bf3metro said: Just one small thing I noticed: you said you included Loyalty as “1” at the last moment, but in the weights you posted, Loyalty is 0.2. Is 0.2 the intended final value, or was Loyalty supposed to be 1?
Also, given what you said about Strength, Vision, Finishing, Teamwork and Loyalty, would you say these are attributes we should actually pay attention to when evaluating a player, or are they basically not worth considering unless the value is particularly low/high?
I’m especially wondering about Teamwork since you still seem a bit uncertain about it.
And one last thing: have you also updated the goalkeeper weights, or are these still the current ones?
PS: What is the correct way to write it? Expand Was referring to the FSMT26 weights. For FMSS I divided by 5 because I think it had problems if you put the numbers too large from memory.
Those 5 attributes are not really worth paying attention to.. except loyalty of course, for non-performance reasons. Not only are they low value, they are non-essential. By contrast, agility is also low value, but moderately essential around ~10. Work rate is absolutely essential to have at least 6. You can have 1 strength/finishing/vision and finish 5th, that's proven.
Overall I don't think teamwork does anything. There's just a small element of uncertainty.
GK weights unchanged. I'm currently doing samples of GKs for rankings list.
I think either way you've typed it is fine.. just test each and see
Kriek said: Is it still needed to use the filters with the new ratings on Genie Scout or only the ratings? Expand Only ratings is fine. Filters still useful, but should be able to phase this out later by adding non-linear weights to FMST26 (not GS though).
bf3metro said: I also noticed that you added 5 attributes that weren’t in the previous set: Strength, Finishing, Ambition, Loyalty and Vision.
Was that intentional as part of the new weighting method? Expand Finishing, strength, and vision were technically in the previous weights. I dropped them from the file because the attributes that are adequate you are bound to get at the required levels most of the time anyway:
Strength - 8 Vision - 7 Finishing - 7
I played around a fair bit with strength in particular in these new weights. I thought maybe it's around 10-20, but turned not to be the case. Vision is 20 on Messi which was food for thought, but it didn't seem to affect ranking alignment for the better.
Actually teamwork is the one I most wonder about.. players like Maeda have high teamwork, and there's some mathematical study on FM24 on github that a guy did for university work that found teamwork was a key correlate of performance.. but I read through it, and overall I think its just full of mistaken findings. My own testing and HarvestGreen's found teamwork doesn't matter, so at the moment I don't see a valid reason to include it.
Loyalty I included as '1' at the last moment pretty much symbolically. It would have some very minor to minor effect via morale, so i felt justified giving it a little '1'. But obviously loyalty deserves some weight for gameplay reasons.
bf3metro said: The WorkRate interaction theory is probably the most interesting part to me. If something like Determination × WorkRate × Stamina is happening, it could explain why isolated attribute testing and the ranking weights seem to tell slightly different stories. Expand
The main thing would be the non-linearity of attributes, which the testing did show, but can't be reflected in linear weightings.
HarvestGreen did do some theorizing on attribute interactions, and I've dug up quotes from the Collyer brothers and in-game tips and whatnot which describe attribute interactions as well. I found that those latter things turn out to be generally untrue, or at least outdated.
I reckon there are attribute interactions though, and HarvestGreen's ideas on it are interesting.. just lack the solid data on it at the moment though.
I don't really have solid data to back it, but I'm personally convinced that aggression x dirtiness is an interaction, it just seems logical and seemed to solve something insoluble with the ratings at one point too.
This is partly why I decided to release it as is now with mostly linear weights, because one could go down the rabbit role of trying out all these different attribute combos and either getting nowhere or being mislead into seeing something that isn't there, while also spending a lot of time doing so. It remains difficult to test attribute combinations, but I'm sure there's some method to bear it out properly.
bf3metro said: Also, I noticed there are some strange extra spaces between a few attributes in the list. Is that intentional, or just a formatting issue? Expand Probably a formatting issue
bf3metro said: One other thing I'm wondering: is it possible that some of your original FMSS weights were simply set too low at the beginning, which would also partly explain why a few attributes have increased so dramatically now?
Do you think these huge increases are entirely a consequence of the different methodology, or could your original FMSS weights also have been underestimating some of these attributes from the start? Expand The FMSS weights I gave before simply reflect the minimum set of attribute values to attain 5th in the Premier League. They represent the best 'bang-for-buck' relative ratio of attributes.
I guess the question that the objective ranking raises in relation to that is, if work rate supposedly caps out at 13, why is it so critical for Maeda, and important in general? And if dribbling isn't so essential, is it really necessary to include it high - even if it does actually have significant benefits. So it does shine a new light on certain things. Personally I strongly favor what is true in realistic testing (the player rankings derived from the team positions), rather than what the isolated or constrained testing shows (including my own).
johnconnerson said: Where does the 118 number come from? I'm trying to split up the visible and hidden attributes into two separate metrics for my own use. Expand The 118 number is just to get it to display with the highest % at ~90%. It's the same as the overall weight option in GS.
bf3metro said: The biggest question for me is: why such massive changes?
From what I understand, the old weights were based on finding the cheapest/minimum attribute balance capable of achieving around 5th in the Premier League, whereas the new weights are being fitted to a much larger set of objective player rankings.
