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?) Expand
This is really interesting. It sounds less like crossing/long shots/technique are individually strong, and more like there may be attribute dependencies or combinations that only become meaningful when certain attributes are high together.
That could explain why some technical attributes look almost irrelevant in isolated testing, but suddenly have an impact when paired with the right ones.
The big thing now would probably be to isolate the combinations: TEC+LON, TEC+CRO, LON+CRO, then maybe TEC+PASS+LON, while keeping the total attribute-point increase identical between tests. That should help show whether there’s a genuine synergy effect rather than just the cumulative value of adding a lot of attributes.
It could be pretty important for player ranking too, because it would mean some attributes we currently give almost zero weight to might still matter as “enablers” when combined with others.
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?
My preferred training schedule remains 214 [Physical]x2[Attacking]x2[Quickness Focus]
331 [Physical][Match Practice][Attacking][Defending][Quickness Focus] has the best absolute attribute gain Expand
Thanks, I’m going with 214 as the main schedule then.
One practical thing I’m still unsure about: where would you place the 2x Physical + 2x Attacking sessions during the week?
The issue I have is that in weeks with away games, Travel can automatically replace training slots, especially around the day before matches, so sometimes the intended 214 sessions disappear.
Would you place the four 214 sessions on specific days/slots to make the schedule as travel-proof as possible?
For example, if I usually have matches on Wednesday and Saturday, where would you ideally put:
Physical x2 Attacking x2
so that Travel interferes with them as little as possible?
So here I am trying to take into account outfield + GK + CA efficiency + intensity.
HarvestGreen didn't include the GK stats for 331 as far as I can see, so I've just inserted a high value of 1000. Even with this high value for GK, it doesn't seem as quite as good overall as 214 or 211, though it does have the highest absolute gain in its favor.
There is one more thing to add into the assessment, which is the effect on other various in-game factors:
331 has a slight edge over 214 due to the better balancing of match sharpness.
211 is much better for match sharpness, but is probably going to wear down condition too much during the competitive season, especially if you have weeks with 2 matches. The main problem with 211 is that it would be so finicky to implement that in all likelihood you're not going to implement it properly every week, and the careful handling of condition would add to this. 211 is nonetheless very good for GK and absolute gain, both of which 214 falls a bit short on.
One more thing to say about 214 is that its what I found to be best using a different method previously. So where things are roughly equal, I'm inclined to lean towards it anyway. And in HarvestGreen's assessment, 214 also scores higher on his 'quality' index than these other 2.
So overall I will still be recommending 214. I just realized that 331 was actually my 2nd recommendation before, I didn't realize until now that that was HarvestGreen's recommendation.
It might be worth setting up 211 for your u21s/u18s, especially if they have no competitive league or cup and you can set and forget it with weekly friendlies for the whole season. Reason is, u21s/u18s easily end up in match sharpness ruts they never get out of, and low match sharpness is a big influence on injury rate, and more injuries = less development.
It's splitting hairs between 214 and 331, so if you feel more comfortable going with 331, that's perfectly fine.
One thing left unaddressed is GK focus. HarvestGreen's data used 'Agility and Balance' as GK focus, so we can't be sure of the effect of any others. But it seems to be the case that Agility isn't as critical as a few others: Determination, Reflexes, Concentration. Personally I've started favoring 'GK reactions' because it focuses on two of those (Reflexes, Concentration). But there's no data on this. Expand
I think I understand the trade-off now:
214 = best overall balance once you include outfield + GK + CA efficiency + workload, and also the safer/easier option over a full competitive season. 331 = highest absolute gain and slightly better match sharpness, but less efficient overall. 211 = very strong, especially for GK and sharpness, but probably too awkward/heavy to use properly with a first team playing 2 matches a week. 260 / 142 = still elite, but not quite as attractive once workload and efficiency are included.
211 = 130% GK, 100% Def, 90% Att 214 = 100% GK, 100% Def, 100% Att 260 = 105% GK, 105% Def, 105% Att 142 = 160% GK, 160% Def, 160% Att
..and now, as I was examining HarvestGreen's own rankings, I gradually cottoned to the fact that there's a huge standout error in my data.
214, 211, 142 and 260 are all at the top of his rankings just like mine.. yet he has 331 as the best. I figured there has to be something amiss here, and sure enough, getting ChatGPT to recalculate it gives 1202.567.
That means HarvestGreen's recommendation of 331, would in fact be the best schedule for outfield.
I could have sworn I verified it with ChatGPT as 1020.63 three times just to be sure at the time. So I went back to check, since I still had the convo open:
Fuckin' ChatGPT man..
I presume it got at least most of them right, but now I can't be certain some weren't miscalculated by ChatGPT.
Anyway, at least we know from the backing of HarvestGreen's rankings that 331, 260, 211, 214 and 142 are the top ones and that the numbers are most likely correct for at least those.
You should be aware also this mirroring of HarvestGreen's data is a pleasant unexpected surprise. I did have HarvestGreen's weights as a starting or reference point for Premier League 2.1, but as you can see with Rain's comparison test of HarvestGreen's weights vs. Premier League 2.1, things should have changed substantially in the end. So it's surprising that the favored training schedules nonetheless align.
I'll get back to you in a day or two, as things are still evolving right now, as you can see from my words above Expand
Got it, thanks — that clears things up a lot.
For now I’m testing 331 (Physical + Match Practice + Attacking + Defending, with Quickness focus) in my save and I’ll compare the actual CA gains against what I was getting with 214.
Since you said you’ll come back with more in 1–2 days, I’ll wait for that before drawing any final conclusions.
Because CA-PA gap is a critical accelerant to growth, you actually want the lowest overall CA growth possible - while highest in the CA growth where it matters. There is a hard cap on CA per season anyway, which is ~20-25 (someone has reported ~30 i think). If your players aren't developing much, it's more likely that they're over 18 and not getting enough match experience. No match experience for older players can reduce growth to ~1 CA/season. Expand
This is how I’m understanding the schedules from everything you’ve posted:
Best universal choice for me Very strong weighted development: 1115.573 You specifically said it produced higher CA gain than the much heavier Physical x5 + Attacking x5 schedule Moderate workload: ~105% Seems suitable whether the player is 16, 20 or 27, provided older players get enough match experience
260 — Physical + Match Practice + Attacking x2 + Quickness focus — 98/100
Best when players have a large CA–PA gap Highest weighted improvement of the tested schedules: 1156.35 Slightly more aggressive CA usage than 214 Seems particularly strong for young players / wonderkids with lots of PA still available
Best once players are closer to PA Lower CA cost: 24.20 Still very strong weighted improvement: 1026.72 Seems useful for preserving the CA–PA gap while still improving valuable attributes
So I’d currently summarize them as:
214 = universal GOAT 260 = big CA–PA gap GOAT 156 = near-PA GOAT
Or another way:
260 = high CA–PA gap / maximum useful development 214 = phenomenal development, very efficient, and apparently strong actual CA gain 156 = once players are getting close to PA
My main goal is simply to get CA increasing as much as possible across the whole squad, roughly ages 16–29.
