So I've known for the better part of a decade now that there's a significant hidden mechanic when it comes to newgens, that seems to be the unique nation itself or something along these lines.
The picture I had about newgen mechanics is:
Junior coaching ~40% PA effect Nation youth rating ~25% PA effect Youth recruitment ~25% PA effect Club reputation acts as tie breaker for youth recruitment Either unique nation or division ID ~25% (or perhaps even more) PA effect (a hidden, unchangeable factor - it may be 'Nation attribute template' that is listed as an inaccessible debug option in the editor, which sounds like it would be similar to the 'Nation personality template' before it was removed in recent versions) All other factors no effect
How I found this is by swapping England and Romania completely, after realizing the numbers didn't add up after deducing the relevant factors and trying to calculate outcomes with them. It took a lot of work so I've never repeated it since.
It occurred to me that there's a simpler way of going about it: Make every club, division, and nation have identical relevant attributes, and measure the median PA differences that result.
The idea worked:
France 120.6 England 116.5 Spain 110.3 Germany 110.2 Brazil 109.5 South Africa 107.4 Argentina 106.7 Russia 104.5 Italy 100.8 Israel 98.6 Switzerland 98.4 Singapore 73.0 Gibraltar 64.5
This means that all things being equal, a newgen at a France Div 1 club will have 20% more PA than a newgen at Italian club, on average.
The formula I've come up with based on this is as follows:
((Junior Coaching x 1.5)+(Youth Recruitment)+(Youth Rating/10)+(Factor X))x1.15
Factor X is the hidden nation/division factor, and it is 62 for England with all other nations to be set proportional to it (i.e. Singapore 73 PA > England 116.5 PA = Singapore 25 Factor X > England 62 Factor X).
It's not perfect, but it seems work quite well (keep in mind random fluctuation of ~5 PA is normal with newgen intakes, and up to ~15 PA fluctuation is not unusual):
((10x1.5)+(10)+(13.5)+(62))x1.15 = 115.575 - England (artificial) average 116.5 ((10x1.5)+(10)+(13.5)+(25))x1.15 = 73.025 - Singapore (artificial) average 73.05
((19x1.5)+(20)+(13.5)+(62))x1.15 = 142.6 - Man City (England) 139
West Ham (15JC/15YR) is equivalent to a maxxed out 20JC/20YR club in Italy. A maxxed out 20JC/20YR Singapore club is exactly equivalent to 1JC/1YR in the English Premier League.
Brentford (7JC/7YR) = 106.95 10JC/10YR Argentina = 106.57 15JC/15YR Switzerland = 107.05 20JC/20YR Singapore = 90.27 1JC/1YR England = 89.7
Rough data for other countries (-/+20):
Norway 112.5 China 111 Hungary 111 Japan 108.8 South Korea 107 Mexico 106 Chile 105.5 Australia 104 Bulgaria 103 Finland 102.5 Scotland 101.5 Romania 100.5 Slovenia 100 Wales 97.5 Turkey 93 Belarus 90 Ireland 83.5 Canada 82.5 Hong Kong 79 N. Ireland 67
Here's what was controlled (made identical):
Nation region Nation game importance Nation state of development Nation youth rating Nation reputation Nation tactical attributes (including preferred formations) Nation economic power Nation FA financial power Nation ranking points Club reputation Club morale Club training Club youth coaching Club youth facilities Club youth recruitment Club youth importance Club corporate facilities Club preferred formation Continent regional strength Division reputation
Only Division level 1 teams were altered Professional status is not controlled, I overlooked this one So the controls aren't exhaustive, but they're pretty extensive
Facilities & morale were set to 10. Club rep to 5000. Nation rep to 7000. Nation youth rating to 135. Basically I went for something moderately high, just to help rule out extremes messing things up somehow.
stefanopt said: For the top tactics, do u use Striker rating for the AMC, or do you use AMC rating? Expand Since the conclusion so far is that all outfield players have the same weightings, you can use any position. For Genie Scout though it has a position proficiency modifier to the %, so in Genie Scout use the position you're targeting (i.e. AMC rating for AMC position).
White Europe said: What's most recent GS ratings to use ? Expand hazar35hazar said: I've been asking about this for FM24 for days too, but no one bothers to treat me like a person and actually answer me. Expand The Premier League 2.1 weights in the updated OP on page 1.
Meta team + 14 crossing/long shots/technique = 7th, 1st, 2nd, 6th, 6th, 4th, 8th = 4.857 position
So that's a little higher (~1 position) than normal which is what I was expecting.
I forgot to mention I retested bravery on DCs only earlier:
6th, 3rd, 5th, 9th (sacked), 5th = 5.6 position
So no significant effect, positive or negative. Note that I've also found previously that 20 bravery on all players is also useless.
I also did a test to see what I can do with a team of 200 CA players at Luton. Got +366 g/d. Best result was 19-0 vs Newcastle. From vague memory the previous best I got at Man City was something like +409.
Might have discovered a meaningful attribute combination.
I started with creating a team of Ederson (GK) + Haaland clones (for each position) at Luton.
A few reasons for this. Man City has sacking issues in tests. Mustermann asked essentially do significant attributes change as environmental attributes change (i.e. condition), which got me thinking about it, so my thinking is let's return to doing some high level tests where technicals may have some impact when physicals drop say. And another reason is my finding is that all positions share the same ideal attributes, so why not take by far the best player and make a team of him and see how it goes.
Ederson + Haalands default = 1st, 101pts, +133
Haalands with most technicals & some mentals at '1' (in line with meta template) = 3rd, 91pts, +58
Haalands with all technicals at '1' except drib, fin = 1st, 89pts, +79
At this point I'm thinking ah geez.. technicals must be back on the menu, but then..
Haalands with all technicals at '1' except drib, fin, cro, lon, tec = 1st, 102pts, +118
However..
Haalands with all technicals at '1' except drib, fin, tec = 2nd, 81pts, +54
Haalands with all technicals at '1' except drib, fin, lon = 1st, 92pts, +84
Haalands with all technicals at '1' except drib, fin, cro = 1st, 94pts, +82
Haalands with all technicals at '1' except drib, fin, cro, tec = 1st, 87pts, +87
So at this point I figured it's probably the combination of crossing + technique + longshots together. This would also explain why it hasn't appeared in prior testing I've done.
So I did more tests on cro + tec + lon:
2nd, 94pts, +100 (this the wildest 2nd I've seen.. beat Man City in the FA cup 8-0, 6-1 in the league) 1st, 97pts, +91 1st, 100pts, +99 1st, 96pts, +84
So that +118 simmered down quite a bit.. but still, it seems to be a significant increase.
