2026-06-15, 10:36 AM - Word count:
Majors Rankings Over History
Interestingly enough (to me at least), I actually started writing this article for the Academy. However, the index is still a little out of sorts, so as I was starting to fill in the stats, I didn’t like that the s25 data was wrong.
So we pivot!
Originally, I pulled the latest stats to look over my class. The first thing I thought of was “Are these stats any good?” The numbers by themselves compared to the rest of the class might look impressive, but how do they rank in history? From here, I pivot to Majors, as the Academy data isn’t correct, so let’s look at the data that is correct for the season.
Step 2 was to pull the stats for every Majors player that has ever played at the Majors level. From s1 until s25, it returns 3022 rows of data, which works out to be 573 unique players.
Interesting Stats when you’re looking across history:
Most Goals
Mikko Rashford II has the most goals with 30 in season 22.
Julian Rubio had 26 in season 23 and tied with Owen Forty-Four from season 4.
Mikko Rashford II also claims third place with 24 goals in season 23.
Most Assists
Yoma Hashimoto leads everyone in Majors in assists with 22 in season 23.
Henry Andrews takes second place in season 17 with 19.
Santos Neymarinho ties Jalen Brooks with 17 for third. Neymarinho accomplished theirs in season 21, while Brooks did it in season 22.
Best Passer
12 different players show up with 100% pass rate.
Wait a minute, how can someone have 100 percent? Well, the answer is that none of them played more than 25 minutes total in that season, and all but 1 of them was a Bot.
You immediately notice the problem. First, get rid of all these bots! No one cares what the robots did, we want player data!
Secondly, for some stats raw count is fine because players who had a limited number of matches won’t have a high number. But for any of the success percent metrics, it’s a problem that those players are at the top of the list with so few stats.
The solution for both is more filters. First, no Bot or Supersub players get to be included. Second, we’ll apply a minimum limit of 450 minutes played to be included in the list. That’s 5 matches, which is enough that most Majors players should have more minutes than that if they were truly an active player to be considered.
Doing that, I was going to spit out the top stats again, but then I realized, this is just ranking by a raw number still. It would be more meaningful to try and rank across a combination of multiple stats, with weighting applied to make sure things like Goals are still very important as a raw stat, but that they can be included with other things.
Almost immediately as I look to group players into roles, I see another issue. You still have the problem of raw stats being higher the more a player is on the field. That’s great if you are looking for “Who has the highest number”, but I’m actually looking to apply some player impact versus just the player who played the most/longest.
This leads me to another tweak, per match averages. Accounting for minutes played gives a better average for statistical impact than just looking at the raw stats.
For example:
Player A has played 1800 minutes and has 12 goals.
Player B has played 900 minutes and has 8 goals.
In a top 10 list, A always beats B.
But if you get a per 90mins average (p90)....
A - (12/1800) * 90 = 0.60
B - (8/900) * 90 = 0.80
If you think about it, B was the better player because they almost scored as much as A with half the minutes. But that needs to be represented with some metric so that the new metric can be used to generate the lists.
The Final Filter Process
As mentioned:
Get all stats for all Majors players from beginning till now (s25)
Remove any bot or supersub players
Filter down so only players with 450 or more minutes show up
Generate p90 metrics for all the raw stats so we have a comparable measurement for every stat regardless of how many minutes the different players played
For the 4th item, what I’m specifically doing is converting raw stats into a percentile within the eligible pool of players in that role. What this means is the score doesn't reward raw production directly. It rewards how exceptional a player is compared to everyone else in each category, then combines those rankings using the chosen weights.
I’ll point out where this matters when I talk about results. But first, the roles!
The Roles
As mentioned above, the intent of roles is to combine similar metrics to show all around rating of players, not just take the top X of Goals, and then Assists, and so on.
Scorer Role
Measures a player's ability to generate and convert scoring opportunities. This role is designed to identify the most dangerous attacking players by combining goal production, shot volume, finishing efficiency, and ability to outperform expected goals.
Stats Included:
Goals (p90) - 30% Weight
XG (p90) - 20% Weight
Shots on Target (p90) - 15% Weight
Shot Accuracy% - 15% Weight
XG Overperformance - 10% Weight
Goals Outside Box (p90) - 10% Weight
Goals is obviously the most important stat here. Second is expected goals, as that means the player is performing in situations that should lead to a goal. Shots on Target and Shot Accuracy % might seem like the same stat, but they are included separately because while accuracy is important, just getting those shots in should also count, even if your accuracy is low. Finally, Overperformance and Goal Outside the Box were included as small bonuses to really elevate the elite players.
Playmaker Role
Measures a player's ability to create chances and generate attacking opportunities for teammates. This role rewards creativity, vision, and progressive passing that leads to dangerous situations.
Stats Included:
Assists (p90) - 25% Weight
XA (p90) - 20% Weight
Key Passes (p90) - 20% Weight
Chances Created (p90) - 15% Weight
Progressive Passes (p90) - 10% Weight
Pass% - 10% Weight
Assists and Expected Assists are the easy stats here, both lead to goals (or expected goals). Key passes are right up there, however, as it’s close to being an assist itself. Chances created is a combo of Assists and Key Passes, so it’s included as a means of getting an extra bump to those two stats. Progressive passes and Pass % are included as a minor increase to reward Playmakers for good passing.
Passer Role
Measures a player's effectiveness in controlling possession and distributing the ball. This role identifies players who are heavily involved in buildup play and can reliably progress possession.
Stats Included:
Successful Passes (p90) - 30% Weight
Pass% - 30% Weight
Progressive Passes (p90) - 20% Weight
Key Passes (p90) - 10% Weight
Attempted Passes (p90) - 10% Weight
Passing, passing, and more passing. Obvious tatts are obvious. Successful passes and pass % are the two most important stats here, but progressive passes are right behind that as it’s moving towards the opponent goal. Chances Created gets a boost, but as a passer role isn’t completely focused on scoring, it’s not the top weight. Then finally, Key passes and Attempted passes are little boosts that raise up any elite passers even further.