So between 214 (Physical x2 + Attacking x2) and 260 (Physical + Match Practice + Attacking x2), which one would you personally use as the main schedule?
I understand now that preserving the CA–PA gap matters, and that match experience becomes crucial after 18, so I’m not necessarily looking for the schedule with the highest CA cost.
What I’m really looking for is the one that gives the best actual CA progression over time, while still developing the important weighted attributes well.
If you had to pick only one between 214 and 260 for the entire squad, which one would you use?
GeorgeFloydOverdosed said: I've re-examined training according to what matters in the Premier League 2.1 weights, for both outfield and GK.
This time I was able to calculate everything quickly and comprehensively utilizing ChatGPT with HarvestGreen's excel sheets.
I haven't calculated every schedule exhaustively, just the ones you see here, most of which were the most promising previously.
Overall I recommend either 260 [Physical][Match Practice][Attacking]x2[Quickness Focus] for squad with high CA-PA gap or 156 [Physical][Quickness][Transition Restrict][Quickness Focus] with low CA-PA gap. Or 214 [Physical]x2[Attacking]x2[Quickness Focus] for something inbetween.
Green = The new best training schedules, in my opinion Light blue = The two schedules I have last recommended. They're both still worth considering. Deep blue = ZaZ's recommended schedule Pink = A long time popular meta Purple = HarvestGreen's last recommendation
Where no focus is listed, it's Quickness Focus.
Workload will be a little inaccurate, as I eyeballed the intensity bars rather than calculated it. Expand
Hey, based on all the testing you’ve done here, do you have what you’d consider an optimal schedule to actually use week-to-week over a full season?
I’ve been using ZaZ’s Growth 2, but in my save it really isn’t delivering the development I expected.
What I’m mainly looking for is a schedule that pushes CA growth as fast as possible so players reach their PA quickly, rather than just maximizing CA efficiency.
Would you recommend switching fully to something like 260 / 214 / 156 depending on the players’ CA–PA gap, or do you have a specific weekly/monthly rotation you think is optimal in practice?
GeorgeFloydOverdosed said: I figured it could be this. I'm using the latest version I believe. I'll try doing a reinstall later on.
You've got it working, and anyone who uses FMSS can use your weights.
Trying to debug code frustrates the hell out of me. I don't know how people willingly do it every day for a living. I've also found it tedious translating the FMST26 weights to GS.
I'm not quite satisfied with the GS weights, especially in light of Rain's recent test, and the 15% discrepancy rate between the FMST26 and GS weights. I want my weights to perform at least slightly better in every respect than anything else ideally. I give 8/10 now though.
I guess Rain's finding that there isn't too much difference overall between different weightings makes sense when you do the math on it.
5/11 players @ +2% = +10% for +2.75 position = +0.55 position per player @ +2% Maeda (83%) 3.0 vs. Dybala (69%) 6.4 = +14% for +3.4 position
+10% for +2.75 position = +3.63% for +1 position +14% for +3.4 position = +4.11% for +1 position
Could be just a coincidental correlation, but I reckon it's probably reflecting a consistent effect. The 80%/78%/76%/74% team test does suggest that the % differences can be simplified to addition, as there were strong distinctions in the results.
I'm putting my comparison test of keithb's biggest discrepancies with Premier League 2.1 on hold for now, but if we just take Maeda vs. Heung-Min Son for a quick picture, there's a ~8.9% discrepancy. Let's say in a worst case scenario you sign 4 of your starting 11 as these discrepancies:
8.9% x 4 = 35.6% If ~4% ≈ 1 position, then 35.6% = 8.9 position
So the most difference you could get from keithb or GS default weights say and mine, or whatever is best, is probably 9th vs 18th.
If we suppose the chance of a Maeda-like discrepancy (+ the curve leading up to Maeda) is ~8% total, then:
1/11 discrepancy ≈ 39.4% chance of -2.22 position 2/11 discrepancy ≈ 17.3% chance of -4.45 position 3/11 discrepancy ≈ 4.6% chance of -6.67 position 4/11 discrepancy ≈ 0.82% chance of -8.9 position
That is to be added on top of the common ~2% difference in ratings.
So let's say its a realistic worst case scenario where its 2x Maeda + 3x identical + 6x the typical ~2% difference:
~15% chance, with result of -7.45 position (i.e. 9 vs 16.45 position) Expand
I really like the work you’ve done with all of this. You’re basically the pivotal part of it — I’ve only translated/adapted what you already built into FMST. The testing, comparisons and all the work you’ve put into the weights are the reason there’s something solid to translate in the first place.
I’m really looking forward to whatever updates you make next, especially if you keep refining the GS weights after Rain’s latest testing. And I genuinely hope you’ll still be around doing this for FM27 as well — it would feel weird going into a new FM without your testing and weight work to build from 😅
On the differences between weighting systems, what you’re saying makes sense to me. The average difference might be relatively small, but I think the important cases are the outliers where one system values a player very differently from another.
If Maeda-type discrepancies are relatively rare, most squads probably won’t be massively affected, but getting even 2–3 of those decisions wrong in the same XI could still create a pretty meaningful performance difference.
The ~4% ≈ 1 league position relationship looks surprisingly consistent from the examples you’ve got. I’d probably still treat it as an empirical approximation rather than something perfectly linear, but there definitely seems to be something there.
And yeah, the FMSS translation seems completely fine on my side now. I stress-tested the full weights across my database and it runs with no missing attributes or calculation errors, so if you or anyone else wants to use/share the FMSS weights, absolutely feel free.
bf3metro said: That makes sense if you actually ran into a limitation in your build.
From the app.js I’m using though, GPT-5.6 checked the scoring path and there doesn’t seem to be a hard attribute-count limit there — it just loops through the weights that are defined and skips values that aren’t available. So the issue you ran into might be somewhere else in the FMSS pipeline/version rather than the Meta calculation itself.
Looking at our two versions side by side, I also think the main difference isn’t really 4.7 vs 5. For the attributes we both include, the relative weighting is basically the same — yours is normalized so Pace = 20, while mine uses ÷5 so the final FMST score translates consistently onto FMSS’s 1–20 scale.
