So I tried a thing. It may be working. In an attempt to construct a roster optimized to the attribute testing results, I did the following (please note, I’ll be using terms like “per player” or “per match” or “per year” - these are more terms of convenience than a literal expectation, more on that later):
1) Took the Goal Diff for each attribute (positive and negative) and divided it by 12 to get Goal Diff per point of attribute - 12 comes from the difference between the “8” and the “20” used in the testing itself. - Acceleration had a Positive or Offensive Goal Diff of 49 going from 8 to 12, so each “point” is worth 4.08 2) Divided that by 380 (10 players, 38 matches) to arrive at per point, per match, per player. - Result is a number (for acceleration and offensive positions, the number was .0107).
I decided to use this number to compare players based on attributes as another “weighting” exercise. While it allows one to compare two players, after experimentation, it was easier for me to consume and compare when against an “average” - I decided to use a player with “12” as that average player.
So now I can compare the aggregate player attributes for (only) those attributes with clear, demonstrable impact in the attribute testing simulations (acceleration, pace, dribbling, anticipation, Jumping Reach, etc.) and create something closer to baseballs “Wins Above Replacement” using a player with all 12s in the attributes that matter.
For any one player explaining in terms of a spreadsheet -
The player is a row, the attributes are columns.
The cells at the intersection are the player’s attribute # from FM24.
There is another column that is a sum of each attribute multiplied against the attribute contribute (per point, per match, per player) in #2 above - I call this # Goals per Match (or Goals Prevented per Match for defense).
I multiply that # by 38 to get the aggregate Goals per Season.
I then subtract Goals Per Season for the player from the Goals Per Season for an “all 12” player to get my “Goals Above Replacement” (GAR) for Attacking Midfielders and Attackers/Strikers - or goals prevented (GPAR) for DL/DR/DC. I took the a similar approach for GK.
For DM - I compiled both the DMs GAR and their GPAR multiplied by -1 to create an aggregate positive number of Goal Contributions Above Replacement (a goal saved is a goal earned).
Ya Boi Eren Dinkci, in my save at age 25, is 3.4 Goals per year greater than a player with all 12s. Even with the haircut.
My man Phil Neumann, in my save at age 30, is 03.75 Goals Prevented per year greater than a player with all 12s.
Bradley Ibrahim as a 23 year old DM in my save is about 5.2 GCAR.
Some observations -
1) These numbers don’t include the impact of the formation (obviously). I’ve been using a meta 424 in the 3 saves I’ve been experimenting with this effort - it would be interesting to see what an “all 12s” 424 team produces and somehow compensate for that (not sure how).
2) I combined this in my XLS (I play on iPad so I my experience is console-ish) against my old ykykyk weights - there are differences in quality (Lucas Bretelle as a DM, for example, tends to have high ykykykyk based weights in my saves - surprised at how “meh” he scored in GCAR.
3) Totally helps financially. I was comparing a player named Connor Bradley for DR against one named Pierre Nadjombe. Similar scores, but the cost per GPAR was crazy in favor of Nadjombe. Even though Connor does seem to have better overall attribute numbers - many of them just don’t matter.
4) I don’t believe at all that if Dango Ouattara is a 27 goal per 38 matches player. I’m sure in some distribution of outcomes, he could be - but that number is useful because I can peg it against an all 12 (23.3 per 38). The labels I use and numbers shouldn’t be thought of as literal expectations.
5) In terms of lineup construction, it really helps to compare lineup A vs. lineup B vs. lineup C (etc.) when planning / scheduling tournaments vs. league play and creating depth.
6) Similar roster construction benefits and lineup comparison benefits when players could be multiple roles. Both Dinkci (AMR/STC) and Neumann (DR/DC) mentioned above qualify at different positions and have (albeit subtly) different GAR/GPAR scores for the positions. But over 38 matches, I’d like to believe that .3 GAR would be a difference that justifies Dinkci at AMR vs. STC.
Anyhow, happy to share more if anyone cares. It’s a fun hobby.
So I tried a thing. It may be working. In an attempt to construct a roster optimized to the attribute testing results, I did the following (please note, I’ll be using terms like “per player” or “per match” or “per year” - these are more terms of convenience than a literal expectation, more on that later):
1) Took the Goal Diff for each attribute (positive and negative) and divided it by 12 to get Goal Diff per point of attribute
- 12 comes from the difference between the “8” and the “20” used in the testing itself.
- Acceleration had a Positive or Offensive Goal Diff of 49 going from 8 to 12, so each “point” is worth 4.08
2) Divided that by 380 (10 players, 38 matches) to arrive at per point, per match, per player.
- Result is a number (for acceleration and offensive positions, the number was .0107).
I decided to use this number to compare players based on attributes as another “weighting” exercise. While it allows one to compare two players, after experimentation, it was easier for me to consume and compare when against an “average” - I decided to use a player with “12” as that average player.
So now I can compare the aggregate player attributes for (only) those attributes with clear, demonstrable impact in the attribute testing simulations (acceleration, pace, dribbling, anticipation, Jumping Reach, etc.) and create something closer to baseballs “Wins Above Replacement” using a player with all 12s in the attributes that matter.
For any one player explaining in terms of a spreadsheet -
The player is a row, the attributes are columns.
The cells at the intersection are the player’s attribute # from FM24.
There is another column that is a sum of each attribute multiplied against the attribute contribute (per point, per match, per player) in #2 above - I call this # Goals per Match (or Goals Prevented per Match for defense).
I multiply that # by 38 to get the aggregate Goals per Season.
I then subtract Goals Per Season for the player from the Goals Per Season for an “all 12” player to get my “Goals Above Replacement” (GAR) for Attacking Midfielders and Attackers/Strikers - or goals prevented (GPAR) for DL/DR/DC. I took the a similar approach for GK.
For DM - I compiled both the DMs GAR and their GPAR multiplied by -1 to create an aggregate positive number of Goal Contributions Above Replacement (a goal saved is a goal earned).
Ya Boi Eren Dinkci, in my save at age 25, is 3.4 Goals per year greater than a player with all 12s. Even with the haircut.
My man Phil Neumann, in my save at age 30, is 03.75 Goals Prevented per year greater than a player with all 12s.
Bradley Ibrahim as a 23 year old DM in my save is about 5.2 GCAR.
Some observations -
1) These numbers don’t include the impact of the formation (obviously). I’ve been using a meta 424 in the 3 saves I’ve been experimenting with this effort - it would be interesting to see what an “all 12s” 424 team produces and somehow compensate for that (not sure how).
2) I combined this in my XLS (I play on iPad so I my experience is console-ish) against my old ykykyk weights - there are differences in quality (Lucas Bretelle as a DM, for example, tends to have high ykykykyk based weights in my saves - surprised at how “meh” he scored in GCAR.
3) Totally helps financially. I was comparing a player named Connor Bradley for DR against one named Pierre Nadjombe. Similar scores, but the cost per GPAR was crazy in favor of Nadjombe. Even though Connor does seem to have better overall attribute numbers - many of them just don’t matter.
4) I don’t believe at all that if Dango Ouattara is a 27 goal per 38 matches player. I’m sure in some distribution of outcomes, he could be - but that number is useful because I can peg it against an all 12 (23.3 per 38). The labels I use and numbers shouldn’t be thought of as literal expectations.
5) In terms of lineup construction, it really helps to compare lineup A vs. lineup B vs. lineup C (etc.) when planning / scheduling tournaments vs. league play and creating depth.
6) Similar roster construction benefits and lineup comparison benefits when players could be multiple roles. Both Dinkci (AMR/STC) and Neumann (DR/DC) mentioned above qualify at different positions and have (albeit subtly) different GAR/GPAR scores for the positions. But over 38 matches, I’d like to believe that .3 GAR would be a difference that justifies Dinkci at AMR vs. STC.
Anyhow, happy to share more if anyone cares. It’s a fun hobby.