I did another test of all technicals except drib, fin at '1' = 3rd, 88pts, +87
To keep this all in context though, we're talking about a sum total of +400 in attribute points for what appears to be ~2 position increase. For comparison, one of the marginally significant attributes, strength, would be equivalently around +140 attribute points for +2 position.
I think it has more application to player ranking dependencies, and perhaps it clues us into a common format that exists (i.e. lon + pass + tec?)
A config file will be created after you run it; see here for a primer on how to configure the weights (but I think it will be straightforward). The app hot reloads after modifying the config so it should make it easy to tweak scales and such.
This was built with speed and convenience in mind so there's no absurd wait time when starting the app. Just run it, load a save and get cracking. (Once you see the number of players cached in the bottom left corner, it's fully primed. Blink and you'll miss it). Expand It was missing some dlls. Worked out I had to install this with both the GTK3 and GTK4 options, and add the '<location>\MSYS2\ucrt64\bin' directory to the system environment path (if anyone else wants to get this working, ask AI how to do the path thing).
The app itself is very janky at the moment.. opening 'squad depth' froze up my 9950x3d for a while!
Some other UI issues that need improvement. Filter by club doesn't seem to be working. First release though, just not ready for use yet.
I'm currently wondering about dribbling, whether it ought to be increased in weight or not.
Having it at low weight just so happened to make the real rankings fit, but of the top players, all have 10+, and more controlled data suggests it is more essential.
I'm thinking that perhaps because I was assessing mainly top players, 10 vs 19 drib didn't make much difference, but something like 6 vs 10 would be a big difference.
I took a look at correlations between the top players using my weightings and the various stat measurements, and the one thing that seemed to be consistent was the inconsistency. So while two did stand out as fairly robust correlates, 'interceptions' and 'fouls committed', overall I suspect that these various stats actually have nothing to do with the win or loss outcome. My guess is that its calculating based on certain attributes and tactics or whatnot, independent of the actual match engine outcome. So cosmetic basically. Just a theory.
I tested HarvestGreen's claim that certain attributes have benefits if one player has them instead of the whole team. I'm sure I must have tested this before, since it's important if true, but I still find that it doesn't improve things. Actually, giving several players each one of the several attributes he listed as '20', seemed make the results worse than standard: 10th, 11th, 5th, 12th. This could be because attributes such as decisions are known to have a negative effect, or it could be another reason. So, unknown cause, but its not inexplicable.
The thing is, HarvestGreen's data has always turned to be generally correct for me, so long as you take it in the confines of what he is measuring. So this claim of his must be true under certain conditions. And the teamwork result (2 wingers having +8 teamwork improves team result, but whole team at +12 teamwork doesn't) seems to be evidence this kind of thing exists. Obviously it probably has something to do with tactics and/or attribute distributions. Whatever it is, it doesn't seem like you can reduce it to 'chuck in this one guy with 20 decisions'.
I decided to take a look at HarvestGreen's excel data on it, not expecting anything interesting but thought I should probably take a quick look to see if anything further could be gleaned. I saw that he included a table I don't think he mentioned in his posts, which I've translated:
So it looks like he's actually tested some of those key stat measures. It's pretty apparent to me that what these results are showing is that the attributes involved matter, not the combo of them as 'dribbles' or 'interceptions' or whatnot.
I came across one of my notes in regards to player reputation:
Home reputation influences national team call-ups by coaches, but not the selection of starters/substitutes at match time
I'm saving these notes for one big compendium sometime, but since it was raised, thought some of you might be interested in knowing that one.
I've done some more tests of Teamwork 8 > 20: 6th (sacked), 13th (sacked), 4th, 7th (sacked) = 7.5 position
So giving all outfield players teamwork is not good. But giving the wingers teamwork is seemingly good. I suspect this could be due to what HarvestGreen identified as certain attributes being less beneficial the more players that use them, rather than it specifically being a winger thing that Mustermann finds. But who knows.
I realized that I'm being too hasty in doing away with sprints & pressures. I got swept up in what Mustermann had to say, that I completely forgot about what the game files actually say:
| Distance run | Work Rate, Stamina, Pace | | Sprints | Work Rate, Stamina | | Pressures attempted | Work Rate, Anticipation | | Pressure completion | Work Rate, Anticipation, Acceleration |
LightningFlik said: I've always suspected that the coach report is vibes-based, really. It makes sense to me that their current reputation (and possibly even form) influences it, as well as the relative strength of the league and team-mates. I don't for one minute think the game is telling me that a 4.5 star player is unquestionably better than a 4 star player. Expand Now I get the weird star ratings my meta team gets. Even though they have equal attributes, the star ratings are all over the place because of different CA. Setting them to 1 CA, they're all equally 3.5 stars.
For normal play, I think this means that wingers are going to be overrated by your staff (i.e. 4 star because of high CA cost for attributes), whereas DM will be underrated (i.e. 2 star because of low CA cost of attributes). Both of these players are equal quality in reality.
I’ve noticed there have been some new findings regarding FM26 recently. What do you make of them?
We’re getting very close to FM27 now, so do you think these discoveries could have any real impact? And are you planning to release PL 2.2 as a result of them?
Looking forward to hearing your thoughts! Expand I've addressed it further up the page, where I also retested a few of the attributes to be sure. Overall a good idea, but the correlations are simply clouding the correct insights. He's getting a bunch of attributes right, but also a few wrong (i.e. Crossing & Off the Ball), even if we test using his specific suggested positions.
Nonetheless it's still useful in several other ways. For instance, I was theorizing before that what leads to more sprints and pressures would perform better, because that is what the Knap tactic seems to favor. So I was initially excited to see his data, because it shows ostensibly what attributes contribute to sprints & pressures. But his correlations, which I don't dispute the validity of, on sprints & dribbles perhaps suggest that certain attributes inexplicably increase win rate even in spite of decreasing or doing little for sprints & pressures. That actually makes sense to me, because if you're a developer, you're probably likely to overlook that then or know about it but ignore it thinking users won't go for it because it makes no sense.
There won't be a Premier League 2.2 for a while. I feel it's at the standard I want it to be, and so I'm taking a break from that for now. If FM27 is any good, I'll probably do a new version for FM27. If not, it depends on how I feel.
iezzex said: Also i feel like that some of counter type tactics which is meta are just designed for high pace physical players Better technical players may act better with posseios type tactic Untested, just a feeling based on my own long save team acting worse on meta ones Expand It's possible, but I think unlikely. I usually always test with Knap tactic, but we know that other tactics do substantially worse with or without technically gifted players. I don't know Knap's methodology, but it's worth bearing in mind, if my following assumption is correct, that he is finding the best tactic based on the real top teams who are often highly technical, not the way I test with more barebones speed machines.
iezzex said: Feels like doing test in a real league like this is absolutely incorrect RNG do more then this changes huh You better make a test league with capped CA, same teams, tactics etc and do 100 samples imo Expand Adding controls for FM testing often does more harm than it helps in my opinion.
