DeFlow said: I wonder if you could share the full lists of coefficients (not only the 8 most prominent ones). This way I can use them to make a squad analysis sheet using the coefficients to score your players for suitability on each of these positions. Expand
Of course it's possible. I'd just have to generate model that have all the attributes. I choose to use only 8 to make it more usable. With 8 attributes you can even make yourself a simple spreadsheet to compare players. With 30-40-50 features a lot of them will literally have coefficients like 0,0001 so they won't bring much to the table. The question is what attributes you'll need. Only the visible ones? By visible I mean those from default player profile page. Because I extracted from the game around 50 features including attributes, hidden attributes, height, weight, player's league rank and foot (left/right) proficiency.
robbr13 said: I think consistently include momentum, taking into consideration the game. The full-backs are involved in aerial disputes, helping on a corner, or when the opposing goalkeeper covers a goal kick. Sometimes I just look at the value of the attribute found by the coefficients and compare it with the best player in the position in the league, or in the list of the best 11. or with the team that is favored to win the league. Expand
That's exactly how the model works. If we wanted to use it 'properly' we should check the average value for given attributes in the league you're in. And then for the attribute value (the one you then multiply by coefficient) you should use the difference between players actual attribute value minus league average.
We use 'straightforwardly' just the attribute value to speed up things. Of course in general it induce the error but since we are comparing players that will play within the same league (all the players that will play for us) it's just much simpler and we're saying that we're ok with the possible error.
At the end of the day it's still better than rely on attributes that the game suggests.
Kamas1 said: Vanarama south team, third season currently on sky bet 2 Expand
I see that, just like in my save, DMs tend to have lower ratings than the rest of the team. I thought that maybe my own DM is just not that good since his average rating was below 7 but on the other hand the others had worse average on that position.
Damn. 48 goals is really like a cheat code. Can you show this striker attributes?
Mrjoser said: I observed that for many positions, very similar type of player is the best. For example I have 3 pacy CDs with great jumping reach and well who would have guessed that, they would also rank as 1, 2 and 4 in my strikers lineup. So I think this weights give us great understanding what stats are most valuable, but only on very general scale. I for example don't believe that FB(att) will benefit from those 8 attributes the same way as IFB(Def). Or similar example wide playmaker VS inside forward.
Maybe the think that is missing in all these test is some atribute variability within the team. Like sure jumping reach is OP, but maybe it's enough to have 3-4 really good players in that and the team would benefit more if other players have better passing, or dribbling or whatever. Expand
As said before, If I was able to filter players by their most played position and role I'd do it in a heartbeat. Sadly I don't know if there is even a possibility to extract such data in any useful way.
VenEttore said: Frankly, I don't think there's a need for loans at all between harvestgreen22 and Zippo's findings. Why waste time quickly developing useless CA in order to get a loan to further quickly develop useless CA when you can spend that time slowly developing only useful CA (unless you're planning on selling the player and never play them).
I also discovered in my vanilla Athletic Bilbao save that players on Basconia still developed as though they were playing matches despite being in a non-playable league. Granted, I was only developing physicals using the optimized training schedules, but still. I saw +3 ACC +4 PAC on one of the players in a single season. Expand
Keep in mind that some B teams/Youth teams playing in non-playable league have this weird situation - I don't know if it's still present but it was like that in previous FMs. Weird situation is that the team does not have any fixtures in the schedule but when you check players they will have 'league games' in their stats (on the bottom of players page) despite no visible fixtures on team schedule. I found about that when I was wondering why despite having this option 'arrange friendlies in a week with no matches' my assman didn't arrange any games so I did it manually just to find out that the players were playing those friendlies (arranged by me) and some 'invisible' league games. Hence why assman didn't arrange friendlies - they had arranged some games that I just couldn't see in the schedule.
CBP87 said: Are those coefficients for the ST a target striker or fast striker? Expand
Yarema said: It doesn't differentiate by role Expand
This. It's just for position no matter the role. If I could somehow filter out players by their most played position and role I'd do it in a heartbeat.
Some B teams play in non-playable leagues or worse not league at all. Since league reputation does not matter could you check if playing friendlies vs playing 'competitive' matches matters? Asking because otherwise we could just leave all youth players in B team and ask staff to arrange friendlies once a week and we're done. No need for loans any more.
AFant said: MP with Unflappable and Reserved is probably the best (20 prof, 15-20 pressure, 15-20 temperament) but MC is safer. Spirited with Reserved is also good (15-17 prof, 15-20 pressure, 15-20 temp). AFAIK pressure is arguably the 2nd most important HA with how it affects on-pitch performance. Expand
High temperament negatively affects players performance. Source
I rerun the model for those 2 positions to use only linear regressions so no we don't get issues with double attributes or anything like that. Results:
STC Attribute Coefficient Jum 0,020557 Pac 0,020096 Acc 0,014496 Cnt 0,013675 Bal 0,012043 Dri 0,010739 Vis 0,009392 Cmp 0,009161
MLR Attribute Coefficient Pac 0,020497 Dri 0,014018 Acc 0,012883 Cmp 0,012072 Vis 0,011542 Jum 0,011150 Cro 0,010598 Sta 0,009658
I'll update first post with the new values. I think this will save us a lot of confusion.
Mrjoser said: Just want to say how much I appreciate the work you put into testing and letting us see behind the curtain. I like the influx of new research regarding attributes and training and whatnot.
I just find it hard to wrap my mind around the fact that this is currently the best Left back I can have (playing as Malmö 3rd season) Expand
Looks really amazing to be honest. He will be able to bully any winger just by raw physicality. I was saying for quite long time that for almost any case the attributes priority is Physical > Mental > Technical.
FREVKY said: That's what I thought, so I created pretty simple excel spreadsheet that make mass player comparison possible and easy.
Here's how it works: First, you need to import specified views. I created two sets of these: one for your team (for squad view) and one for scouting tab - both with CA & PA hidden - and another set with CA & PA visible for those who like to spoil the fun a little bit. Download and paste them into "views" folder in your Documents (C:\Users\your_name\Documents\Sports Interactive\Football Manager 2024\views is the default path).
When you load the view, you need to select every player, so click on one player and than ctrl+a to select everyone in the team or in the scouting range. Just bear in mind the more players you select, the more time it takes, so if you're about to select over 1000 players, give it a few seconds to work. Then, press cltr+p to "print" the selection into HTML file. Save it wherever you want, name it whatever you want. Then, you need the spreadsheet (MS Excel file). Open it and then in the Excel go to to File->Open and select the html file with your set of players. Copy it's whole contents (ctrl+a, then ctrl+c) and paste them into my spreadsheet in the blank sheet called "IMPORT" then switch the sheet to the one called "CALCULATION" and it should automatically calculate values for every player for each position using coefficients from this thread. Additionalli I added sections with CA, PA and difference between them (it will work only when you used views with PA and CA obviously).
Of course you can use whatever filters you want on the scouting section to narrow down the amount of players to whatever you really need.
At first glance it may sound complicated a bit but it's pretty easy to use. If you find any trouble using it, I'll try to help.
The spreadsheet is editable so do whatever you want with it, if you find any room for improvements, go for it. Expand
Bafici said: I made a genie scout file. Biggest difference from fmarena ratings is this is bit more realistic. Great players in real life takes higher ratings.
Just to keep you guys up to date I've decided to make a small adjustment for the features that use two attributes - so the last one for MLR and the last one for Striker. I've decided to take square of a product from multiplication those features (SQR(Attribute1 * Attribute2) * Coefficient). This way you can keep it proportional to the coefficient. I don't know how it works with your files. I assume that for spreadsheet it's not an issue. What about GenieScout? Does it support other mathematical operations? If not I'd suggest just to get rid of those 2 features (since they are last important for given positions) and adjust others to sum up to 100% just like you did for the other positions.
Yarema said: Yes but the model was working with adjusted numbers. Let me give an example. Average JR in the league is 12, a striker has 17. The way you explained stuff in the original post and later on basically says you take 17-12=5 so you do the calculations with 5. And isn't it possible that quadratic fits well for adjusted number (5) but not the actual number 17? Squaring numbers like 5 is a lot different that 17. Above 8 it basically dominates every other attribute basically double dipping (or triple since it's also the best attribute for a striker anyway).
Edit: Or to put it another way. Comparing JR contribution in above scenario if striker has 17 or 18 JR. Taking numbers at face up value you'd get roughly 1,27 vs 1,40. So a difference of 0,13 which would roughly be the effect of +6 pace. Using adjusted numbers of 5 and 6 makes the scores 0,49 and 0,55 respectively, a difference of 0,06 so half of the previous difference. Still equals +3 pace which feels a lot. Expand
For features using multiple attributes my suggestion is to multiply them and then take a square before multiplying by coefficient. So when you have for MLR Acc, Agi and coeff 0,003329 do: SQR(Acc * Agi) * Coeff This way you can balance the impact of this double attribute multiplication and you keep the 'partial score' properly scaled so for example when I tried said MLR using the same attribute value for all features, the 'partial score' for the double attribute feature stayed proportional to it's coefficient compared to other features.
Thanks. I really looks on the first glance that Konate should be far superior to the Chermiti. Like really the only thing that Chermiti has better than Konate is solely jumping reach and nothing else.
kvasir said: Could Chermiti’s higher goal tally be because he was your team’s main aerial threat, with extra goals coming from set pieces?
These ratings have worked well for me too, though I find the high Jumping Reach requirement for full-backs a bit off-putting. Just finished my first season with Schalke and comfortably won the 2. Bundesliga—curious to see if I can win the top division as well. In a previous save using FM-Arena ratings, I nearly won La Liga with Alavés in the first season, finishing just one point behind a dominant Real Madrid, who only lost once. Expand
One of my wild guesses behind Jumping Reach being high rated attributed for other positions, beside obviously scoring a lot from set pieces, was that it has high correlation with Strength. So player with high jumping reach usually have also high height, high weight and high Strength. So maybe strength also plays part in it because player with high strength will be able to just push opposition player and get the ball back.
pek80 said: Yes bro, the one in your model is the only "rating model" that rate Chermiti higher than Konate since all other model value pace acceleration, and finishing but not weight much in jumping reach! Expand
Just out of curiosity can you post the screenshot of attributes for both player?
pek80 said: This was in my head for several weeks before this post reveal the answer. I sold my Youssef Chermiti who has 140CA with striker rating 79 and upgrading my Striker to Karim Konate 160PA with 84 striker rating. Konate never perform better than chermiti and i keep asking... how come? with new rating in this post, its clear that chermiti rating is 77%TS comparing to konate 73%TS. Expand
Just to clarify. So at first you used GenieScout default 'rating' and then one from my model?
RFC said: So I've been testing this on my journeyman save, previously I was a spreadsheet with calculations for attacking and defending based on attributes weighted by the FM Arena new attribute testing.
That got me three promotions and then three years title wins on the top league (Japan)
I'm currently in Korea and with the weighting based on Orions approach, I'm still winning at the same rate but the football seems better to watch and average player ratings are higher. I'm claiming more player & goals of the month.
In general, my players are slower than than what I would sign on my previous set up but using harvestgreen22 training, I could build up pace and acceleration over time if needed.
One thing that does feel off is the jumping reach ratings. I didn't understand the calculations at first so I'm obviously not an expert on any of this but my suspicions are you need one or two players with high ratings there to score from set pieces and it's those goals that boost the average rating rating - potentially any position could have a high attribute.
It might be along the same line of high free kicks/corners/penalty taking having an impact, but that's purely guesswork on my part Expand
Good to hear that it works for you. I'm really looking forward to feedback since as said, the model is one thing, we know how it works and in theory I can calculate the average error for the model but knowing that it actually works in 'real life' is something different.
In terms of rating - FM Arena test is based on team Points. harvestgreen22 test is using goal difference. And my model is using players rating as a target variable. Hence my model will look for attributes that increase rating, but not team performance per se. We know that in general average rating will be somehow connected with team results. And I know about a flaw of this model that since it looks for players for high rating it will usually prioritise offensive player but that's due to FM rating system.
Yarema said: Yes but the model was working with adjusted numbers. Let me give an example. Average JR in the league is 12, a striker has 17. The way you explained stuff in the original post and later on basically says you take 17-12=5 so you do the calculations with 5. And isn't it possible that quadratic fits well for adjusted number (5) but not the actual number 17? Squaring numbers like 5 is a lot different that 17. Above 8 it basically dominates every other attribute basically double dipping (or triple since it's also the best attribute for a striker anyway).
Edit: Or to put it another way. Comparing JR contribution in above scenario if striker has 17 or 18 JR. Taking numbers at face up value you'd get roughly 1,27 vs 1,40. So a difference of 0,13 which would roughly be the effect of +6 pace. Using adjusted numbers of 5 and 6 makes the scores 0,49 and 0,55 respectively, a difference of 0,06 so half of the previous difference. Still equals +3 pace which feels a lot. Expand
Ok. I get your point. You are right. In such case you can exclude feautres that are quadratic. As said in the topic the results are shown related to the average attributes in a league and in perfect scenario they should be applie that way. My proposal was to simplyfy players comparison. I'll try to figure something out for this scenario. I might even just run the model for those 2 positions again and allow only 'single' feautres since those are majority of the results anyway.
They used different methodology. Keep in mind that for a striker coefficient for Jumping Reach is ~0,024 while for Pace it's ~0,021 so high pace is 'almost' the same 'good' as high Jumping Reach but having both high is even better. They use very different approach to the testing. If they proved that Jumping Reach above 17 doesn't give significant increase in the outcome so it be. If you want to be precise in model results interpretation it basically says that every single point of Jumping Reach higher than the average for a certain league gives a striker +0,024768 for his average rating. So this +3 point difference (20 compared to 17) gives around +0,074 to average rating. If in reality it doesn't it's not much off. Especially if we consider that it represents features mostly in linear way. I was really surprised that basically a linear regression model was able to be that accurate in this particular application.
Of course it's possible. I'd just have to generate model that have all the attributes.
I choose to use only 8 to make it more usable. With 8 attributes you can even make yourself a simple spreadsheet to compare players.
With 30-40-50 features a lot of them will literally have coefficients like 0,0001 so they won't bring much to the table.
The question is what attributes you'll need. Only the visible ones? By visible I mean those from default player profile page. Because I extracted from the game around 50 features including attributes, hidden attributes, height, weight, player's league rank and foot (left/right) proficiency.
That's exactly how the model works. If we wanted to use it 'properly' we should check the average value for given attributes in the league you're in. And then for the attribute value (the one you then multiply by coefficient) you should use the difference between players actual attribute value minus league average.
We use 'straightforwardly' just the attribute value to speed up things. Of course in general it induce the error but since we are comparing players that will play within the same league (all the players that will play for us) it's just much simpler and we're saying that we're ok with the possible error.
At the end of the day it's still better than rely on attributes that the game suggests.
I see that, just like in my save, DMs tend to have lower ratings than the rest of the team.
I thought that maybe my own DM is just not that good since his average rating was below 7 but on the other hand the others had worse average on that position.
Damn. 48 goals is really like a cheat code. Can you show this striker attributes?
For example I have 3 pacy CDs with great jumping reach and well who would have guessed that, they would also rank as 1, 2 and 4 in my strikers lineup.
So I think this weights give us great understanding what stats are most valuable, but only on very general scale. I for example don't believe that FB(att) will benefit from those 8 attributes the same way as IFB(Def). Or similar example wide playmaker VS inside forward.
Maybe the think that is missing in all these test is some atribute variability within the team. Like sure jumping reach is OP, but maybe it's enough to have 3-4 really good players in that and the team would benefit more if other players have better passing, or dribbling or whatever.
As said before, If I was able to filter players by their most played position and role I'd do it in a heartbeat. Sadly I don't know if there is even a possibility to extract such data in any useful way.
I also discovered in my vanilla Athletic Bilbao save that players on Basconia still developed as though they were playing matches despite being in a non-playable league. Granted, I was only developing physicals using the optimized training schedules, but still. I saw +3 ACC +4 PAC on one of the players in a single season.
Keep in mind that some B teams/Youth teams playing in non-playable league have this weird situation - I don't know if it's still present but it was like that in previous FMs. Weird situation is that the team does not have any fixtures in the schedule but when you check players they will have 'league games' in their stats (on the bottom of players page) despite no visible fixtures on team schedule.
I found about that when I was wondering why despite having this option 'arrange friendlies in a week with no matches' my assman didn't arrange any games so I did it manually just to find out that the players were playing those friendlies (arranged by me) and some 'invisible' league games. Hence why assman didn't arrange friendlies - they had arranged some games that I just couldn't see in the schedule.
According to this model it's almost equally important as Pace but yes. These are 2 most important attributes for a striker.
Yarema said: It doesn't differentiate by role
This. It's just for position no matter the role. If I could somehow filter out players by their most played position and role I'd do it in a heartbeat.
Some B teams play in non-playable leagues or worse not league at all. Since league reputation does not matter could you check if playing friendlies vs playing 'competitive' matches matters?
Asking because otherwise we could just leave all youth players in B team and ask staff to arrange friendlies once a week and we're done. No need for loans any more.
High temperament negatively affects players performance.
Source
Results:
STC
Attribute Coefficient
Jum 0,020557
Pac 0,020096
Acc 0,014496
Cnt 0,013675
Bal 0,012043
Dri 0,010739
Vis 0,009392
Cmp 0,009161
MLR
Attribute Coefficient
Pac 0,020497
Dri 0,014018
Acc 0,012883
Cmp 0,012072
Vis 0,011542
Jum 0,011150
Cro 0,010598
Sta 0,009658
I'll update first post with the new values.
I think this will save us a lot of confusion.
I just find it hard to wrap my mind around the fact that this is currently the best Left back I can have (playing as Malmö 3rd season)
Looks really amazing to be honest. He will be able to bully any winger just by raw physicality.
I was saying for quite long time that for almost any case the attributes priority is Physical > Mental > Technical.
Here's how it works:
First, you need to import specified views. I created two sets of these: one for your team (for squad view) and one for scouting tab - both with CA & PA hidden - and another set with CA & PA visible for those who like to spoil the fun a little bit. Download and paste them into "views" folder in your Documents (C:\Users\your_name\Documents\Sports Interactive\Football Manager 2024\views is the default path).
When you load the view, you need to select every player, so click on one player and than ctrl+a to select everyone in the team or in the scouting range. Just bear in mind the more players you select, the more time it takes, so if you're about to select over 1000 players, give it a few seconds to work.
Then, press cltr+p to "print" the selection into HTML file. Save it wherever you want, name it whatever you want.
Then, you need the spreadsheet (MS Excel file). Open it and then in the Excel go to to File->Open and select the html file with your set of players. Copy it's whole contents (ctrl+a, then ctrl+c) and paste them into my spreadsheet in the blank sheet called "IMPORT" then switch the sheet to the one called "CALCULATION" and it should automatically calculate values for every player for each position using coefficients from this thread. Additionalli I added sections with CA, PA and difference between them (it will work only when you used views with PA and CA obviously).
Of course you can use whatever filters you want on the scouting section to narrow down the amount of players to whatever you really need.
At first glance it may sound complicated a bit but it's pretty easy to use. If you find any trouble using it, I'll try to help.
Spreadsheet link: https://www.mediafire.com/file/huj2qrmavoqnd6x/meta.xlsx/file
The spreadsheet is editable so do whatever you want with it, if you find any room for improvements, go for it.
Bafici said: I made a genie scout file. Biggest difference from fmarena ratings is this is bit more realistic. Great players in real life takes higher ratings.
Ratings
Just to keep you guys up to date I've decided to make a small adjustment for the features that use two attributes - so the last one for MLR and the last one for Striker. I've decided to take square of a product from multiplication those features (SQR(Attribute1 * Attribute2) * Coefficient). This way you can keep it proportional to the coefficient.
I don't know how it works with your files. I assume that for spreadsheet it's not an issue.
What about GenieScout? Does it support other mathematical operations? If not I'd suggest just to get rid of those 2 features (since they are last important for given positions) and adjust others to sum up to 100% just like you did for the other positions.
Edit: Or to put it another way. Comparing JR contribution in above scenario if striker has 17 or 18 JR. Taking numbers at face up value you'd get roughly 1,27 vs 1,40. So a difference of 0,13 which would roughly be the effect of +6 pace.
Using adjusted numbers of 5 and 6 makes the scores 0,49 and 0,55 respectively, a difference of 0,06 so half of the previous difference. Still equals +3 pace which feels a lot.
For features using multiple attributes my suggestion is to multiply them and then take a square before multiplying by coefficient. So when you have for MLR Acc, Agi and coeff 0,003329 do:
SQR(Acc * Agi) * Coeff
This way you can balance the impact of this double attribute multiplication and you keep the 'partial score' properly scaled so for example when I tried said MLR using the same attribute value for all features, the 'partial score' for the double attribute feature stayed proportional to it's coefficient compared to other features.
Thanks. I really looks on the first glance that Konate should be far superior to the Chermiti. Like really the only thing that Chermiti has better than Konate is solely jumping reach and nothing else.
These ratings have worked well for me too, though I find the high Jumping Reach requirement for full-backs a bit off-putting. Just finished my first season with Schalke and comfortably won the 2. Bundesliga—curious to see if I can win the top division as well. In a previous save using FM-Arena ratings, I nearly won La Liga with Alavés in the first season, finishing just one point behind a dominant Real Madrid, who only lost once.
One of my wild guesses behind Jumping Reach being high rated attributed for other positions, beside obviously scoring a lot from set pieces, was that it has high correlation with Strength. So player with high jumping reach usually have also high height, high weight and high Strength. So maybe strength also plays part in it because player with high strength will be able to just push opposition player and get the ball back.
Just out of curiosity can you post the screenshot of attributes for both player?
Just to clarify. So at first you used GenieScout default 'rating' and then one from my model?
That got me three promotions and then three years title wins on the top league (Japan)
I'm currently in Korea and with the weighting based on Orions approach, I'm still winning at the same rate but the football seems better to watch and average player ratings are higher. I'm claiming more player & goals of the month.
In general, my players are slower than than what I would sign on my previous set up but using harvestgreen22 training, I could build up pace and acceleration over time if needed.
One thing that does feel off is the jumping reach ratings. I didn't understand the calculations at first so I'm obviously not an expert on any of this but my suspicions are you need one or two players with high ratings there to score from set pieces and it's those goals that boost the average rating rating - potentially any position could have a high attribute.
It might be along the same line of high free kicks/corners/penalty taking having an impact, but that's purely guesswork on my part
Good to hear that it works for you. I'm really looking forward to feedback since as said, the model is one thing, we know how it works and in theory I can calculate the average error for the model but knowing that it actually works in 'real life' is something different.
In terms of rating - FM Arena test is based on team Points. harvestgreen22 test is using goal difference. And my model is using players rating as a target variable. Hence my model will look for attributes that increase rating, but not team performance per se. We know that in general average rating will be somehow connected with team results. And I know about a flaw of this model that since it looks for players for high rating it will usually prioritise offensive player but that's due to FM rating system.
Edit: Or to put it another way. Comparing JR contribution in above scenario if striker has 17 or 18 JR. Taking numbers at face up value you'd get roughly 1,27 vs 1,40. So a difference of 0,13 which would roughly be the effect of +6 pace.
Using adjusted numbers of 5 and 6 makes the scores 0,49 and 0,55 respectively, a difference of 0,06 so half of the previous difference. Still equals +3 pace which feels a lot.
Ok. I get your point. You are right. In such case you can exclude feautres that are quadratic. As said in the topic the results are shown related to the average attributes in a league and in perfect scenario they should be applie that way. My proposal was to simplyfy players comparison.
I'll try to figure something out for this scenario. I might even just run the model for those 2 positions again and allow only 'single' feautres since those are majority of the results anyway.
Looks like jumping reach is most import attr for a striker, but how does this work when this thread (https://fm-arena.com/thread/14009-attribute-testing-football-manager-24/) indicates it's capped out at 17?
They used different methodology. Keep in mind that for a striker coefficient for Jumping Reach is ~0,024 while for Pace it's ~0,021 so high pace is 'almost' the same 'good' as high Jumping Reach but having both high is even better.
They use very different approach to the testing. If they proved that Jumping Reach above 17 doesn't give significant increase in the outcome so it be.
If you want to be precise in model results interpretation it basically says that every single point of Jumping Reach higher than the average for a certain league gives a striker +0,024768 for his average rating. So this +3 point difference (20 compared to 17) gives around +0,074 to average rating. If in reality it doesn't it's not much off. Especially if we consider that it represents features mostly in linear way.
I was really surprised that basically a linear regression model was able to be that accurate in this particular application.