So it makes sense that the result could be very different: instead of asking “what minimum combination of attributes gets results?”, you're now asking “which attributes best explain the actual ranking of players?” Expand Yes, that is precisely the correct summary of it, and I daresay a better explanation than I did.
I'm glad you immediately understand, because I think it's easy to misinterpret this latest thing I'm doing with the weights.
bf3metro said: The new model seems to put an enormous premium on Pace, Acceleration, Stamina, Work Rate and Anticipation, while attributes such as Dribbling, Consistency, Important Matches or Natural Fitness become much less influential.
And then you have Finishing at only 0.4, which is pretty wild too. Expand I started with the presumption that finishing is almost completely unnecessary, but ChatGPT independently came to the conclusion that finishing should be removed too when I fed it all the player's attributes and asked it to come up a set of weights to fit the rankings.
It was surprising how strongly weighted anticipation and work rate seems to be. Maeda is still down a bit, so work rate could in fact do with being a bit higher perhaps. It's strange to me, because I know that (and you can see HarvestGreen's data to verify this) that work rate almost caps out at 13. But that is when work rate has been tested in isolation or partial isolation. Perhaps there is something like 'determination x work rate x stamina' going on. I couldn't find a way of getting around weighting work rate highly.
Anticipation in my observations did continue to scale, but had diminishing returns. But putting that aside for now, it makes sense that it would be a key attribute because ostensibly it's the movement ('off the ball' as speed?) to the area before possessing the ball. So it sounds like it cuts down on the pace/acc requirement to me, because once they have the ball, they have less distance to travel. Concentration would be a similar kind of effect, where low concentration leads to a delay in getting into position to receive or stop a ball (the latter offset by 'positioning' perhaps).
Consistency, Important Matches, and Natural fitness has not been incorporated with much accuracy. They are kind of afterthoughts, as I know they will matter somewhat, but not sure to what degree. I've used HarvestGreen's data to set them, and so long as they're not interfering with the ranking, I generally leave them alone. Same with pressure, professionalism, etc. - and the left foot & right foot figures I've just pulled out of my ass, to give a little extra weight to the modest footedness bonus which we know exists. This stuff can be refined further later. I think HarvestGreen found that foot should also match the side they're on, and I think FMST26 allows us to do this as well.
Dribbling I've been in two minds about. Most of the time I had it set pretty high at ~40, sometimes as high as 60. I ended up reducing it to 15. ChatGPT actually said early on to do this too. Both were fairly equally functional. From testing, we know dribbling is a very strong attribute that scales well.. I suspect it's not needed much to match the rankings, because hardly any players have high dribbling anyway. It's the hardest attribute to get high in the game I believe.
bf3metro said: PS: Quick question: why are attributes like WorkRate, ImportantMatches, and NaturalFitness written without spaces? Is that just the naming format, and do they need to be written exactly like that in app.js as well? Also, should I add a comma after the final value, Consistency: 2.82, or should the last entry be left without one? Expand Yes, that is how they have to be written for FMSuperScout, otherwise the program won't work. That is how labels the attributes internally.
bf3metro said: Legend! Expand Actually this is proving more difficult than I thought it would be
I'm not a programmer, so I don't know how to implement the injury proneness & dirtiness weights. I thought, maybe I'll just add them as simple negatives, like for the GS weights, and adjust it until it matches the rankings, but then I realized it doesn't do FM24 db, only FM26.
Replace the weights in the app.js file with those (search for 'FM-Arena' ).
For adding injury proneness & dirtiness, which are in fact crucial (but I guess you could also just check players manually to have low inj and dirt values), we're going to need someone like Panneton0 or perhaps the creator of FMSuperScout itself to help out. Could probably do it myself, but can't be bothered doing an hour of research honestly.
bf3metro said: any release for FMSS? Expand Yeah, I left it out because I have to work out how to best translate it from the FMST26 weights first. I'll do that now.
This new file is a lot like mine LOL! Expand You have stamina weighted at 0 for DC, 20 for ST/TS/AMC/AML/AMR, and 60 elsewhere. It's not my fault your own weights contradict your own words.
Meanwhile, the key attribute of dirtiness you leave on 0, as you have 0 clue of course about what attributes actually matter beyond what we knew from HarvestGreen's testing a year or two ago.
But as I've said, you can have a variety of combinations that work to get to match the final ranking result. And at least you're implicitly accepting now that my objective rankings are correct.
Let's do a comparison of our two files to find out just how similar they really are. First, yours:
Junior is 1st for FS, even with 16 ST proficiency. Mbappe is above Haaland. Messi is 5% above Lewandowski and 10% above Maeda. Even Malen is above Maeda. Dybala is above Maeda and Furuhashi. Mbappe (8 jump, 8 head, 11 str) is a better target man than Glatzel (16 jump/head/str). It goes on.
Mine:
There are some minor issues, such as that Maeda should be ~3% higher, and that Malen & Glatzel should have a wider percentage gap between them.
keithb said: Why have you still used negative values and scored the hidden attributes in Genie Scout? Expand The only fair point you've raised in your entire posting history I believe
It is true that hiddens remain invalid for GS. I decided in the end to include them for my final GS adjustment at the end to try and closely match what I got in FMST26, halved for everything except dirtiness & injury proneness & natural fitness. Dirtiness & injury proneness are essential to the weightings, there's no way to do without them.