Suppose you do test against teams that have 10 in every attribute. Well then a player of Jumping Reach 11 is going to beat them all, but then in the actual game, Jumping Reach 11 would get your team relegated. You can't take opposition players and tactics out of the equation.
Besides, this has been done. HarvestGreen uses this controlled kind of testing, and so does FM Arena - although I believe FM Arena is a little different where uses a representative set of competitive attributes for its opposition teams (i.e. 13 instead of 10).
Another angle I would tackle it from is that RNG is part of the game, and therefore must also be taken into account. So with teamwork for example, we see the RNG range is 2nd-8th. With real Luton, the top Knap tactic produces ~2nd-9th range.
It's worth pointing out that that RNG range for real Luton is actually easily squashed down a lot by pre-selecting the team so it uses the same players. If pre-selected, the range goes down to ~2nd-4th. So really the RNG is only about -/+1 position difference. People overestimate the impact of the RNG of this game IMO.
With the meta team but with Raheem Sterling replacing AMR, the variance is also very low, getting ~6th almost all the time.
Why does the meta team, having identical outfield players, have a increased variance of 2nd-8th, compared to the meta team with Sterling? Don't know, but keep in mind I'm not using pre-selection for my meta tests either.
Now regardless of whether it's RNG or player fitness or bad luck injuries or whatnot, let's just surmise the reality as this:
You can expect to vary ~2-6 positions each season from expectation. An average save might last say 6 seasons. Are you going to experience the difference between 10 and 15 pace in 6 seasons? Yes, very much so. Are you going to experience the difference between 10 and 15 teamwork in 6 seasons? Probably not really, but when you have 10 of these minor attributes together as part of a weighting system for buying/assessing players, then you will.
This also goes to why I don't think 100 samples is necessary. No one is playing 100 seasons. If I take 9 samples representing 9 seasons, and the end result is an uncertainty of -/+ 1.81 position, then that reflects the reality of normal play and it should be left at that.
Which is the more useful and relevant information?:
The life expectancy of a nation as 82.59 based on measurements of millions of people, or
As a 60 year old with a particular cancer my doctor has guesstimated I most likely have 5-7 years to live based on several similar cases he has seen, or
The doctor telling me 'its all random...we all die sometime anyway'
So teamwork does appear to be doing something here. It shouldn't be above 5 after 9 samples otherwise.
There is upside to having it at high values, but not as much downside to lacking it completely (7th at '1', vs 9th for Aggression at '1' for example, and many other key attributes fare far worse at '1' ).
8 > 16 is quite a big jump for a relatively small position gain of ~2 position, but that's only +1.6 per outfield player.
Overall I would now classify teamwork an attribute of minor importance, above Strength but probably below Aggression. Or maybe equal to Aggression, since the test team has 11 Aggression and only manages ~5th-7th on average.
Another thing to note is that because the test team's outfield players are identical, it wouldn't be the case that teamwork is simply handing over the ball to the players with the better attributes.
I'll be testing it at 20 on all players again to see what results that shows.
So versatility doesn't seem to do much. Possibly a very slight benefit. Too hard to tell like crossing.
Dirtiness surprisingly did worse at '1'. Perhaps it just requires more samples, but even so, one would expect '1' dirtiness to do better than 5th-7th given its deduced importance based on real player performance. In comparing the real players, there is a significant difference between 8 and 2 dirtiness, so it's not just about it being under '10' say. Only possibility I can think of, aside from simply requiring more samples, is that perhaps dirtiness interacts with temperament and/or sportsmanship and/or controversy, where high dirtiness can be beneficial (more hard tackles) when machiavellian sociopath but not when prone to losing temper and getting red carded for no gain.
Here's a very quick test. There are three metrics for reputation in the game:
- Home - Current - World
Other websites can probably explain (or guess) what they do but I'm just here to see if they affect contract negotiations. (How could they not?) I'll play with the values (and use my unchanged player as a baseline) and record them below.
| Home rep | Current rep | World rep | Salary demand | 3,115 | 4,372 | 1,1911 | Important player, £53k p/a | 10,000 | 4,372 | 1,1911 | Important player, £55k p/a (plus small bonus increases) | 0 | 4,372 | 1,1911 | Same as baseline | 3,115 | 10,000 | 1,1911 | Wants to talk but thinks I can't afford him (1) | 3,115 | 0 | 1,1911 | Important player, £36k p/a (also his bonus demands have halved and the coach's estimation of his ability has decreased) | 3,115 | 4,372 | 10,1000 | Important player, £86k p/a (plus large bonus increases) | 3,115 | 4,372 | 0 | Important player, £51.5k p/a (plus small bonus decreases)
(1) This was true even after I adjusted my budget sliders and tried again and after I adjusted the club's reputation to be 10,000
N.b. after changing reputations I advance a few in-game hours so the system registers it
Other notes 1. I saw no change after setting my wage budget as low as it would go (£428k p/a) and as high as it would go (£1.2m p/a) even for the 10,000 world rep experiment. (But if someone on Reddit said it did then I'm not going to dispute that.) 2. World reputation is the only thing that changed the player's reputation on his Personal Information page. It was 5 grey stars ("Minimal" at 0 and 5 gold stars ("Exceptional" at 10,000. 3. It makes sense that Current Reputation would have such a big impact on demands + your star rating if it's meant to be the sense of how hot you are as a player. 4. I also tested the baseline player when the club had a reputation of 0 and 10,000.
0: Important player, £58k p/a 2,920 (baseline): Important player, £53k p/a 3,115 (player's home rep): Important player, £54k p/a 10,000: Important player, £64k p/a
5. I also tried setting the CA/PA of the player to various values:
6. I changed the manager's Home, Current and World reputations to 10,000 and it made no difference (although it made people like me more).
Here's a dump of screenshots showing (most) of the above. I know a lot of this might not be new information but it's a start.
Baseline
CA 120/120
Club rep 0
Club rep 10,000
Player current rep 0
Player current rep 10,000
Player home rep 0
Player home rep 10,000
Player world rep 10,000
EDIT: From reading the game's code I can tell you that the reputation of the league you're going to be playing in next season is a factor (or the current league, if next season's isn't decided yet). It's a minimal impact though, seemingly. If I tell Isaac that we're going to the Premier League next season instead of League Two, he asks for a tiny bit more money and his coach summary rating drops an entire star.
From the looks of the code, it seems like a few things are summed (reputations) and then scored (given a grade?) That is, I don't think changes are linear; I think you get bucketed in to discrete grades, which represent how strong your bargaining position is. After enough reputation points gained/lost, you might find yourself in the next bucket.