Defender Role
Measures a player's ability to stop opposition attacks through tackling, interceptions, positioning, and defensive actions. This role is focused on defensive reliability and disruption.
Stats Included:
Tackles Won (p90) - 25% Weight
Tackle% - 20% Weight
Interceptions (p90) - 20% Weight
Clearances (p90) - 10% Weight
Blocks (p90) - 10% Weight
Successful Presses (p90) - 10% Weight
Header% - 5% Weight
Tackles are the top stat. % are won lead the way in defining defender success. Right behind that are interceptions, which is self-explanatory as well. Clearances, Blocks, and Successful Presses are all included to give another bump. Then finally, I’ve included Header % as a small bonus just because situationally it might matter.
Presser Role
Measures a player's work rate, pressing intensity, and ability to regain possession. This role rewards players who consistently apply pressure and disrupt opposition buildup.
Stats Included:
Successful Presses (p90) - 35% Weight
Press% - 20% Weight
Distance Run (p90) - 20% Weight
Tackles Won (p90) - 10% Weight
Interceptions (p90) - 10% Weight
Fouls Against (p90) - 5% Weight
This role is hyperfocused on pressing, which is why it has the high weights on Successful presses and Press %. Distance Run makes an appearance as a means of showcasing someone who is moving around across the field to break up the opponents offense. Tackles Won and Interceptions are added as smaller bonuses to use success as a means of distinguishing the best players for this role. Finally, Fouls Against adds some extra flavor for players who are successful in getting the ball and the other team to commit a foul to stop them.
Aerial Role
Measures a player's effectiveness in aerial duels and ability to dominate in the air. We want to see the jumpers here.
Stats Included:
Successful Headers (p90) - 35% Weight
Header% - 35% Weight
Key Headers (p90) - 20% Weight
Clearances (p90) - 10% Weight
Headers are the key stat for anything Aerial, so it’s obvious why they have the most weight. 3 of the 4 stats are header related. Clearances are thrown in mostly as an additional stat that looks at the ball flying through the air.
Wide Creator Role
Measures a player's ability to create chances from wide areas through crossing and attacking delivery. This role is designed for wingers and wing-backs who provide service into dangerous areas.
Stats Included:
Successful Crosses (p90) - 35% Weight
Cross% - 25% Weight
Chances Created (p90) - 15% Weight
Open Play Key Passes (p90) - 15% Weight
Assists (p90) - 10% Weight
Crosses are the key stat here, holding the first and second most important weights. Chances created and Open Play Key passes follow that by giving more emphasis on creating shots. Finally, assists is the cherry on top, giving an extra boost when a goal is actually scored.
Dribbler Role
Measures a player's ability to progress the ball through carrying, beating defenders, and creating opportunities through individual actions. This role highlights players who can break defensive lines on their own.
Stats Included:
Dribbles (p90) - 40% Weight
Chances Created (p90) - 20% Weight
Key Passes (p90) - 15% Weight
Progressive Passes (p90) - 15% Weight
Assists (p90) - 10% Weight
Dribbles being the largest weight is obvious here. Layered into that are the results of getting the ball ahead with Chances Created, Key Passes, and Progressive passes. Like the Wide Creator role above, Assists is added to give a bonus for actually leading to a goal.
Goalkeeper Role
Measures a goalkeeper's shot-stopping quality, efficiency, and ability to prevent goals beyond expectation. This role focuses on individual goalkeeping performance rather than team results.
Stats Included:
Average Rating - 25% Weight
Save% - 25% Weight
XSave% - 15% Weight
XG Prevented (p90) - 20% Weight
Clean Sheets (p90) - 10% Weight
Penalties Saved (p90) - 5% Weight
Probably the most self-explanatory stats, as they’re all about stopping goals. I noted it’s about individual performance, but it’s important to note that all the roles above play into Goalkeeper stats. If they don’t do their jobs, the Goalkeeper has to work a lot harder, and probably ends up with worse stats because of it. The summary there, Good stats, good GK. Bad stats, probably the team’s fault.
Top 10 In Roles
Now that we have our roles setup and their stats and stat weights assigned, it’s time to apply those against every player in Majors who has ever played and meets our filter criteria (Not a bot, 450+ mins played).
Scorer Role
Right away we have our first test of logic. Gerd Klose in season 17 topping Mikko Rashford II’s season 22 performance. I’m sure most of you reading this article can tell why that seems off, but for those who haven’t looked at the history, above I pointed out that the most goals in a season in Major’s history was s22 Mikko Rashford II. As you look at the p90s, you see that Mikko has higher values for goals, xg, shots on target, and xg overperformance. Gerd only led the categories of shot accuracy% and goals outside the box when compared to Mikko.
So why is Gerd barely edging out Mikko for 1st? The answer is the weighted category differences. The stats you see above are the raw p90 stats. Then, those stats are put into a percentile of everyone in the group. So for Mikko, the 1.39 p90 for goals is the 99.9th percentile of everyone. Then, you apply the weight of 30% for Goals and you get a weighted goals score of 29.97.
Where this matters is when you do the same for Gerd (1.14 is 99.38th) you get a weighted goals score of 29.81. Only a 0.16 difference.
This matters the most because as you go across the categories that Mikko won, the difference between the two is a total of 0.48. When you look at the two categories that Gerd won (accuracy and shots outside the box), Gerd actually won those by 0.87. So all in all, it’s a 0.39 difference in Gerd’s favor, which you see in the final score difference of 0.4 (rounding).
I explain this in depth because looking at raw numbers above isn’t enough to explain the order, but I can’t put all of this data in the table because then you couldn’t really read it.
The short version is, Mikko was an elite goal scorer but Gerd was close enough that his much better accuracy pushed them into the top slot.
The rest of the list isn’t that far behind, either, with s23’s Julian Rubio only 0.4 away from tying Mikko. S18’s Zlatan Ibruhimovic, the 10th place finisher, is only 2.4points out of tying for 1st place. There is not a huge gap between these players.
Playmaker Role
The Playmaker rankings ended up being one of the most concentrated categories in the entire project. Of the ten spots available, six belong to just two players: Marco Tentacles and Furious Chicken. When one player appears three times in the top ten, that's impressive. When two players combine for more than half of the list, it tells you they were operating on a completely different level from their peers. Remember, this is over all seasons, so not only were they able to better their own group of same season players, but everyone before and after them as well.
At the top sits Marco Tentacles' s22 season for Reykjavik United, narrowly edging out Furious Chicken's s16 campaign by just 0.1 points. Looking at the raw numbers, there isn't much separating them. Chicken produced more assists, more key passes, and more chances created, while Tentacles held advantages in expected assists, progressive passes, and passing percentage. Since the weighting favors assists, expected assists, and key passes most heavily, both players end up scoring exceptionally well across the board, leaving only tiny percentile differences to determine first place.
That theme continues throughout the rankings. These aren't players winning because they dominate a single category. They're winning because they're elite in all of them. The Playmaker score rewards players who consistently create opportunities through multiple methods, whether that's directly providing assists, generating quality chances, progressing the ball into dangerous areas, or simply retaining possession efficiently enough to keep attacks flowing.
The player who manages to break up the Reykjavik monopoly is Jude Greer's s18 season for Tokyo S.C. Greer actually matches Furious Chicken's assist rate and posts the highest progressive passing figure among the top three. However, a noticeably lower key pass total and slightly weaker passing efficiency leave them just behind the two Reykjavik legends. It's a perfect example of how the weighted percentile system works. Being the best in one category is helpful, but the rankings heavily reward players who can maintain elite percentile rankings across every category.
One of the more interesting observations from this list is how frequently the same players reappear. Tentacles occupies first, sixth, and seventh. Furious Chicken claims second, fourth, and eighth. Rather than identifying one-off peak seasons, the rankings are highlighting players who sustained elite creative production year after year. That's arguably more impressive than a single dominant campaign.
The margins are also incredibly thin. Only 1.6 points separate first place from tenth, meaning virtually every player on this list has a legitimate argument for belonging among the greatest creative seasons in league history. The order matters, but the real takeaway is just how small the gap is between these performances once they are converted into weighted percentile scores.
Passer Role
Unlike the Scorer and Playmaker roles, the Passer rankings are dominated by deeper-lying players. Successful Passes p90 and Pass % account for 60% of the score by themselves, so this category is heavily geared toward players who can move the ball efficiently and consistently over large volumes.
At the top of the list sits s16 Karl Schenkinger of Schwarzwälder FV. Looking at the raw numbers, it's easy to see why. He combines an elite 68.21 successful passes per 90 with a 92.90% completion rate while also maintaining strong progressive passing numbers. What separates them from the rest of the field is that they have no obvious weakness. Some players have higher passing volume, some have slightly higher completion percentages, and some offer more key passes, but nobody matches their combination across all five categories.
David Brukspatron's s18 season finishes second, just 1.9 points behind. Their passing accuracy is actually higher than Schenkinger's, but the gap in successful passes per 90 and progressive passes is enough to keep them from the top spot. Momo Adamu rounds out the podium with perhaps the most unusual profile in the top three. Despite playing primarily as a wide defender (compared to center like everyone else), they posted the highest successful passes per 90 among the top three and nearly matched Schenkinger's progressive passing output. What ultimately costs them is a much lower key pass rate, which carries a 10% weight in the formula.
One of the more interesting patterns here is how little key passes seem to matter relative to the playmaker rankings. Jude Greer's s14 season, for example, leads the entire top ten in key passes p90 at 3.21, yet only finishes tenth overall. The reason is simple: key passes only account for 10% of the Passer score, while successful passes and pass percentage account for 60%. A player can create chances, but if they aren't also completing huge numbers of passes efficiently, they will struggle to climb this list.
The clubs represented here also tell an interesting story. Schwarzwälder FV places four seasons in the top ten, including the top spot and two appearances from Schenkinger. CA Buenos Aires contributes three entries, while Tokyo S.C. places two. Unlike the Playmaker rankings, which were largely a battle between Marco Tentacles and Furious Chicken, the Passer category is spread across several clubs and player archetypes.
Perhaps the biggest takeaway from this list is how compressed the scores are. Schenkinger's league-leading 86.7 is only 4.5 points clear of Jude Greer's tenth-place finish at 82.2. While the names are different from the Playmaker rankings, the same trend appears again: once a player reaches an elite level in the weighted categories, small percentile differences become the deciding factor.
Defender Role
The Defender rankings produced one of the most balanced top tens of any category. Unlike Playmaker, which was dominated by a handful of names, or Scorer, where a few elite attacking seasons separated themselves from the pack, the Defender list is filled with players who arrived at elite scores in very different ways.
Sitting at the top is Ilya Prusikin's s16 season for CF Catalunya. What's interesting about Prusikin is that they don't actually lead many of the raw categories. Matthew Mayhem wins comfortably in tackles won, Hippity Hoppity dominates the aerial game, and several players post similar pressing numbers. What Prusikin does have is perhaps the most complete defensive profile in the group. Their combination of elite interception numbers, strong tackling efficiency, and solid production across every weighted category allows them to narrowly claim first place.
The margins at the top are incredibly thin. Just 0.5 points separate first place Prusikin from third place Dina Skovgaard. In fact, only 2.2 points separate the entire top ten. That is the smallest spread we've seen so far and highlights how difficult it is to distinguish between elite defensive seasons once they are converted into weighted percentile scores.
Matthew Mayhem's s14 campaign is a good example of how the weighting influences the results. Their 4.93 tackles won per 90 is comfortably the best figure in the top ten and one of the strongest individual numbers in any category we've examined. However, tackles won only account for 25% of the Defender score. Prusikin's advantages in interceptions and tackling efficiency are enough to overcome Mayhem's massive lead in raw tackling volume and secure the top spot.
A.C. Romana is perhaps the biggest winner here, placing four seasons in the top ten. Hippity Hoppity appears twice, while Milton Núñez also claims two spots.
What stands out most from this list is the absence of a truly dominant defensive season. No player separates from the field. Instead, the Defender rankings are defined by consistency. Every player in the top ten excelled in multiple defensive categories, and the final order ultimately comes down to tiny percentile differences spread across seven separate metrics.
Presser Role
If the Defender rankings rewarded complete defensive profiles, the Presser rankings reward pure work rate. Successful Presses p90 alone accounts for 35% of the score, while Press %, Distance Run, Tackles Won, and Interceptions make up another 60%. The result is a list dominated by players who seemed determined to make life miserable for anyone trying to keep possession.
At the top sits Tamanna Whittington's s16 season for Schwarzwälder FV, edging out Shinji Kaido's s15 campaign by the slimmest of margins. Just 0.1 points separate the two. Looking at the numbers, it's easy to see why. Kaido actually records more successful presses and interceptions, while Whittington holds advantages in pressing efficiency, distance covered, and tackles won. Neither player has a meaningful weakness, and the final ranking comes down to tiny percentile differences across six different categories.
What immediately stands out from this list is how concentrated it is around a specific player profile. Nearly every player here has the defender or wing back position. That isn't a coincidence. These positions naturally favor players operating in larger spaces and covering greater distances, and the metrics used in this category heavily reward those responsibilities.
No player embodies that more than Puma Superhoops. Tokyo's relentless right-sided engine appears three times in the top six, including fourth, fifth, and sixth place. What's remarkable is how similar the three seasons are. Each year produced elite pressing numbers, excellent work rate, and strong defensive contributions. Rather than a single standout campaign, the rankings suggest Superhoops maintained an elite pressing standard for several consecutive seasons.
Distance covered also plays a larger role here than it has in any previous category. Hun Possible's s13 season provides a great example. Their 42.00 distance run per 90 is the highest figure in the entire top ten, helping them secure a place despite more modest tackling and pressing numbers than some of the players above them. Likewise, Tom Pedersen's s16 season posts the second-highest pressing efficiency in the group at 40.23%, allowing them to remain competitive despite lower tackle numbers.
The club distribution is also noteworthy. Schwarzwälder FV places four seasons in the top ten, while Tokyo S.C. claims another four. Between Whittington, Pedersen, Kaido, and Superhoops, these two clubs essentially define the category. Whatever tactical systems they employed, they consistently produced players capable of sustaining elite defensive pressure over an entire season.
Perhaps the most impressive takeaway is just how high the overall standard is. First place sits at 94.4, while tenth still clears 91.0. Unlike the Defender rankings, where the field was tightly packed but varied in style, the Presser rankings are packed with specialists who excelled at the exact same job.
Aerial Role
The Aerial rankings may be the most lopsided category in the entire project. While other roles featured players from across multiple eras and clubs competing on relatively even footing, the Aerial list is almost entirely dominated by a small group of early league central defenders who appear to have been playing a different sport from everyone else.
At the center of it all is Henrik Lind. Four of the top ten spots belong to them, including the number one overall ranking from their s10 campaign at Athênai F.C. Their numbers are absurd across the board. More than 23 successful headers per 90, a 92.29% success rate, 4.64 key headers per 90, and over 19 clearances per match. Those figures aren't just elite within this ranking, they're outliers even compared to the rest of an already aerially dominant top ten.
In fact, Henrik's greatest competition comes from players who posted nearly identical profiles. Bob Divesinalot, Cole Mertz, Pierre Houde, Jökull Júlíusson, and Spack Jarrow all combine exceptional heading volume with success rates above 90%. The result is one of the closest finishes we've seen so far. Only 0.7 points separate first place from tenth, despite the category spanning multiple clubs and seasons.
What's particularly interesting is how compressed the scores become when a category is built around only four metrics. Successful Headers p90 and Header % account for 70% of the score by themselves, meaning players who dominate aerial duels start with a massive advantage. Key Headers and Clearances then serve primarily as tiebreakers between players who are already elite in the air. Once a player reaches the low 90s in Header % while maintaining enormous volume, there simply isn't much room left to separate them from their peers.
The recurring names tell the story. Henrik Lind appears four times while several other defenders appear with seasons that would have comfortably topped later eras. Rather than highlighting a single dominant year, the rankings suggest that a specific generation of defenders consistently produced aerial numbers that have rarely been matched since.
The other thing that jumps out is how traditional these players are. Unlike the Defender or Presser rankings, where wing backs and wide players frequently appeared, the Aerial category belongs almost exclusively to center-backs and defensive midfielders. That's not surprising given the weighting. Winning headers, winning them efficiently, and then turning those wins into defensive actions are exactly the traits the formula is designed to reward.
More than any other role so far, the Aerial rankings feel like a snapshot of a particular era. Whether that reflects tactical differences, league evolution, or simply a generation of great players roaming the back lines is up for debate. What isn't debatable is Henrik Lind's place at the top. When one player claims four of the top ten spots, including the best score ever recorded in the category, the discussion begins with them and works downward from there.
Wide Creator Role
The Wide Creator rankings feel like the natural mix between the Playmaker and Passer categories. Crossing volume and crossing efficiency account for 60% of the score, but players are also rewarded for creating chances, generating open-play key passes, and producing assists. The result is a category that favors players who consistently create attacking opportunities from wide areas, whether through traditional crossing or more modern creative play.
At the top sits Furious Chicken's s19 season for Reykjavik United. While several players on this list post stronger numbers in individual categories, Chicken's profile is remarkably complete. They combine elite crossing volume with strong accuracy, solid chance creation, and the highest assist rate among the top two finishers. As we've seen throughout these rankings, having no weaknesses often matters more than dominating a single category.
The battle for first is surprisingly close. Yoma Hashimoto's s22 season finishes just 0.4 points behind despite posting more successful crosses and creating more chances. The difference ultimately comes down to crossing efficiency and assists, where Chicken maintains a small edge. Given how heavily crossing metrics are weighted, even minor percentile advantages can make the difference between first and second place.
The most fascinating player on the list may be Henry Andrews. Unlike the players around them, Andrews doesn't rely on crossing volume at all. Their 2.64 successful crosses per 90 is nearly two full crosses behind the leaders. Instead, they compensated with extraordinary creative numbers: 2.57 chances created, 4.93 open-play key passes, and 1.36 assists per 90. Those figures are among the best in the entire table and demonstrate that there is more than one path to success in this category.
Much like the Playmaker rankings, certain names appear repeatedly. Furious Chicken claims two spots in the top four, while Yoma Hashimoto appears three times in the top ten. Marco Tentacles, Jude Greer, and Fara Dian also make appearances after already showing up in earlier creative categories. This suggests that many of the league's best creators were capable of producing chances in multiple ways rather than fitting neatly into a single role.
One player who stands out from the rest is Evan Hunt's s23 season. Their crossing accuracy of 40.97% is comfortably the highest in the top ten, yet they only ranks ninth overall. That's a perfect illustration of how the weighting system works. Cross percentage is important, but it only accounts for 25% of the score. Lower crossing volume and fewer chance creation numbers prevent Hunt from climbing higher despite his exceptional efficiency.
The club distribution also tells an interesting story. Reykjavik United claims four of the top ten spots, while União São Paulo contributes three. Together they account for seven of the ten best wide-creator seasons ever recorded.
Unlike the Aerial rankings, where a handful of historical outliers separated themselves from the field, the Wide Creator category remains remarkably competitive. Only 1.9 points separate first place from tenth, and nearly every player on the list has a compelling argument built around a different strength. Some overwhelmed opponents with crossing volume, others with efficiency, and a few through pure creativity. The rankings ultimately reward the players who managed to do all three at once.
Dribbler Role
The Dribbler rankings may have the most misleading name of any category in the project. Dribbles account for 40% of the score, making them the single most important metric, but the remaining 60% comes from chance creation, key passes, progressive passes, and assists. The result isn't a ranking of players who simply beat opponents off the dribble. It's a ranking of players who turn ball progression into attacking production.
At the top sits Yoma Hashimoto's s21 season for União São Paulo. What makes Hashimoto's performance so impressive is how balanced it is. They don’t lead the table in dribbles, assists, or chance creation, but they rank near the top in every category. As we've seen repeatedly throughout these rankings, the weighted percentile system heavily rewards players who avoid weaknesses. Being elite everywhere is often more valuable than being historically great in a single area.
The race for first is exceptionally close. Furious Chicken's s16 campaign finishes just 0.2 points behind despite posting the highest dribble rate among the top contenders. In many ways, Chicken's season is exactly what people imagine when they hear "dribbler." They attack defenders relentlessly, create chances, and generate key passes at an elite rate. What ultimately costs them is a lower progressive passing contribution compared to Hashimoto, whose ability to consistently move the ball into dangerous areas boosts their overall profile.
One of the biggest surprises is how heavily the rankings are dominated by defenders and wing backs. Nine of the ten entries come from players whose primary positions are in defense or wide areas. Rather than traditional wingers or attacking midfielders, the formula consistently rewards players who can carry the ball forward from deeper positions while also contributing creatively.
No player embodies that better than Yoma Hashimoto. They appeared four times in the top ten, including the number one spot. Roquefort Cotswold adds two more appearances for União São Paulo, meaning the club claims six of the ten positions outright. For a category built around ball progression and creativity, that's a staggering level of dominance by a single club.
Roquefort's two appearances are particularly interesting because they demonstrate a different route to success. Their s23 season records the highest progressive passing figure in the entire top ten at 9.12 per 90 while also leading the table in key passes. The dribbling numbers themselves are relatively modest compared to some of the players around them, but their ability to advance attacks through passing compensates for that and keeps them firmly among the elite.
Hun Possible's s13 campaign provides another example of the formula at work. They lead the entire top ten in dribbles per 90 with an incredible 5.14, yet only finish eighth overall. The reason is simple: dribbling may be the largest individual category, but it is still only worth 40% of the score. Lower creative output in the remaining metrics prevents them from climbing higher despite posting the category's most eye-catching headline statistic.
Perhaps the biggest takeaway from the Dribbler rankings is how closely intertwined dribbling and creativity have become. The players at the top aren't simply carrying the ball. They're using those carries to create chances, generate assists, and progress attacks. That's why the list feels less like a ranking of flashy dribblers and more like a ranking of the league's most effective ball progressors.
The margins reflect that quality. Only 1.7 points separate first place from tenth, and several players are separated by just a few tenths of a point. As with many of the creative categories, the difference between the best and the tenth-best season is remarkably small once everything is converted into weighted percentile scores.
Goalkeeper Role
The Goalkeeper rankings produced one of the most interesting results in the entire project because they highlight a fundamental difference between evaluating outfield players and evaluating goalkeepers. While most of the other categories are built around volume statistics, the Goalkeeper score is much more heavily influenced by efficiency. Average Rating and Save Percentage alone account for half of the total score, while xSave%, xG Prevented, Clean Sheets, and Penalties Saved fill out the remainder.
At the top of the rankings is Bartholomew Twinkletoes' s24 season for CF Catalunya. Unlike some of the runaway winners we've seen in other categories, Twinkletoes doesn't dominate every individual statistic. Shingo Takechi, Denis Mobek, and even several lower-ranked goalkeepers hold advantages in specific categories. What separates Twinkletoes is their combination of elite shot-stopping, strong xG prevention, and an outstanding clean sheet rate. they don't simply excel in one area, they rank near the top across almost all of them.
The most striking aspect of the list is how heavily it favors recent seasons. Three of the top four spots belong to season 24 goalkeepers, and four of the top five come from seasons 24 and 25. Whether that reflects improved goalkeeping quality, tactical evolution, or changes in the way teams defend is difficult to say, but the rankings clearly suggest modern keepers are producing stronger all-around statistical profiles than many of their predecessors.
The biggest outlier on the list is undoubtedly Elmis The Heretic's s13 season for A.C. Romana. While most goalkeepers cluster in the mid-80s for xSave%, Elmis posts an astonishing 107.27%. That number immediately jumps off the page because it implies they saved substantially more goals than expected over the course of the season. Despite posting by far the most eye-catching xSave% figure in the rankings, he only finishes fourth overall. That's a perfect illustration of how the weighting works. xSave% is important, but it only accounts for 15% of the score. Lower save percentage and clean sheet numbers prevent them from challenging for first place.
Another interesting case is Certified Problem's s6 campaign for Sydney City. They own the highest save percentage in the top ten at 91.18%, an absurd figure by any standard. Yet he only finishes seventh overall because their xG prevented value is zero and their clean sheet rate trails several of the players above them. Once again, the rankings reward complete goalkeeping profiles rather than singular standout statistics.
Hollywood FC quietly places three separate seasons in the top ten, led by Duncan Walrusson's s25 and s23 campaigns and Dexter Hall's s18 season. No other club matches that level of representation, suggesting Hollywood enjoyed sustained excellence in goal rather than relying on a single elite season.
Perhaps the most surprising takeaway is the gap at the top. Unlike many of the outfield categories where a point or two separated the entire top ten, Twinkletoes finished 2.3 points ahead of second place. In weighted percentile terms, that's a significant margin. While the rest of the rankings are tightly contested, the numbers suggest that their s24 season stands alone as the strongest goalkeeping campaign on record.
Whether that makes Bartholomew Twinkletoes the greatest goalkeeper in league history is a debate for another day. What this ranking does show is that no other season combined elite shot-stopping, goal prevention, and team success quite as effectively as they did.
Conclusion
If you made it to this point, then I hope you enjoyed the deep statistics dump. There was a lot more to look at here than I thought there would be, but similar to when I made scouting profiles for the Academy class, I really enjoy looking at players more by the type of role they are playing rather than trying to map into raw positions.
Given how many different tactics setups I have seen examples of, I'm not sure positions are ever consistent enough to be useful in actually categorizing.
More importantly, I'm still extremely happy with the index and the ability to get it via API. I have a massive Google sheet for this and it wouldn't be possible to write articles like this if I could get table summary data that I can then visually determine what I think is worth exploring further.
If the s25 Academy Index gets fixed, I might do this article again for the Academy class so that my class can see where we rank in terms of the all time Academy classes.
Interestingly enough (to me at least), I actually started writing this article for the Academy. However, the index is still a little out of sorts, so as I was starting to fill in the stats, I didn’t like that the s25 data was wrong.
So we pivot!
Originally, I pulled the latest stats to look over my class. The first thing I thought of was “Are these stats any good?” The numbers by themselves compared to the rest of the class might look impressive, but how do they rank in history? From here, I pivot to Majors, as the Academy data isn’t correct, so let’s look at the data that is correct for the season.
Step 2 was to pull the stats for every Majors player that has ever played at the Majors level. From s1 until s25, it returns 3022 rows of data, which works out to be 573 unique players.
Interesting Stats when you’re looking across history:
Most Goals
Mikko Rashford II has the most goals with 30 in season 22.
Julian Rubio had 26 in season 23 and tied with Owen Forty-Four from season 4.
Mikko Rashford II also claims third place with 24 goals in season 23.
Most Assists
Yoma Hashimoto leads everyone in Majors in assists with 22 in season 23.
Henry Andrews takes second place in season 17 with 19.
Santos Neymarinho ties Jalen Brooks with 17 for third. Neymarinho accomplished theirs in season 21, while Brooks did it in season 22.
Best Passer
12 different players show up with 100% pass rate.
Wait a minute, how can someone have 100 percent? Well, the answer is that none of them played more than 25 minutes total in that season, and all but 1 of them was a Bot.
You immediately notice the problem. First, get rid of all these bots! No one cares what the robots did, we want player data!
Secondly, for some stats raw count is fine because players who had a limited number of matches won’t have a high number. But for any of the success percent metrics, it’s a problem that those players are at the top of the list with so few stats.
The solution for both is more filters. First, no Bot or Supersub players get to be included. Second, we’ll apply a minimum limit of 450 minutes played to be included in the list. That’s 5 matches, which is enough that most Majors players should have more minutes than that if they were truly an active player to be considered.
Doing that, I was going to spit out the top stats again, but then I realized, this is just ranking by a raw number still. It would be more meaningful to try and rank across a combination of multiple stats, with weighting applied to make sure things like Goals are still very important as a raw stat, but that they can be included with other things.
Almost immediately as I look to group players into roles, I see another issue. You still have the problem of raw stats being higher the more a player is on the field. That’s great if you are looking for “Who has the highest number”, but I’m actually looking to apply some player impact versus just the player who played the most/longest.
This leads me to another tweak, per match averages. Accounting for minutes played gives a better average for statistical impact than just looking at the raw stats.
For example:
Player A has played 1800 minutes and has 12 goals.
Player B has played 900 minutes and has 8 goals.
In a top 10 list, A always beats B.
But if you get a per 90mins average (p90)....
A - (12/1800) * 90 = 0.60
B - (8/900) * 90 = 0.80
If you think about it, B was the better player because they almost scored as much as A with half the minutes. But that needs to be represented with some metric so that the new metric can be used to generate the lists.
The Final Filter Process
As mentioned:
Get all stats for all Majors players from beginning till now (s25)
Remove any bot or supersub players
Filter down so only players with 450 or more minutes show up
Generate p90 metrics for all the raw stats so we have a comparable measurement for every stat regardless of how many minutes the different players played
For the 4th item, what I’m specifically doing is converting raw stats into a percentile within the eligible pool of players in that role. What this means is the score doesn't reward raw production directly. It rewards how exceptional a player is compared to everyone else in each category, then combines those rankings using the chosen weights.
I’ll point out where this matters when I talk about results. But first, the roles!
The Roles
As mentioned above, the intent of roles is to combine similar metrics to show all around rating of players, not just take the top X of Goals, and then Assists, and so on.
Scorer Role
Measures a player's ability to generate and convert scoring opportunities. This role is designed to identify the most dangerous attacking players by combining goal production, shot volume, finishing efficiency, and ability to outperform expected goals.
Stats Included:
Goals (p90) - 30% Weight
XG (p90) - 20% Weight
Shots on Target (p90) - 15% Weight
Shot Accuracy% - 15% Weight
XG Overperformance - 10% Weight
Goals Outside Box (p90) - 10% Weight
Goals is obviously the most important stat here. Second is expected goals, as that means the player is performing in situations that should lead to a goal. Shots on Target and Shot Accuracy % might seem like the same stat, but they are included separately because while accuracy is important, just getting those shots in should also count, even if your accuracy is low. Finally, Overperformance and Goal Outside the Box were included as small bonuses to really elevate the elite players.
Playmaker Role
Measures a player's ability to create chances and generate attacking opportunities for teammates. This role rewards creativity, vision, and progressive passing that leads to dangerous situations.
Stats Included:
Assists (p90) - 25% Weight
XA (p90) - 20% Weight
Key Passes (p90) - 20% Weight
Chances Created (p90) - 15% Weight
Progressive Passes (p90) - 10% Weight
Pass% - 10% Weight
Assists and Expected Assists are the easy stats here, both lead to goals (or expected goals). Key passes are right up there, however, as it’s close to being an assist itself. Chances created is a combo of Assists and Key Passes, so it’s included as a means of getting an extra bump to those two stats. Progressive passes and Pass % are included as a minor increase to reward Playmakers for good passing.
Passer Role
Measures a player's effectiveness in controlling possession and distributing the ball. This role identifies players who are heavily involved in buildup play and can reliably progress possession.
Stats Included:
Successful Passes (p90) - 30% Weight
Pass% - 30% Weight
Progressive Passes (p90) - 20% Weight
Key Passes (p90) - 10% Weight
Attempted Passes (p90) - 10% Weight
Passing, passing, and more passing. Obvious tatts are obvious. Successful passes and pass % are the two most important stats here, but progressive passes are right behind that as it’s moving towards the opponent goal. Chances Created gets a boost, but as a passer role isn’t completely focused on scoring, it’s not the top weight. Then finally, Key passes and Attempted passes are little boosts that raise up any elite passers even further.
Defender Role
Measures a player's ability to stop opposition attacks through tackling, interceptions, positioning, and defensive actions. This role is focused on defensive reliability and disruption.
Stats Included:
Tackles Won (p90) - 25% Weight
Tackle% - 20% Weight
Interceptions (p90) - 20% Weight
Clearances (p90) - 10% Weight
Blocks (p90) - 10% Weight
Successful Presses (p90) - 10% Weight
Header% - 5% Weight
Tackles are the top stat. % are won lead the way in defining defender success. Right behind that are interceptions, which is self-explanatory as well. Clearances, Blocks, and Successful Presses are all included to give another bump. Then finally, I’ve included Header % as a small bonus just because situationally it might matter.
Presser Role
Measures a player's work rate, pressing intensity, and ability to regain possession. This role rewards players who consistently apply pressure and disrupt opposition buildup.
Stats Included:
Successful Presses (p90) - 35% Weight
Press% - 20% Weight
Distance Run (p90) - 20% Weight
Tackles Won (p90) - 10% Weight
Interceptions (p90) - 10% Weight
Fouls Against (p90) - 5% Weight
This role is hyperfocused on pressing, which is why it has the high weights on Successful presses and Press %. Distance Run makes an appearance as a means of showcasing someone who is moving around across the field to break up the opponents offense. Tackles Won and Interceptions are added as smaller bonuses to use success as a means of distinguishing the best players for this role. Finally, Fouls Against adds some extra flavor for players who are successful in getting the ball and the other team to commit a foul to stop them.
Aerial Role
Measures a player's effectiveness in aerial duels and ability to dominate in the air. We want to see the jumpers here.
Stats Included:
Successful Headers (p90) - 35% Weight
Header% - 35% Weight
Key Headers (p90) - 20% Weight
Clearances (p90) - 10% Weight
Headers are the key stat for anything Aerial, so it’s obvious why they have the most weight. 3 of the 4 stats are header related. Clearances are thrown in mostly as an additional stat that looks at the ball flying through the air.
Wide Creator Role
Measures a player's ability to create chances from wide areas through crossing and attacking delivery. This role is designed for wingers and wing-backs who provide service into dangerous areas.
Stats Included:
Successful Crosses (p90) - 35% Weight
Cross% - 25% Weight
Chances Created (p90) - 15% Weight
Open Play Key Passes (p90) - 15% Weight
Assists (p90) - 10% Weight
Crosses are the key stat here, holding the first and second most important weights. Chances created and Open Play Key passes follow that by giving more emphasis on creating shots. Finally, assists is the cherry on top, giving an extra boost when a goal is actually scored.
Dribbler Role
Measures a player's ability to progress the ball through carrying, beating defenders, and creating opportunities through individual actions. This role highlights players who can break defensive lines on their own.
Stats Included:
Dribbles (p90) - 40% Weight
Chances Created (p90) - 20% Weight
Key Passes (p90) - 15% Weight
Progressive Passes (p90) - 15% Weight
Assists (p90) - 10% Weight
Dribbles being the largest weight is obvious here. Layered into that are the results of getting the ball ahead with Chances Created, Key Passes, and Progressive passes. Like the Wide Creator role above, Assists is added to give a bonus for actually leading to a goal.
Goalkeeper Role
Measures a goalkeeper's shot-stopping quality, efficiency, and ability to prevent goals beyond expectation. This role focuses on individual goalkeeping performance rather than team results.
Stats Included:
Average Rating - 25% Weight
Save% - 25% Weight
XSave% - 15% Weight
XG Prevented (p90) - 20% Weight
Clean Sheets (p90) - 10% Weight
Penalties Saved (p90) - 5% Weight
Probably the most self-explanatory stats, as they’re all about stopping goals. I noted it’s about individual performance, but it’s important to note that all the roles above play into Goalkeeper stats. If they don’t do their jobs, the Goalkeeper has to work a lot harder, and probably ends up with worse stats because of it. The summary there, Good stats, good GK. Bad stats, probably the team’s fault.
Top 10 In Roles
Now that we have our roles setup and their stats and stat weights assigned, it’s time to apply those against every player in Majors who has ever played and meets our filter criteria (Not a bot, 450+ mins played).
Scorer Role
| Rank | Name | Club | Season | Position | Score | Goals p90 | xG p90 | Shots on Target p90 | Shot Accuracy % | xG Overperformance | Goals Outside Box p90 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Gerd Klose | Hollywood FC | 17 | AM (RL), ST ( C ) | 98.60 | 1.14 | 0.81 | 2.14 | 78.95 | 4.63 | 0.14 |
| 2 | Mikko Rashford II | Reykjavik United | 22 | AM (RL), ST ( C ) | 98.20 | 1.39 | 0.86 | 2.55 | 64.71 | 11.43 | 0.09 |
| 3 | Julian Rubio | União São Paulo | 23 | AM (LC), ST ( C ) | 97.80 | 1.18 | 0.93 | 2.32 | 61.45 | 5.60 | 0.14 |
| 4 | Jean-Claude Goddamn | Reykjavik United | 16 | M/AM ( C ), ST ( C ) | 97.60 | 1.36 | 1.17 | 2.79 | 63.93 | 2.67 | 0.07 |
| 5 | Jake Bradford | CA Buenos Aires | 17 | AM (RL), ST ( C ) | 97.00 | 1.00 | 0.83 | 2.36 | 57.89 | 2.45 | 0.29 |
| 6 | Julian Rubio | União São Paulo | 21 | AM (LC), ST ( C ) | 96.80 | 1.17 | 1.11 | 2.50 | 70.31 | 1.03 | 0.06 |
| 7 | Wang Zhihao | Schwarzwälder FV | 20 | AM (RC), ST ( C ) | 96.70 | 1.17 | 0.61 | 2.00 | 61.02 | 9.99 | 0.11 |
| 8 | Pete Martell | Hollywood FC | 21 | M/AM ( C ), ST ( C ) | 96.70 | 1.00 | 0.79 | 2.39 | 56.58 | 3.84 | 0.17 |
| 9 | Predrag Dobrić | A.C. Romana | 23 | AM (LC), ST ( C ) | 96.40 | 0.95 | 0.67 | 1.45 | 68.09 | 6.21 | 0.09 |
| 10 | Zlatan Ibruhimovic | Tokyo S.C. | 18 | AM (LC), ST ( C ) | 96.20 | 0.79 | 0.56 | 3.29 | 60.53 | 3.23 | 0.14 |
Right away we have our first test of logic. Gerd Klose in season 17 topping Mikko Rashford II’s season 22 performance. I’m sure most of you reading this article can tell why that seems off, but for those who haven’t looked at the history, above I pointed out that the most goals in a season in Major’s history was s22 Mikko Rashford II. As you look at the p90s, you see that Mikko has higher values for goals, xg, shots on target, and xg overperformance. Gerd only led the categories of shot accuracy% and goals outside the box when compared to Mikko.
So why is Gerd barely edging out Mikko for 1st? The answer is the weighted category differences. The stats you see above are the raw p90 stats. Then, those stats are put into a percentile of everyone in the group. So for Mikko, the 1.39 p90 for goals is the 99.9th percentile of everyone. Then, you apply the weight of 30% for Goals and you get a weighted goals score of 29.97.
Where this matters is when you do the same for Gerd (1.14 is 99.38th) you get a weighted goals score of 29.81. Only a 0.16 difference.
This matters the most because as you go across the categories that Mikko won, the difference between the two is a total of 0.48. When you look at the two categories that Gerd won (accuracy and shots outside the box), Gerd actually won those by 0.87. So all in all, it’s a 0.39 difference in Gerd’s favor, which you see in the final score difference of 0.4 (rounding).
I explain this in depth because looking at raw numbers above isn’t enough to explain the order, but I can’t put all of this data in the table because then you couldn’t really read it.
The short version is, Mikko was an elite goal scorer but Gerd was close enough that his much better accuracy pushed them into the top slot.
The rest of the list isn’t that far behind, either, with s23’s Julian Rubio only 0.4 away from tying Mikko. S18’s Zlatan Ibruhimovic, the 10th place finisher, is only 2.4points out of tying for 1st place. There is not a huge gap between these players.
Playmaker Role
| Rank | Name | Club | Season | Position | Score | Assists p90 | xA p90 | Key Passes p90 | Chances Created p90 | Progressive Passes p90 | Pass % |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Marco Tentacles | Reykjavik United | 22 | D/M/AM (L) | 93.30 | 0.55 | 0.55 | 5.32 | 1.23 | 6.09 | 86.07 |
| 2 | Furious Chicken | Reykjavik United | 16 | D (RL), AM (L) | 93.20 | 0.71 | 0.50 | 5.64 | 1.50 | 5.57 | 85.16 |
| 3 | Jude Greer | Tokyo S.C. | 18 | D/WB/AM (L) | 92.90 | 0.71 | 0.56 | 4.43 | 1.50 | 7.21 | 84.36 |
| 4 | