2026-08-01, 01:37 AM - Word count:
Hello everybody once again,
In case you missed the first post in this "series", here is the link to the previous article about defencemen in the SSL
Does Defence Win Championships? An analysis of TPE Investment
In this series, I analyze the key statistics that measure success in a particular position, and then use a metric I created in order to create a final 1-24 ranking of which teams' midfields are the most effective for their TPE investments. However, unlike the previous article, I will also be going over the attributes which an effective midfielder has.
Now, my first step in this endeavour is to find every starting midfielder across every division, and find their TPE. I then take an average of every team's average midfield TPE, and also use these numbers to find the divisional average TPE investment in the midfield. Now something important to keep in mind is that I made the executive decision to count AMCs as midfielders, but not count AMLs/AMRs. This is because to create a distinction between attacking and supporting wingers. WL and WR will be counted as midfielders, because in the game engine, these players normally support their team more, look to make plays, and drop back on defence more often than a winger playing further up. (These players will instead be counted in next week's article about every team's offence)
Once the TPE averages are set, I subtract every team's average midfield TPE by the divisional average midfield TPE to create an over/under system. I then rank these values 1-24. The purpose of this is to ensure that MAJ D1 teams have a shot of being considered efficient by their divisional standards, and they are not considered "overpriced", even if they have midfielders with less investment than most in their division.
An interesting fact brought up by this ranking is that Shanghai Dragons FC, despite being in the 2nd Majors division, has the 2nd highest average TPE in the midfield with 1814, only behind United Sao Paulo, with 1861.
Now, let's talk about what makes a midfield good.
Forwards and defencemen are normally pretty easy to quantify in terms of value, you can simply check Goals For and Goals Against respectively. However, the importance of a strong midfield is not nearly as easy to find. In my search to find a set of statistics that accurately depict what a midfielder actually does, something I had to keep in mind was the other players on the field. When I take data from a game file, it shows me the results for the entire team, and not only the players that I cherry-picked as part of a midfield group. So to combat this, I must define a "good midfield" by a combination of statistics that do not lean heavily in the direction of an offence or defence, and I must use enough metrics such that a tactical choice does not influence the quality of the player.
So, this is what I decided upon.
1. Team Pass Completion %
Rather cut and dry, but represents a team's ability to make passes. This statistic may be misconstrued by a faultier measurement than mine, as a team that likes to use a route one style of play that utilizes long, reaching passes across the field will generally have a low pass %. However, in my research I found that many of the teams with low PC% actually use shorter passing in their player instructions (looking at you Catalunya). Either way however, there is a reason this is not the only thing I am measuring.
2. Possession W/L%
For this stat, I took note of how many times each team has gained or lost possession of the ball on any given play. I then used the standard formula to convert these 2 numbers into a percentage
3. Tackle Win %
This stat may be heavily in favour of a team with a strong defence, but I believe it must be included to balance out the offensive nature of the next measurement
4. Chances Created
As above states, although both of these statistics are import markers of a strong midfield, they are heavily weighted to other parts of the field. Yet, both must be included to introduce a balance bwteen the offence and defence that is present in the midfield in-game
5. Possession %
This is the simplest stat of them all, but is important as the midfield is often in charge of dictating the tempo of the play, and doing this ineffectively often leads to a team with unruly possession percentages.
Now, every team will be given a ranking of 1-24 between all of these attributes. Curious where your team lands on these? Good, I'll tell you even if you aren't.
Team Pass Completion %
T1.
AS Paris - 87%
T1.
AS Masques Sacres - 87%
T3.
CS Rova Mpanjaka - 86%
T3.
Liffeyside Celtic FC - 86%
...
...
...
22.
Seoul MFC - 81%
T23.
CF Catalunya - 80%
T23.
Shwartzwalder FV - 80%
Possession W/L %
1. AS Paris - 51%
T2. AF Masques Sacres - 49%
T2.
Shanghai Dragons FC - 49%
T2.
Hollywood FC - 49%
...
...
...
T22. CF Catalunya - 46%
T22.
North Shore United - 46%
T22.
Inter London - 46%
Tackle Win %
T1. Liffeyside Celtic FC - 79%
T1.
Krung Thep FC - 79%
T1. AF Masques Sacres - 79%
...
...
...
22.
AC Romana - 71%
T23. Shwartzwalder FV - 70%
T23.
Tokyo SC - 70%
Chances Created
1. AS Paris - 96
2. CS Rova Mpanjaka - 78
3. AF Masques Sacres - 77
...