This has taken _far_ longer than I anticipated and I'm knackered so I might look at the actual calculations tomorrow (no promises though).
EDIT again: I can't help myself.
If you have a "Front End" Sugar Daddy, the player's demands increase. (+15 to the score it keeps track of). Expand I found out recently what the three different reps mean exactly. I've forgot it now, but I'll post it later on. I think it was explained in a 2011 guide by two of the developers.
But yeah, there's not much to go on in regards to what affects wage demands. Perhaps someone on Youtube has done it in the past few years. I haven't looked into it myself.
Interesting that the coach rating decreased with lower current rep. Wouldn't have thought that.
Some handy findings there. So CA matters and PA doesn't. Club rep and manager rep doesn't matter, would have expected somewhat otherwise.
You know, I'm thinking this could kind of be integrated into the rating system if you use FMSS. You could do something like add 'divide by current/world rep & CA' to the formula. Makes it more useful for gameplay.
Normal result is ~5th-7th, so perhaps a hint of a benefit to crossing in there due to the 1st which is not unprecedented but quite rare. Not quite enough to conclude crossing has any benefit though. I think a few more samples with high consistency & technique would settle this one way or the other.
No revelations here. Again, perhaps a very marginal benefit outweighed by CA cost, similar to strength or vision.
I forgot to change Man City's media expectation to 20th, hence the high rate of sackings. Doesn't invalidate the results, just means some samples are semi-wasted (9th sacked means they wont end up first, but they could have ended up 4th say).
I am also doing this in FM24, not FM26, but I doubt that that is why he finds crossing matters and whatnot.
LightningFlik said: How do you feel about the premise that different positions require different attributes? I know that flies in the face of your theory of a generalised make-up (last I checked, anyway). I thought for a while that the positional requirements of attributes was overblown because FM always punishes me for expecting too much of it.
Now I'm unsure, but I've had great success promoting Warrington up the leagues using your old, old weights. Expand Before I played around with tactics, I would have made this critique of his findings, citing my attribute brute force testing, but now I am more open to the idea again of there being positional differences.
My impression now is that what is going on is that the Knap tactic favors a certain set of attributes, and this would extend to all the very similar top tactics in general (different roles or even formations, but all high pressing/intensity).
So if you play a defensive tactic, maybe then positioning would matter. Thing is, all defensive tactics lose. So effectively positioning never matters.
So it can be true at the same time that (1) only certain attributes matter for winning games, and (2) different tactics need different attributes.
Now I have tried positional variation in my attribute tests with the Knap tactic, and found there isn't any I can see (this is after being convinced for the longest time that concentration mattered for defenders but not forwards), but it's possible I am mistaken on one or two, or perhaps I didn't try the right combo (i.e. maybe I tested flair alone on ST, but not flair alone on MC).
I'll be doing some tests today based on Mustermann's findings, so we'll see if there are in fact position differences that actually increase/decrease win rate.
LightningFlik said: I just can't find where that code above is executed. I've decided to search for "weig" in hopes that I can find somewhere else in the code where that logic is performed. In doing so, I've found the nuttiest string references in the binary. What the actual hell is in this game?
Expand That's funny
I wonder what they've included that brought that stuff into it. I noticed there was some chinese characters, that links to a chinese website in the code for free template resources and whatnot - must be that they used an asset from it.
How did you get it to display the phrases as single lines like that? Maybe its cause I didn't let it fully finish processing I guess.
Maggh said: Thank you for all these discoveries, small question that i have, do staff have any relevant role in player development, i tend to try and have the best staff there is, but with all you all discovered im not sure if it really matters or not. Assitant managers, scouts, phisios, all that, does it really matter? Thank you Expand I would surmise overall that most staff do matter moderately, both for growth and performance. It can also matter for players being willing to sign for your club.
People are going to have different impressions, but for me I'd say staff are about 70% as impactful as I'd intuitively think they should be.
Gerrard said: Expand I came here today with the intention of making an assessment of this video lol
Very interesting findings
Flair is strongly negatively correlated with possession won for midfielders. Is this because of the attribute itself altering the role, or the high flair causing a low-possession-won role to be selected? And then, does more possession won lead to more or less wins?
I think overall this can actually be triangulated with the key attribute template and EF 424 IF HP V2 P101 AC tactic to determine what tactical measures are beneficial and which are detrimental, and then refine the key attributes - to per position specificity - based on that. Perhaps it might even clue us into how the tactic can be edged out too.
We see that flair and off-the-ball have good & bad results for different positions/measures. And he finds bravery & one-on-ones for GK were significant in relation to goals conceded. So I will do some testing to see.
There are some caveats to his conclusions I think most will miss. For instance, the GK goals conceded thing, isn't actually measuring team goals conceded, and finishing produces more goals on wingers, but that doesn't mean the team scores more goals as a whole (my own testing on finishing shows that it simply take off goals from ST and give it to the winger).
But there's a lot of valid and potentially fresh information in it.
LightningFlik said: Yes that's right. Everything in constants.h is right, that's from my app which scouts players in memory. I'm saying the names I gave to the attribute groups were made up by me.
Also I may have found the location in code where role/position suitability is calculated, but I can't get the code to fire in the course of the game, so testing it is proving difficult. It looks like that's what it's doing though. Expand So just to clarify, it is the case that 0x239 = acceleration, right?
What led you to 0x0144b40180 anyway? I've been looking through instances of 0x239 for example for something interesting, but maybe there's a better way of going about it
Would be interested in seeing the suspected role location
So I've known for the better part of a decade now that there's a significant hidden mechanic when it comes to newgens, that seems to be the unique nation itself or something along these lines.
The picture I had about newgen mechanics is:
Junior coaching ~40% PA effect
Nation youth rating ~25% PA effect
Youth recruitment ~25% PA effect
Club reputation acts as tie breaker for youth recruitment
Either unique nation or division ID ~25% (or perhaps even more) PA effect (a hidden, unchangeable factor - it may be 'Nation attribute template' that is listed as an inaccessible debug option in the editor, which sounds like it would be similar to the 'Nation personality template' before it was removed in recent versions)
All other factors no effect
How I found this is by swapping England and Romania completely, after realizing the numbers didn't add up after deducing the relevant factors and trying to calculate outcomes with them. It took a lot of work so I've never repeated it since.
It occurred to me that there's a simpler way of going about it: Make every club, division, and nation have identical relevant attributes, and measure the median PA differences that result.
The idea worked:
France 120.6
England 116.5
Spain 110.3
Germany 110.2
Brazil 109.5
South Africa 107.4
Argentina 106.7
Russia 104.5
Italy 100.8
Israel 98.6
Switzerland 98.4
Singapore 73.0
Gibraltar 64.5
This means that all things being equal, a newgen at a France Div 1 club will have 20% more PA than a newgen at Italian club, on average.