Because GS calculates incorrectly, the ratings remain a bit off compared to what you'll get in FMST26, hence I highly recommend FMST26, but I know many people aren't going to be willing to switch over.
ZaZ said: If you want people to respect the results of your tests, you need to respect the results of other people too. Saying stuff like "it is all wrong" is not very scientific. Their results are valid for their methodology and data set, and their interpretation is based on the method of their analysis. It is not up to you to decide which of the thesis are more trustworthy. Expand I think you've got the picture wrong.
As I referred to in my post, the OP of that thread said 'For goalkeepers, read the top two or three only.'
I didn't do the full quote, which follows with:
'Reflexes and Agility separate cleanly; below that it is noise, and Handling ranking under Corners is not a finding. One keeper per match against ten outfielders means that column has about a tenth of the statistical power.'
I'm not saying his other findings are 'completely wrong' - only the GK weights.
I'm using my own data to refute the data he got on GKs, that's the sound basis for me saying 'it is all wrong'. And it is up to me to decide which of the thesis are more trustworthy.
Panneton0 said: I'd be interested to see how this specific attribute ends up being even more important than pace/acc. Expand BTW I'll tell you how I got to such a high value for stamina, and the other values in general, since I didn't really explain.
I start with the existing weights, changed a few weights until the players roughly aligned properly, then I carefully compared players who would remain stubbornly out of alignment (fixing one, ruins another).
Maeda was key to this process. He's near the top, but he only has greens in aggression, determination, teamwork, work rate, acceleration, natural fitness, pace, stamina.
I start with ruling out teamwork, cause I know from testing before that that's useless.
Long story short, moving everything up and down except stamina and work rate were a no go. Stamina ended up being an indispensable part of the puzzle. And it worked for other players too. So this is how I ended up at such a high figure for stamina.
And as I said, I tried to revisit and lower this later, because I wanted the weights to more closely match HarvestGreen's data, but couldn't find a way of doing it.
Panneton0 said: Do you force a fixed starting XI so no rotation is possible? That might explain the abnormaly high Stamina required to fit the season result. I'd be interested to see how this specific attribute ends up being even more important than pace/acc. I feel like it would have this importance for a club with a poor backup, but not as much if the backup is still decent. Anyway, interesting! Expand Good question
I anticipate this by having 4 of the same player as the position I'm testing. The rest of the team also has extras for each position.
So it's not that fatigue is leading to stamina being overpowered. I was scratching my head over stamina until I thought about it, realizing that stamina is actually compensating for the 2 key attributes of pace and acc, not just one, so it makes sense in that way that it's so strongly weighted.
I did try and get it to work with say 25 stamina. Couldn't get it working. Other attributes, I found you could use 14 or 50 for it and it'd work either way, just shifting the other weights around. I think composure was one such attribute.
I actually paid some close attention to the game time the 4 players were getting, because obviously I wanted them to get adequate games (for match fitness & reliable scoring) without being overworked. I also didn't want a situation where injury leads to being a player short. I've got the precise data, but generally speaking all of the two most played players each got ~28-35 Premier League games/season. That doesn't include cup games and whatnot, which totaled all combined to 41-50 games/season for the assistant's most picked player of the four.
I did not force XI selection, just let the assistant select who he thought was best each game.
OpticFawn said: the same weightings for each position? Expand Yes
I was hesitant to release this before testing all positions, but these combined team results gave me enough confidence to believe it should be the same or about the same for all positions.
I did do another DR and a DC
82% | Kim Min Jae (DC) - 3rd, 3rd, 6th, 6th, 3rd, 4th, 5th = 4.286 position
compare to:
83% | Giovanni Di Lorenzo (DR) - 3rd, 3rd, 2nd, 9th, 2nd, 5th, 5th = 4.143 position
82% | Daizen Maeda - 2nd, 3rd, 4th, 4th, 2nd, 3rd, 3rd = 3 position 81% | Kyogo Furuhashi - 4th, 2nd, 3rd, 5th, 3rd, 2nd, 2nd = 3 position 82% | Vinicius Junior - 5th, 4th, 1st, 3rd = 3.25 position 83% | Harry Kane - 5th, 2nd, 3rd, 3rd, 1st, 7th = 3.5 position 83% | Lautaro Martinez - 7th, 3rd, 1st, 5th, 2nd, 5th, 2nd, 5th = 3.75 position 81% | Victor Osimhen - 3rd, 3rd, 7th, 3rd, 3rd, 4th, 3rd, 4th = 3.833 position 81% | Viktor Gyokeres - 6th, 4th, 3rd, 4th, 5th, 5th, 2nd, 5th, 2nd = 4 position
Both DR and DC appear a bit ineffectual compared to ST, but it's not off by much.
I had two options: Release what I have based on the above results, or test each position (so at least 100 samples) which would probably take a week or two. I could also end up getting burn out by that point. So I decided to just release it now, since I've got some stuff to do these next few days anyway. Testing of other positions will continue to be done. If it turns out it doesn't work for other positions properly, it'll be adjusted.
The weights for GK in that thread are completely wrong. For instance, Aerial Reach is 1.8 while Reflexes is 20. In fact Aerial Reach is the most important attribute while reflexes is the least important of the big three, and less important than has always been believed. I didn't comment on this in that thread, because the OP does denote 'For goalkeepers, read the top two or three only.'
