Hello everyone. We're back again with a new experiment regarding most important attributes for each respective position in FM24. It's based on the same methodology as in these two topics regarding Outfield players in FM23 and Goalkeepers in FM24. The difference is this time we'll get most important attributes for all positions in the game and their coefficients.
Game setup and testing environment
Game setup and testing environment is exactly the same as in the Goalkeepers in FM24 topic. Just this time we've selected only the players for each respective position.
Results
Instead of using all 3 models, due to their similarities, I decided to use only polynomial regression to get 8 key attributes for each position. The number of attributes was selected arbitrary so the number is not too high, since most of attributes will have very minor coefficients, and not to small, so we have more attributes to compare our players.
The attributes that were excluded from the model are - Corners, Free Kick Taking, Penalty Taking, Long Throws. It was decided due to those attributes affecting the outcome. Penalty takers, no matter the position, are expected to get higher ratings due to their goalscoring potential. The same applies to other set pieces that allow players to get more goals or assist, hence higher ratings, no matter their respective position.
The 'side' positions, like ML and MR were merged since the side aspect should not affect player ratings.
The results are listed below:
GK Attribute Coefficient Agi 0,014640 Ref 0,012837 Aer 0,011812 Thr 0,007465 Com 0,007436 Han 0,006255 Dec 0,005089 TRO -0,003310
STC OLD VALUES Attribute Coefficient Jum 0,024768 Pac 0,021030 Acc 0,015754 Cnt 0,014398 Dri 0,012353 Vis 0,011814 Bal 0,010338 Jum^ 0,002953
STC NEW VALUES 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
How to use it?
This attributes coefficients regards difference between players actual attributes and their league average - but since we are comparing players that will potentially play in the same league we don't have to care about it. The results will be a little bit skewed towards attributes with higher coefficients but for our purpose it's acceptable.
Take any position - for example GK. Take given attribute value and multiply it by coefficient - for example 10 Agi * 0,014640 = 0,14640. Do the same for all other attributes listed. Sum the results. This is your player 'score'. You can use your existing player as a benchmark. Look for other players in your scouting view and calculate score for every one of them. Pick a player that has the highest score.
Disclaimer
If there are 2 attributes listed, like for example 'Acc Agi' for the MLR multiply those by themselves, then square it and then use coefficient ( SQR(Acc * Agi) * Coeff). Jum^ is the power of two (so SQR(Jum * Jum) * Coeff).
Attributes names are corresponding with their English naming.
Pos_x is Positioning.
TRO is 'Rushing out (Tendency) - it's just how FM names that attribute when exporting.
Example of usage
I've use this method recently to find a replacement for the goalkeeper that wanted to leave. I've found one that had little bit higher 'score' than my current goalkeeper so in theory should be proper replacement. This is the result.
Old goalkeeper that I had to sell at the beginning of the season:
And his replacement results
Keep in mind that we had a recent promotion so previous goalkeeper rating of 6,91 was in 'weaker' environment while our current goalkeeper rating of 6,95 is in higher division.
I hope you had a good reading and will find this method useful for finding a proper replacements for your other players. [edit]
Just out of curiosity - since I have quite limited time to play the game - if you use this method please share some results if you'll find them interesting - not matter if it proves the model or negates it. Feedback about model not being right is also important for model evaluation.
[edit2]
Recalculated results for STC and MLR to use only linear coefficients to remove the issue with double attributes being used in the model.
[edit3]
NEW MATCH ENGINES UPDATE!
I've made similar simulations for the new match engines. Method was the same like for the original model. 11 season were simulated for each match engine using exactly the same setup as original project - it was actually simulating further in the game (from year 2033 to 2044). Methodology is also the same. I calculated 8 attributes for each position with their respective coefficients ranked descending. The list of engines and their respective results are listed below:
AMC Attribute Coefficient Pac 0,019289 Acc 0,018151 Ant 0,015457 Tec 0,015131 Pas 0,012255 Cnt 0,009733 Lon 0,009488 Jum 0,007665
STC Attribute Coefficient Pac 0,021277 Ant 0,013547 Jum 0,013172 Agi 0,011111 Dri 0,010546 Cro 0,009884 Cnt 0,009881 Bal 0,007499
ALL Attribute Coefficient Agi 0,015292 Acc 0,014105 Vis 0,012979 Jum 0,010721 Pas 0,009205 Dri 0,007296 Lon 0,007237 Fla 0,005854
FM Genie Scout
I've also prepared Genie Scout ratings for all 3 models (Google Drive Link). Disclaimer: - negative attributes like Rushing Out (Tendency) are not included since Genie Scout does not support negative coefficients - GK is GK, there is no Sweeper - MLR is Winger - AMLR is Fast Striker - STC is Target Striker - ALL is not included
I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME. Could you maybe check again to see if there's a mistake in there?
Thank you very much. I am very successful with this tactic! Where would you place your striker with a good jumping reach? Probably AF, right? The PF seems to be more of a F9 or playmaker, right?
ZaZ said: You can probably normalize by multiplying the weight of all attributes for some X factor. I'll give an example using the case @lordus used: "For example, if I create a player with 20 in all attributes, a winger gets 120% and a FS gets 109%."
In this case, X factor would be 1.0 for winger and 0.90 for fast striker (120% is 1.0, 109% is X, basic rule of three). Then you multiply all weights from winger by 0.9, and they will now have a normalized rating. You can do the same to all roles, based on the position with lowest rating (since all others will be reduced to reach similar level). Expand
I am new to this... What exactly does the weight coefficient do in Genie Scout? The values there are exactly the values you get when generating a player with 20 in all attributes for a specific position. For example FS is 109% and the weight in Genie Scout is 109 (based on "ykykyk balanced" ).
First of all good work! I love meta-analyses like this! If you compare within a position, that's great! But if I want to find out which position a youth player should play in the future, the result is usually Winger or FB. For example, if I create a player with 20 in all attributes, a winger gets 120% and a FS gets 109%. Or do I just normalize the result to 100%? Is it even possible to decide in this way which position is the best for a player?
Hello everyone. We're back again with a new experiment regarding most important attributes for each respective position in FM24. It's based on the same methodology as in these two topics regarding Outfield players in FM23 and Goalkeepers in FM24. The difference is this time we'll get most important attributes for all positions in the game and their coefficients.
Game setup and testing environment
Game setup and testing environment is exactly the same as in the Goalkeepers in FM24 topic. Just this time we've selected only the players for each respective position.
Results
Instead of using all 3 models, due to their similarities, I decided to use only polynomial regression to get 8 key attributes for each position. The number of attributes was selected arbitrary so the number is not too high, since most of attributes will have very minor coefficients, and not to small, so we have more attributes to compare our players.
The attributes that were excluded from the model are - Corners, Free Kick Taking, Penalty Taking, Long Throws. It was decided due to those attributes affecting the outcome. Penalty takers, no matter the position, are expected to get higher ratings due to their goalscoring potential. The same applies to other set pieces that allow players to get more goals or assist, hence higher ratings, no matter their respective position.
The 'side' positions, like ML and MR were merged since the side aspect should not affect player ratings.
The results are listed below:
GK
Attribute Coefficient
Agi 0,014640
Ref 0,012837
Aer 0,011812
Thr 0,007465
Com 0,007436
Han 0,006255
Dec 0,005089
TRO -0,003310
DLR
Attribute Coefficient
Pac 0,018991
Jum 0,014582
Acc 0,012670
Ant 0,012269
Cnt 0,009911
Dri 0,008246
Cmp 0,007720
Cro 0,006812
DC
Attribute Coefficient
Jum 0,022608
Pac 0,0158820
Acc 0,013536
Wor 0,010819
Ant 0,010326
Pos_x 0,008864
Pas 0,008826
Cnt 0,008358
WBLR
Attribute Coefficient
Pac 0,019196
Acc 0,018585
Jum 0,013761
Cmp 0,011762
Vis 0,010025
Cro 0,009596
Wor 0,008166
Det 0,005121
DMC
Attribute Coefficient
Acc 0,020572
Ant 0,013047
Sta 0,010352
Jum 0,009796
Cmp 0,009470
Pas 0,009114
Lon 0,007952
Dri 0,007338
MLR OLD VALUES
Attribute Coefficient
Pac 0,022852
Acc 0,018679
Dri 0,016960
Tec 0,013665
Jum 0,010993
Vis 0,010804
Cnt 0,009576
Acc Agi 0,003329
MLR NEW VALUES
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
MC
Attribute Coefficient
Ant 0,014011
Acc 0,012595
Cmp 0,012589
Pac 0,012156
Cro 0,010100
Dri 0,008134
Jum 0,007918
Str 0,006528
AMLR
Attribute Coefficient
Pac 0,023458
Acc 0,019640
Ant 0,015160
Cro 0,014857
Dri 0,013533
Jum 0,013029
Tec 0,012662
Cmp 0,012295
AMC
Attribute Coefficient
Pac 0,016763
Acc 0,016348
Cnt 0,013697
Cmp 0,012813
Tec 0,011647
Lon 0,009914
Jum 0,009524
Dri 0,008679
STC OLD VALUES
Attribute Coefficient
Jum 0,024768
Pac 0,021030
Acc 0,015754
Cnt 0,014398
Dri 0,012353
Vis 0,011814
Bal 0,010338
Jum^ 0,002953
STC NEW VALUES
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
How to use it?
This attributes coefficients regards difference between players actual attributes and their league average - but since we are comparing players that will potentially play in the same league we don't have to care about it. The results will be a little bit skewed towards attributes with higher coefficients but for our purpose it's acceptable.
Take any position - for example GK. Take given attribute value and multiply it by coefficient - for example 10 Agi * 0,014640 = 0,14640. Do the same for all other attributes listed. Sum the results. This is your player 'score'. You can use your existing player as a benchmark. Look for other players in your scouting view and calculate score for every one of them. Pick a player that has the highest score.
Disclaimer
If there are 2 attributes listed, like for example 'Acc Agi' for the MLR multiply those by themselves, then square it and then use coefficient ( SQR(Acc * Agi) * Coeff). Jum^ is the power of two (so SQR(Jum * Jum) * Coeff).
Attributes names are corresponding with their English naming.
Pos_x is Positioning.
TRO is 'Rushing out (Tendency) - it's just how FM names that attribute when exporting.
Example of usage
I've use this method recently to find a replacement for the goalkeeper that wanted to leave. I've found one that had little bit higher 'score' than my current goalkeeper so in theory should be proper replacement. This is the result.
Old goalkeeper that I had to sell at the beginning of the season:
And his replacement results
Keep in mind that we had a recent promotion so previous goalkeeper rating of 6,91 was in 'weaker' environment while our current goalkeeper rating of 6,95 is in higher division.
I hope you had a good reading and will find this method useful for finding a proper replacements for your other players.
[edit]
Just out of curiosity - since I have quite limited time to play the game - if you use this method please share some results if you'll find them interesting - not matter if it proves the model or negates it. Feedback about model not being right is also important for model evaluation.
[edit2]
Recalculated results for STC and MLR to use only linear coefficients to remove the issue with double attributes being used in the model.
[edit3]
NEW MATCH ENGINES UPDATE!
I've made similar simulations for the new match engines.
Method was the same like for the original model. 11 season were simulated for each match engine using exactly the same setup as original project - it was actually simulating further in the game (from year 2033 to 2044).
Methodology is also the same. I calculated 8 attributes for each position with their respective coefficients ranked descending.
The list of engines and their respective results are listed below:
Asian Fusion
GK
Attribute Coefficient
Agi 0,013862
Ref 0,01223
Aer 0,011153
Dec 0,007721
Com 0,006883
Thr 0,005364
TRO -0,004394
Wor 0,003918
DLR
Attribute Coefficient
Acc 0,014419
Pac 0,01425
Jum 0,012579
Ant 0,011949
Cmp 0,010953
Cnt 0,010615
Agi 0,00894
Cro 0,006053
DC
Jum 0,020336
Acc 0,013794
Cnt 0,012809
Ant 0,012786
Pac 0,011036
Agi 0,007869
Dri 0,007604
Vis 0,007175
WBLR
Pac 0,020999
Acc 0,01613
Ant 0,014263
Cmp 0,01227
Jum 0,011309
Cro 0,009785
Nat 0,00598
Bal 0,005673
DMC
Attribute Coefficient
Pac 0,015115
Acc 0,014695
Ant 0,009336
Jum 0,008994
Cnt 0,008862
Pas 0,008854
Dri 0,008205
OtB 0,007905
MLR
Attribute Coefficient
Pac 0,020733
Acc 0,017484
Ant 0,014751
Dri 0,011262
Vis 0,010851
Cro 0,009974
OtB 0,009782
Jum 0,009303
MC
Attribute Coefficient
Pac 0,015818
Acc 0,013322
Dri 0,010707
Cmp 0,010609
Cnt 0,009394
Ant 0,009217
Lon 0,008809
Jum 0,006312
AMLR
Attribute Coefficient
Pac 0,023192
Acc 0,020691
Ant 0,015043
Dri 0,013602
Cro 0,012183
Vis 0,012012
Tec 0,011933
Jum 0,011169
AMC
Attribute Coefficient
Pac 0,0202
Acc 0,014382
Cnt 0,011833
Ant 0,011825
Fla 0,011125
Dri 0,010094
Lon 0,010083
Jum 0,007762
STC
Attribute Coefficient
Pac 0,021063
Ant 0,015483
Acc 0,015151
Jum 0,014779
Fla 0,013387
Cnt 0,012599
Dri 0,01092
Cro 0,009355
ALL (model including outfield players from all positions)
Attribute Coefficient
Ant 0,014785
Pac 0,013272
Acc 0,01191
Jum 0,011831
Agi 0,009973
Lon 0,007401
Cmp 0,006749
Dri 0,006559
FMTweak
GK
Attribute Coefficient
Agi 0,012505
Aer 0,011164
Ref 0,010801
Com 0,006723
Han 0,00641
Thr 0,006367
TRO -0,006245
Pac 0,004643
DLR
Attribute Coefficient
Acc 0,013597
Jum 0,01306
Pac 0,012236
Ant 0,011565
Agi 0,009327
Fir 0,008223
Cmp 0,007745
Cnt 0,007133
DC
Attribute Coefficient
Jum 0,01907
Acc 0,015935
Agi 0,012246
Pos_x 0,010717
Cnt 0,01056
Ant 0,008858
Vis 0,008293
Fla 0,007868
WBLR
Attribute Coefficient
Pac 0,017325
Ant 0,012306
Jum 0,011456
Cmp 0,011074
Agi 0,01054
Tec 0,009274
Acc 0,008873
Dri 0,008574
DMC
Attribute Coefficient
Pac 0,014999
Acc 0,013865
OtB 0,012342
Ant 0,010224
Jum 0,00853
Pas 0,00806
Vis 0,007703
Wor 0,007163
MLR
Attribute Coefficient
Pac 0,021969
Acc 0,016877
Ant 0,016714
OtB 0,015771
Pas 0,013718
Dri 0,010168
Cro 0,008961
Jum 0,008427
MC
Attribute Coefficient
Pac 0,01696
Dri 0,01232
Acc 0,010894
Cmp 0,010411
Ant 0,010086
Vis 0,009814
Cro 0,009064
Jum 0,006533
AMLR
Attribute Coefficient
Pac 0,025619
Acc 0,017786
Vis 0,015335
Tec 0,014416
Dri 0,014384
Cro 0,010459
Jum 0,009989
Cnt 0,009832
AMC
Attribute Coefficient
Pac 0,019289
Acc 0,018151
Ant 0,015457
Tec 0,015131
Pas 0,012255
Cnt 0,009733
Lon 0,009488
Jum 0,007665
STC
Attribute Coefficient
Pac 0,021277
Ant 0,013547
Jum 0,013172
Agi 0,011111
Dri 0,010546
Cro 0,009884
Cnt 0,009881
Bal 0,007499
ALL
Attribute Coefficient
Agi 0,015292
Acc 0,014105
Vis 0,012979
Jum 0,010721
Pas 0,009205
Dri 0,007296
Lon 0,007237
Fla 0,005854
FM Genie Scout
I've also prepared Genie Scout ratings for all 3 models (Google Drive Link).
Disclaimer:
- negative attributes like Rushing Out (Tendency) are not included since Genie Scout does not support negative coefficients
- GK is GK, there is no Sweeper
- MLR is Winger
- AMLR is Fast Striker
- STC is Target Striker
- ALL is not included
Excel sheet with all the coefficients and calculated Weights
I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME.
Could you maybe check again to see if there's a mistake in there?
Where would you place your striker with a good jumping reach? Probably AF, right? The PF seems to be more of a F9 or playmaker, right?
"For example, if I create a player with 20 in all attributes, a winger gets 120% and a FS gets 109%."
In this case, X factor would be 1.0 for winger and 0.90 for fast striker (120% is 1.0, 109% is X, basic rule of three). Then you multiply all weights from winger by 0.9, and they will now have a normalized rating. You can do the same to all roles, based on the position with lowest rating (since all others will be reduced to reach similar level).
I am new to this... What exactly does the weight coefficient do in Genie Scout?
The values there are exactly the values you get when generating a player with 20 in all attributes for a specific position. For example FS is 109% and the weight in Genie Scout is 109 (based on "ykykyk balanced" ).
If you compare within a position, that's great! But if I want to find out which position a youth player should play in the future, the result is usually Winger or FB. For example, if I create a player with 20 in all attributes, a winger gets 120% and a FS gets 109%. Or do I just normalize the result to 100%? Is it even possible to decide in this way which position is the best for a player?