening on the pitch in front of us. It is a battle then for all players to understand where we hang our flag on Attributes v Statistics.

For FMers of all ages & experiences, delving into data and statistics can seem like a gargantuan leap from the norm…and it is. Understanding the relevance and use of certain measures is difficult at best for unguided players, and requires a level of depth that many players may not wish to engage with as they play through the game. The addition of new metrics this year, e.g. non-Penalty xG, is welcomed by players who use the measures. As knowing a penalty has an xG of 0.78 will skew the xG performance of a penalty taker, in comparison to their xG from open play and other set piece opportunities. However, while the case for Attributes remain strong, because it is familiar, there is an oversight on the SI side of things that the Data Hub Tutorial does not explain the true depth of data. Which forces players to either ignore data completely, or seek help from the community and the real world to try to understand what the game is telling them.

My own understanding is far from complete, and I fall into this category as well. Trying to make sense of the relevance of some data to specific players, despite easily highlighting their relevant attributes using the Default Skin. Some FMers have made their own Data-based skins to replace the attribute screen altogether, but I can see how many players may see this and feel overwhelmed by the total difference of what is displayed in these.

Star Power

My own approach to going Attributeless in FM is to embrace, as I call it, Star Power. Using the Star Skin, I still see the traditional Attribute Profile page of players and coaches, however the numerical values have been replaced by Stars. Just like the attribute figures, these stars are colour-coded to quickly display the strengths of a player, but hide the level of their strengths and weaknesses from being on a scale of 1-20. If I want to then see how a player is performing, I must delve into the data. I can have an idea of the players strengths, but in order to put a numerical value on their strengths/weaknesses, I have up to date, game-by-game figures. While not a totally Attributeless approach, I can avoid making snap judgements based on numerical values and instead recognise areas in which I can shape a team and see where each player fits in based on these.

Starting out with Arendal Fotball in Norway’s Third Tier, I had no pre-existing information on the playing squa. The stars have just shown me a player’s strengths. I have had to rely on the Coach Reports to set a foundation of my playing squad to flesh out the basic strengths/weaknesses of each player, before following the statistics to make informed decisions game-by-game in order to develop the player, the squad and playing style. Then using these smaller judgements to help me recruit and develop the future of the playing squad. What has hampering my progress had been the lack of a Data Department at the club. We’re Semi-Professional and working from a small budget, so I have become the defacto Data Analyst, informed only by the basic reports from my Assistant. It has been a steep learning curve, but already I feel the benefits of not having anyone to hold my hand through the initial phases of the journey with the club. I have had to jump two feet in and learn on the job. It has been a good start to the season as well, so that has helped my confidence grow.

My Plan

My determination is to keep the squad small, to rely on development of Youth Players as my primary method of squad building, and as such, developing this level of knowledge of the statistics of my first squad in-game, I feel I can better make informed decisions about each of them from this point forward. Attributes v Statistics at this early stage sees the lead begin with Attributes, but with each game week, Statistics are drawing back one closer.

The catch with this data will be the wiping of data season-on-season in FM. I have practiced starting a new save file at the end of each season for the past couple of editions of FM. This will take on a new importance this year as each save file will preserve the data from each season that I can use to make the necessary squad decisions each year. I also have the use of some of the Excel Sheets that have been circulated around the community recently to help me make clearer sense of the data. While my Excel skills may be limited, I am grateful to those in the Community (FM Stag especially, who’s views I have used in the Screenshots above) who make resources like these to make life easier for the rest of us.

In painting a picture of a player, there are two schools of information gathering –  the eye test and the data test. Moneyball gave us a glimpse into the Oakland A’s Recruitment Meeting. There were the Scouts, who scouted the way scouts have always scouted, and there was Billy, converted by Peter Brand into seeing the true value of players who scouts did not appreciate. As we have watched football become more entrenched with numbers, we need to make sure that we do not move from one extreme to the other. Instead of having players pass the eye test or the data test, we need them to pass both tests. We need our Scouts to watch the player and our Data Team to analyse them. Together, we can put together the full puzzle and see the clearest picture of the player we can. That’s why I’m using Star Power to put together the pieces. Not abandoning the ways of the past, but altering them ever so slightly that keeps me tethered, stopping me from flying away with the data-driven ways of the present. It’s not so much Attributes v Statistics, but Attributes & Statistics.

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