HarvestGreen's GK data is also wrong. The problem both these people are having is low sample size leading to invalid results.
Here are the ideal minimum weights I derived from brute force testing:
Of those, Aerial Reach is the least flexible in terms how low you can go and get away with it.
Traits do not seem to effect performance at all for any player.
I can't remember if height itself plays any role, but jumping reach in indeed important, so not just Aerial Reach, and jumping reach is semi-determined by height I think (for starting database players it is, and I reckon they probably coded it for newgens too).
One attribute that matters for GK that I think pretty much everybody would be unaware of, and isn't shown on that image, is technique. Technique actually matters quite a lot for GK. It's useless for outfield players, but important for GK.
- Designed to align with the objective results of real players within the English Premier League - GK not yet done, weights from Premier League 1.0 are used which are decent - Do not read too much into the specific values for each attribute, I used HarvestGreen's and my own data as a basis to start with, but it's largely just plugging in numbers until I get the output I'm looking for, which is alignment with the rankings. Individual attribute weights will be inaccurate or even superfluous, but it's what they sum up to that matters. - GS file is a bit less accurate than the FMST26 weights. Highly recommend FMST26 over GS if possible.
I could keep working on this for forever and a day, but I feel like it's good enough right now for a first version.
You can make your own adjustments, add in non-linear formulas, etc. to line up with the rankings better if you like, since the ranking data is really what's solid.
bf3metro said: and btw, may i ask if you still refer to your ratings, or did you changed/tweaked them?
Pace: 6, Acceleration: 6, JumpingReach: 5.2, Dribbling: 5.2, Balance: 4.8, Concentration: 5.2, Anticipation: 5.2, Determination: 5.6, Agility: 4, Stamina: 5.2, Composure: 4.4, WorkRate: 4.4, Aggression: 4.4, ImportantMatches: 5.2, NaturalFitness: 4.8, Consistency: 5.2, Pressure: 5.2, Professionalism: 5.2, Expand Yes, those are the most recent. It's simply my findings for the balance of attributes that achieves 5th in the Premier League at minimal cost/requirements.
But now I have a bunch of objective player rankings to work with, so I'm currently working a set of weights that lines up with this objective ranking list. And I'm close to done on the first go at this.
bf3metro said: what's better in your opinion? FMST26 or FMSS? Expand I haven't used either enough to form an opinion on it. I'll be using FMST26 simply because it supports FM24, but FMSuperScout doesn't.
Note that an edited Man City was left in. All four teams played in the same league together. GKs were controlled players, as I haven't done GK yet.
The best players in each position are ~85-90% for reference.
I'm actually surprised with how consistent the results turned out, as when I was testing Sacha Boey (5th best DR, 80.2%) I got first 2 results of 7th and 12th. Perhaps it's that some positions contribute more than others.
This is also giving an idea now what % difference 1 position difference is. ~1% = 1 position difference.
88% = 1st?, Premier League 80% = 8th, Premier League 74% = 14th, Premier League 70% = 18th?, relegated, Premier League
Indeed for DR, at 71.1% I see a player from Bournemouth who finished 18th in the season I'm looking at.
I think I'll be testing a few players in the DC position, and if they align neatly if the current weights, I'll be posting them soon after.
The meta team players are 71.8%. Based on the ST % values, we would expect the meta team to finish ~6.2, which is almost correct (~5th average from prior testing). It makes Knap's tactic worth 11%..
Panneton0 said: Just FYI, I checked the FM Scouting Tool26 and I realize I was doing the exact thing by modifying the code within FMSuperScout (being open-source paves the way to fully customizable score). Expand Yes, it would be good for more people to come up with their own weights for the rankings, as there's so many possibilities it could be right now.
I'm using a couple of formula weights, but I'm leaving the non-linear adjustments until after I have some confidence all outfield positions are the same and get a set of weights out.
Also, given what you said about Strength, Vision, Finishing, Teamwork and Loyalty, would you say these are attributes we should actually pay attention to when evaluating a player, or are they basically not worth considering unless the value is particularly low/high?
I’m especially wondering about Teamwork since you still seem a bit uncertain about it.
And one last thing: have you also updated the goalkeeper weights, or are these still the current ones?
Reflexes: 12.8, Agility: 8.0, Acceleration: 4.7, Pressure: 4.1, Pace: 3.5, AerialReach: 3.4
PS: What is the correct way to write it?
Was referring to the FSMT26 weights. For FMSS I divided by 5 because I think it had problems if you put the numbers too large from memory.
Those 5 attributes are not really worth paying attention to.. except loyalty of course, for non-performance reasons. Not only are they low value, they are non-essential. By contrast, agility is also low value, but moderately essential around ~10. Work rate is absolutely essential to have at least 6. You can have 1 strength/finishing/vision and finish 5th, that's proven.
Overall I don't think teamwork does anything. There's just a small element of uncertainty.
GK weights unchanged. I'm currently doing samples of GKs for rankings list.
I think either way you've typed it is fine.. just test each and see
Kriek said: Is it still needed to use the filters with the new ratings on Genie Scout or only the ratings?
Only ratings is fine. Filters still useful, but should be able to phase this out later by adding non-linear weights to FMST26 (not GS though).
Was that intentional as part of the new weighting method?
Finishing, strength, and vision were technically in the previous weights. I dropped them from the file because the attributes that are adequate you are bound to get at the required levels most of the time anyway:
Strength - 8
Vision - 7
Finishing - 7
I played around a fair bit with strength in particular in these new weights. I thought maybe it's around 10-20, but turned not to be the case. Vision is 20 on Messi which was food for thought, but it didn't seem to affect ranking alignment for the better.
Actually teamwork is the one I most wonder about.. players like Maeda have high teamwork, and there's some mathematical study on FM24 on github that a guy did for university work that found teamwork was a key correlate of performance.. but I read through it, and overall I think its just full of mistaken findings. My own testing and HarvestGreen's found teamwork doesn't matter, so at the moment I don't see a valid reason to include it.
Loyalty I included as '1' at the last moment pretty much symbolically. It would have some very minor to minor effect via morale, so i felt justified giving it a little '1'. But obviously loyalty deserves some weight for gameplay reasons.
The main thing would be the non-linearity of attributes, which the testing did show, but can't be reflected in linear weightings.
HarvestGreen did do some theorizing on attribute interactions, and I've dug up quotes from the Collyer brothers and in-game tips and whatnot which describe attribute interactions as well. I found that those latter things turn out to be generally untrue, or at least outdated.
I reckon there are attribute interactions though, and HarvestGreen's ideas on it are interesting.. just lack the solid data on it at the moment though.
I don't really have solid data to back it, but I'm personally convinced that aggression x dirtiness is an interaction, it just seems logical and seemed to solve something insoluble with the ratings at one point too.
This is partly why I decided to release it as is now with mostly linear weights, because one could go down the rabbit role of trying out all these different attribute combos and either getting nowhere or being mislead into seeing something that isn't there, while also spending a lot of time doing so. It remains difficult to test attribute combinations, but I'm sure there's some method to bear it out properly.
bf3metro said: Also, I noticed there are some strange extra spaces between a few attributes in the list. Is that intentional, or just a formatting issue?
Probably a formatting issue
bf3metro said: One other thing I'm wondering: is it possible that some of your original FMSS weights were simply set too low at the beginning, which would also partly explain why a few attributes have increased so dramatically now?
Do you think these huge increases are entirely a consequence of the different methodology, or could your original FMSS weights also have been underestimating some of these attributes from the start?
The FMSS weights I gave before simply reflect the minimum set of attribute values to attain 5th in the Premier League. They represent the best 'bang-for-buck' relative ratio of attributes.
I guess the question that the objective ranking raises in relation to that is, if work rate supposedly caps out at 13, why is it so critical for Maeda, and important in general? And if dribbling isn't so essential, is it really necessary to include it high - even if it does actually have significant benefits. So it does shine a new light on certain things. Personally I strongly favor what is true in realistic testing (the player rankings derived from the team positions), rather than what the isolated or constrained testing shows (including my own).
The 118 number is just to get it to display with the highest % at ~90%. It's the same as the overall weight option in GS.
From what I understand, the old weights were based on finding the cheapest/minimum attribute balance capable of achieving around 5th in the Premier League, whereas the new weights are being fitted to a much larger set of objective player rankings.
So it makes sense that the result could be very different: instead of asking “what minimum combination of attributes gets results?”, you're now asking “which attributes best explain the actual ranking of players?”
Yes, that is precisely the correct summary of it, and I daresay a better explanation than I did.
I'm glad you immediately understand, because I think it's easy to misinterpret this latest thing I'm doing with the weights.
bf3metro said: The new model seems to put an enormous premium on Pace, Acceleration, Stamina, Work Rate and Anticipation, while attributes such as Dribbling, Consistency, Important Matches or Natural Fitness become much less influential.
And then you have Finishing at only 0.4, which is pretty wild too.
I started with the presumption that finishing is almost completely unnecessary, but ChatGPT independently came to the conclusion that finishing should be removed too when I fed it all the player's attributes and asked it to come up a set of weights to fit the rankings.
It was surprising how strongly weighted anticipation and work rate seems to be. Maeda is still down a bit, so work rate could in fact do with being a bit higher perhaps. It's strange to me, because I know that (and you can see HarvestGreen's data to verify this) that work rate almost caps out at 13. But that is when work rate has been tested in isolation or partial isolation. Perhaps there is something like 'determination x work rate x stamina' going on. I couldn't find a way of getting around weighting work rate highly.
Anticipation in my observations did continue to scale, but had diminishing returns. But putting that aside for now, it makes sense that it would be a key attribute because ostensibly it's the movement ('off the ball' as speed?) to the area before possessing the ball. So it sounds like it cuts down on the pace/acc requirement to me, because once they have the ball, they have less distance to travel. Concentration would be a similar kind of effect, where low concentration leads to a delay in getting into position to receive or stop a ball (the latter offset by 'positioning' perhaps).
Consistency, Important Matches, and Natural fitness has not been incorporated with much accuracy. They are kind of afterthoughts, as I know they will matter somewhat, but not sure to what degree. I've used HarvestGreen's data to set them, and so long as they're not interfering with the ranking, I generally leave them alone. Same with pressure, professionalism, etc. - and the left foot & right foot figures I've just pulled out of my ass, to give a little extra weight to the modest footedness bonus which we know exists. This stuff can be refined further later. I think HarvestGreen found that foot should also match the side they're on, and I think FMST26 allows us to do this as well.
Dribbling I've been in two minds about. Most of the time I had it set pretty high at ~40, sometimes as high as 60. I ended up reducing it to 15. ChatGPT actually said early on to do this too. Both were fairly equally functional. From testing, we know dribbling is a very strong attribute that scales well.. I suspect it's not needed much to match the rankings, because hardly any players have high dribbling anyway. It's the hardest attribute to get high in the game I believe.
bf3metro said: PS: Quick question: why are attributes like WorkRate, ImportantMatches, and NaturalFitness written without spaces? Is that just the naming format, and do they need to be written exactly like that in app.js as well? Also, should I add a comma after the final value, Consistency: 2.82, or should the last entry be left without one?
Yes, that is how they have to be written for FMSuperScout, otherwise the program won't work. That is how labels the attributes internally.
I would add the comma.
Actually this is proving more difficult than I thought it would be
I'm not a programmer, so I don't know how to implement the injury proneness & dirtiness weights. I thought, maybe I'll just add them as simple negatives, like for the GS weights, and adjust it until it matches the rankings, but then I realized it doesn't do FM24 db, only FM26.
Here's the weights at least that will work:
Pace: 17.36, Acceleration: 17.24, JumpingReach: 9.24, Dribbling: 2.92, Pressure: 3.34, Balance: 2.7,
Concentration: 9.46, Anticipation: 11.64, Determination: 6, Agility: 3.4, Stamina: 18.78,
Strength: 0.2, Composure: 5.12, WorkRate: 12.6, Finishing: 0.4,
Aggression: 1.26, Professionalism: 3, Ambition: 0.4, Loyalty: 0.2, Vision: 0.1, ImportantMatches: 2.04, NaturalFitness: 1.58, Consistency: 2.82
Replace the weights in the app.js file with those (search for 'FM-Arena' ).
For adding injury proneness & dirtiness, which are in fact crucial (but I guess you could also just check players manually to have low inj and dirt values), we're going to need someone like Panneton0 or perhaps the creator of FMSuperScout itself to help out. Could probably do it myself, but can't be bothered doing an hour of research honestly.
Yeah, I left it out because I have to work out how to best translate it from the FMST26 weights first. I'll do that now.
Any insights about how newgen generation works under the hood?
Not stuff we already know generally about what effects newgens, but is your detection method say revealing anything that's going on?
This new file is a lot like mine LOL!
You have stamina weighted at 0 for DC, 20 for ST/TS/AMC/AML/AMR, and 60 elsewhere. It's not my fault your own weights contradict your own words.
Meanwhile, the key attribute of dirtiness you leave on 0, as you have 0 clue of course about what attributes actually matter beyond what we knew from HarvestGreen's testing a year or two ago.
But as I've said, you can have a variety of combinations that work to get to match the final ranking result. And at least you're implicitly accepting now that my objective rankings are correct.
Let's do a comparison of our two files to find out just how similar they really are. First, yours:
Junior is 1st for FS, even with 16 ST proficiency. Mbappe is above Haaland. Messi is 5% above Lewandowski and 10% above Maeda. Even Malen is above Maeda. Dybala is above Maeda and Furuhashi. Mbappe (8 jump, 8 head, 11 str) is a better target man than Glatzel (16 jump/head/str). It goes on.
Mine:
There are some minor issues, such as that Maeda should be ~3% higher, and that Malen & Glatzel should have a wider percentage gap between them.
keithb said: Why have you still used negative values and scored the hidden attributes in Genie Scout?
The only fair point you've raised in your entire posting history I believe
It is true that hiddens remain invalid for GS. I decided in the end to include them for my final GS adjustment at the end to try and closely match what I got in FMST26, halved for everything except dirtiness & injury proneness & natural fitness. Dirtiness & injury proneness are essential to the weightings, there's no way to do without them.
Because GS calculates incorrectly, the ratings remain a bit off compared to what you'll get in FMST26, hence I highly recommend FMST26, but I know many people aren't going to be willing to switch over.
I think you've got the picture wrong.
As I referred to in my post, the OP of that thread said 'For goalkeepers, read the top two or three only.'
I didn't do the full quote, which follows with:
'Reflexes and Agility separate cleanly; below that it is noise, and Handling ranking under Corners is not a finding.
One keeper per match against ten outfielders means that column has about a tenth of the statistical power.'
I'm not saying his other findings are 'completely wrong' - only the GK weights.
I'm using my own data to refute the data he got on GKs, that's the sound basis for me saying 'it is all wrong'. And it is up to me to decide which of the thesis are more trustworthy.
BTW I'll tell you how I got to such a high value for stamina, and the other values in general, since I didn't really explain.
I start with the existing weights, changed a few weights until the players roughly aligned properly, then I carefully compared players who would remain stubbornly out of alignment (fixing one, ruins another).
Maeda was key to this process. He's near the top, but he only has greens in aggression, determination, teamwork, work rate, acceleration, natural fitness, pace, stamina.
I start with ruling out teamwork, cause I know from testing before that that's useless.
Long story short, moving everything up and down except stamina and work rate were a no go. Stamina ended up being an indispensable part of the puzzle. And it worked for other players too. So this is how I ended up at such a high figure for stamina.
And as I said, I tried to revisit and lower this later, because I wanted the weights to more closely match HarvestGreen's data, but couldn't find a way of doing it.
Good question
I anticipate this by having 4 of the same player as the position I'm testing. The rest of the team also has extras for each position.
So it's not that fatigue is leading to stamina being overpowered. I was scratching my head over stamina until I thought about it, realizing that stamina is actually compensating for the 2 key attributes of pace and acc, not just one, so it makes sense in that way that it's so strongly weighted.
I did try and get it to work with say 25 stamina. Couldn't get it working. Other attributes, I found you could use 14 or 50 for it and it'd work either way, just shifting the other weights around. I think composure was one such attribute.
I actually paid some close attention to the game time the 4 players were getting, because obviously I wanted them to get adequate games (for match fitness & reliable scoring) without being overworked. I also didn't want a situation where injury leads to being a player short. I've got the precise data, but generally speaking all of the two most played players each got ~28-35 Premier League games/season. That doesn't include cup games and whatnot, which totaled all combined to 41-50 games/season for the assistant's most picked player of the four.
I did not force XI selection, just let the assistant select who he thought was best each game.
Yes
I was hesitant to release this before testing all positions, but these combined team results gave me enough confidence to believe it should be the same or about the same for all positions.
I did do another DR and a DC
82% | Kim Min Jae (DC) - 3rd, 3rd, 6th, 6th, 3rd, 4th, 5th = 4.286 position
compare to:
83% | Giovanni Di Lorenzo (DR) - 3rd, 3rd, 2nd, 9th, 2nd, 5th, 5th = 4.143 position
82% | Daizen Maeda - 2nd, 3rd, 4th, 4th, 2nd, 3rd, 3rd = 3 position
81% | Kyogo Furuhashi - 4th, 2nd, 3rd, 5th, 3rd, 2nd, 2nd = 3 position
82% | Vinicius Junior - 5th, 4th, 1st, 3rd = 3.25 position
83% | Harry Kane - 5th, 2nd, 3rd, 3rd, 1st, 7th = 3.5 position
83% | Lautaro Martinez - 7th, 3rd, 1st, 5th, 2nd, 5th, 2nd, 5th = 3.75 position
81% | Victor Osimhen - 3rd, 3rd, 7th, 3rd, 3rd, 4th, 3rd, 4th = 3.833 position
81% | Viktor Gyokeres - 6th, 4th, 3rd, 4th, 5th, 5th, 2nd, 5th, 2nd = 4 position
Both DR and DC appear a bit ineffectual compared to ST, but it's not off by much.
I had two options: Release what I have based on the above results, or test each position (so at least 100 samples) which would probably take a week or two. I could also end up getting burn out by that point. So I decided to just release it now, since I've got some stuff to do these next few days anyway. Testing of other positions will continue to be done. If it turns out it doesn't work for other positions properly, it'll be adjusted.
HarvestGreen's GK data is also wrong. The problem both these people are having is low sample size leading to invalid results.
Here are the ideal minimum weights I derived from brute force testing:
Of those, Aerial Reach is the least flexible in terms how low you can go and get away with it.
Traits do not seem to effect performance at all for any player.
I can't remember if height itself plays any role, but jumping reach in indeed important, so not just Aerial Reach, and jumping reach is semi-determined by height I think (for starting database players it is, and I reckon they probably coded it for newgens too).
One attribute that matters for GK that I think pretty much everybody would be unaware of, and isn't shown on that image, is technique. Technique actually matters quite a lot for GK. It's useless for outfield players, but important for GK.
Use FMST26 (works for FM24 & FM26) and copy paste this into statistics > custom metrics (advanced):
(acceleration * 86.8 + pace * 86.2 + jumping_reach * 46.2 + pressure * 16.7 + dribbling * 14.6 + work_rate * 63.0 + agility * 17.0 + anticipation * 58.2 + composure * 25.6 + stamina * 93.9 + consistency * 14.1 + determination * 30.0 + balance * 13.5 + strength * 1.0 + concentration * 47.3 + finishing * 2.0 + important_matches * 10.2 + natural_fitness * 7.9 + professionalism * 15.0 + ambition * 2.0 + loyalty * 1.0 + aggression * 6.3 + vision * 0.5 + left_foot * 6 + right_foot * 5 - (injury_proneness * (50 / (natural_fitness * 0.85))) - (dirtiness * 32 * (aggression * 0.1))) / 118
Genie Scout file:
https://files.catbox.moe/z7hsqg.grf
Notes:
- Designed to align with the objective results of real players within the English Premier League
- GK not yet done, weights from Premier League 1.0 are used which are decent
- Do not read too much into the specific values for each attribute, I used HarvestGreen's and my own data as a basis to start with, but it's largely just plugging in numbers until I get the output I'm looking for, which is alignment with the rankings. Individual attribute weights will be inaccurate or even superfluous, but it's what they sum up to that matters.
- GS file is a bit less accurate than the FMST26 weights. Highly recommend FMST26 over GS if possible.
I could keep working on this for forever and a day, but I feel like it's good enough right now for a first version.
You can make your own adjustments, add in non-linear formulas, etc. to line up with the rankings better if you like, since the ranking data is really what's solid.