The formula I've come up with based on this is as follows:
((Junior Coaching x 1.5)+(Youth Recruitment)+(Youth Rating/10)+(Factor X))x1.15
Factor X is the hidden nation/division factor, and it is 62 for England with all other nations to be set proportional to it (i.e. Singapore 73 PA > England 116.5 PA = Singapore 25 Factor X > England 62 Factor X).
It's not perfect, but it seems work quite well (keep in mind random fluctuation of ~5 PA is normal with newgen intakes, and up to ~15 PA fluctuation is not unusual):
((10x1.5)+(10)+(13.5)+(62))x1.15 = 115.575 - England (artificial) average 116.5
((10x1.5)+(10)+(13.5)+(25))x1.15 = 73.025 - Singapore (artificial) average 73.05
((19x1.5)+(20)+(13.5)+(62))x1.15 = 142.6 - Man City (England) 139
((15x1.5)+(19)+(14.4)+(49.14))x1.15 = 120.796 - Blu-neri (Italy) 120
((14x1.5)+(14)+(14.4)+(49.14))x1.15 = 113.32 - Torino (Italy) 108
((3x1.5)+(15)+(3.5)+(25))x1.15 = 55.2 - Tampines (Singapore) 54.5, 51
((2x1.5)+(9)+(3.5)+(25))x1.15 = 46.575 Balestier (Singapore) 24, 28
((7x1.5)+(8)+(9)+(46.59))x1.15 = 85.2 Yverdon (Switzerland) 70, 90
((17x1.5)+(18)+(9)+(46.59))x1.15 = 113.95 Zurich (Switzerland) 83.5, 111.5
((11x1.5)+(6)+(9)+(46.59))x1.15 = 89.8 - Stade Lausanne-Ouchy (Switzerland) 90.5, 96
((13x1.5)+(13)+(16.3)+(56.05))x1.15 = 120.57 - AMG (Brazil) 126
((11x1.5)+(9)+(16.3)+(56.05))x1.15 = 112.53 - CUI (Brazil) 103
((14x1.5)+(18)+(16.3)+(56.05))x1.15 = 128.05 - FLU (Brazil) 121
((15x1.5)+(17)+(16.3)+(56.05))x1.15 = 128.63 - SEP (Brazil) 130.5
((7x1.5)+(12)+(16.3)+(56.05))x1.15 = 109.08 - FOR (Brazil) 100
((15x1.5)+(14)+(14)+(53.67))x1.15 = 119.795 - Argentinos Jrs. (Argentina) 116
((9x1.5)+(13)+(14)+(53.67))x1.15 = 108.29 - Atl Tucuman (Argentina) 105
((7x1.5)+(10)+(14)+(53.67))x1.15 = 101.395 - Central Cordoba (SdE) (Argentina) 104
((14x1.5)+(14)+(14)+(53.67))x1.15 = 118.07 - Estudiantes (LP) (Argentina) 124.5
((4x1.5)+(11)+(14)+(53.67))x1.15 = 97.37 - Barracas Central (Argentina) 101.5
((19x1.5)+(20)+(14.5)+(56.71))x1.15 = 137.66 - Barcelona (Spain) 126, 138.5
((14x1.5)+(15)+(14.5)+(56.71))x1.15 = 123.29 - Real Hispalis (Spain) 125, 124
((11x1.5)+(13)+(14.5)+(56.71))x1.15 = 115.816 - Almeria (Spain) 112.5, 101
West Ham (15JC/15YR) is equivalent to a maxxed out 20JC/20YR club in Italy.
A maxxed out 20JC/20YR Singapore club is exactly equivalent to 1JC/1YR in the English Premier League.
Brentford (7JC/7YR) = 106.95
10JC/10YR Argentina = 106.57
15JC/15YR Switzerland = 107.05
20JC/20YR Singapore = 90.27
1JC/1YR England = 89.7
Rough data for other countries (-/+20):
Norway 112.5
China 111
Hungary 111
Japan 108.8
South Korea 107
Mexico 106
Chile 105.5
Australia 104
Bulgaria 103
Finland 102.5
Scotland 101.5
Romania 100.5
Slovenia 100
Wales 97.5
Turkey 93
Belarus 90
Ireland 83.5
Canada 82.5
Hong Kong 79
N. Ireland 67
Here's what was controlled (made identical):
Nation region
Nation game importance
Nation state of development
Nation youth rating
Nation reputation
Nation tactical attributes (including preferred formations)
Nation economic power
Nation FA financial power
Nation ranking points
Club reputation
Club morale
Club training
Club youth coaching
Club youth facilities
Club youth recruitment
Club youth importance
Club corporate facilities
Club preferred formation
Continent regional strength
Division reputation
Only Division level 1 teams were altered
Professional status is not controlled, I overlooked this one
So the controls aren't exhaustive, but they're pretty extensive
Facilities & morale were set to 10. Club rep to 5000. Nation rep to 7000. Nation youth rating to 135. Basically I went for something moderately high, just to help rule out extremes messing things up somehow.
Since the conclusion so far is that all outfield players have the same weightings, you can use any position. For Genie Scout though it has a position proficiency modifier to the %, so in Genie Scout use the position you're targeting (i.e. AMC rating for AMC position).
hazar35hazar said: I've been asking about this for FM24 for days too, but no one bothers to treat me like a person and actually answer me.
The Premier League 2.1 weights in the updated OP on page 1.
Link: https://files.catbox.moe/9n18q0.grf
I strongly recommend using FMST26 with the weights provided on page 1 instead of Genie Scout though if you can.
I won't be doing an update until at least the release of FM27, but so far there's no major changes to make anyway.
So that's a little higher (~1 position) than normal which is what I was expecting.
I forgot to mention I retested bravery on DCs only earlier:
6th, 3rd, 5th, 9th (sacked), 5th = 5.6 position
So no significant effect, positive or negative. Note that I've also found previously that 20 bravery on all players is also useless.
I also did a test to see what I can do with a team of 200 CA players at Luton. Got +366 g/d. Best result was 19-0 vs Newcastle. From vague memory the previous best I got at Man City was something like +409.
I started with creating a team of Ederson (GK) + Haaland clones (for each position) at Luton.
A few reasons for this. Man City has sacking issues in tests. Mustermann asked essentially do significant attributes change as environmental attributes change (i.e. condition), which got me thinking about it, so my thinking is let's return to doing some high level tests where technicals may have some impact when physicals drop say. And another reason is my finding is that all positions share the same ideal attributes, so why not take by far the best player and make a team of him and see how it goes.