The bigger difference is completeness. Mine keeps more of the original FMST formula wherever FMSS exposes the data: Consistency, Important Matches, Professionalism, Natural Fitness, Aggression, etc. It also keeps the original Injury Proneness and Dirtiness interactions rather than simplifying them into fixed weights.
So I’d describe yours as a more compact/simplified FMST implementation, while mine is trying to be a more literal port of the full formula.
If the full version really does cause problems in certain FMSS builds, then your reduced version obviously makes sense for robustness. Mine is currently running fine with the full set though, so if you want I can send you my app.js and maybe you can spot what caused the limitation on your side. Expand
edit: I tested it properly on my current build. I ran the full weightedMeta() directly, uncached, across 94,111 players using all 23 outfield weights and 21 GK weights. It completed in ~74 ms with 0 errors and 0 invalid scores. I also checked for missing attributes and got none, and PA-Meta passed all 94,111 players too. So whatever attribute-count limitation you ran into doesn’t seem to reproduce on my current FMSS version. Maybe it was related to an older build/plugin or a different part of the data pipeline.
GeorgeFloydOverdosed said: With your formula, the problem is that it uses too many attributes. For some reason, FMSS will fail with all of them included. At least it did for me.
Normalizing them however you like is fine though Expand
That makes sense if you actually ran into a limitation in your build.
From the app.js I’m using though, GPT-5.6 checked the scoring path and there doesn’t seem to be a hard attribute-count limit there — it just loops through the weights that are defined and skips values that aren’t available. So the issue you ran into might be somewhere else in the FMSS pipeline/version rather than the Meta calculation itself.
Looking at our two versions side by side, I also think the main difference isn’t really 4.7 vs 5. For the attributes we both include, the relative weighting is basically the same — yours is normalized so Pace = 20, while mine uses ÷5 so the final FMST score translates consistently onto FMSS’s 1–20 scale.
The bigger difference is completeness. Mine keeps more of the original FMST formula wherever FMSS exposes the data: Consistency, Important Matches, Professionalism, Natural Fitness, Aggression, etc. It also keeps the original Injury Proneness and Dirtiness interactions rather than simplifying them into fixed weights.
So I’d describe yours as a more compact/simplified FMST implementation, while mine is trying to be a more literal port of the full formula.
If the full version really does cause problems in certain FMSS builds, then your reduced version obviously makes sense for robustness. Mine is currently running fine with the full set though, so if you want I can send you my app.js and maybe you can spot what caused the limitation on your side.
Your translation isn’t wrong at all — I think we’re just optimizing for two slightly different things.
Your method normalizes the coefficients so the strongest attribute weight becomes 20, which makes a lot of sense if the goal is to express the weights themselves on an FM-style 1–20 scale.
For mine, I used GPT-5.6’s reasoning to preserve the full FMST formula as literally as possible and translate the final FMST score onto FMSS’s 1–20 scale.
The main difference is that I use the same ÷5 conversion for both outfield and GK, because that gives:
FMSS Meta = FMST score ÷ 5
So 100 → 20, 90 → 18, 75 → 15, etc.
That also means GK and outfield remain on exactly the same scale. With the “highest coefficient = 20” approach, outfield ends up using roughly ÷4.7 while GK uses roughly ÷4.4445, so the two formulas are normalized slightly differently.
I also kept every FMST term that FMSS can access, including hidden/personality attributes, the CA penalty for GKs, and the original nonlinear Injury Proneness and Dirtiness calculations rather than simplifying them into fixed weights.
So I definitely wouldn’t call your version wrong — it’s a good normalized/simplified implementation. I just think the GPT-5.6 version is more faithful if the objective is specifically to port the complete FMST calculation into FMSS while keeping the output on a consistent 1–20 scale.
If you want, I can send/upload the exact code I’m using so you can compare the implementations directly.
I couldn't include every attribute, there's some limit occurring in FMSS, but it'll only be a few minor ones missing. I had to take off professionalism from outfield though. You could swap 'Balance: 3.03' for 'Professionalism: 3.19' if you like.
I've simplified dirtiness & injury proneness. I also can't verify the accuracy at all because it's FM26 only. Expand
GeorgeFloydOverdosed said: I don't have an opinion of which is better, but I'm using FMST right now because I can put in more sophisticated weighting formulas easier Expand
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?)
This is really interesting. It sounds less like crossing/long shots/technique are individually strong, and more like there may be attribute dependencies or combinations that only become meaningful when certain attributes are high together.
That could explain why some technical attributes look almost irrelevant in isolated testing, but suddenly have an impact when paired with the right ones.
The big thing now would probably be to isolate the combinations: TEC+LON, TEC+CRO, LON+CRO, then maybe TEC+PASS+LON, while keeping the total attribute-point increase identical between tests. That should help show whether there’s a genuine synergy effect rather than just the cumulative value of adding a lot of attributes.
It could be pretty important for player ranking too, because it would mean some attributes we currently give almost zero weight to might still matter as “enablers” when combined with others.
Definitely curious to see where this goes.
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!
My preferred training schedule remains 214 [Physical]x2[Attacking]x2[Quickness Focus]
331 [Physical][Match Practice][Attacking][Defending][Quickness Focus] has the best absolute attribute gain
Thanks, I’m going with 214 as the main schedule then.
One practical thing I’m still unsure about: where would you place the 2x Physical + 2x Attacking sessions during the week?
The issue I have is that in weeks with away games, Travel can automatically replace training slots, especially around the day before matches, so sometimes the intended 214 sessions disappear.
Would you place the four 214 sessions on specific days/slots to make the schedule as travel-proof as possible?
For example, if I usually have matches on Wednesday and Saturday, where would you ideally put:
Physical x2
Attacking x2
so that Travel interferes with them as little as possible?
any news on trainings? which one came out on top?
211 = (((10x1182.2185) + 1000.59) / ((10x37.75) + 43.25)) / 0.977272 = 31.1847
331 = (((10x1202.567) + 1000) / ((10x39.62) + 43)) / 1.05 = 28.2454
260 = (((10x1156.35) + 965.23) / ((10x38.98) + 43.00)) / 1.05 = 27.5696
142 = (((10x1135.2835) + 1048.42) / ((10x36.43) + 42.33)) / 1.25 = 24.3981
So here I am trying to take into account outfield + GK + CA efficiency + intensity.
HarvestGreen didn't include the GK stats for 331 as far as I can see, so I've just inserted a high value of 1000. Even with this high value for GK, it doesn't seem as quite as good overall as 214 or 211, though it does have the highest absolute gain in its favor.
There is one more thing to add into the assessment, which is the effect on other various in-game factors:
214 [Physical]x2[Attacking]x2[Quickness Focus]
331 [Physical][Match Practice][Attacking][Defending][Quickness Focus]
211 [Handling][Shot Stopping][Attacking][Physical][Chance Conversion][Aerial Defence][Ground Defence] [Distribution][Quickness Focus]
331 has a slight edge over 214 due to the better balancing of match sharpness.