Pace: 6, Acceleration: 6, JumpingReach: 5.2, Dribbling: 5.2, Balance: 4.8, Concentration: 5.2, Anticipation: 5.2, Determination: 5.6, Agility: 4, Stamina: 5.2, Composure: 4.4, WorkRate: 4.4, Aggression: 4.4, ImportantMatches: 5.2, NaturalFitness: 4.8, Consistency: 5.2, Pressure: 5.2, Professionalism: 5.2,
Yes, those are the most recent. It's simply my findings for the balance of attributes that achieves 5th in the Premier League at minimal cost/requirements.
But now I have a bunch of objective player rankings to work with, so I'm currently working a set of weights that lines up with this objective ranking list. And I'm close to done on the first go at this.
I haven't used either enough to form an opinion on it. I'll be using FMST26 simply because it supports FM24, but FMSuperScout doesn't.
Results:
80% | Sheffield Utd - 9th, 9th, 7th, 6th, 7th = 7.6 position
78% | Luton - 12th, 7th, 11th, 11th, 12th = 10.6 position
76% | Burnley - 20th, 11th, 12th, 12th, 14th = 13.8 position
74% | Brentford - 10th, 17th, 16th, 14th, 15th = 14.4 position
Note that an edited Man City was left in. All four teams played in the same league together. GKs were controlled players, as I haven't done GK yet.
The best players in each position are ~85-90% for reference.
I'm actually surprised with how consistent the results turned out, as when I was testing Sacha Boey (5th best DR, 80.2%) I got first 2 results of 7th and 12th. Perhaps it's that some positions contribute more than others.
This is also giving an idea now what % difference 1 position difference is. ~1% = 1 position difference.
88% = 1st?, Premier League
80% = 8th, Premier League
74% = 14th, Premier League
70% = 18th?, relegated, Premier League
Indeed for DR, at 71.1% I see a player from Bournemouth who finished 18th in the season I'm looking at.
I think I'll be testing a few players in the DC position, and if they align neatly if the current weights, I'll be posting them soon after.
The meta team players are 71.8%. Based on the ST % values, we would expect the meta team to finish ~6.2, which is almost correct (~5th average from prior testing). It makes Knap's tactic worth 11%..
ST
90% | Erling Haaland - 1st, 1st, 4th, 1st, 2nd, 1st = 1.666 position
87% | Kylian Mbappe - 2nd, 3rd, 3rd, 3rd, 5th, 2nd, 1st, 1st = 2.5 position
87% | Mohamed Salah* - 1st, 3rd, 5th, 3rd, 3rd, 2nd, 1st = 2.571 position
84% | Heung-Min Son - 3rd, 2nd, 2nd, 2nd, 2nd, 6th, 4th = 3 position
82% | Daizen Maeda - 2nd, 3rd, 4th, 4th, 2nd, 3rd, 3rd = 3 position
81% | Kyogo Furuhashi - 4th, 2nd, 3rd, 5th, 3rd, 2nd, 2nd = 3 position
82% | Vinicius Junior - 5th, 4th, 1st, 3rd = 3.25 position
83% | Harry Kane - 5th, 2nd, 3rd, 3rd, 1st, 7th = 3.5 position
84% | Robert Lewandowski - 2nd, 2nd, 2nd, 3rd, 5th, 2nd, 8th, 5th, 3rd = 3.555 position
79% | Dusan Vlahovic - 1st, 4th, 4th, 5th, 1st, 6th, 4th, 4th = 3.625
83% | Lautaro Martinez - 7th, 3rd, 1st, 5th, 2nd, 5th, 2nd, 5th = 3.75 position
81% | Victor Osimhen - 3rd, 3rd, 7th, 3rd, 3rd, 4th, 3rd, 4th = 3.833 position
78% | Lionel Messi - 4th, 2nd, 1st, 5th, 9th, 3rd = 4 position
81% | Viktor Gyokeres - 6th, 4th, 3rd, 4th, 5th, 5th, 2nd, 5th, 2nd = 4 position
74% | Donyell Malen - 2nd, 2nd, 7th, 5th, 5th, 7th = 4.666 position
73% | Robert Glatzel - 5th, 7th, 6th, 3rd, 12th, 6th, 2nd, 6th, 8th = 6.111 position
69% | Paulo Dybala - 5th, 5th, 6th, 10th, 3rd, 12th, 6th, 5th, 6th, 6th = 6.4 position
64% | Adam Le Fondre - 8th, 11th (sacked), 5th, 7th (sacked), 6th (sacked), 8th (sacked) = 7.5 position
63% | Luis Suarez - 7th, 9th, 10th, 8th, 8th = 8.4 position
DR
83% | Giovanni Di Lorenzo - 3rd, 3rd, 2nd, 9th, 2nd, 5th, 5th = 4.143 position
66% | Kingsley Schindler - 4th, 11th, 6th, 11th = 8 position
It's worth mentioning again that all players were set to 20 ST and nothing else. This will affect a few players slightly.
* I forgot to duplicate Salah first before moving him, so there was no Salah for the opposition. Could redo, but shouldn't make much difference.
Yes, it would be good for more people to come up with their own weights for the rankings, as there's so many possibilities it could be right now.
I'm using a couple of formula weights, but I'm leaving the non-linear adjustments until after I have some confidence all outfield positions are the same and get a set of weights out.