Ederson + Haalands default = 1st, 101pts, +133
Haalands with most technicals & some mentals at '1' (in line with meta template) = 3rd, 91pts, +58
Haalands with all technicals at '1' except drib, fin = 1st, 89pts, +79
At this point I'm thinking ah geez.. technicals must be back on the menu, but then..
Haalands with all technicals at '1' except drib, fin, cro, lon, tec = 1st, 102pts, +118
However..
Haalands with all technicals at '1' except drib, fin, tec = 2nd, 81pts, +54
Haalands with all technicals at '1' except drib, fin, lon = 1st, 92pts, +84
Haalands with all technicals at '1' except drib, fin, cro = 1st, 94pts, +82
Haalands with all technicals at '1' except drib, fin, cro, tec = 1st, 87pts, +87
So at this point I figured it's probably the combination of crossing + technique + longshots together. This would also explain why it hasn't appeared in prior testing I've done.
So I did more tests on cro + tec + lon:
2nd, 94pts, +100 (this the wildest 2nd I've seen.. beat Man City in the FA cup 8-0, 6-1 in the league)
1st, 97pts, +91
1st, 100pts, +99
1st, 96pts, +84
So that +118 simmered down quite a bit.. but still, it seems to be a significant increase.
I did another test of all technicals except drib, fin at '1' = 3rd, 88pts, +87
To keep this all in context though, we're talking about a sum total of +400 in attribute points for what appears to be ~2 position increase. For comparison, one of the marginally significant attributes, strength, would be equivalently around +140 attribute points for +2 position.
I think it has more application to player ranking dependencies, and perhaps it clues us into a common format that exists (i.e. lon + pass + tec?)
A config file will be created after you run it; see here for a primer on how to configure the weights (but I think it will be straightforward). The app hot reloads after modifying the config so it should make it easy to tweak scales and such.
This was built with speed and convenience in mind so there's no absurd wait time when starting the app. Just run it, load a save and get cracking. (Once you see the number of players cached in the bottom left corner, it's fully primed. Blink and you'll miss it).
It was missing some dlls. Worked out I had to install this with both the GTK3 and GTK4 options, and add the '<location>\MSYS2\ucrt64\bin' directory to the system environment path (if anyone else wants to get this working, ask AI how to do the path thing).
The app itself is very janky at the moment.. opening 'squad depth' froze up my 9950x3d for a while!
Some other UI issues that need improvement. Filter by club doesn't seem to be working. First release though, just not ready for use yet.
@GeorgeFloydOverdosed
Crazy CB 88,6 Rating
And how does he fair?
I'm currently wondering about dribbling, whether it ought to be increased in weight or not.
Having it at low weight just so happened to make the real rankings fit, but of the top players, all have 10+, and more controlled data suggests it is more essential.
I'm thinking that perhaps because I was assessing mainly top players, 10 vs 19 drib didn't make much difference, but something like 6 vs 10 would be a big difference.
I tested HarvestGreen's claim that certain attributes have benefits if one player has them instead of the whole team. I'm sure I must have tested this before, since it's important if true, but I still find that it doesn't improve things. Actually, giving several players each one of the several attributes he listed as '20', seemed make the results worse than standard: 10th, 11th, 5th, 12th. This could be because attributes such as decisions are known to have a negative effect, or it could be another reason. So, unknown cause, but its not inexplicable.
The thing is, HarvestGreen's data has always turned to be generally correct for me, so long as you take it in the confines of what he is measuring. So this claim of his must be true under certain conditions. And the teamwork result (2 wingers having +8 teamwork improves team result, but whole team at +12 teamwork doesn't) seems to be evidence this kind of thing exists. Obviously it probably has something to do with tactics and/or attribute distributions. Whatever it is, it doesn't seem like you can reduce it to 'chuck in this one guy with 20 decisions'.
I decided to take a look at HarvestGreen's excel data on it, not expecting anything interesting but thought I should probably take a quick look to see if anything further could be gleaned. I saw that he included a table I don't think he mentioned in his posts, which I've translated:
So it looks like he's actually tested some of those key stat measures. It's pretty apparent to me that what these results are showing is that the attributes involved matter, not the combo of them as 'dribbles' or 'interceptions' or whatnot.
Home reputation influences national team call-ups by coaches, but not the selection of starters/substitutes at match time
I'm saving these notes for one big compendium sometime, but since it was raised, thought some of you might be interested in knowing that one.
I've done some more tests of Teamwork 8 > 20: 6th (sacked), 13th (sacked), 4th, 7th (sacked) = 7.5 position
So giving all outfield players teamwork is not good. But giving the wingers teamwork is seemingly good. I suspect this could be due to what HarvestGreen identified as certain attributes being less beneficial the more players that use them, rather than it specifically being a winger thing that Mustermann finds. But who knows.
I realized that I'm being too hasty in doing away with sprints & pressures. I got swept up in what Mustermann had to say, that I completely forgot about what the game files actually say:
| Distance run | Work Rate, Stamina, Pace |
| Sprints | Work Rate, Stamina |
| Pressures attempted | Work Rate, Anticipation |
| Pressure completion | Work Rate, Anticipation, Acceleration |
I've got some ideas I'm going to look into now.
Now I get the weird star ratings my meta team gets. Even though they have equal attributes, the star ratings are all over the place because of different CA. Setting them to 1 CA, they're all equally 3.5 stars.
For normal play, I think this means that wingers are going to be overrated by your staff (i.e. 4 star because of high CA cost for attributes), whereas DM will be underrated (i.e. 2 star because of low CA cost of attributes). Both of these players are equal quality in reality.
I’ve noticed there have been some new findings regarding FM26 recently. What do you make of them?
We’re getting very close to FM27 now, so do you think these discoveries could have any real impact? And are you planning to release PL 2.2 as a result of them?
Looking forward to hearing your thoughts!
I've addressed it further up the page, where I also retested a few of the attributes to be sure. Overall a good idea, but the correlations are simply clouding the correct insights. He's getting a bunch of attributes right, but also a few wrong (i.e. Crossing & Off the Ball), even if we test using his specific suggested positions.
Nonetheless it's still useful in several other ways. For instance, I was theorizing before that what leads to more sprints and pressures would perform better, because that is what the Knap tactic seems to favor. So I was initially excited to see his data, because it shows ostensibly what attributes contribute to sprints & pressures. But his correlations, which I don't dispute the validity of, on sprints & dribbles perhaps suggest that certain attributes inexplicably increase win rate even in spite of decreasing or doing little for sprints & pressures. That actually makes sense to me, because if you're a developer, you're probably likely to overlook that then or know about it but ignore it thinking users won't go for it because it makes no sense.
