GerardFM24 said: Interesting topic that I will keep following. Is there also such research about staff and staffroles, Like what are the most important roles and which attribute are the best. Expand EBFM has important findings about various staff. This video shows the impact of coach attributes, which I would surmise as somewhat impactful but probably a fair bit less important than you would think.
From memory he also found that you only need 1 physio to get the full benefit, and it doesn't matter if this 1 physio has '1' physiotherapy. Hopefully I'm recalling that correctly. I think the idea is that a physio's attributes only estimate length of injury better, they can't actually reduce injury rate/length aside from their presence as a staff member.
For newgen impact, staff are mostly irrelevant. The only effect is that HoYD will influence 1-2 players coming in each intake with their personality. This possibly extends to other 'youth coaches' who supposedly have half this effect.
IMO an aspect that is emerging as important is stuff to do with tactical & team selection, because these appear to significantly influence winning - even if it's just selecting between similar players you have in your squad. So tactical knowledge, judging player ability, motivating for whoever you set the relevant responsibility for. One thing this would mean in practice is getting an assistant manager who is a great manager, rather than great coach.
I've been disassembling the "fm.exe" application for FM 24, which means I've turned the machine code back in to something resembling C so that I can look at it. I'm doing this because I want to find where shortlists/search results are stored in RAM so I can read them in my scouting tool but I might have found something else.
There's a function beginning at memory address 0x0144b40180 (at least in my Windows, Steam copy of FM 24.4.2+2081827), which appears to be grouping together player attributes and scoring them. I'll share the approximated C code here (feel free to ask Chat GPT what it means) as well as what I think it's doing. If you do feed this in to an LLM to decipher, give it this file too. That will map the offset codes (e.g. 0x23d, Pace) to the player's actual attributes.
From what I can see, this code is reading player attributes in groups, multiplying them together and creating a score for each group. It then returns the highest scoring group and the group itself.
For example, starting on line 65 of the code snippet:
- The lines beginning sVar1 = (short)((uint)(*(char *) read a different attribute from the Player entity (Acceleration and Pace in this example (0x239 and 0x23d)). - The lines uVar8 = (sVar1 >> 1) - (sVar1 >> 0xf); divide the attribute by 2, rounding down. - The next two if conditions ensure the number is between 1-20 inclusive. - The final if condition sums uVar11 (which is Pace / 2) and uVar6 (which is Acceleration / 2) and then multiplies the value by 5. If that result is > uVar2 (which is the highest rating it has seen so far), then it sets that to be the highest rating (*param_2 = uVar2) and sets the return value of the function to be the category ID (9 in this case).
Now here's the exciting part: the attributes it groups together and how it scores them.
Now I'm not saying that the game uses the same aggregation/scaling in the match engine--the only place that I've found it make a difference is the coach report (see below; this is me hacking the return value from the function so it thinks groups 9, 8 and 3 are this player's best)--but it's worth bearing in mind. I'm going to keep this line of enquiry up and I'll report back anything I learn.
Expand Although what you discovered isn't exactly the golden ticket we are looking for, it's nonetheless a significant insight for me.
Perhaps it's something obvious to a programmer, but as someone who doesn't know programming and couldn't make heads or tails of the code before, finding out what the attributes are called in the code gives me a solid foothold to work with.
I spent a while trying to find attribute combos in the code using this info. I came across a few intriguing ones, such as Reflexes interacting with CommandOfArea for some reason, but no clear formula I'm looking for. Strangely 'versatility' seems to interact with certain other attributes, but maybe I'm just misinterpreting what the code is about. Even without the formula, these attribute associations are perhaps useful clues for forming hypotheses to test about attributes.
Another thing I noticed is that there are references to four-letter names such as 'TrDp' (made-up example), after translating it into plain english with AI. If anyone else has a go at it, I know from previously trying to find the newgen mechanics in the code that these four-letter names are the shorthand they use for the actual mechanic. So a real example of this for instance is you can change the fitness heart icon back to fitness % by changing the mechanic it reads from 'PcOI' to 'PCRF' in the skin. If you go through the other game files, you can find a bunch of these four letter names, even ones that are meant to be hidden mechanics.
So I think I already found an example of say TrDp = anticipation + work rate, where if TrDp was what you were interested in finding out the formula for, you'd have your answer.
I reckon that the attribute combinations, and other things like newgen mechanics, are lurking in there waiting to be found.
GerardFM24 said: Interesting topic that I will keep following.
Is there also such research about staff and staffroles,
Like what are the most important roles and which attribute are the best.
EBFM has important findings about various staff. This video shows the impact of coach attributes, which I would surmise as somewhat impactful but probably a fair bit less important than you would think.
From memory he also found that you only need 1 physio to get the full benefit, and it doesn't matter if this 1 physio has '1' physiotherapy. Hopefully I'm recalling that correctly. I think the idea is that a physio's attributes only estimate length of injury better, they can't actually reduce injury rate/length aside from their presence as a staff member.
For newgen impact, staff are mostly irrelevant. The only effect is that HoYD will influence 1-2 players coming in each intake with their personality. This possibly extends to other 'youth coaches' who supposedly have half this effect.
IMO an aspect that is emerging as important is stuff to do with tactical & team selection, because these appear to significantly influence winning - even if it's just selecting between similar players you have in your squad. So tactical knowledge, judging player ability, motivating for whoever you set the relevant responsibility for. One thing this would mean in practice is getting an assistant manager who is a great manager, rather than great coach.
LightningFlik said: I want to share something of potential interest.
I've been disassembling the "fm.exe" application for FM 24, which means I've turned the machine code back in to something resembling C so that I can look at it. I'm doing this because I want to find where shortlists/search results are stored in RAM so I can read them in my scouting tool but I might have found something else.
There's a function beginning at memory address 0x0144b40180 (at least in my Windows, Steam copy of FM 24.4.2+2081827), which appears to be grouping together player attributes and scoring them. I'll share the approximated C code here (feel free to ask Chat GPT what it means) as well as what I think it's doing. If you do feed this in to an LLM to decipher, give it this file too. That will map the offset codes (e.g. 0x23d, Pace) to the player's actual attributes.
From what I can see, this code is reading player attributes in groups, multiplying them together and creating a score for each group. It then returns the highest scoring group and the group itself.
For example, starting on line 65 of the code snippet:
Spoiler sVar1 = (short)((uint)(*(char *)(param_1 + 0x239) * 0x6667 + 0xccce) >> 0x10);
uVar8 = (sVar1 >> 1) - (sVar1 >> 0xf);
if ((short)uVar8 < 2) {
uVar8 = 1;
}
uVar6 = (uint)uVar8;
if (0x13 < uVar6) {
uVar6 = 0x14;
}
sVar1 = (short)((uint)(*(char *)(param_1 + 0x23d) * 0x6667 + 0xccce) >> 0x10);
uVar8 = (sVar1 >> 1) - (sVar1 >> 0xf);
if ((short)uVar8 < 2) {
uVar8 = 1;
}
uVar11 = 0x14;
if (uVar8 < 0x14) {
uVar11 = (uint)uVar8;
}
if ((int)(short)uVar2 < (int)((uVar11 + uVar6) * 5)) {
uVar2 = (short)(uVar11 + uVar6) * 5;
*param_2 = uVar2;
uVar3 = CONCAT71((int7)((ulonglong)uVar3 >> 8),9);
}
- The lines beginning sVar1 = (short)((uint)(*(char *) read a different attribute from the Player entity (Acceleration and Pace in this example (0x239 and 0x23d)).
- The lines uVar8 = (sVar1 >> 1) - (sVar1 >> 0xf); divide the attribute by 2, rounding down.
- The next two if conditions ensure the number is between 1-20 inclusive.
- The final if condition sums uVar11 (which is Pace / 2) and uVar6 (which is Acceleration / 2) and then multiplies the value by 5. If that result is > uVar2 (which is the highest rating it has seen so far), then it sets that to be the highest rating (*param_2 = uVar2) and sets the return value of the function to be the category ID (9 in this case).
Now here's the exciting part: the attributes it groups together and how it scores them.
Group 1: Set pieces
((Corners + Free Kicks + Penalties + Long Throws) * 10) / 4
Group 2: Attacking technique
(Crossing + Finishing + Long Shots + Passing + Technique) * 2
Group 3: Attacking intelligence
((Anticipation + Vision + Decisions + Positioning) * 10) / 4
(Yes, positioning.)
Group 4: Leadership
((Bravery + Work Rate + Teamwork) * 10) / 3
Group 5: Defensive intelligence
((Tackling + Marking + Concentration + Positioning) * 10) / 4
Group 6: Technical control
(Flair + Dribbling + First Touch + Composure + Balance) * 2
Group 7: Aerial Ability
(Heading + Jumping Reach) * 5
Group 8: Physique
((Strength + Natural Fitness + Stamina + Agility) * 10) / 4
Group 9: Speed
(Acceleration + Pace) * 5
Now I'm not saying that the game uses the same aggregation/scaling in the match engine--the only place that I've found it make a difference is the coach report (see below; this is me hacking the return value from the function so it thinks groups 9, 8 and 3 are this player's best)--but it's worth bearing in mind. I'm going to keep this line of enquiry up and I'll report back anything I learn.
Although what you discovered isn't exactly the golden ticket we are looking for, it's nonetheless a significant insight for me.
Perhaps it's something obvious to a programmer, but as someone who doesn't know programming and couldn't make heads or tails of the code before, finding out what the attributes are called in the code gives me a solid foothold to work with.
I spent a while trying to find attribute combos in the code using this info. I came across a few intriguing ones, such as Reflexes interacting with CommandOfArea for some reason, but no clear formula I'm looking for. Strangely 'versatility' seems to interact with certain other attributes, but maybe I'm just misinterpreting what the code is about. Even without the formula, these attribute associations are perhaps useful clues for forming hypotheses to test about attributes.
Another thing I noticed is that there are references to four-letter names such as 'TrDp' (made-up example), after translating it into plain english with AI. If anyone else has a go at it, I know from previously trying to find the newgen mechanics in the code that these four-letter names are the shorthand they use for the actual mechanic. So a real example of this for instance is you can change the fitness heart icon back to fitness % by changing the mechanic it reads from 'PcOI' to 'PCRF' in the skin. If you go through the other game files, you can find a bunch of these four letter names, even ones that are meant to be hidden mechanics.
So I think I already found an example of say TrDp = anticipation + work rate, where if TrDp was what you were interested in finding out the formula for, you'd have your answer.
I reckon that the attribute combinations, and other things like newgen mechanics, are lurking in there waiting to be found.