Orion said: Keep in mind that this is for difference between actual attribute value and the average for the league. So for example if average finishing was 10, the higher than that would result in -0.000456 per every finishing point. So if the average was 10 and players value was 20 it still results in lowering average rating by 0,00456. So for features with coefficients this small you can basically say that they just don't make any difference. Finishing having negative coefficient this low could be possibly just 0. Simply for such numerical models it's very unlikely to have coefficient equal exactly 0. Tl;dr coefficients this low are basically noise and they don't contribute either way into target variable. Expand
Yes, the underlying assessment that for strikers (the one position where the attribute would be essential to successfully accomplish the main task of the position), having above average finishing is does not make any meaningful difference in average rating (knowing that scoring goals is practically the one certain and main way of substantially increasing the average rating for strikers) is mind boggling.
This one experiment was also conducted with the same underlying parameters (A very significant amount of leagues and divisions, running for 11 seasons)?
Orion said: This is the result for linear model for STC in FM23 that uses all the features. Where would you put the line which features are still important and which are not? Expand
This can't be right...
So according to your results, STC having higher Finishing equals having worse average rating?
FREVKY said: I believe there is a typo in DC coefficient:
It should be 0,015882, right?
Also you said:
But I can see that crossing is still a valid coefficient in your lists on a number of positions. Did you mean corners by any chance? Expand
Both correct. He has sorted attributes by highest to lowest, and the only one that is a massive outlier and is not sorted correctly. In regards to Crossing, several positions have Crossing in their most important attributes, so it's definitely Corners.
Jolt said: That is extremely surprising. So every combination tried merely increases cumulatively with the increase of the combined chosen attributes, with no statistical difference from the sum of the chosen attributes?
EDIT: So what does testing of a 20 Pace and 20 Acceleration look like, in terms of points, goals for and goals against, as opposed to a 20 Pace 10 Acceleration, or a 10 Pace 20 Acceleration?
EDIT 2: Or perhaps even a more clear example of synergy: "Goals for" with both Crossing and Heading in 20. For an attacking team to fully exploit high heading attributes, accurate crossings with high Crossing attributes generally means higher opportunities of goal scoring, as opposed great crosses to mediocre heading players or mediocre crosses that don't frequently reach great heading players. I wonder what's the testing difference specifically goals for, between 20 Crossing 20 Heading, and 20 Crossing 10 Heading, and 10 Crossing 20 Heading. Expand
What's the practical difference in points, Goals For, Goals Against, between the current tests where only one attribute gets raised, and when two with theorerical synergies get raised (Pace+Acceleration; Crossing+Heading; or others such as Heading+Jumping Reach). I'm curious to understand in practical terms what "there's no difference" means.
Zippo said: We've tired testing attributes in combinations, assuming that one attribute might somehow amplify another, but we found out that wasn't the case so there's no point testing attribute combinations. Expand
That is extremely surprising. So every combination tried merely increases cumulatively with the increase of the combined chosen attributes, with no statistical difference from the sum of the chosen attributes?
EDIT: So what does testing of a 20 Pace and 20 Acceleration look like, in terms of points, goals for and goals against, as opposed to a 20 Pace 10 Acceleration, or a 10 Pace 20 Acceleration?
EDIT 2: Or perhaps even a more clear example of synergy: "Goals for" with both Crossing and Heading in 20. For an attacking team to fully exploit high heading attributes, accurate crossings with high Crossing attributes generally means higher opportunities of goal scoring, as opposed great crosses to mediocre heading players or mediocre crosses that don't frequently reach great heading players. I wonder what's the testing difference specifically goals for, between 20 Crossing 20 Heading, and 20 Crossing 10 Heading, and 10 Crossing 20 Heading.
Ultimately the major thing that matters from a developmental point of view is professionalism. Everything else is of secondary importance.
1. We know that the ideal numerical setup is one mentor to two mentees. 2. We know (roughly) the factors that make someone have more or less influence in the group. 3. We know which personalities have professionalism thresholds.
As such, this is a game of pairing up comparatively high professionalism established (playtime and age wise) players, with comparatively lower professionalism young players.
Because of the random nature of what goes up and down and the many attributes that can go up or down (or favoured moves gained or lost), depending on the differences between the mentor and the two mentees, mentoring shouldn't really be used for anything else, on the risks of backfiring and making the youngsters worse.
Summary: 7. Under Control variable, From 1, 2, and 3, you can see, Even in the multivariable case, considering multiple technical type of attributes,
technical type of attributes 15 + Technique 10 (73.7) > technical type of attributes 15+ Technique 15 (60.3) ，This means that in this case, Technique is bad technical type of attributes 15 + Flair 10 (73.7) ≈/> technical type of attributes 15 + Flair 10 (71.7) ，This means that in this case, Flair have little/no influence
8. In the case of 4, 5, and 6, it's a little special, Dribbling 20 + Technique 20 (44.2) < Dribbling 20 + Technique 10 (53.6) Higher Technique is having a negative effect with Dribbling This means that in this case, Technique is bad
Passing 20 + Technique 20 (27.9) ≈/> Passing 20 + Technique 10 (24.5) Higher Technique may or may not have a positive effect with Passing Not much difference. It is also possible that the sample size is not large enough and resulting in random error
Finishing 20 + Technique 20 (33.7) ≈ Finishing 20 + Technique 10 (33.3) Higher Technique had no effect on Finishing passing
9. What is surprising is number 5, which means that it is possible for Technique to have some positive Stats and negative effects on others Expand
That does help understanding that Technique and Flair are bad in general, or with most attributes.
But because it is not an extensive test with all other attributes, we don't know if there's an attribute that will unlock great potential in Technique and Flair.
My suggestion is the same as my previous post here: Test all 3 attribute combinations with a small sample size. There are 36 player attributes. That should be 648 separate tests. Then averaging the results for every 2 attribute combination, we can also see how good each 2 attribute combination is.
some comments said that the performance of attributes is A multi-variable problem, for example, the A attribute is evaluated at the same time as the B attribute.
Some people will think that testing a player who First touch 10 and Finishing 15, because the player can't stop the ball and it doesn't matter how high the Finishing is, so this test is wrong.
However, if an attribute is weighted with one or more other attributes, all associated attributes must be found. In the end all testing is either pointless or too tedious to do. Since it is statistically possible to tell the difference, let's use this result as a conclusion
Now, imagine a mathematical problem. Player A's Composure is 10, provides 20% of the goal scoring ability. Finishing 10, providing 10 accuracy shots on goal per game. The end result is 10x20%=2 balls.
If this is a multivariate problem, then Composure 20, assuming it provides 50% of the goal scoring ability. The end result is 10x50%=5 balls. So 2 becomes 5. The difference "5-2=3" may or may not be obvious. Those that are not obvious are actually the effect of interaction is not good, since it is so small, it can simply be ignored. If there is an effect, it must be reflected in the statistical results.
Unless it is using some more complex mechanism, for example, it detects composure 20, and if the shot is not ≥15, the goal scoring ability % does not increase. I'm not going to consider that possibility. Expand
But that is the flaw with these tests. If only the attribute 'Off the ball' or 'Flair' is increased, but the player doesn't have the physical or technical attributes to take advantage of the 'Off the ball' or 'Flair', of course it is going to look like a bad attribute. If these attributes are multiplicative of other attributes, and the other attributes are low, then of course these multiplicative attributes will look bad.
Your finishing & composure attributes example is not good. The truth is we don't know how much impact those two attributes have together, to say if it makes no difference, little difference, or a lot of difference.
The only way of making it clear is tedious work: You (or someone else) needs to run tests in groups of three attributes, raising them or lowering them together. This way, we'll gain insight into every 3 attribute combination, and by doing the average of every 3 attribute combination that have the same 2 attributes, we can also gain insight into which 2 attribute combination is good.
If you are correct, then the 3 attribute combination of 'Acceleration'+'Pace'+'Jumping Reach' should perform much better than any other combination, including 'Acceleration'+'Pace'+'Strength', or 'Passing'+'Decisions'+'Work Rate'.
Zippo said: Ohh... it's that thing. Ok, I'll check whether it can be tested or not. Expand
Pick a middle table team with average CA for the league, that doesn't have continental football. Freeze transfers, injuries and morale but nothing else.
Run 20 seasons in low, 20 in medium and 20 in high bonuses.
Compare league and cup results. If they are roughly the same, do another 20 seasons each run, but with only injuries and transfers frozen, but let morale fluctuate.
That should indicate: 1. Whether the boost is solely morale based (if it is, freezing morale should make the first results be roughly the same regardless of the season bonus, if there are other benefits, we should see a reflection of that). 2. How powerful the boost is, if at all (if the benefit is minimal, it will make sense to save up on the money).
It would be amusing to create a fantasy league with 20 teams, where each team can only use one of the top 20 tactics, and would pit the tactics ones against the others (each team named after the tactic it will apply). Each team would have exactly the same players, reputation, etc.
And then we would see who is the tactic that would manage to finish on top of all other tactics.
Tl;dr coefficients this low are basically noise and they don't contribute either way into target variable.
Yes, the underlying assessment that for strikers (the one position where the attribute would be essential to successfully accomplish the main task of the position), having above average finishing is does not make any meaningful difference in average rating (knowing that scoring goals is practically the one certain and main way of substantially increasing the average rating for strikers) is mind boggling.
This one experiment was also conducted with the same underlying parameters (A very significant amount of leagues and divisions, running for 11 seasons)?
This can't be right...
So according to your results, STC having higher Finishing equals having worse average rating?
It should be 0,015882, right?
Also you said:
But I can see that crossing is still a valid coefficient in your lists on a number of positions. Did you mean corners by any chance?
Both correct. He has sorted attributes by highest to lowest, and the only one that is a massive outlier and is not sorted correctly. In regards to Crossing, several positions have Crossing in their most important attributes, so it's definitely Corners.
EDIT: So what does testing of a 20 Pace and 20 Acceleration look like, in terms of points, goals for and goals against, as opposed to a 20 Pace 10 Acceleration, or a 10 Pace 20 Acceleration?
EDIT 2: Or perhaps even a more clear example of synergy: "Goals for" with both Crossing and Heading in 20. For an attacking team to fully exploit high heading attributes, accurate crossings with high Crossing attributes generally means higher opportunities of goal scoring, as opposed great crosses to mediocre heading players or mediocre crosses that don't frequently reach great heading players. I wonder what's the testing difference specifically goals for, between 20 Crossing 20 Heading, and 20 Crossing 10 Heading, and 10 Crossing 20 Heading.
@Zippo Could you clarify this?
What's the practical difference in points, Goals For, Goals Against, between the current tests where only one attribute gets raised, and when two with theorerical synergies get raised (Pace+Acceleration; Crossing+Heading; or others such as Heading+Jumping Reach). I'm curious to understand in practical terms what "there's no difference" means.
That is extremely surprising. So every combination tried merely increases cumulatively with the increase of the combined chosen attributes, with no statistical difference from the sum of the chosen attributes?
EDIT: So what does testing of a 20 Pace and 20 Acceleration look like, in terms of points, goals for and goals against, as opposed to a 20 Pace 10 Acceleration, or a 10 Pace 20 Acceleration?
EDIT 2: Or perhaps even a more clear example of synergy: "Goals for" with both Crossing and Heading in 20. For an attacking team to fully exploit high heading attributes, accurate crossings with high Crossing attributes generally means higher opportunities of goal scoring, as opposed great crosses to mediocre heading players or mediocre crosses that don't frequently reach great heading players. I wonder what's the testing difference specifically goals for, between 20 Crossing 20 Heading, and 20 Crossing 10 Heading, and 10 Crossing 20 Heading.
1. We know that the ideal numerical setup is one mentor to two mentees.
2. We know (roughly) the factors that make someone have more or less influence in the group.
3. We know which personalities have professionalism thresholds.
As such, this is a game of pairing up comparatively high professionalism established (playtime and age wise) players, with comparatively lower professionalism young players.
Because of the random nature of what goes up and down and the many attributes that can go up or down (or favoured moves gained or lost), depending on the differences between the mentor and the two mentees, mentoring shouldn't really be used for anything else, on the risks of backfiring and making the youngsters worse.
1.
Passing传球15
Crossing传中15
Dribbling盘带15
Tackling抢断15
Finishing射门15
First touch停球15
Heading头球15
longshot远射15
Technique技术10
Flair才华10
other其他属性10
Goal difference 净胜球≈73.7
2.
Passing传球15
Crossing传中15
Dribbling盘带15
Tackling抢断15
Finishing射门15
First touch停球15
Heading头球15
longshot远射15
Technique技术15
Flair才华10
other其他属性10
Goal difference 净胜球≈60.3
3.
Passing传球15
Crossing传中15
Dribbling盘带15
Tackling抢断15
Finishing射门15
First touch停球15
Heading头球15
longshot远射15
Technique技术10
Flair才华15
other其他属性10
Goal difference 净胜球≈71.7
4.
Dribbling盘带20
Technique技术20
other其他属性10
Goal difference 净胜球≈44.2
Dribbling盘带20
Technique技术10
other其他属性10
Goal difference 净胜球=53.6
5.
Passing传球20
Technique技术20
other其他属性10
Goal difference 净胜球≈27.9
Passing传球20
Technique技术10
other其他属性10
Goal difference 净胜球=24.5
6.
Finishing传球20
Technique技术20
other其他属性10
Goal difference 净胜球≈33.7
Finishing传球20
Technique技术10
other其他属性10
Goal difference 净胜球=33.3
Summary:
7.
Under Control variable,
From 1, 2, and 3, you can see,
Even in the multivariable case, considering multiple technical type of attributes,
technical type of attributes 15 + Technique 10 (73.7) > technical type of attributes 15+ Technique 15 (60.3) ，This means that in this case, Technique is bad
technical type of attributes 15 + Flair 10 (73.7) ≈/> technical type of attributes 15 + Flair 10 (71.7) ，This means that in this case, Flair have little/no influence
8.
In the case of 4, 5, and 6, it's a little special,
Dribbling 20 + Technique 20 (44.2) < Dribbling 20 + Technique 10 (53.6)
Higher Technique is having a negative effect with Dribbling
This means that in this case, Technique is bad
Passing 20 + Technique 20 (27.9) ≈/> Passing 20 + Technique 10 (24.5)
Higher Technique may or may not have a positive effect with Passing
Not much difference.
It is also possible that the sample size is not large enough and resulting in random error
Finishing 20 + Technique 20 (33.7) ≈ Finishing 20 + Technique 10 (33.3)
Higher Technique had no effect on Finishing passing
9.
What is surprising is number 5,
which means that it is possible for Technique to have some positive Stats and negative effects on others
That does help understanding that Technique and Flair are bad in general, or with most attributes.
But because it is not an extensive test with all other attributes, we don't know if there's an attribute that will unlock great potential in Technique and Flair.
My suggestion is the same as my previous post here: Test all 3 attribute combinations with a small sample size. There are 36 player attributes. That should be 648 separate tests. Then averaging the results for every 2 attribute combination, we can also see how good each 2 attribute combination is.
some comments said that the performance of attributes is A multi-variable problem, for example, the A attribute is evaluated at the same time as the B attribute.
Some people will think that testing a player who First touch 10 and Finishing 15, because the player can't stop the ball and it doesn't matter how high the Finishing is, so this test is wrong.
However, if an attribute is weighted with one or more other attributes, all associated attributes must be found. In the end all testing is either pointless or too tedious to do.
Since it is statistically possible to tell the difference, let's use this result as a conclusion
Now, imagine a mathematical problem. Player A's Composure is 10, provides 20% of the goal scoring ability. Finishing 10, providing 10 accuracy shots on goal per game. The end result is 10x20%=2 balls.
If this is a multivariate problem, then Composure 20, assuming it provides 50% of the goal scoring ability. The end result is 10x50%=5 balls. So 2 becomes 5. The difference "5-2=3" may or may not be obvious.
Those that are not obvious are actually the effect of interaction is not good, since it is so small, it can simply be ignored.
If there is an effect, it must be reflected in the statistical results.
Unless it is using some more complex mechanism, for example, it detects composure 20, and if the shot is not ≥15, the goal scoring ability % does not increase. I'm not going to consider that possibility.
But that is the flaw with these tests. If only the attribute 'Off the ball' or 'Flair' is increased, but the player doesn't have the physical or technical attributes to take advantage of the 'Off the ball' or 'Flair', of course it is going to look like a bad attribute. If these attributes are multiplicative of other attributes, and the other attributes are low, then of course these multiplicative attributes will look bad.
Your finishing & composure attributes example is not good. The truth is we don't know how much impact those two attributes have together, to say if it makes no difference, little difference, or a lot of difference.
The only way of making it clear is tedious work: You (or someone else) needs to run tests in groups of three attributes, raising them or lowering them together. This way, we'll gain insight into every 3 attribute combination, and by doing the average of every 3 attribute combination that have the same 2 attributes, we can also gain insight into which 2 attribute combination is good.
If you are correct, then the 3 attribute combination of 'Acceleration'+'Pace'+'Jumping Reach' should perform much better than any other combination, including 'Acceleration'+'Pace'+'Strength', or 'Passing'+'Decisions'+'Work Rate'.
Pick a middle table team with average CA for the league, that doesn't have continental football. Freeze transfers, injuries and morale but nothing else.
Run 20 seasons in low, 20 in medium and 20 in high bonuses.
Compare league and cup results. If they are roughly the same, do another 20 seasons each run, but with only injuries and transfers frozen, but let morale fluctuate.
That should indicate:
1. Whether the boost is solely morale based (if it is, freezing morale should make the first results be roughly the same regardless of the season bonus, if there are other benefits, we should see a reflection of that).
2. How powerful the boost is, if at all (if the benefit is minimal, it will make sense to save up on the money).
And then we would see who is the tactic that would manage to finish on top of all other tactics.
First season with Porto, not using that tactic:
Second season with Porto, first time using the tactic, first time I finish the season undefeated (lost the CL in the Quarters):
Current season with Porto, second season using just the tactic:
It's been great so far. I'd like to see how the tactic fares against the others.
https://community.sigames.com/topic/451185-fm-20-tactic-list-and-fm-19-recommended-tactic-list/page/143/?tab=comments#comment-12286362