There won't be a Premier League 2.2 for a while. I feel it's at the standard I want it to be, and so I'm taking a break from that for now. If FM27 is any good, I'll probably do a new version for FM27. If not, it depends on how I feel.
iezzex said: Also i feel like that some of counter type tactics which is meta are just designed for high pace physical players
Better technical players may act better with posseios type tactic
Untested, just a feeling based on my own long save team acting worse on meta ones
It's possible, but I think unlikely. I usually always test with Knap tactic, but we know that other tactics do substantially worse with or without technically gifted players. I don't know Knap's methodology, but it's worth bearing in mind, if my following assumption is correct, that he is finding the best tactic based on the real top teams who are often highly technical, not the way I test with more barebones speed machines.
RNG do more then this changes huh
You better make a test league with capped CA, same teams, tactics etc and do 100 samples imo
Adding controls for FM testing often does more harm than it helps in my opinion.
Suppose you do test against teams that have 10 in every attribute. Well then a player of Jumping Reach 11 is going to beat them all, but then in the actual game, Jumping Reach 11 would get your team relegated. You can't take opposition players and tactics out of the equation.
Besides, this has been done. HarvestGreen uses this controlled kind of testing, and so does FM Arena - although I believe FM Arena is a little different where uses a representative set of competitive attributes for its opposition teams (i.e. 13 instead of 10).
Another angle I would tackle it from is that RNG is part of the game, and therefore must also be taken into account. So with teamwork for example, we see the RNG range is 2nd-8th. With real Luton, the top Knap tactic produces ~2nd-9th range.
It's worth pointing out that that RNG range for real Luton is actually easily squashed down a lot by pre-selecting the team so it uses the same players. If pre-selected, the range goes down to ~2nd-4th. So really the RNG is only about -/+1 position difference. People overestimate the impact of the RNG of this game IMO.
With the meta team but with Raheem Sterling replacing AMR, the variance is also very low, getting ~6th almost all the time.
Why does the meta team, having identical outfield players, have a increased variance of 2nd-8th, compared to the meta team with Sterling? Don't know, but keep in mind I'm not using pre-selection for my meta tests either.
Now regardless of whether it's RNG or player fitness or bad luck injuries or whatnot, let's just surmise the reality as this:
You can expect to vary ~2-6 positions each season from expectation.
An average save might last say 6 seasons.
Are you going to experience the difference between 10 and 15 pace in 6 seasons? Yes, very much so.
Are you going to experience the difference between 10 and 15 teamwork in 6 seasons? Probably not really, but when you have 10 of these minor attributes together as part of a weighting system for buying/assessing players, then you will.
This also goes to why I don't think 100 samples is necessary. No one is playing 100 seasons. If I take 9 samples representing 9 seasons, and the end result is an uncertainty of -/+ 1.81 position, then that reflects the reality of normal play and it should be left at that.
Which is the more useful and relevant information?:
The life expectancy of a nation as 82.59 based on measurements of millions of people, or
As a 60 year old with a particular cancer my doctor has guesstimated I most likely have 5-7 years to live based on several similar cases he has seen, or
The doctor telling me 'its all random...we all die sometime anyway'
So teamwork does appear to be doing something here. It shouldn't be above 5 after 9 samples otherwise.
There is upside to having it at high values, but not as much downside to lacking it completely (7th at '1', vs 9th for Aggression at '1' for example, and many other key attributes fare far worse at '1' ).
8 > 16 is quite a big jump for a relatively small position gain of ~2 position, but that's only +1.6 per outfield player.
Overall I would now classify teamwork an attribute of minor importance, above Strength but probably below Aggression. Or maybe equal to Aggression, since the test team has 11 Aggression and only manages ~5th-7th on average.
Another thing to note is that because the test team's outfield players are identical, it wouldn't be the case that teamwork is simply handing over the ball to the players with the better attributes.
I'll be testing it at 20 on all players again to see what results that shows.
I also tested two other attributes:
Dirtiness 8 > 1 = 4th, 10th (sacked), 9th (sacked), 9th (sacked) = 8 position
Versatility 8 > 20 = 5th, 10th (sacked), 3rd, 3rd, 8th (sacked), 5th = 5.666 position
So versatility doesn't seem to do much. Possibly a very slight benefit. Too hard to tell like crossing.
Dirtiness surprisingly did worse at '1'. Perhaps it just requires more samples, but even so, one would expect '1' dirtiness to do better than 5th-7th given its deduced importance based on real player performance. In comparing the real players, there is a significant difference between 8 and 2 dirtiness, so it's not just about it being under '10' say. Only possibility I can think of, aside from simply requiring more samples, is that perhaps dirtiness interacts with temperament and/or sportsmanship and/or controversy, where high dirtiness can be beneficial (more hard tackles) when machiavellian sociopath but not when prone to losing temper and getting red carded for no gain.
Here's a very quick test. There are three metrics for reputation in the game:
- Home
- Current
- World
Other websites can probably explain (or guess) what they do but I'm just here to see if they affect contract negotiations. (How could they not?) I'll play with the values (and use my unchanged player as a baseline) and record them below.
| Home rep | Current rep | World rep | Salary demand
| 3,115 | 4,372 | 1,1911 | Important player, £53k p/a
| 10,000 | 4,372 | 1,1911 | Important player, £55k p/a (plus small bonus increases)
| 0 | 4,372 | 1,1911 | Same as baseline
| 3,115 | 10,000 | 1,1911 | Wants to talk but thinks I can't afford him (1)
| 3,115 | 0 | 1,1911 | Important player, £36k p/a (also his bonus demands have halved and the coach's estimation of his ability has decreased)
| 3,115 | 4,372 | 10,1000 | Important player, £86k p/a (plus large bonus increases)
| 3,115 | 4,372 | 0 | Important player, £51.5k p/a (plus small bonus decreases)
(1) This was true even after I adjusted my budget sliders and tried again and after I adjusted the club's reputation to be 10,000
N.b. after changing reputations I advance a few in-game hours so the system registers it
Other notes
1. I saw no change after setting my wage budget as low as it would go (£428k p/a) and as high as it would go (£1.2m p/a) even for the 10,000 world rep experiment. (But if someone on Reddit said it did then I'm not going to dispute that.)
2. World reputation is the only thing that changed the player's reputation on his Personal Information page. It was 5 grey stars ("Minimal"
3. It makes sense that Current Reputation would have such a big impact on demands + your star rating if it's meant to be the sense of how hot you are as a player.
4. I also tested the baseline player when the club had a reputation of 0 and 10,000.
0: Important player, £58k p/a
2,920 (baseline): Important player, £53k p/a
3,115 (player's home rep): Important player, £54k p/a
10,000: Important player, £64k p/a
5. I also tried setting the CA/PA of the player to various values:
| CA | PA | Demands
| 67 | 120 | £53k p/a (baseline)
| 67 | 200 | Unchanged
| 67 | 67 | Unchanged
| 120 | 120 | Not willing to discuss!
| 1 | 200 | Unchanged
6. I changed the manager's Home, Current and World reputations to 10,000 and it made no difference (although it made people like me more).
Here's a dump of screenshots showing (most) of the above. I know a lot of this might not be new information but it's a start.
Baseline
CA 120/120
Club rep 0
Club rep 10,000
Player current rep 0
Player current rep 10,000
Player home rep 0
Player home rep 10,000
Player world rep 10,000
EDIT: From reading the game's code I can tell you that the reputation of the league you're going to be playing in next season is a factor (or the current league, if next season's isn't decided yet).
It's a minimal impact though, seemingly. If I tell Isaac that we're going to the Premier League next season instead of League Two, he asks for a tiny bit more money and his coach summary rating drops an entire star.
From the looks of the code, it seems like a few things are summed (reputations) and then scored (given a grade?) That is, I don't think changes are linear; I think you get bucketed in to discrete grades, which represent how strong your bargaining position is. After enough reputation points gained/lost, you might find yourself in the next bucket.
This has taken _far_ longer than I anticipated and I'm knackered so I might look at the actual calculations tomorrow (no promises though).
EDIT again: I can't help myself.
If you have a "Front End" Sugar Daddy, the player's demands increase. (+15 to the score it keeps track of).
I found out recently what the three different reps mean exactly. I've forgot it now, but I'll post it later on. I think it was explained in a 2011 guide by two of the developers.
But yeah, there's not much to go on in regards to what affects wage demands. Perhaps someone on Youtube has done it in the past few years. I haven't looked into it myself.
Interesting that the coach rating decreased with lower current rep. Wouldn't have thought that.
Some handy findings there. So CA matters and PA doesn't. Club rep and manager rep doesn't matter, would have expected somewhat otherwise.
You know, I'm thinking this could kind of be integrated into the rating system if you use FMSS. You could do something like add 'divide by current/world rep & CA' to the formula. Makes it more useful for gameplay.
11th (sacked), 8th (sacked), 6th, 8th (sacked), 12th (sacked) = 9 position
fb = tackling (cross success %)
fb = crossing, off the ball (high intensity sprints)
dm = low flair (posession won)
dm = off the ball, low marking (pressures attempted)
winger = low decisions, low off the ball (dribbles)
winger = crossing, teamwork, low heading (pressures completed)
st = off the ball, low technique (xG overperformance)
'Low' attributes were reduced to 1, while the others were increased to 20. CA was generally higher, with the highest gain being +20 CA on fullbacks.
Results: 7th, 7th
So no performance difference
I then tried just crossing 16 on fullbacks & wingers.
Results: 9th (sacked), 1st, 3rd, 7th, 6th (sacked), 8th (sacked) = 5.666 average
Normal result is ~5th-7th, so perhaps a hint of a benefit to crossing in there due to the 1st which is not unprecedented but quite rare. Not quite enough to conclude crossing has any benefit though. I think a few more samples with high consistency & technique would settle this one way or the other.
Next, bravery 20 for DCs.
Results: 6th (sacked), 3rd, 5th, 9th (sacked), 5th = 5.6 position
No revelations here. Again, perhaps a very marginal benefit outweighed by CA cost, similar to strength or vision.
I forgot to change Man City's media expectation to 20th, hence the high rate of sackings. Doesn't invalidate the results, just means some samples are semi-wasted (9th sacked means they wont end up first, but they could have ended up 4th say).
I am also doing this in FM24, not FM26, but I doubt that that is why he finds crossing matters and whatnot.
Now I'm unsure, but I've had great success promoting Warrington up the leagues using your old, old weights.
Before I played around with tactics, I would have made this critique of his findings, citing my attribute brute force testing, but now I am more open to the idea again of there being positional differences.
My impression now is that what is going on is that the Knap tactic favors a certain set of attributes, and this would extend to all the very similar top tactics in general (different roles or even formations, but all high pressing/intensity).
So if you play a defensive tactic, maybe then positioning would matter. Thing is, all defensive tactics lose. So effectively positioning never matters.
So it can be true at the same time that (1) only certain attributes matter for winning games, and (2) different tactics need different attributes.
Now I have tried positional variation in my attribute tests with the Knap tactic, and found there isn't any I can see (this is after being convinced for the longest time that concentration mattered for defenders but not forwards), but it's possible I am mistaken on one or two, or perhaps I didn't try the right combo (i.e. maybe I tested flair alone on ST, but not flair alone on MC).
I'll be doing some tests today based on Mustermann's findings, so we'll see if there are in fact position differences that actually increase/decrease win rate.
LightningFlik said: I just can't find where that code above is executed. I've decided to search for "weig" in hopes that I can find somewhere else in the code where that logic is performed. In doing so, I've found the nuttiest string references in the binary. What the actual hell is in this game?
That's funny
I wonder what they've included that brought that stuff into it. I noticed there was some chinese characters, that links to a chinese website in the code for free template resources and whatnot - must be that they used an asset from it.
How did you get it to display the phrases as single lines like that? Maybe its cause I didn't let it fully finish processing I guess.
I would surmise overall that most staff do matter moderately, both for growth and performance. It can also matter for players being willing to sign for your club.
People are going to have different impressions, but for me I'd say staff are about 70% as impactful as I'd intuitively think they should be.
I came here today with the intention of making an assessment of this video lol
Very interesting findings
Flair is strongly negatively correlated with possession won for midfielders. Is this because of the attribute itself altering the role, or the high flair causing a low-possession-won role to be selected? And then, does more possession won lead to more or less wins?
I think overall this can actually be triangulated with the key attribute template and EF 424 IF HP V2 P101 AC tactic to determine what tactical measures are beneficial and which are detrimental, and then refine the key attributes - to per position specificity - based on that. Perhaps it might even clue us into how the tactic can be edged out too.
We see that flair and off-the-ball have good & bad results for different positions/measures. And he finds bravery & one-on-ones for GK were significant in relation to goals conceded. So I will do some testing to see.
There are some caveats to his conclusions I think most will miss. For instance, the GK goals conceded thing, isn't actually measuring team goals conceded, and finishing produces more goals on wingers, but that doesn't mean the team scores more goals as a whole (my own testing on finishing shows that it simply take off goals from ST and give it to the winger).
But there's a lot of valid and potentially fresh information in it.
Also I may have found the location in code where role/position suitability is calculated, but I can't get the code to fire in the course of the game, so testing it is proving difficult. It looks like that's what it's doing though.
So just to clarify, it is the case that 0x239 = acceleration, right?
What led you to 0x0144b40180 anyway?
I've been looking through instances of 0x239 for example for something interesting, but maybe there's a better way of going about it
Would be interested in seeing the suspected role location