...
...
22.
Athenai FC - 44
23. Shwartzwalder FV - 39
24. CF Catalunya - 38
Possession %
1. AS Paris - 55%
T2. Reykjavik United - 53%
T2. Inter London - 53%
T2. AF Masques Sacres - 53%
...
...
...
22. Krung Thep FC - 45%
T23. CF Catalunya - 44%
T23. Seoul MFC - 44%
Now, all of this must be run through the earlier ranking I made for the divisional TPE above average value. To do this, I take every ranking of the statisctics shown, divide them by 5 to find the average, then subtract that value by the Divisional TPE Above Average Ranking
Excited for the final results?
1st. AF Masques Sacres - 17.2
2nd. Liffeyside Celtic FC - 15.4
3rd.
Xelaju Cosmico FC - 13.2
4.
Rapid Magyar SC - 11.2
5. Hollywood FC - 8.8
6.
FC Kaapstad - 8.2
7. CS Rova Mpanjaka - 8.2
8. Inter London - 5.6
9. Tokyo SC - 5.6
10. AC Romana - 4
11. AS Paris - 3.4
12. Athenai FC - 3.4
13.
Reykjavik United - 1
14.
Cairo City - -0.2
15.
Montreal United - -0.6
16. Shanghai Dragons FC - -2.8
17.
CD Techochtitlan - -3.2
18.
United Sao Paulo - -4.4
19.
CA Buenos Aires - -6.4
20. CF Catalunya - -7.2
21. Krung Thep FC - -7.4
22. Shwartzwalder FV - -10.8
23. Seoul MFC - -11.6
24. North Shore United - -13
Now, for the last part of this article, I will create John Midfield, the ultimate midfielder out of an amalgamation of the midfielders from the most efficient teams
What are John Midfield's highest attributes you ask?
T1. Pace (20)
T1. Acceleration (20)
3. Anticipation (18)
T4. Jumping Reach (17)
T4. Work Rate (17)
T4. Dribbling (17)
T4. Agility (17)
T8. Concentration (15)
T8. Passing (15)
10. Strength (14)
I hope this all was interesting or helpful information to you all, thank you for spending the time to read my (sometimes incoherent) ramblings, and let me know if you have any ideas to improve the methods to my madness!
In case you missed the first post in this "series", here is the link to the previous article about defencemen in the SSL
Does Defence Win Championships? An analysis of TPE Investment
In this series, I analyze the key statistics that measure success in a particular position, and then use a metric I created in order to create a final 1-24 ranking of which teams' midfields are the most effective for their TPE investments. However, unlike the previous article, I will also be going over the attributes which an effective midfielder has.
Now, my first step in this endeavour is to find every starting midfielder across every division, and find their TPE. I then take an average of every team's average midfield TPE, and also use these numbers to find the divisional average TPE investment in the midfield. Now something important to keep in mind is that I made the executive decision to count AMCs as midfielders, but not count AMLs/AMRs. This is because to create a distinction between attacking and supporting wingers. WL and WR will be counted as midfielders, because in the game engine, these players normally support their team more, look to make plays, and drop back on defence more often than a winger playing further up. (These players will instead be counted in next week's article about every team's offence)
Once the TPE averages are set, I subtract every team's average midfield TPE by the divisional average midfield TPE to create an over/under system. I then rank these values 1-24. The purpose of this is to ensure that MAJ D1 teams have a shot of being considered efficient by their divisional standards, and they are not considered "overpriced", even if they have midfielders with less investment than most in their division.
An interesting fact brought up by this ranking is that Shanghai Dragons FC, despite being in the 2nd Majors division, has the 2nd highest average TPE in the midfield with 1814, only behind United Sao Paulo, with 1861.
Now, let's talk about what makes a midfield good.
Forwards and defencemen are normally pretty easy to quantify in terms of value, you can simply check Goals For and Goals Against respectively. However, the importance of a strong midfield is not nearly as easy to find. In my search to find a set of statistics that accurately depict what a midfielder actually does, something I had to keep in mind was the other players on the field. When I take data from a game file, it shows me the results for the entire team, and not only the players that I cherry-picked as part of a midfield group. So to combat this, I must define a "good midfield" by a combination of statistics that do not lean heavily in the direction of an offence or defence, and I must use enough metrics such that a tactical choice does not influence the quality of the player.
So, this is what I decided upon.
1. Team Pass Completion %
Rather cut and dry, but represents a team's ability to make passes. This statistic may be misconstrued by a faultier measurement than mine, as a team that likes to use a route one style of play that utilizes long, reaching passes across the field will generally have a low pass %. However, in my research I found that many of the teams with low PC% actually use shorter passing in their player instructions (looking at you Catalunya). Either way however, there is a reason this is not the only thing I am measuring.
2. Possession W/L%
For this stat, I took note of how many times each team has gained or lost possession of the ball on any given play. I then used the standard formula to convert these 2 numbers into a percentage
3. Tackle Win %
This stat may be heavily in favour of a team with a strong defence, but I believe it must be included to balance out the offensive nature of the next measurement
4. Chances Created
As above states, although both of these statistics are import markers of a strong midfield, they are heavily weighted to other parts of the field. Yet, both must be included to introduce a balance bwteen the offence and defence that is present in the midfield in-game
5. Possession %
This is the simplest stat of them all, but is important as the midfield is often in charge of dictating the tempo of the play, and doing this ineffectively often leads to a team with unruly possession percentages.
Now, every team will be given a ranking of 1-24 between all of these attributes. Curious where your team lands on these? Good, I'll tell you even if you aren't.
Team Pass Completion %
T1.
AS Paris - 87%T1.
AS Masques Sacres - 87%T3.
CS Rova Mpanjaka - 86%T3.
Liffeyside Celtic FC - 86%...
...
...
22.
Seoul MFC - 81%T23.
CF Catalunya - 80%T23.
Shwartzwalder FV - 80%Possession W/L %
1.
AS Paris - 51%T2.
AF Masques Sacres - 49%T2.
Shanghai Dragons FC - 49%T2.
Hollywood FC - 49%...
...
...
T22.
CF Catalunya - 46%T22.
North Shore United - 46%T22.
Inter London - 46%Tackle Win %
T1.
Liffeyside Celtic FC - 79%T1.
Krung Thep FC - 79%T1.
AF Masques Sacres - 79%...
...
...
22.
AC Romana - 71%T23.
Shwartzwalder FV - 70%T23.
Tokyo SC - 70%Chances Created
1.
AS Paris - 962.
CS Rova Mpanjaka - 783.
AF Masques Sacres - 77...
...
...
22.
Athenai FC - 4423.
Shwartzwalder FV - 3924.
CF Catalunya - 38Possession %
1. AS Paris - 55%
T2. Reykjavik United - 53%
T2. Inter London - 53%
T2. AF Masques Sacres - 53%
...
...
...
22. Krung Thep FC - 45%
T23. CF Catalunya - 44%
T23. Seoul MFC - 44%
Now, all of this must be run through the earlier ranking I made for the divisional TPE above average value. To do this, I take every ranking of the statisctics shown, divide them by 5 to find the average, then subtract that value by the Divisional TPE Above Average Ranking
Excited for the final results?
1st.
AF Masques Sacres - 17.22nd.
Liffeyside Celtic FC - 15.43rd.
Xelaju Cosmico FC - 13.24.
Rapid Magyar SC - 11.25.
Hollywood FC - 8.86.
FC Kaapstad - 8.27.
CS Rova Mpanjaka - 8.28.
Inter London - 5.69.
Tokyo SC - 5.610.
AC Romana - 411.
AS Paris - 3.412.
Athenai FC - 3.413.
Reykjavik United - 1 14.
Cairo City - -0.215.
Montreal United - -0.616.
Shanghai Dragons FC - -2.817.
CD Techochtitlan - -3.218.
United Sao Paulo - -4.419.
CA Buenos Aires - -6.420.
CF Catalunya - -7.221.
Krung Thep FC - -7.422.
Shwartzwalder FV - -10.823.
Seoul MFC - -11.624.
North Shore United - -13Now, for the last part of this article, I will create John Midfield, the ultimate midfielder out of an amalgamation of the midfielders from the most efficient teams
What are John Midfield's highest attributes you ask?
T1. Pace (20)
T1. Acceleration (20)
3. Anticipation (18)
T4. Jumping Reach (17)
T4. Work Rate (17)
T4. Dribbling (17)
T4. Agility (17)
T8. Concentration (15)
T8. Passing (15)
10. Strength (14)
I hope this all was interesting or helpful information to you all, thank you for spending the time to read my (sometimes incoherent) ramblings, and let me know if you have any ideas to improve the methods to my madness!