211 is much better for match sharpness, but is probably going to wear down condition too much during the competitive season, especially if you have weeks with 2 matches. The main problem with 211 is that it would be so finicky to implement that in all likelihood you're not going to implement it properly every week, and the careful handling of condition would add to this. 211 is nonetheless very good for GK and absolute gain, both of which 214 falls a bit short on.
One more thing to say about 214 is that its what I found to be best using a different method previously. So where things are roughly equal, I'm inclined to lean towards it anyway. And in HarvestGreen's assessment, 214 also scores higher on his 'quality' index than these other 2.
So overall I will still be recommending 214. I just realized that 331 was actually my 2nd recommendation before, I didn't realize until now that that was HarvestGreen's recommendation.
It might be worth setting up 211 for your u21s/u18s, especially if they have no competitive league or cup and you can set and forget it with weekly friendlies for the whole season. Reason is, u21s/u18s easily end up in match sharpness ruts they never get out of, and low match sharpness is a big influence on injury rate, and more injuries = less development.
It's splitting hairs between 214 and 331, so if you feel more comfortable going with 331, that's perfectly fine.
One thing left unaddressed is GK focus. HarvestGreen's data used 'Agility and Balance' as GK focus, so we can't be sure of the effect of any others. But it seems to be the case that Agility isn't as critical as a few others: Determination, Reflexes, Concentration. Personally I've started favoring 'GK reactions' because it focuses on two of those (Reflexes, Concentration). But there's no data on this.
I think I understand the trade-off now:
214 = best overall balance once you include outfield + GK + CA efficiency + workload, and also the safer/easier option over a full competitive season.
331 = highest absolute gain and slightly better match sharpness, but less efficient overall.
211 = very strong, especially for GK and sharpness, but probably too awkward/heavy to use properly with a first team playing 2 matches a week.
260 / 142 = still elite, but not quite as attractive once workload and efficiency are included.
142 GK - 1048.42 / 42.33 = 24.76643
211 GK - 1000.59 / 43.25 = 23.14023
260 GK - 965.23 / 43.00 = 22.44721
212 GK - 868.76 / 41.00 = 21.18927
214 GK - 805.20 / 39.33 = 20.45888
211 = 130% GK, 100% Def, 90% Att
214 = 100% GK, 100% Def, 100% Att
260 = 105% GK, 105% Def, 105% Att
142 = 160% GK, 160% Def, 160% Att
..and now, as I was examining HarvestGreen's own rankings, I gradually cottoned to the fact that there's a huge standout error in my data.
214, 211, 142 and 260 are all at the top of his rankings just like mine.. yet he has 331 as the best. I figured there has to be something amiss here, and sure enough, getting ChatGPT to recalculate it gives 1202.567.
That means HarvestGreen's recommendation of 331, would in fact be the best schedule for outfield.
I could have sworn I verified it with ChatGPT as 1020.63 three times just to be sure at the time. So I went back to check, since I still had the convo open:
Fuckin' ChatGPT man..
I presume it got at least most of them right, but now I can't be certain some weren't miscalculated by ChatGPT.
Anyway, at least we know from the backing of HarvestGreen's rankings that 331, 260, 211, 214 and 142 are the top ones and that the numbers are most likely correct for at least those.
You should be aware also this mirroring of HarvestGreen's data is a pleasant unexpected surprise. I did have HarvestGreen's weights as a starting or reference point for Premier League 2.1, but as you can see with Rain's comparison test of HarvestGreen's weights vs. Premier League 2.1, things should have changed substantially in the end. So it's surprising that the favored training schedules nonetheless align.
I'll get back to you in a day or two, as things are still evolving right now, as you can see from my words above
Got it, thanks — that clears things up a lot.
For now I’m testing 331 (Physical + Match Practice + Attacking + Defending, with Quickness focus) in my save and I’ll compare the actual CA gains against what I was getting with 214.
Since you said you’ll come back with more in 1–2 days, I’ll wait for that before drawing any final conclusions.
Oddly enough the 2-match Physicalx2 Attackingx2 scores better than the Physicalx5 Attackingx5 and results in higher CA gain too.
[Physical]x2[Attacking]x2 = 1115.573 / 33.83 = 32.976
[Physical]x5[Attacking]x5 = 1064.2675 / 27.85 = 38.214
Both of these are very close to the best.
Because CA-PA gap is a critical accelerant to growth, you actually want the lowest overall CA growth possible - while highest in the CA growth where it matters. There is a hard cap on CA per season anyway, which is ~20-25 (someone has reported ~30 i think). If your players aren't developing much, it's more likely that they're over 18 and not getting enough match experience. No match experience for older players can reduce growth to ~1 CA/season.
This is how I’m understanding the schedules from everything you’ve posted:
214 — Physical x2 + Attacking x2 + Quickness focus — 100/100
Best universal choice for me
Very strong weighted development: 1115.573
You specifically said it produced higher CA gain than the much heavier Physical x5 + Attacking x5 schedule
Moderate workload: ~105%
Seems suitable whether the player is 16, 20 or 27, provided older players get enough match experience
260 — Physical + Match Practice + Attacking x2 + Quickness focus — 98/100
Best when players have a large CA–PA gap
Highest weighted improvement of the tested schedules: 1156.35
Slightly more aggressive CA usage than 214
Seems particularly strong for young players / wonderkids with lots of PA still available
156 — Physical + Quickness + Transition Restrict + Quickness focus — 92/100
Best once players are closer to PA
Lower CA cost: 24.20
Still very strong weighted improvement: 1026.72
Seems useful for preserving the CA–PA gap while still improving valuable attributes
So I’d currently summarize them as:
214 = universal GOAT
260 = big CA–PA gap GOAT
156 = near-PA GOAT
Or another way:
260 = high CA–PA gap / maximum useful development
214 = phenomenal development, very efficient, and apparently strong actual CA gain
156 = once players are getting close to PA
My main goal is simply to get CA increasing as much as possible across the whole squad, roughly ages 16–29.
So between 214 (Physical x2 + Attacking x2) and 260 (Physical + Match Practice + Attacking x2), which one would you personally use as the main schedule?
I understand now that preserving the CA–PA gap matters, and that match experience becomes crucial after 18, so I’m not necessarily looking for the schedule with the highest CA cost.
What I’m really looking for is the one that gives the best actual CA progression over time, while still developing the important weighted attributes well.
If you had to pick only one between 214 and 260 for the entire squad, which one would you use?
This time I was able to calculate everything quickly and comprehensively utilizing ChatGPT with HarvestGreen's excel sheets.
I haven't calculated every schedule exhaustively, just the ones you see here, most of which were the most promising previously.
Overall I recommend either 260 [Physical][Match Practice][Attacking]x2[Quickness Focus] for squad with high CA-PA gap or 156 [Physical][Quickness][Transition Restrict][Quickness Focus] with low CA-PA gap. Or 214 [Physical]x2[Attacking]x2[Quickness Focus] for something inbetween.
Weighted attribute improvement:
260: [Physical][Match Practice][Attacking]x2 = 1156.35
142: [Physical][Quickness][Attacking]x3 = 1135.2835
212: 2x Physical + 2x Match Practice + Attack + Defend + Set Pieces = 1126.97
338: [Physical][Match Practice][Attacking][Defending][Recovery]x7[Quickness Focus] = 1117.508
214: [Physical]x2[Attacking]x2[Quickness Focus] = 1115.573
149: [Match Practice][Attacking][Match Tactics]x2[Tactical]x2 = 1101.13
139: [Physical][Quickness][Resistance][Defending] = 1091.794
339: [Quickness][Attacking][Match Practice] = 1086.43
243: [Quickness][Match Practice[Attackingx2][Quickness focus] = 1079.689
150: [Attackingx6][Quickness focus] = 1079.6675
334: [Physical]x2[Aerial Defence][Attacking] = 1078.23
90: [Attacking]x3 = 1061.16
466: [Quickness][Physical][Attacking] Additional focus: Ball Control = 1056.47
147: [Attacking][Match Tactics]x3 = 1046.77
164: [Physical][Quickness][Aerial Defence]x2[Match Review] = 1039.84
97: [Chance creation][Attacking][Aerial Defense][Handling][Defending from front][Quickness][Quick focus] = 1039.29
166: [Resistance][Quickness][Chance Conversion] = 1028.64
156: [Physical][Quickness][Transition Restrict] = 1026.7215
331: [Physical][Match Practice][Attacking][Defending] = 1020.63
160: [Physical][Quickness][Resistance][Transition Restrict]x2 = 1018.89
148: [Attacking][Match Tactics]x3[Tactical]x2 = 1011.46
327: [Quickness][Match Practice][Attacking]x2[Strength Focus] = 1005.93
116: [Physical]x2[Chance Conversion] = 1001.58
65: [Chance Creation] = 1000.90
53: [Match Practice] = 992.97
170: [Endurance][Quickness][Chance Conversion] = 962.78
123: [Quickness][Attacking][Transition Restrict] = 958.12
106: [Quickness][Attacking][Overall] = 948.62
83: [Community Outreach] = 940.47
113: [Quickness][Match Practice][Chance Conversion][Quickness focus] = 927.185
161: [Physical][Quickness][Aerial Defence][Recovery]x7 = 916.81
75: [One on Ones] = 910.01
54: [Attacking Wings] = 907.79
55: [Attacking Patient] = 891.86
99: [Chance Conversion][Match Review] = 848.14
168: [Quickness]x2[Chance Conversion] = 838.56
86: [Rest]Quickness Focus] = 792.26
43: [Rest][No Focus] = 735.1305
Weighted attribute improvement divided by CA cost:
86: [Rest]Quickness Focus] = 792.26 / 17.53 = 45.194
43: [Rest][No Focus] = 735.1305 / 16.85 = 43.628
156: [Physical][Quickness][Transition Restrict] = 1026.7215 / 24.20 = 42.42
166: [Resistance][Quickness][Chance Conversion] = 1028.64 / 25.71 = 40.010
65: [Chance Creation] = 1000.90 / 25.80 = 38.794
139: [Physical][Quickness][Resistance][Defending] = 1091.794 / 28.71 = 38.027
466: [Quickness][Physical][Attacking] Additional focus: Ball Control = 1056.47 / 29.00 = 36.430
334: [Physical]x2[Aerial Defence][Attacking] = 1078.23 / 31.21 = 34.545
214: [Physical]x2[Attacking]x2[Quickness Focus] = 1115.573 / 33.83 = 32.973
338: [Physical][Match Practice][Attacking][Defending][Recovery]x7[Quickness Focus] = 1117.508 / 34.99 = 31.939
212: 2x Physical + 2x Match Practice + Attack + Defend + Set Pieces = 1126.97 / 35.78 = 31.495
142: [Physical][Quickness][Attacking]x3 = 1135.2835 / 36.43 = 31.164
149: [Match Practice][Attacking][Match Tactics]x2[Tactical]x2 = 1101.13 / 35.50 = 31.017
339: [Quickness][Attacking][Match Practice] = 1086.43 / 35.72 = 30.416
90: [Attacking]x3 = 1061.16 / 34.98 = 30.335
150: [Attackingx6][Quickness focus] = 1079.6675 / 36.20 = 29.825
260: [Physical][Match Practice][Attacking]x2 = 1156.35 / 38.98 = 29.664
243: [Quickness][Match Practice[Attackingx2][Quickness focus] = 1079.689 / 37.93 = 28.460
331: [Physical][Match Practice][Attacking][Defending] = 1020.63 / 39.62 = 25.759
GK:
142: [Physical][Quickness][Attacking]x3 = 1048.42
260: [Physical][Match Practice][Attacking]x2 = 965.23
214: [Physical]x2[Attacking]x2[Quickness Focus] = 805.20
156: [Physical][Quickness][Transition Restrict] = 717.03
139: [Physical][Quickness][Resistance][Defending] = 662.44
243: [Quickness][Match Practice[Attackingx2][Quickness focus] = 614.57
86: [Rest][Quickness Focus] = 310.616
43: [Rest][No Focus] = 282.70
10x outfield + 1x GK:
260: [Physical][Match Practice][Attacking]x2 = 1138.975
142: [Physical][Quickness][Attacking]x3 = 1127.386
214: [Physical]x2[Attacking]x2[Quickness Focus] = 1087.357
139: [Physical][Quickness][Resistance][Defending] = 1052.762
243: [Quickness][Match Practice[Attackingx2][Quickness focus] = 1037.405
156: [Physical][Quickness][Transition Restrict] = 998.567
86: [Rest][Quickness Focus] = 748.474
43: [Rest][No Focus] = 694.000
Workload:
156: [Physical][Quickness][Transition Restrict] = 95%
260: [Physical][Match Practice][Attacking]x2 = 100%
214: [Physical]x2[Attacking]x2 = 105%
243: [Quickness][Match Practice[Attackingx2][Quickness focus] = 120%
142: [Physical][Quickness][Attacking]x3 = 125%
139: [Physical][Quickness][Resistance][Defending] = 135%
Notes:
Green = The new best training schedules, in my opinion
Light blue = The two schedules I have last recommended. They're both still worth considering.
Deep blue = ZaZ's recommended schedule
Pink = A long time popular meta
Purple = HarvestGreen's last recommendation
Where no focus is listed, it's Quickness Focus.
Workload will be a little inaccurate, as I eyeballed the intensity bars rather than calculated it.
Hey, based on all the testing you’ve done here, do you have what you’d consider an optimal schedule to actually use week-to-week over a full season?
I’ve been using ZaZ’s Growth 2, but in my save it really isn’t delivering the development I expected.
What I’m mainly looking for is a schedule that pushes CA growth as fast as possible so players reach their PA quickly, rather than just maximizing CA efficiency.
Would you recommend switching fully to something like 260 / 214 / 156 depending on the players’ CA–PA gap, or do you have a specific weekly/monthly rotation you think is optimal in practice?
You've got it working, and anyone who uses FMSS can use your weights.
Trying to debug code frustrates the hell out of me. I don't know how people willingly do it every day for a living. I've also found it tedious translating the FMST26 weights to GS.
I'm not quite satisfied with the GS weights, especially in light of Rain's recent test, and the 15% discrepancy rate between the FMST26 and GS weights. I want my weights to perform at least slightly better in every respect than anything else ideally. I give 8/10 now though.
I guess Rain's finding that there isn't too much difference overall between different weightings makes sense when you do the math on it.
5/11 players @ +2% = +10% for +2.75 position = +0.55 position per player @ +2%
Maeda (83%) 3.0 vs. Dybala (69%) 6.4 = +14% for +3.4 position
+10% for +2.75 position = +3.63% for +1 position
+14% for +3.4 position = +4.11% for +1 position
Could be just a coincidental correlation, but I reckon it's probably reflecting a consistent effect. The 80%/78%/76%/74% team test does suggest that the % differences can be simplified to addition, as there were strong distinctions in the results.
I'm putting my comparison test of keithb's biggest discrepancies with Premier League 2.1 on hold for now, but if we just take Maeda vs. Heung-Min Son for a quick picture, there's a ~8.9% discrepancy. Let's say in a worst case scenario you sign 4 of your starting 11 as these discrepancies:
8.9% x 4 = 35.6%
If ~4% ≈ 1 position, then 35.6% = 8.9 position
So the most difference you could get from keithb or GS default weights say and mine, or whatever is best, is probably 9th vs 18th.
If we suppose the chance of a Maeda-like discrepancy (+ the curve leading up to Maeda) is ~8% total, then:
1/11 discrepancy ≈ 39.4% chance of -2.22 position
2/11 discrepancy ≈ 17.3% chance of -4.45 position
3/11 discrepancy ≈ 4.6% chance of -6.67 position
4/11 discrepancy ≈ 0.82% chance of -8.9 position
That is to be added on top of the common ~2% difference in ratings.
So let's say its a realistic worst case scenario where its 2x Maeda + 3x identical + 6x the typical ~2% difference:
~15% chance, with result of -7.45 position (i.e. 9 vs 16.45 position)
I really like the work you’ve done with all of this. You’re basically the pivotal part of it — I’ve only translated/adapted what you already built into FMST. The testing, comparisons and all the work you’ve put into the weights are the reason there’s something solid to translate in the first place.
I’m really looking forward to whatever updates you make next, especially if you keep refining the GS weights after Rain’s latest testing. And I genuinely hope you’ll still be around doing this for FM27 as well — it would feel weird going into a new FM without your testing and weight work to build from 😅
On the differences between weighting systems, what you’re saying makes sense to me. The average difference might be relatively small, but I think the important cases are the outliers where one system values a player very differently from another.
If Maeda-type discrepancies are relatively rare, most squads probably won’t be massively affected, but getting even 2–3 of those decisions wrong in the same XI could still create a pretty meaningful performance difference.
The ~4% ≈ 1 league position relationship looks surprisingly consistent from the examples you’ve got. I’d probably still treat it as an empirical approximation rather than something perfectly linear, but there definitely seems to be something there.
And yeah, the FMSS translation seems completely fine on my side now. I stress-tested the full weights across my database and it runs with no missing attributes or calculation errors, so if you or anyone else wants to use/share the FMSS weights, absolutely feel free.
that's FMSS.
From the app.js I’m using though, GPT-5.6 checked the scoring path and there doesn’t seem to be a hard attribute-count limit there — it just loops through the weights that are defined and skips values that aren’t available. So the issue you ran into might be somewhere else in the FMSS pipeline/version rather than the Meta calculation itself.
Looking at our two versions side by side, I also think the main difference isn’t really 4.7 vs 5. For the attributes we both include, the relative weighting is basically the same — yours is normalized so Pace = 20, while mine uses ÷5 so the final FMST score translates consistently onto FMSS’s 1–20 scale.
The bigger difference is completeness. Mine keeps more of the original FMST formula wherever FMSS exposes the data: Consistency, Important Matches, Professionalism, Natural Fitness, Aggression, etc. It also keeps the original Injury Proneness and Dirtiness interactions rather than simplifying them into fixed weights.
So I’d describe yours as a more compact/simplified FMST implementation, while mine is trying to be a more literal port of the full formula.
If the full version really does cause problems in certain FMSS builds, then your reduced version obviously makes sense for robustness. Mine is currently running fine with the full set though, so if you want I can send you my app.js and maybe you can spot what caused the limitation on your side.
const META_W = {
Acceleration: 18.1, Pace: 18.8, JumpingReach: 8.6, Pressure: 3.4, Dribbling: 2.86,
WorkRate: 13.3, Agility: 3.2, Anticipation: 8, Composure: 5.2, Stamina: 10,
Consistency: 2.81, Determination: 8.7, Balance: 2.85, Strength: 1,
Concentration: 9, Finishing: 0.4, ImportantMatches: 2.02, NaturalFitness: 1.29,
Professionalism: 3, Ambition: 0.4, Loyalty: 0.2, Aggression: 2, Vision: 0.1,
};
const META_GK_W = {
AerialReach: 5.736, CommandOfArea: 3.138, FirstTouch: 3.398, Passing: 2.092,
Reflexes: 17.2, Concentration: 16.47, Determination: 17.778, WorkRate: 7.736,
Acceleration: 3.006, Balance: 2.092, Agility: 6.536, JumpingReach: 2.86,
NaturalFitness: 2.092, Pace: 5.66, Stamina: 6.536, Strength: 2.092,
Technique: 3.138, Pressure: 3.006, Professionalism: 1.96, Flair: 9.804,
Vision: 5,
};
edit: I tested it properly on my current build. I ran the full weightedMeta() directly, uncached, across 94,111 players using all 23 outfield weights and 21 GK weights. It completed in ~74 ms with 0 errors and 0 invalid scores. I also checked for missing attributes and got none, and PA-Meta passed all 94,111 players too. So whatever attribute-count limitation you ran into doesn’t seem to reproduce on my current FMSS version. Maybe it was related to an older build/plugin or a different part of the data pipeline.
Normalizing them however you like is fine though
That makes sense if you actually ran into a limitation in your build.
From the app.js I’m using though, GPT-5.6 checked the scoring path and there doesn’t seem to be a hard attribute-count limit there — it just loops through the weights that are defined and skips values that aren’t available. So the issue you ran into might be somewhere else in the FMSS pipeline/version rather than the Meta calculation itself.
Looking at our two versions side by side, I also think the main difference isn’t really 4.7 vs 5. For the attributes we both include, the relative weighting is basically the same — yours is normalized so Pace = 20, while mine uses ÷5 so the final FMST score translates consistently onto FMSS’s 1–20 scale.
The bigger difference is completeness. Mine keeps more of the original FMST formula wherever FMSS exposes the data: Consistency, Important Matches, Professionalism, Natural Fitness, Aggression, etc. It also keeps the original Injury Proneness and Dirtiness interactions rather than simplifying them into fixed weights.
So I’d describe yours as a more compact/simplified FMST implementation, while mine is trying to be a more literal port of the full formula.
If the full version really does cause problems in certain FMSS builds, then your reduced version obviously makes sense for robustness. Mine is currently running fine with the full set though, so if you want I can send you my app.js and maybe you can spot what caused the limitation on your side.
AerialReach: 5.736, CommandOfArea: 3.138, FirstTouch: 3.398, Passing: 2.092, Reflexes: 17.2, Concentration: 16.47, Determination: 17.778, WorkRate: 7.736, Acceleration: 3.006, Balance: 2.092, Agility: 6.536, JumpingReach: 2.86, NaturalFitness: 2.092, Pace: 5.66, Stamina: 6.536, Strength: 2.092, Technique: 3.138, Pressure: 3.006, Professionalism: 1.96, Flair: 9.804, Vision: 5,
what do you think of that? i think it translates better FMST to FMSS.
@GeorgeFloydOverdosed
Your translation isn’t wrong at all — I think we’re just optimizing for two slightly different things.
Your method normalizes the coefficients so the strongest attribute weight becomes 20, which makes a lot of sense if the goal is to express the weights themselves on an FM-style 1–20 scale.
For mine, I used GPT-5.6’s reasoning to preserve the full FMST formula as literally as possible and translate the final FMST score onto FMSS’s 1–20 scale.
The main difference is that I use the same ÷5 conversion for both outfield and GK, because that gives:
FMSS Meta = FMST score ÷ 5
So 100 → 20, 90 → 18, 75 → 15, etc.
That also means GK and outfield remain on exactly the same scale. With the “highest coefficient = 20” approach, outfield ends up using roughly ÷4.7 while GK uses roughly ÷4.4445, so the two formulas are normalized slightly differently.
I also kept every FMST term that FMSS can access, including hidden/personality attributes, the CA penalty for GKs, and the original nonlinear Injury Proneness and Dirtiness calculations rather than simplifying them into fixed weights.
So I definitely wouldn’t call your version wrong — it’s a good normalized/simplified implementation. I just think the GPT-5.6 version is more faithful if the objective is specifically to port the complete FMST calculation into FMSS while keeping the output on a consistent 1–20 scale.
If you want, I can send/upload the exact code I’m using so you can compare the implementations directly.
Outfield:
const META_W = {
Pace: 20, Acceleration: 19.26, JumpingReach: 9.15, Dribbling: 3.04, Balance: 3.03,
Concentration: 9.57, Anticipation: 8.51, Determination: 9.25, Agility: 3.40, Stamina: 10.64,
Dirtiness: 6.8, Composure: 5.53, WorkRate: 14.15, Pressure: 3.62, InjuryProneness: -4.8,
};
GK:
const META_GK_W = { Determination: 20, Concentration: 18.53, Reflexes: 19.35, AerialReach: 6.45, Agility: 7.35, Pace: 6.37, Acceleration: 3.38, InjuryProneness: -11.03, Dirtiness: -3.68, FirstTouch: 3.83, WorkRate: 8.7, JumpingReach: 3.22, Stamina: 7.35, Technique: 3.53, Flair: 11.03, CommandOfArea: 3.53, Pressure: 3.38, Professionalism: 2.2, NaturalFitness: 2.35, };
I couldn't include every attribute, there's some limit occurring in FMSS, but it'll only be a few minor ones missing. I had to take off professionalism from outfield though. You could swap 'Balance: 3.03' for 'Professionalism: 3.19' if you like.
I've simplified dirtiness & injury proneness. I also can't verify the accuracy at all because it's FM26 only.
Acceleration: 18.1, Pace: 18.8, JumpingReach: 8.6, Pressure: 3.4, Dribbling: 2.86, WorkRate: 13.3, Agility: 3.2, Anticipation: 8, Composure: 5.2, Stamina: 10, Consistency: 2.81, Determination: 8.7, Balance: 2.85, Strength: 1, Concentration: 9, Finishing: 0.4, ImportantMatches: 2.02, NaturalFitness: 1.29, Professionalism: 3, Ambition: 0.4, Loyalty: 0.2, Aggression: 2, Vision: 0.1,
AerialReach: 5.736, CommandOfArea: 3.138, FirstTouch: 3.398, Passing: 2.092, Reflexes: 17.2, Concentration: 16.47, Determination: 17.778, WorkRate: 7.736, Acceleration: 3.006, Balance: 2.092, Agility: 6.536, JumpingReach: 2.86, NaturalFitness: 2.092, Pace: 5.66, Stamina: 6.536, Strength: 2.092, Technique: 3.138, Pressure: 3.006, Professionalism: 1.96, Flair: 9.804, Vision: 5,
what do you think of that? i think it translates better FMST to FMSS.
can’t wait for it, mate! good luck in life too!!
FMST26 weights (copy paste this into statistics > custom metrics (advanced)):
Outfield:
(acceleration * 90.5 + pace * 94 + jumping_reach * 43 + pressure * 17 + dribbling * 14.3 + work_rate * 66.5 + agility * 16.0 + anticipation * 40 + composure * 26 + stamina * 50 + consistency * 14.05 + determination * 43.5 + balance * 14.25 + strength * 5.0 + concentration * 45 + finishing * 2.0 + important_matches * 10.1 + natural_fitness * 6.45 + professionalism * 15.0 + ambition * 2.0 + loyalty * 1.0 + aggression * 10 + vision * 0.5 + left_foot * 6 + right_foot * 5 - (injury_proneness * (48 / (natural_fitness * 0.75))) - (dirtiness * 32 * (aggression * 0.1))) / 113.2
GK:
((aerial_reach * 28.68 + command_of_area * 15.69 + first_touch * 16.99 + passing * 10.46 + reflexes * 86 + concentration * 82.35 + determination * 88.89 + work_rate * 38.68 + acceleration * 15.03 + balance * 10.46 + agility * 32.68 + jumping_reach * 14.30 + natural_fitness * 10.46 + pace * 28.30 + stamina * 32.68 + strength * 10.46 + technique * 15.69 + pressure * 15.03 + professionalism * 9.80 + flair * 49.02 - injury_proneness * 49.02 + vision * 25 - dirtiness * 16.34 - ca * 9.80)) / 84.5
Genie Scout file: https://files.catbox.moe/9n18q0.grf
I translated it to GS as best I could.
Outfield:
90% | Erling Haaland - 1st, 1st, 4th, 1st, 2nd, 1st = 1.666 position
88% | 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
88% | Kylian Mbappe (AML) - 1st, 2nd, 3rd, 5th = 2.75 position
82% | Bukayo Saka (AMR) - 2nd, 3rd, 3rd, 3rd = 2.75 position
82% | Heung-Min Son - 3rd, 2nd, 2nd, 2nd, 2nd, 6th, 4th = 3 position
83% | Daizen Maeda - 2nd, 3rd, 4th, 4th, 2nd, 3rd, 3rd = 3 position
80% | Kyogo Furuhashi - 4th, 2nd, 3rd, 5th, 3rd, 2nd, 2nd = 3 position
82% | Vinicius Junior - 5th, 4th, 1st, 3rd = 3.25 position
83% | Eduardo Camavinga (DM) = 3rd, 5th, 2nd, 3rd, 4th = 3.4 position
82% | Harry Kane - 5th, 2nd, 3rd, 3rd, 1st, 7th = 3.5 position
83% | Robert Lewandowski - 2nd, 2nd, 2nd, 3rd, 5th, 2nd, 8th, 5th, 3rd = 3.555 position
77% | Dusan Vlahovic - 1st, 4th, 4th, 5th, 1st, 6th, 4th, 4th = 3.625
82% | Lautaro Martinez - 7th, 3rd, 1st, 5th, 2nd, 5th, 2nd, 5th = 3.75 position
80% | Victor Osimhen - 3rd, 3rd, 7th, 3rd, 3rd, 4th, 3rd, 4th = 3.833 position
78% | Lionel Messi - 4th, 2nd, 1st, 5th, 9th, 3rd = 4 position
79% | Viktor Gyokeres - 6th, 4th, 3rd, 4th, 5th, 5th, 2nd, 5th, 2nd = 4 position
81% | Giovanni Di Lorenzo (DR) - 3rd, 3rd, 2nd, 9th, 2nd, 5th, 5th = 4.143 position
82% | Kim Min Jae (DC) - 3rd, 3rd, 6th, 6th, 3rd, 4th, 5th = 4.286 position
82% | Ronald Araujo (DR) - 4th, 8th, 3rd, 1st, 7th = 4.6 position
73% | Donyell Malen - 2nd, 2nd, 7th, 5th, 5th, 7th = 4.666 position
78% | Jules Kounde (DR) - 5th, 4th, 7th 5th, 3rd = 4.8 position
75% | Kurt Zouma (DC) - 7th, 5th, 6th, 3rd = 5.6 position
72% | Robert Glatzel - 5th, 7th, 6th, 3rd, 12th, 6th, 2nd, 6th, 8th = 6.111 position
81% | Raheem Sterling (AMR) - 6th, 7th, 7th, 7th, 6th, 6th, 6th, 6th = 6.375 position
69% | Paulo Dybala - 5th, 5th, 6th, 10th, 3rd, 12th, 6th, 5th, 6th, 6th = 6.4 position
72% | Andrian Kraev (DM) = 9th, 8th, 5th, 5th = 6.75 position
62% | Adam Le Fondre - 8th, 11th (sacked), 5th, 7th (sacked), 6th (sacked), 8th (sacked) = 7.5 position
66% | Kingsley Schindler (DR) - 4th, 11th, 6th, 11th = 8 position
63% | Luis Suarez - 7th, 9th, 10th, 8th, 8th = 8.4 position
71% | Sean Goldberg (DC) - 5th, 16th (sacked), 8th (sacked), 5th = 8.5 position
77% | Sacha Boey (DR) - 7th (sacked), 12th (sacked), 12th (sacked) = 10.333 position
Error rate = 4/32 = 12.5%
Vlahovic = 6.5% off
Malen = 6.8% off
Sterling = 12.3% off
Boey = 22.1% off
GK:
90% | Alisson - 6th, 3rd, 4th, 4th, 3rd = 4 position
84% | Thibaut Courtois - 3rd, 7th, 2nd, 5th, 8th = 5 position
82% | Jan Oblak - 5th, 8th, 6th, 4th, 3rd = 5.2 position
82% | Walter Benitez - 4th, 9th, 4th, 6th, 5th = 5.6 position
82% | Ugurcan Cakir - 7th, 9th, 2nd = 6 position
79% | Ederson - 6th, 6th, 4th, 8th, 7th, 7th = 6.333 position
81% | Marc-Andre ter Stegen - 10th, 11th, 4th, 4th, 3rd = 6.4 position
84% | Gregor Kobel - 4th, 12th, 2nd, 10th (sacked), 3rd, 10th (sacked), 5th = 6.571 position
78% | Anatolii Trubin - 15th (sacked), 6th, 4th (sacked), 5th = 7.5 position
70% | Davy Roef - 13th (sacked), 4th, 14th (sacked), 6th (sacked), 1st = 7.6 position
61% | Jack Stevens - 13th (sacked), 4th, 9th (sacked), 8th (sacked), 8th (sacked) = 8.4 position
75% | Sinan Bolat - 4th, 2nd, 19th (sacked), 5th, 14th (sacked), 9th (sacked), 12th (sacked), 5th = 8.75 position
67% | Ben Winterbottom - 10th (sacked), 5th (sacked), 12th (sacked), 18th (sacked) = 11.25 position
Harder to tell who is inaccurate and who is error for GK..
Error rate = 3/13 = 23.1%
Kobel = 7.1% off
Roef = 7.1%(?) off
Stevens = 18.0%(?) off
any FMSS update? please, thanks mate!
FMST more accurate or just simpler for you?