OpticFawn said: can you give us the pics for each position on genie scout with the data for the new files for best fit gs file Expand I'll be posting it soon, but it's still a work in progress right now.
Translating the real results to Genie Scout has been proving to be really tricky. As ChatGPT has confirmed to me, there's no straightforward way of deriving the specific combination of attribute weightings from the results alone. For example say I find drib 30 + ant 30 + pace 70 is what works.. how do I know it's not just ant 30 + pace 70, and dribbling has 0 effect?
Thankfully I can rule out a lot of attributes from the get-go from previous testing, but I still have to just manually fiddle around with all sorts of different combinations to find out what works and what doesn't until I have something that at least aligns with the real rankings as closely as possible.
I started with the following process:
1. Start with Premier League 1.0 'weighted weights' version 2. Compare players who have completely different sets of attributes but had similar results 3. Adjust weighting of 1-3 attributes that I know matter (i.e. pace, anticipation, work rate, etc.) starting with the most likely attribute (i.e. pace) until all players are roughly where they should be ranked. 4. If one player's rank is fixed, but then another falls out, return to step 3. 5. Find a player with high rep or high CA who has unusually low % rating with the changed weights. Test performance in controlled test first, then do step 3 with them if performance doesn't match rank. 6. Keep repeating step 5.
Right now I've got a functional set of weights, but everything has to be changed around again the next time a sample finds a player changes their ranking. I think 6 samples is giving us a fairly accurate picture now to work with, but for true accuracy I think it's going to require at least 10.
Then there is the matter of position variation. But my first result in another position so far seems to suggest the weights could be the same for every outfield position. Once I've done all positions, with perhaps 10 samples for STs for calibration, then I'll post it.
I mean it's kind of the basis of all these weighting systems, because if a point of pace was vastly different from 9 to 10 as opposed to 15 to 16 there would be no point in having a fixed value for an attribute weight.
So you either accept they are pretty much linear (with a few exceptions) or throw GS ratings out the window. Expand It's not the basis of my weightings, as non-linear values do not make useful weightings in Genie Scout impossible.
Here's how I handle the problem of non-linearity:
Finishing - I know from testing it has no advantage above ~7, and is only of moderate value even below that all the way down to one. Therefore I give it a very low weight, or perhaps even no weight at all.
Work Rate - It's very clear that work rate is crucial going from 1 to 6 but has greatly diminishing returns beyond that, and pretty much tops out at around ~11-13. If you don't believe the testing done on this, just look at player like Haaland, Mbappe and Messi - their work rate is 13, 12, and 9 respectively. The compensation in GS for this is fairly straightforward, instead of weighting Work Rate equal to pace or acc if we were just looking at the 1-to-6 effect, we give it around 20-40 instead of 70-100.
Jumping Reach - This has one of the most difficult curves of all to accommodate. It is very important going from 1 to ~7, then moderately important ~8-13, has a massive jump in performance once going above the league threshold (~14-17 for Premier League), but is then near useless going from ~18 to 20 since probably no other player will challenge above 17 anyway. Here we target either ~8-13 (moderate rating), or the ~14-17 threshold (high rating, perhaps the highest even above pace/acc).
Of course you run into situations where GS considers 1 pace 20 acc equal to 10 pace 10 acc, but those 1 pace 20 acc players are the exceptions not the rule, so it doesn't render the whole enterprize futile.
keithb said: haahah. You lie and misrepresent what I say often. You're a clown.
No Mbappe and Haaland are not rated the same. And when you increase TS to 107, like I said in the post, the gap becomes even bigger. Its 2% and then becomes 4%. They are not rated the same. Is there actually something wrong with you?
AGAIN how can you create a file and use and not see this?? There's a worrying disconnect. Expand To be honest I didn't even check if you had a TS rating, because I took one look at the FS rating and knew it was rubbish.
The screenshot I provided is, you'll agree, the FS ratings you've given.
keithb said: Ps I dont have Mbappe and Haaland rated the same. You dont even know how to use and read GS. How can you make a file for it? I love that you lie all the time, it just makes it funnier. Expand I wouldn't want to misrepresent you. This is what your GS file says does it not?
Default database, start of game (England - 3/7/2023)
There are now 6 samples per player. That's 2,736 Premier League matches simulated on full detail + all other relevant first team competitions (i.e. Community Shield, Champions Cup, Club World Cup, etc.) simulated on full detail as well.
Edit: I've added a color-coded column to compare 4 different GS rating files. 1st is new Best fit file, 2nd is Premier League 1.0, 3rd is keithb, 4th is Falbravv's META FMA 2.0 file based on skawkclsrn's recent data. Green = 1% or less difference. Yellow = 2-4% difference. Red = 5%+ difference. Ignore Harry Kane, he needs a slight readjustment right now due to the latest samples.
There are a lot of new deductions that can be made.
One is that now we have a rough idea what GS % corresponds to in terms of concrete performance numbers, and another is that we can tell at what point random variation makes distinguishing between % numbers pointless.
We can always do with more samples, but if we just take the difference between whoever is highest and lowest between Lewandowski, Mbappe, Maeda, Kane, Vlahovic, Osimhen, and Messi (total +/- 1 position difference maximum) we get:
((4.08/80.70)x100) = 5.06% - My best fit of the data so far in GS (not to minimize %, but to correct player rank) ((14.30/91.99)x100) = 15.54% - Premier League 1.0 'weighted weights' version ((14.94/88.11)x100) = 16.96% - Premier League 1.0 ((14.14/92.25)x100) = 17.14% - 'Pure Performance' FM24 ((13.45/77.59)x100) = 17.33% - My first/original FM24 ratings file ((15.89/84.94)x100) = 18.71% - 'Blended' FM24 ((18.34/83.29)x100) = 22.02% - ykykyk05251's weights file ((22.88/88.01)x100) = 26.00% - Orion's coefficients file ((29.27/91.47)x100) = 32.00% - Genie Scout Default
That is where we are accounting for extreme cases. In other words, a 4-5% difference in GS is almost certainly an objectively better player.
But what if we make it a bit leniant, and tolerate ~10-20% being exceptions we miss. In other words, this is the more typical uncertainty range. To discover this, I exclude the bottom % player from the list:
So we see that whatever the case, ~4-5% is going to be the range within which the player who is better becomes uncertain, but also the difference is going to be pretty meaningless objectively.
Wigo said: Guys i read all threads about atributes testing ant meta ones... this topic is the most intresting to me and i think i have a question that nobody answers. We found that speed in general is the king, but everytime i start a new save i see that (fm26) first season Harry Kane with speed of turtle is scoring unbelievable amount of goals and is gets player of the year award. So technically is speed is everything how come this happening??? so still plenty theories in my head that maybe for some positions speed is not so overpowered as we all think? Expand I can answer this for you because I'm currently doing isolated testing and analyzing the attribute differences closely for a number of players, and one of them is Harry Kane.
So far my testing finds that Kane is in fact objectively one of top STs in FM24.
I've already come up with a draft set of weights that allows Kane's low acc/pace and other quirks to align smoothly with all other players tested so far, but I'll just tell you a more abstract version of what I'm seeing.
This is my impression so far (for ST at least specifically):
1. Pace/acc, and stamina to keep it going through a game, are still the top factors. 2. Pace/acc is not *strictly* necessary to do well. A player with 9 acceleration can function decently as ST in the Premier League (I've tested this). If you have a team of them, you'll be relegated, but as individual exceptions to the rule they can do the job. Although I haven't tested it, I would expect similar with a team of Harry Kanes. You need at least ~13-14 average for the team as a whole in Premier League just to survive. So pace/acc remains very important. 3. Only about one third of attributes matter. Anticipation and dribbling are in this third for ST, but all of these are at least a bit less crucial than pace/acc. For instance, Messi has 20 dribbling, and while he is not no.1, it does appear to be a key factor in him being one of the top STs according to testing even at age 36 in FM24. None of these attributes are strictly necessary either except perhaps 1 or 2, see: Daizen Maeda 4. This is still dubious, but through analyzing examples such as Kane vs. Lewandowski specifically, it would appear that some attributes such as passing may actually have negative performance impact.
Here is something I already wrote that I was thinking of posting earlier:
There is no reason why Lewandowski should be better than Harry Kane, and yet he is. In fact, Lewandowski is slightly inferior on a number of things.
Lewandowski has advantages in the following areas:
+3 aggression +3 first touch +3 balance +2 anticipation +2 concentration +2 natural fitness +1 bravery +1 off the ball +1 acceleration +1 agility +1 strength +1 important matches
None of those are adequate to explain the difference, given Kane has the following advantages (I'll list just a few):
5 years younger +7 crossing +5 passing +5 vision +4 teamwork +3 left foot +2 consistency +1 determination +1 composure +1 work rate
I suspect that high passing and possibly crossing reduces team performance. Perhaps what is happening in the engine is that Kane is passing to a team-mate do to the job, who is always an inferior player in this case.
---
Note: A possible conclusion of course is that +1 acceleration or +2 anticipation and so forth is just very important, but what you'd be missing is that I'm not just comparing these two players together, I'm comparing them to a dozen others, who each all have to align with the rest of each other, in line with the objective performance results. So if I weight anticipation more say, that would throw 3 others out of whack with each other. There is no simply no way I can see that Lewandowski is better than Kane, and yet after 6 seasons of testing for each, Lewandowski in fact is. The only thing that seems possible is that passing and perhaps other attributes Kane is better in, are having a negative effect on his performance.
Then again, it could be that we just need a few more samples to set things straight. And here we are talking the minor differences of who is say 7th and who is 6th.
Fraudiola01 said: Guys I am a returning player, I already have FM24, but I can get FM26 for cheap. How good is the ME compared to FM24? I know the UI sucks but I’ve heard the Tactical Creator gives you more options due to the OOP/IP formations and that you can play more varied styles of play. Is that true? Expand If you mean the ME under the hood rather than the visuals, from what I've seen and read, it's about the same, the new OOP/IP is almost placebo, and it gets dominated by strikerless tactics.
Yarema said: I think FM-Arena attribute tests for the past few editions show linear effect for pretty much all of the attributes. Expand HarvestGreen, Orion, and my own findings don't though.
And the effects can be pretty massive not subtle, for example Orion finds pressure 1 to 10 is +47.33% but pressure 10 to 20 is +10.18%, and HarvestGreen confirmed this with pressure 1 to 6 18.2% and pressure 6 to 18 8.9%. I've found the same.
I think the indisputable contribution of this data is that it tells us what matters in FM26, and it seems like there's not much difference to FM24.
I don't know why you're finding every attribute effect to be linear, I think something going wrong somewhere in regards to that. I suspect it could be that you're randomizing attributes of every player at every club in the league, so that there's no longer set levels to compete against (i.e. in a real league, most players will have ~14 pace for example, or most GKs will have ~14 aerial reach, producing thresholds - it's not necessarily innate to the attribute itself, but a product of the relative competition. So 13 > 14 pace isn't a threshold in Vanarama South, but it is in Premier League). It's not entirely clear though if you're randomizing every player at every club, or just one club.
The part that intrigues me the most are the position weights. Have you simply divided the general results by the CA costs for each position, or have you done comparative testing for each position here?
Although I don't agree with the reasoning given as critique of my weightings, I do believe the underlying idea that the precise rankings may be somewhat impaired.
I attempted to address this with weighting the weights, but it produced worse results, or at least no better.
I've tried comparative deduction by comparing several players match ratings & goal tallies with their attributes. Some key problems with this approach:
- Match ratings have been demonstrated to favor presence of technical/mental ability over actual wins - Goals scored can simply reflect finishing, and poor finishing has minimal effect on the team win rate (it simply means someone else with higher finishing in the team scores the goals) - I actually managed to get the players to 'line up' with the ratings/goals a lot better, but it took a very strange set of GS weightings to get there. Either I'm on the wrong track, or even if it's right, I would be building a house without being able to know the foundations.
I concluded that what's actually needed is more result-based data, but on individual players. It's more tedious, and will require a fair amount of samples for good accuracy, but I think it's the best approach.
Method
I use my meta team at Man City, but replace a position with duplicates of a particular player. If I'm testing ST, I remove proficiency in other positions so as not to mess up the results, as what I'm most interested in is deducing what constitutes the precise ranking of STs. Full detail is used for all first team competitions.
Results
Here's some data I've collected so far (ranked by team performance with player as ST):
The reason I picked Le Fondre to include is because I saw him compared in a Youtube video, and even though he's a 36-year-old with 9 pace/acc, he managed the results you see here. A takeaway from this for me is that pace/acc isn't strictly necessary. Using the comparative deduction I mentioned before, I found that anticipation and composure are more critical than pace/acc.. as I said, these are strange conclusions, so rather than make the facts fit the theory, I'm going to be getting the facts straight first and then seeing what theory fits the data.
I'll probably do about 5-6 samples each of ~10 players for ST and then see if I can deduce consistent patterns to create accurate weightings.
iezzex said: So heres my question If i still win, but i wanna find a keeper that actually saves, what i shall look for Rn Chevalier looks good for me, best keeper for me yet, but he's old in my save, so i wanna find or make a good regen, waht to look for? Expand I may have a look into it later, but I suspect that it's fundamentally your tactic and overall level of quality of your players, not the attributes of your GK, that determine the number of goals he concedes.
We can influence who scores goals in a team, but that's because at the end of the day a goal is going to be scored. But you can't reduce the number of goals your GK concedes, because he's the only one who can concede them.
iezzex said: Is it all about team overall performance or not? Cuz what i've seen on my long real save, as example, Courtouis were bad (with my high def line at least) Yeah i won alot, and gd was insane, but he's avg rating was like 6.8 or something like that haha Expand To clarify, my template, and therefore the weightings, are based on team position at end of season, not match ratings.
keithb said: Here you go. It will make the game very easy if used with a top tactic and set pieces which score a lot. Thats one of the main things about high jumping reach is those players will score a lot from set pieces. Expand Interesting that you give all hidden attributes 0 weighting even though you claim Genie Scout isn't bugged.
According to those weights, Donnarumma is ranked 9th, while Muric is 202nd.
Meanwhile, actual Premier League test results in reality:
Muric - 3rd 6.81, Sacked March (10th) 6.67, Sacked Febuary (10th) 6.78 = Average 7.666th 6.75 Oblak - 6th 6.87, 7th 6.76, 1st 7.03 = Average 4.666th 6.88 Donnarumma - Sacked November (16th) 6.51, Sacked December (7th) 6.83, 5th 6.91 = Average 9.333th 6.75
For the outfield, the big problems come up where he strays from convention. For instance, he gives 0 weighting to concentration for STs, resulting in this player being rank 40th ST (classed alongside Kane and de Bruyne):
He is limited by his low concentration. I have him ranked 114th alongside Danny Ings at West Ham, rather than Kane at Bayern.
He has Mbappe rated higher than Haaland. You can see on Youtube or my own test results that Haaland does significantly better than Mbappe when Mbappe is put in the same division.
And this is whom he reckons is the 7th best DC in the world:
This is what you get when you weight dribbling at 0, composure at 5, and strength at 50.. or conversely, when you put pace/acc to 100 and don't factor in other attributes properly.
For comparison, this is what my weightings say is the 8th best DC in the world (John Stones was 7th; a little too old to do):
Zouma isn't bad, I have him at 65th. But he's not an absolute top player.. he's a West Ham player.
Yarema said: You can absolutely win PL without a single player having 18 pace or acceleration, as long as their CA is high enough. And that is the entire point. CA isn't worthless, it may not be best distributed but it is very very hard for a 120 CA player to outperform 160. Even with engineered perfect attribute distribution on 120 it's close and we know those don't actually exist ingame.
Not to mention that somehow you claim that under 18 pace you can't win the league and at the same time it's weighting is almost the same as 12 other attributes. So the scoring only works once you reach the minimum thresholds or what? Expand I'm pretty sure I could devise a team of players where they have 18 pace/acc and high CA and yet still finish towards the bottom. But of course it is generally true that a team of high CA players will dominate, but if that is sufficient and viable for you, then why bother with GS ratings at all, and instead just sort by CA? The truth is a lot of CA used is useless, and expensive to boot. Muric did better than ter Stegen, even though ter Stegen has much higher CA and cost/reputation.
The perfect attribute combination to win the English Premier League is 1 CA. The minimum realistic attribute distribution is ~85 CA as shown with my player templates. Obviously real players will have some extra padding on top of that in places, but we have been talking about GKs I favor have CA as low as ~100-110. I have specifically tailored my template to be realistic.
How I found that 18 pace/acc alone is insufficient is through my 1 CA testing. Even 20 pace + 20 acc + 20 drib cannot win.
Here are some quotes from the thread to show you I am not just making this up on the spot now:
GeorgeFloydOverdosed said: Mid-table results were easy with just 20 pace/acc/drib (not sure if I included jump), but it required careful tweaks — closer to Orion’s data than HarvestGreen’s — to push into the top four.
- Pace+Acc alone = 0 points, 0-7 loss typical - Jump+Acc alone = a draw or two, 1-7 loss typical - Drib+Pace+Acc alone = mid-table Expand
But it doesn't take much extra if your player does have 18 pace/acc. Generally speaking, any real player with 18 pace/acc will dominate because his other attributes will all be at least 6-8.
However a 16 pace/acc player is not so straightforward. They do require at least moderate values in certain other attributes to win. The two most obvious are dribbling and jumping reach, but '6' concentration or pressure and he'll be a dud.
With my player templates, I try to leave at least a bit of leeway to account for times when players deficient in a certain key attribute pop up in your top ratings when they really shouldn't (the ter Stegen example explored earlier is a good example of this problem). So for instance, the real minimum for pace/acc to win the Premier League isn't 14, it's 13 from memory or even 12 in certain cases (think Harry Kane). But the requirements for other attributes are so rigid and high with that, so I left pace/acc at 14.
Another example is the less critical an attribute is, the lower I will generally put a threshold on it, even if you can gain an extra position from a single '1' boost. An example of this is agility. Agility '11' or '12' has decent benefit, but it's not strictly necessary. This is an aim for 5th or 3rd debate. Ultimately I keep at the back of my mind always that the more attributes you put high thresholds on, the less player options you end up with.
blackbird said: If thresholds are more important why not use filters instead? Expand Both have downsides. For instance, if you filtered using my template, ter Stegen would be excluded due to his 13 Aerial Reach, and there's also the classic problem of if you filter for more than ~3 attributes no one would qualify.
Obviously the best solution would be some combination of both. A lenient set of filters combined with attribute weightings. But the practical reality I think for most of us is that we're rarely bothered to use filters at all. I think most of us just want a list sorted by rating % to look at.
Yarema said: Bottom line is people are looking for who is a better player right now kind of rating, not who you can train to be better in 5 seasons. You severely undervalue pace and acceleration for example because you argue that it's easy to train it up, which might be true but it takes a lot of time and some luck.
Secondly if you compare 2 players with same "key" attributes, one is 120 CA the other is 160 CA the 2nd one will perform better. Probably even significantly, those 1% here and 1% there add up. Can a team of 120s finish 7th in PL? Sure, but most of people looking for ratings aren't after that. They simply want a scoring system that will tell them player X is better than player Y, and I think you've strayed further and further from the original intention with each iteration. Expand
treath said: I never said Coutouis is not the best player at the start of the game, i said he is the best player and in the rating he lost to Ter Stegen.
Oblak with 20ish reflexes 19 one-one, 18 handling and 15 aerieal reach lost to some dudes in Equador with 120-130ca just because he lost some pace (??????????) due this age.
There is no way he is not a better goalkeeper than dudes with 120-130ca just because they are taller tham him.
When i use genie scout i want to know who is the best players in the filter i selected (like just english players for example), not who is the best cost effective players.
People are misguiding using this rating because of that.
I get that yeah 1 speed is better to train than 5 passing because of Ca cost, i agree with you, but in the rating when you put ZERO evaluate in passing, is like it's useless in the match engine and that's not true at all. Expand I have conducted some tests using the following players:
Arijanet Muric - 128 CA, 69.23%, Burnley Jan Oblak - 171 CA, 69.13%, Real Madrid Gianluigi Donnarumma - 161 CA, 65.84%, Paris SG
In each test I have replaced the GKs at Manchester City with 2 duplicates of the player. First, dropping them into my team of meta players and using Knap tactic that would normally come ~5th:
Muric - 3rd 6.81, Sacked March (10th) 6.67, Sacked Febuary (10th) 6.78 = Average 7.666th 6.75 Oblak - 6th 6.87, 7th 6.76, 1st 7.03 = Average 4.666th 6.88 Donnarumma - Sacked November (16th) 6.51, Sacked December (7th) 6.83, 5th 6.91 = Average 9.333th 6.75
Next, putting them a default Manchester City team and selecting the default 424 gegenpress tactic:
So we can see that in normal play, you wouldn't be able to tell the difference between these three players. When we put them in a struggling team with less variation allowing us to isolate the GK differences more clearly, all three hold up decently, but clearly Oblak has an edge on Muric, and Muric has an edge on Donnarumma.
To clarify just how much of an edge Oblak has, I have also tested the highest rated GK (Marc-Andre ter Stegen, 175 CA, 75.17%, Barcelona) to compare:
ter Stegen - Sacked November (17th) 6.60, Sacked April (11th) 6.64, 5th 6.88, 4th 6.80, 6th 7.05 = Average 8.6th 6.79
Comparing ter Stegen to the template, the two areas he is deficient in are Aerial Reach and Pace. In fact, a close look at Oblak's stats shows he fits the template better. The reason ter Stegen is rated higher is because of some very high values for key mentals and generally higher physicals. As mentioned before, this is the inherent downside of weightings, it can't tell the difference between 1 pace 20 acc and 10 pace 10 acc.
Comparing ter Stegen and Muric, the only advantages Muric has are Aerial Reach (16 > 13) and Jumping Reach (18 > 16). ter Stegen absolutely dominates him in other areas. Yet we see that Muric performs as good or slightly better. For Oblak vs. ter Stegen it is largely the same story; ter Stegen mogs Oblak in everything except Aerial Reach and Handling.
The conclusion I draw is that Aerial Reach has been slightly underweighted.
It is worth noting that if we assessed by passing, first touch, and whatnot, then ter Stegen would be rated even higher above Oblak and Muric.
I know from testing that Aerial Reach is the least forgiving GK attribute, and you can see evidence of me believing this here where I give Aerial Reach the highest rating of 80 in a 'weighted weights' version of my file. You can see that by contrast I give Reflexes only 28 and Jumping Reach 32. So ter Stegen going under the 14 minimum is pretty clearly to me the cause of his poor performance. The problem with the weighted weights is that they simply didn't perform better in comparison testing - it's not the end of the story, I just haven't progressed further with it yet.
Now hopefully that settles the facts of the matter.
I would now like to address the theoretical part.
1) I see ongoing conflation of two separate things. Criticizing basing weights on what gets ~5th-7th instead of 1st is one thing, criticizing that certain attributes are weighted at zero is another. They are two separate things. In regards to weighting useless attributes at zero, I stand firm on this - there is no '1%' gain.. there is 0% gain, or negative gain. Now I hear you, you say 'just suppose it is 1%, what's the harm in giving it a little weight?' - the harm would be that if start weighting all these worthless attributes even a little, you would end up with 36 year olds being recommended as 1st-choice strikers.
2) I did not wake up and decide to aim for 5th place in my making my latest weightings file. As you'd expect, I aimed for 1st. But what I quickly came to realize is that coming 1st is only feasible if all your players have 18+ pace/acc, or otherwise simply spend a billion dollars on standard players that will win without using the speed exploit. Now you could say, well that's how it has to be. But the conclusion I arrived at is that GS ratings are kind of pointless if there aren't any players you can buy or even exist. That 18 pace/acc requirement I mentioned.. it's not just that, you need to pair it with say high drib and jumping reach and a few other things; high pace/acc alone is not enough. So I thought, ok what can we realistically get this pace/acc requirement down to.. and that turned out to be ~14, which so happened to manage ~5th. 13 = relegated, and 17 = no win. You could argue that I should pick 15 for 4th, or 16 for 3rd, that's a fair point of debate. But you are not aware of the nuance of it if you believe that calibrating weightings towards ensuring 5th means they ensure 5th more than they ensure 1st. Here are the parts of the puzzle you are missing:
- Generally speaking, '20' pace is worth no more than '18' pace in the Premier League. If you have 18, you win all games. If you have 20, you win all games. - I have tested every such threshold. As you can see with the GKs above, '18' concentration shows negligible benefit compared to '13'. With some attributes, such as dribbling, it's generally the higher the better (linear). With Aerial Reach, it has a concrete minimum ('14' ) but quickly diminishing returns above that minimum. - If I weight attribute X at '15', that does not mean it is weighted to get 5th. It is weighted to get 1st so long as the cost is not too great. In the end, only a few attributes are weighted below 1st. These are: pace, acc, jump, drib and perhaps another I have forgotten. - If you say 'well let's boost pace, acc, jump, drib to 100 weighting!' then you would end up with key technical and mentals falling out of favor, and the whole optimized structure falling apart. It is more important to get 12 concentration instead of 11 concentration, than 17 pace instead of 16 pace. Obviously this gets too complicated to fully think out, but I have a hunch it's why my 'weighted weightings' did worse than the standard weightings in my comparison test, and hence why it's given me current pause for thought on the matter. - If you say 'to hell with the 'structure'! give me copious amounts of pace, acc, jump and drib at 100 with some passing/tackling slop on top and be done with it.. keithb, give me your ratings file!', then you end up with bloated players on top of the list that fall short in some key regard.
treath said: mate i don't discredit your research, i think it's valid when we talk about cost effective relatively.
but people don't use the ratings for that, they use to find the best players, so when you put in your ratings ZERO for long shot, technique, first touch, etc. It will fall apart. It will not show the best players, but the more cost effective maybe.
Take a look at your picture here from GenieScout, there is a GK from KV Mechelen 112Ca better than Mamardashville 148ca..near as good as Diogo Costa and Verbruggen.
But he is not that good, am i right?
Your rating show that, well forGk with 110-120ca he is the best cost effective in the game but not the best player to play for my team now.
There is no way in hell Courtouis it's not the best Gk in the game, there is no way a Gk from Indepediente Del Valle and Burnley better than Oblak, just because the Aerial Reach and Pace (???) is a little bit better than Oblak (discredit for all other atributes).
So you misguiding people when we talk about find the best players to play for my team now.
But you help a lot, and helped me a lot when we talk about cost effective for training. Expand For many attributes, including all the ones you mention, there is simply no benefit to having them. They are in fact often a net negative because they take up CA, reducing available CA for useful attributes such as pace/acc and slowing development (CA-PA gap strongly influences growth).
That may sound hyperbolic, but consider the following example:
A player has 5 extra passing. +5 passing = Approx +0.96 win rate according to HarvestGreen's data I took a random 140 CA player and gave him +5 passing. His RCA went to 142 CA. So +2 CA for +5 passing. If I give him +1 acceleration instead, his RCA is 143. So +3 CA for +1 acceleration. +1 acceleration = Approx +4.78% win rate according to HarvestGreen's data. If we adjust the CA to match, it's +0.96 (passing) vs +3.186 (acceleration). So extra passing is just losing.
That's HarvestGreen's data.
For my own reference, I use my own data. I change an attribute from 1 to 20 for all players in a team, and if the team has an identical or worse position after a full season, I say that attribute is worthless, end of story, and mark it '1'. If going from 1 to 20 changes the position by 1 place, I put it in my weights - even if it costs a lot of CA. An example of this is strength.
A zero weighting does not mean they are penalized for having the attribute, it just doesn't give them an advantage for it.
Now as for saying Messi is obviously better than Joe Blow from Slough.. I have resigned myself to not making presumptions. First off, I'm not an expert on every great player in the footballing world, and my knowledge is probably a bit outdated. Second, it would obviously be a form of confirmation bias to judge results by name recognition.
The gold standard for me is results. I have compared a few top strikers, testing them in the same league as a control.
A good second option I believe is to see who the top clubs actually start with or buy. You say Courtois is not the best GK in the game. Well, he's the 1st choice GK for Real Madrid, and in a save I checked he averaged 7.21 rating over 5 years with 2 runner-up World Goalkeeper of the Year Awards and 3 third-place awards, which doesn't sound too shabby to me. I guess my question to you is.. on what basis do you know he is not one of the best GKs?
In regards to best players vs CA cost-effective, the current weightings I present do not take CA cost-effectiveness into account. I previously have, if you are familiar with the 'Blended' vs. 'Pure Performance' files. The only way it is currently coming into consideration is as follows:
Suppose I test X attribute '20' finishes 5th. '12' finishes 5th. '11' finishes 6th. '10' finishes 7th. '9' finishes 10th.
My thinking would be that I should choose somewhere between '10' and '12'. If attribute X is very CA costly, or more importantly difficult to find/train, then I will likely choose '11' or possibly '10'. Otherwise I will choose '12'.
How you should actually present your argument therefore is that I should target 1st, not 7th. But I think your typical player is taking Slough to the Champions League, not assembling the World's best XI in Season 1.
I mean honestly, what do you even suppose to propose? I can only imagine you would suggest weighting passing as 5 instead of 0 for example. But then you would have 36 year olds giving Div 2 strikers a run for their money, because their 16s in various dud technicals & mentals would make up for the difference in speed.
and also have tried to emphasize that Unique Nation ID matters a lot - as important as junior coaching or more.
But I've never discussed the randomness factor, let alone put a precise figure on it. And many people just handwave discussion of newgens away with 'it's all dominated by randomness anyway'.
So I think it is time to start determining and stating just how random it is exactly. After a brief thinkle I've put it at 50% and junior coaching at 13% for a comparison, but this is just a starting point for a more precise estimation.
treath said: not mention other positions that infer a player like a winger or midfield with 20 passing 20 first touch 20 long shoot 20 technic and 14 driblling are worst than a player with 20 driblling and 5 on the rest. Expand Passing, first touch, long shots, and technique have negligible benefit or even possibly negative correlation for outfield players.
If you don't want to take my word for it, refer to HarvestGreen's data on this:
You're also not attempting a fair example. No players are going to have 20 passing, first touch, long shots, and technique. Nor is a player going to have 20 dribbling and 5 in those other stats.
treath said: he blindly trust this prem 1.0 ratings that show pace is more importante for a GK than reflexes and his gk are a phisical monster.
the problem is people trust this ratings blindly i made tests with this rating with gsscout and it show goalkeepers with 100 or 110 pa better than great goalkeepers just because they are tall and have enough aerial reach and jumping reach to surpass lack of reflexes, 1-1, etc. Expand If you doubt it, you can edit an English Premier League's team in the editor to match these attributes and see if they come ~7th in combination with Knap tactic as I claim they do. For hiddens, convert the values here (simply divide by 4).
It requires a bit of work, but the difficult legwork is already done. It would take about 30 minutes, and then perhaps another 30 minutes to test.
I have tried to state clearly that while the attribute templates are very reliable (plug those figures in and you *will* get 7th on average and with not too much variation), weightings have the downside of being unable to distinguish between 1 acc/20 pace and 10 acc/10 pace.
You mention 'prem 1.0 ratings that show pace is more important for a GK than reflexes' and 'aerial reach and jumping reach to surpass lack of reflexes, 1vs1', which are examples of this inherent lack of discrimination. I have attempted a weighted weights version in attempt to try and compensate for this problem, however my comparison testing found that it did performed worse than the standard Premier League 1.0 weights under normal playing conditions (8.333 position result average vs 7.333 for standard Premier League 1.0 weights).
The comparison tests included a control of Orion's weights, which averaged 13.166 position, so my weightings are definitely worth using above any random set of values, and I daresay anyone else's weights presented so far.
You say that my weightings 'show goalkeepers with 100 or 110 pa better than great goalkeepers'. This irks me because this is just straight up disinfo. Now maybe what you mean to say is that sometimes a 100 CA goalkeeper can supposedly perform at the highest level, but cmon man, anyone can load up my ratings with the starting database in 3 minutes and see this:
I'll be posting it soon, but it's still a work in progress right now.
Translating the real results to Genie Scout has been proving to be really tricky. As ChatGPT has confirmed to me, there's no straightforward way of deriving the specific combination of attribute weightings from the results alone. For example say I find drib 30 + ant 30 + pace 70 is what works.. how do I know it's not just ant 30 + pace 70, and dribbling has 0 effect?
Thankfully I can rule out a lot of attributes from the get-go from previous testing, but I still have to just manually fiddle around with all sorts of different combinations to find out what works and what doesn't until I have something that at least aligns with the real rankings as closely as possible.
I started with the following process:
1. Start with Premier League 1.0 'weighted weights' version
2. Compare players who have completely different sets of attributes but had similar results
3. Adjust weighting of 1-3 attributes that I know matter (i.e. pace, anticipation, work rate, etc.) starting with the most likely attribute (i.e. pace) until all players are roughly where they should be ranked.
4. If one player's rank is fixed, but then another falls out, return to step 3.
5. Find a player with high rep or high CA who has unusually low % rating with the changed weights. Test performance in controlled test first, then do step 3 with them if performance doesn't match rank.
6. Keep repeating step 5.
Right now I've got a functional set of weights, but everything has to be changed around again the next time a sample finds a player changes their ranking. I think 6 samples is giving us a fairly accurate picture now to work with, but for true accuracy I think it's going to require at least 10.
Then there is the matter of position variation. But my first result in another position so far seems to suggest the weights could be the same for every outfield position. Once I've done all positions, with perhaps 10 samples for STs for calibration, then I'll post it.
I mean it's kind of the basis of all these weighting systems, because if a point of pace was vastly different from 9 to 10 as opposed to 15 to 16 there would be no point in having a fixed value for an attribute weight.
So you either accept they are pretty much linear (with a few exceptions) or throw GS ratings out the window.
It's not the basis of my weightings, as non-linear values do not make useful weightings in Genie Scout impossible.
Here's how I handle the problem of non-linearity:
Finishing - I know from testing it has no advantage above ~7, and is only of moderate value even below that all the way down to one. Therefore I give it a very low weight, or perhaps even no weight at all.
Work Rate - It's very clear that work rate is crucial going from 1 to 6 but has greatly diminishing returns beyond that, and pretty much tops out at around ~11-13. If you don't believe the testing done on this, just look at player like Haaland, Mbappe and Messi - their work rate is 13, 12, and 9 respectively. The compensation in GS for this is fairly straightforward, instead of weighting Work Rate equal to pace or acc if we were just looking at the 1-to-6 effect, we give it around 20-40 instead of 70-100.
Jumping Reach - This has one of the most difficult curves of all to accommodate. It is very important going from 1 to ~7, then moderately important ~8-13, has a massive jump in performance once going above the league threshold (~14-17 for Premier League), but is then near useless going from ~18 to 20 since probably no other player will challenge above 17 anyway. Here we target either ~8-13 (moderate rating), or the ~14-17 threshold (high rating, perhaps the highest even above pace/acc).
Of course you run into situations where GS considers 1 pace 20 acc equal to 10 pace 10 acc, but those 1 pace 20 acc players are the exceptions not the rule, so it doesn't render the whole enterprize futile.
No Mbappe and Haaland are not rated the same. And when you increase TS to 107, like I said in the post, the gap becomes even bigger. Its 2% and then becomes 4%. They are not rated the same. Is there actually something wrong with you?
AGAIN how can you create a file and use and not see this?? There's a worrying disconnect.
To be honest I didn't even check if you had a TS rating, because I took one look at the FS rating and knew it was rubbish.
The screenshot I provided is, you'll agree, the FS ratings you've given.
So do you want me to use the FS or TS rating?
Looking at your TS ratings, you have Osimhem above Lewandowski even though both having 15 jumping reach, so more of the same đź’© either way.
I wouldn't want to misrepresent you. This is what your GS file says does it not?
Default database, start of game (England - 3/7/2023)
90% | 90% | 90% | 90% | Erling Haaland - 1st, 1st, 4th, 1st, 2nd, 1st = 1.666 position, 1.119 goals/match, 7.80 rating
84% | 84% | 80% | 79% | Robert Lewandowski - 2nd, 2nd, 2nd, 3rd, 5th, 2nd = 2.666 position, 0.721 goals/match, 7.33 rating
84% | 81% | 90% | 87% | Kylian Mbappe - 2nd, 3rd, 3rd, 3rd, 5th, 2nd = 3 position, 0.701 goals/match, 7.48 rating
80% | 69% | 76% | 72% | Daizen Maeda - 2nd, 3rd, 4th, 4th, 2nd, 3rd = 3 position, 0.586 goals/match, 7.23 rating
83% | 84% | 80% | 78% | Harry Kane - 5th, 2nd, 3rd, 3rd, 1st, 7th = 3.5 position, 0.563 goals/match, 7.29 rating
80% | 81% | 77% | 78% | Dusan Vlahovic - 1st, 4th, 4th, 5th, 1st, 6th, 4th = 3.571 position, 0.634 goals/match, 7.23 rating
80% | 81% | 84% | 85% | Victor Osimhen - 3rd, 3rd, 7th, 3rd, 3rd, 4th = 3.833 position, 0.714 goals/match, 7.29 rating
80% | 78% | 85% | 80% | Lionel Messi - 4th, 2nd, 1st, 5th, 9th, 3rd = 4 position, 0.588 goals/match, 7.16 rating
75% | 72% | 77% | 74% | Donyell Malen - 2nd, 2nd, 7th, 5th, 5th, 7th = 4.666 position, 0.514 goals/match, 7.22 rating
75% | 74% | 69% | 72% | Robert Glatzel - 5th, 7th, 6th, 3rd, 12th, 6th = 6.5 position, 0.634 goals/match, 7.16 rating
72% | 72% | 79% | 71% | Paulo Dybala - 5th, 5th, 6th, 10th, 3rd, 12th = 6.833 position, 0.492 goals/match, 7.07 rating
61% | 63% | 62% | 61% | Adam Le Fondre - 8th, 11th (sacked), 5th, 7th (sacked), 6th (sacked), 8th (sacked) = 7.5 position
Edit: I've added a color-coded column to compare 4 different GS rating files. 1st is new Best fit file, 2nd is Premier League 1.0, 3rd is keithb, 4th is Falbravv's META FMA 2.0 file based on skawkclsrn's recent data. Green = 1% or less difference. Yellow = 2-4% difference. Red = 5%+ difference. Ignore Harry Kane, he needs a slight readjustment right now due to the latest samples.
There are a lot of new deductions that can be made.
One is that now we have a rough idea what GS % corresponds to in terms of concrete performance numbers, and another is that we can tell at what point random variation makes distinguishing between % numbers pointless.
We can always do with more samples, but if we just take the difference between whoever is highest and lowest between Lewandowski, Mbappe, Maeda, Kane, Vlahovic, Osimhen, and Messi (total +/- 1 position difference maximum) we get:
((4.08/80.70)x100) = 5.06% - My best fit of the data so far in GS (not to minimize %, but to correct player rank)
((14.30/91.99)x100) = 15.54% - Premier League 1.0 'weighted weights' version
((14.94/88.11)x100) = 16.96% - Premier League 1.0
((14.14/92.25)x100) = 17.14% - 'Pure Performance' FM24
((13.45/77.59)x100) = 17.33% - My first/original FM24 ratings file
((15.89/84.94)x100) = 18.71% - 'Blended' FM24
((18.34/83.29)x100) = 22.02% - ykykyk05251's weights file
((22.88/88.01)x100) = 26.00% - Orion's coefficients file
((29.27/91.47)x100) = 32.00% - Genie Scout Default
That is where we are accounting for extreme cases. In other words, a 4-5% difference in GS is almost certainly an objectively better player.
But what if we make it a bit leniant, and tolerate ~10-20% being exceptions we miss. In other words, this is the more typical uncertainty range. To discover this, I exclude the bottom % player from the list:
((4.03/80.70)x100) = 4.99% - My best fit of the data so far in GS (not to minimize %, but to correct player rank)
((6.21/88.11)x100) = 7.05% - Premier League 1.0
((6.00/83.29)x100) = 7.20% - ykykyk05251's weights file
((7.34/75.95)x100) = 9.66% - Falbravv's 'META FMA 2.0' file based on skawkclsrn's data this week
((10.39/89.51)x100) = 11.60% - Falbravv's 'META FMA' file based I think FM Arena's testing(?)
((11.67/91.47)x100) = 12.76% - Genie Scout Default
((13.45/77.59)x100) = 13.20% - My first/original FM24 ratings file
((12.61/95.25)x100) = 13.24% - 'Pure Performance' FM24
((12.42/91.99)x100) = 13.50% - Premier League 1.0 'weighted weights' version
((13.21/94.59)x100) = 13.97% - keithb's weights đź’©
((13.64/88.01)x100) = 15.50% - Orion's coefficients file
((14.14/84.94)x100) = 16.65% - 'Blended' FM24
So we see that whatever the case, ~4-5% is going to be the range within which the player who is better becomes uncertain, but also the difference is going to be pretty meaningless objectively.
I can answer this for you because I'm currently doing isolated testing and analyzing the attribute differences closely for a number of players, and one of them is Harry Kane.
So far my testing finds that Kane is in fact objectively one of top STs in FM24.
I've already come up with a draft set of weights that allows Kane's low acc/pace and other quirks to align smoothly with all other players tested so far, but I'll just tell you a more abstract version of what I'm seeing.
This is my impression so far (for ST at least specifically):
1. Pace/acc, and stamina to keep it going through a game, are still the top factors.
2. Pace/acc is not *strictly* necessary to do well. A player with 9 acceleration can function decently as ST in the Premier League (I've tested this). If you have a team of them, you'll be relegated, but as individual exceptions to the rule they can do the job. Although I haven't tested it, I would expect similar with a team of Harry Kanes. You need at least ~13-14 average for the team as a whole in Premier League just to survive. So pace/acc remains very important.
3. Only about one third of attributes matter. Anticipation and dribbling are in this third for ST, but all of these are at least a bit less crucial than pace/acc. For instance, Messi has 20 dribbling, and while he is not no.1, it does appear to be a key factor in him being one of the top STs according to testing even at age 36 in FM24. None of these attributes are strictly necessary either except perhaps 1 or 2, see: Daizen Maeda
4. This is still dubious, but through analyzing examples such as Kane vs. Lewandowski specifically, it would appear that some attributes such as passing may actually have negative performance impact.
Here is something I already wrote that I was thinking of posting earlier:
There is no reason why Lewandowski should be better than Harry Kane, and yet he is. In fact, Lewandowski is slightly inferior on a number of things.
Lewandowski has advantages in the following areas:
+3 aggression
+3 first touch
+3 balance
+2 anticipation
+2 concentration
+2 natural fitness
+1 bravery
+1 off the ball
+1 acceleration
+1 agility
+1 strength
+1 important matches
None of those are adequate to explain the difference, given Kane has the following advantages (I'll list just a few):
5 years younger
+7 crossing
+5 passing
+5 vision
+4 teamwork
+3 left foot
+2 consistency
+1 determination
+1 composure
+1 work rate
I suspect that high passing and possibly crossing reduces team performance. Perhaps what is happening in the engine is that Kane is passing to a team-mate do to the job, who is always an inferior player in this case.
---
Note: A possible conclusion of course is that +1 acceleration or +2 anticipation and so forth is just very important, but what you'd be missing is that I'm not just comparing these two players together, I'm comparing them to a dozen others, who each all have to align with the rest of each other, in line with the objective performance results. So if I weight anticipation more say, that would throw 3 others out of whack with each other. There is no simply no way I can see that Lewandowski is better than Kane, and yet after 6 seasons of testing for each, Lewandowski in fact is. The only thing that seems possible is that passing and perhaps other attributes Kane is better in, are having a negative effect on his performance.
Then again, it could be that we just need a few more samples to set things straight. And here we are talking the minor differences of who is say 7th and who is 6th.
If you mean the ME under the hood rather than the visuals, from what I've seen and read, it's about the same, the new OOP/IP is almost placebo, and it gets dominated by strikerless tactics.
HarvestGreen, Orion, and my own findings don't though.
And the effects can be pretty massive not subtle, for example Orion finds pressure 1 to 10 is +47.33% but pressure 10 to 20 is +10.18%, and HarvestGreen confirmed this with pressure 1 to 6 18.2% and pressure 6 to 18 8.9%. I've found the same.
I don't know why you're finding every attribute effect to be linear, I think something going wrong somewhere in regards to that. I suspect it could be that you're randomizing attributes of every player at every club in the league, so that there's no longer set levels to compete against (i.e. in a real league, most players will have ~14 pace for example, or most GKs will have ~14 aerial reach, producing thresholds - it's not necessarily innate to the attribute itself, but a product of the relative competition. So 13 > 14 pace isn't a threshold in Vanarama South, but it is in Premier League). It's not entirely clear though if you're randomizing every player at every club, or just one club.
The part that intrigues me the most are the position weights. Have you simply divided the general results by the CA costs for each position, or have you done comparative testing for each position here?
I attempted to address this with weighting the weights, but it produced worse results, or at least no better.
I've tried comparative deduction by comparing several players match ratings & goal tallies with their attributes. Some key problems with this approach:
- Match ratings have been demonstrated to favor presence of technical/mental ability over actual wins
- Goals scored can simply reflect finishing, and poor finishing has minimal effect on the team win rate (it simply means someone else with higher finishing in the team scores the goals)
- I actually managed to get the players to 'line up' with the ratings/goals a lot better, but it took a very strange set of GS weightings to get there. Either I'm on the wrong track, or even if it's right, I would be building a house without being able to know the foundations.
I concluded that what's actually needed is more result-based data, but on individual players. It's more tedious, and will require a fair amount of samples for good accuracy, but I think it's the best approach.
Method
I use my meta team at Man City, but replace a position with duplicates of a particular player. If I'm testing ST, I remove proficiency in other positions so as not to mess up the results, as what I'm most interested in is deducing what constitutes the precise ranking of STs. Full detail is used for all first team competitions.
Results
Here's some data I've collected so far (ranked by team performance with player as ST):
Erling Haaland đź‘Ť - 1st, 1st, 4th = 2nd position, 1.263 goals/match, 7.93 rating
Kylian Mbappe đź’© - 5th, 1st, 3rd = 3rd position, 0.708 goals/match, 7.54 rating
Daizen Maeda - 2nd, 3rd, 4th = 3rd position, 0.533 goals/match, 7.28 rating
Harry Kane - 5th, 2nd = 3.5th position, 0.429 goals/match, 7.25 rating
Victor Osimhen - 3rd, 3rd, 7th = 4.333th position, 0.699 goals/match, 7.27 rating
Adam Le Fondre - 8th, 11th (sacked), 5th = 8th position, 0.416 goals/match, 6.82 rating
The reason I picked Le Fondre to include is because I saw him compared in a Youtube video, and even though he's a 36-year-old with 9 pace/acc, he managed the results you see here. A takeaway from this for me is that pace/acc isn't strictly necessary. Using the comparative deduction I mentioned before, I found that anticipation and composure are more critical than pace/acc.. as I said, these are strange conclusions, so rather than make the facts fit the theory, I'm going to be getting the facts straight first and then seeing what theory fits the data.
I'll probably do about 5-6 samples each of ~10 players for ST and then see if I can deduce consistent patterns to create accurate weightings.
If i still win, but i wanna find a keeper that actually saves, what i shall look for
Rn Chevalier looks good for me, best keeper for me yet, but he's old in my save, so i wanna find or make a good regen, waht to look for?
I may have a look into it later, but I suspect that it's fundamentally your tactic and overall level of quality of your players, not the attributes of your GK, that determine the number of goals he concedes.
We can influence who scores goals in a team, but that's because at the end of the day a goal is going to be scored. But you can't reduce the number of goals your GK concedes, because he's the only one who can concede them.
Cuz what i've seen on my long real save, as example, Courtouis were bad (with my high def line at least)
Yeah i won alot, and gd was insane, but he's avg rating was like 6.8 or something like that haha
To clarify, my template, and therefore the weightings, are based on team position at end of season, not match ratings.
Interesting that you give all hidden attributes 0 weighting even though you claim Genie Scout isn't bugged.
According to those weights, Donnarumma is ranked 9th, while Muric is 202nd.
Meanwhile, actual Premier League test results in reality:
Muric - 3rd 6.81, Sacked March (10th) 6.67, Sacked Febuary (10th) 6.78 = Average 7.666th 6.75
Oblak - 6th 6.87, 7th 6.76, 1st 7.03 = Average 4.666th 6.88
Donnarumma - Sacked November (16th) 6.51, Sacked December (7th) 6.83, 5th 6.91 = Average 9.333th 6.75
For the outfield, the big problems come up where he strays from convention. For instance, he gives 0 weighting to concentration for STs, resulting in this player being rank 40th ST (classed alongside Kane and de Bruyne):
He is limited by his low concentration. I have him ranked 114th alongside Danny Ings at West Ham, rather than Kane at Bayern.
He has Mbappe rated higher than Haaland. You can see on Youtube or my own test results that Haaland does significantly better than Mbappe when Mbappe is put in the same division.
And this is whom he reckons is the 7th best DC in the world:
This is what you get when you weight dribbling at 0, composure at 5, and strength at 50.. or conversely, when you put pace/acc to 100 and don't factor in other attributes properly.
For comparison, this is what my weightings say is the 8th best DC in the world (John Stones was 7th; a little too old to do):
Zouma isn't bad, I have him at 65th. But he's not an absolute top player.. he's a West Ham player.
Not to mention that somehow you claim that under 18 pace you can't win the league and at the same time it's weighting is almost the same as 12 other attributes. So the scoring only works once you reach the minimum thresholds or what?
I'm pretty sure I could devise a team of players where they have 18 pace/acc and high CA and yet still finish towards the bottom. But of course it is generally true that a team of high CA players will dominate, but if that is sufficient and viable for you, then why bother with GS ratings at all, and instead just sort by CA? The truth is a lot of CA used is useless, and expensive to boot. Muric did better than ter Stegen, even though ter Stegen has much higher CA and cost/reputation.
The perfect attribute combination to win the English Premier League is 1 CA. The minimum realistic attribute distribution is ~85 CA as shown with my player templates. Obviously real players will have some extra padding on top of that in places, but we have been talking about GKs I favor have CA as low as ~100-110. I have specifically tailored my template to be realistic.
How I found that 18 pace/acc alone is insufficient is through my 1 CA testing. Even 20 pace + 20 acc + 20 drib cannot win.
Here are some quotes from the thread to show you I am not just making this up on the spot now:
GeorgeFloydOverdosed said: Mid-table results were easy with just 20 pace/acc/drib (not sure if I included jump), but it required careful tweaks — closer to Orion’s data than HarvestGreen’s — to push into the top four.
- Pace+Acc alone = 0 points, 0-7 loss typical
- Jump+Acc alone = a draw or two, 1-7 loss typical
- Drib+Pace+Acc alone = mid-table
But it doesn't take much extra if your player does have 18 pace/acc. Generally speaking, any real player with 18 pace/acc will dominate because his other attributes will all be at least 6-8.
However a 16 pace/acc player is not so straightforward. They do require at least moderate values in certain other attributes to win. The two most obvious are dribbling and jumping reach, but '6' concentration or pressure and he'll be a dud.
With my player templates, I try to leave at least a bit of leeway to account for times when players deficient in a certain key attribute pop up in your top ratings when they really shouldn't (the ter Stegen example explored earlier is a good example of this problem). So for instance, the real minimum for pace/acc to win the Premier League isn't 14, it's 13 from memory or even 12 in certain cases (think Harry Kane). But the requirements for other attributes are so rigid and high with that, so I left pace/acc at 14.
Another example is the less critical an attribute is, the lower I will generally put a threshold on it, even if you can gain an extra position from a single '1' boost. An example of this is agility. Agility '11' or '12' has decent benefit, but it's not strictly necessary. This is an aim for 5th or 3rd debate. Ultimately I keep at the back of my mind always that the more attributes you put high thresholds on, the less player options you end up with.
Both have downsides. For instance, if you filtered using my template, ter Stegen would be excluded due to his 13 Aerial Reach, and there's also the classic problem of if you filter for more than ~3 attributes no one would qualify.
Obviously the best solution would be some combination of both. A lenient set of filters combined with attribute weightings. But the practical reality I think for most of us is that we're rarely bothered to use filters at all. I think most of us just want a list sorted by rating % to look at.
Secondly if you compare 2 players with same "key" attributes, one is 120 CA the other is 160 CA the 2nd one will perform better. Probably even significantly, those 1% here and 1% there add up. Can a team of 120s finish 7th in PL? Sure, but most of people looking for ratings aren't after that. They simply want a scoring system that will tell them player X is better than player Y, and I think you've strayed further and further from the original intention with each iteration.
treath said: I never said Coutouis is not the best player at the start of the game, i said he is the best player and in the rating he lost to Ter Stegen.
Oblak with 20ish reflexes 19 one-one, 18 handling and 15 aerieal reach lost to some dudes in Equador with 120-130ca just because he lost some pace (??????????) due this age.
There is no way he is not a better goalkeeper than dudes with 120-130ca just because they are taller tham him.
When i use genie scout i want to know who is the best players in the filter i selected (like just english players for example), not who is the best cost effective players.
People are misguiding using this rating because of that.
I get that yeah 1 speed is better to train than 5 passing because of Ca cost, i agree with you, but in the rating when you put ZERO evaluate in passing, is like it's useless in the match engine and that's not true at all.
I have conducted some tests using the following players:
Arijanet Muric - 128 CA, 69.23%, Burnley
Jan Oblak - 171 CA, 69.13%, Real Madrid
Gianluigi Donnarumma - 161 CA, 65.84%, Paris SG
In each test I have replaced the GKs at Manchester City with 2 duplicates of the player. First, dropping them into my team of meta players and using Knap tactic that would normally come ~5th:
Muric - 3rd 6.81, Sacked March (10th) 6.67, Sacked Febuary (10th) 6.78 = Average 7.666th 6.75
Oblak - 6th 6.87, 7th 6.76, 1st 7.03 = Average 4.666th 6.88
Donnarumma - Sacked November (16th) 6.51, Sacked December (7th) 6.83, 5th 6.91 = Average 9.333th 6.75
Next, putting them a default Manchester City team and selecting the default 424 gegenpress tactic:
Jan Oblak - 1st 6.99, 2nd 7.12
Muric - 2nd 7.00, 1st 6.94
Donnarumma - 1st 7.07, 1st 6.97
So we can see that in normal play, you wouldn't be able to tell the difference between these three players. When we put them in a struggling team with less variation allowing us to isolate the GK differences more clearly, all three hold up decently, but clearly Oblak has an edge on Muric, and Muric has an edge on Donnarumma.
To clarify just how much of an edge Oblak has, I have also tested the highest rated GK (Marc-Andre ter Stegen, 175 CA, 75.17%, Barcelona) to compare:
ter Stegen - Sacked November (17th) 6.60, Sacked April (11th) 6.64, 5th 6.88, 4th 6.80, 6th 7.05 = Average 8.6th 6.79
Comparing ter Stegen to the template, the two areas he is deficient in are Aerial Reach and Pace. In fact, a close look at Oblak's stats shows he fits the template better. The reason ter Stegen is rated higher is because of some very high values for key mentals and generally higher physicals. As mentioned before, this is the inherent downside of weightings, it can't tell the difference between 1 pace 20 acc and 10 pace 10 acc.
Comparing ter Stegen and Muric, the only advantages Muric has are Aerial Reach (16 > 13) and Jumping Reach (18 > 16). ter Stegen absolutely dominates him in other areas. Yet we see that Muric performs as good or slightly better. For Oblak vs. ter Stegen it is largely the same story; ter Stegen mogs Oblak in everything except Aerial Reach and Handling.
The conclusion I draw is that Aerial Reach has been slightly underweighted.
It is worth noting that if we assessed by passing, first touch, and whatnot, then ter Stegen would be rated even higher above Oblak and Muric.
I know from testing that Aerial Reach is the least forgiving GK attribute, and you can see evidence of me believing this here where I give Aerial Reach the highest rating of 80 in a 'weighted weights' version of my file. You can see that by contrast I give Reflexes only 28 and Jumping Reach 32. So ter Stegen going under the 14 minimum is pretty clearly to me the cause of his poor performance. The problem with the weighted weights is that they simply didn't perform better in comparison testing - it's not the end of the story, I just haven't progressed further with it yet.
Now hopefully that settles the facts of the matter.
I would now like to address the theoretical part.
1) I see ongoing conflation of two separate things. Criticizing basing weights on what gets ~5th-7th instead of 1st is one thing, criticizing that certain attributes are weighted at zero is another. They are two separate things. In regards to weighting useless attributes at zero, I stand firm on this - there is no '1%' gain.. there is 0% gain, or negative gain. Now I hear you, you say 'just suppose it is 1%, what's the harm in giving it a little weight?' - the harm would be that if start weighting all these worthless attributes even a little, you would end up with 36 year olds being recommended as 1st-choice strikers.
2) I did not wake up and decide to aim for 5th place in my making my latest weightings file. As you'd expect, I aimed for 1st. But what I quickly came to realize is that coming 1st is only feasible if all your players have 18+ pace/acc, or otherwise simply spend a billion dollars on standard players that will win without using the speed exploit. Now you could say, well that's how it has to be. But the conclusion I arrived at is that GS ratings are kind of pointless if there aren't any players you can buy or even exist. That 18 pace/acc requirement I mentioned.. it's not just that, you need to pair it with say high drib and jumping reach and a few other things; high pace/acc alone is not enough. So I thought, ok what can we realistically get this pace/acc requirement down to.. and that turned out to be ~14, which so happened to manage ~5th. 13 = relegated, and 17 = no win. You could argue that I should pick 15 for 4th, or 16 for 3rd, that's a fair point of debate. But you are not aware of the nuance of it if you believe that calibrating weightings towards ensuring 5th means they ensure 5th more than they ensure 1st. Here are the parts of the puzzle you are missing:
- Generally speaking, '20' pace is worth no more than '18' pace in the Premier League. If you have 18, you win all games. If you have 20, you win all games.
- I have tested every such threshold. As you can see with the GKs above, '18' concentration shows negligible benefit compared to '13'. With some attributes, such as dribbling, it's generally the higher the better (linear). With Aerial Reach, it has a concrete minimum ('14' ) but quickly diminishing returns above that minimum.
- If I weight attribute X at '15', that does not mean it is weighted to get 5th. It is weighted to get 1st so long as the cost is not too great. In the end, only a few attributes are weighted below 1st. These are: pace, acc, jump, drib and perhaps another I have forgotten.
- If you say 'well let's boost pace, acc, jump, drib to 100 weighting!' then you would end up with key technical and mentals falling out of favor, and the whole optimized structure falling apart. It is more important to get 12 concentration instead of 11 concentration, than 17 pace instead of 16 pace. Obviously this gets too complicated to fully think out, but I have a hunch it's why my 'weighted weightings' did worse than the standard weightings in my comparison test, and hence why it's given me current pause for thought on the matter.
- If you say 'to hell with the 'structure'! give me copious amounts of pace, acc, jump and drib at 100 with some passing/tackling slop on top and be done with it.. keithb, give me your ratings file!', then you end up with bloated players on top of the list that fall short in some key regard.
but people don't use the ratings for that, they use to find the best players, so when you put in your ratings ZERO for long shot, technique, first touch, etc. It will fall apart. It will not show the best players, but the more cost effective maybe.
Take a look at your picture here from GenieScout, there is a GK from KV Mechelen 112Ca better than Mamardashville 148ca..near as good as Diogo Costa and Verbruggen.
But he is not that good, am i right?
Your rating show that, well forGk with 110-120ca he is the best cost effective in the game but not the best player to play for my team now.
There is no way in hell Courtouis it's not the best Gk in the game, there is no way a Gk from Indepediente Del Valle and Burnley better than Oblak, just because the Aerial Reach and Pace (???) is a little bit better than Oblak (discredit for all other atributes).
So you misguiding people when we talk about find the best players to play for my team now.
But you help a lot, and helped me a lot when we talk about cost effective for training.
For many attributes, including all the ones you mention, there is simply no benefit to having them. They are in fact often a net negative because they take up CA, reducing available CA for useful attributes such as pace/acc and slowing development (CA-PA gap strongly influences growth).
That may sound hyperbolic, but consider the following example:
A player has 5 extra passing.
+5 passing = Approx +0.96 win rate according to HarvestGreen's data
I took a random 140 CA player and gave him +5 passing. His RCA went to 142 CA. So +2 CA for +5 passing.
If I give him +1 acceleration instead, his RCA is 143. So +3 CA for +1 acceleration.
+1 acceleration = Approx +4.78% win rate according to HarvestGreen's data.
If we adjust the CA to match, it's +0.96 (passing) vs +3.186 (acceleration).
So extra passing is just losing.
That's HarvestGreen's data.
For my own reference, I use my own data. I change an attribute from 1 to 20 for all players in a team, and if the team has an identical or worse position after a full season, I say that attribute is worthless, end of story, and mark it '1'. If going from 1 to 20 changes the position by 1 place, I put it in my weights - even if it costs a lot of CA. An example of this is strength.
A zero weighting does not mean they are penalized for having the attribute, it just doesn't give them an advantage for it.
Now as for saying Messi is obviously better than Joe Blow from Slough.. I have resigned myself to not making presumptions. First off, I'm not an expert on every great player in the footballing world, and my knowledge is probably a bit outdated. Second, it would obviously be a form of confirmation bias to judge results by name recognition.
The gold standard for me is results. I have compared a few top strikers, testing them in the same league as a control.
A good second option I believe is to see who the top clubs actually start with or buy. You say Courtois is not the best GK in the game. Well, he's the 1st choice GK for Real Madrid, and in a save I checked he averaged 7.21 rating over 5 years with 2 runner-up World Goalkeeper of the Year Awards and 3 third-place awards, which doesn't sound too shabby to me. I guess my question to you is.. on what basis do you know he is not one of the best GKs?
In regards to best players vs CA cost-effective, the current weightings I present do not take CA cost-effectiveness into account. I previously have, if you are familiar with the 'Blended' vs. 'Pure Performance' files. The only way it is currently coming into consideration is as follows:
Suppose I test X attribute
'20' finishes 5th. '12' finishes 5th. '11' finishes 6th. '10' finishes 7th. '9' finishes 10th.
My thinking would be that I should choose somewhere between '10' and '12'. If attribute X is very CA costly, or more importantly difficult to find/train, then I will likely choose '11' or possibly '10'. Otherwise I will choose '12'.
How you should actually present your argument therefore is that I should target 1st, not 7th. But I think your typical player is taking Slough to the Champions League, not assembling the World's best XI in Season 1.
I mean honestly, what do you even suppose to propose? I can only imagine you would suggest weighting passing as 5 instead of 0 for example. But then you would have 36 year olds giving Div 2 strikers a run for their money, because their 16s in various dud technicals & mentals would make up for the difference in speed.
Initially I explained things in more extensive and precise detail, but realized it would be too burdensome to read through.
It's still fairly long and could do with another touch up soon, but it has new sections and new info.
For instance I have previously characterized newgen PA as essentially:
40% Junior Coaching
25% Youth Recruitment
25% Nation Youth Rating
~10% Game Importance
0% Youth Facilities
and also have tried to emphasize that Unique Nation ID matters a lot - as important as junior coaching or more.
But I've never discussed the randomness factor, let alone put a precise figure on it. And many people just handwave discussion of newgens away with 'it's all dominated by randomness anyway'.
So I think it is time to start determining and stating just how random it is exactly. After a brief thinkle I've put it at 50% and junior coaching at 13% for a comparison, but this is just a starting point for a more precise estimation.
Passing, first touch, long shots, and technique have negligible benefit or even possibly negative correlation for outfield players.
If you don't want to take my word for it, refer to HarvestGreen's data on this:
You're also not attempting a fair example. No players are going to have 20 passing, first touch, long shots, and technique. Nor is a player going to have 20 dribbling and 5 in those other stats.
the problem is people trust this ratings blindly i made tests with this rating with gsscout and it show goalkeepers with 100 or 110 pa better than great goalkeepers just because they are tall and have enough aerial reach and jumping reach to surpass lack of reflexes, 1-1, etc.
If you doubt it, you can edit an English Premier League's team in the editor to match these attributes and see if they come ~7th in combination with Knap tactic as I claim they do. For hiddens, convert the values here (simply divide by 4).
It requires a bit of work, but the difficult legwork is already done. It would take about 30 minutes, and then perhaps another 30 minutes to test.
I have tried to state clearly that while the attribute templates are very reliable (plug those figures in and you *will* get 7th on average and with not too much variation), weightings have the downside of being unable to distinguish between 1 acc/20 pace and 10 acc/10 pace.
You mention 'prem 1.0 ratings that show pace is more important for a GK than reflexes' and 'aerial reach and jumping reach to surpass lack of reflexes, 1vs1', which are examples of this inherent lack of discrimination. I have attempted a weighted weights version in attempt to try and compensate for this problem, however my comparison testing found that it did performed worse than the standard Premier League 1.0 weights under normal playing conditions (8.333 position result average vs 7.333 for standard Premier League 1.0 weights).
The comparison tests included a control of Orion's weights, which averaged 13.166 position, so my weightings are definitely worth using above any random set of values, and I daresay anyone else's weights presented so far.
You say that my weightings 'show goalkeepers with 100 or 110 pa better than great goalkeepers'. This irks me because this is just straight up disinfo. Now maybe what you mean to say is that sometimes a 100 CA goalkeeper can supposedly perform at the highest level, but cmon man, anyone can load up my ratings with the starting database in 3 minutes and see this: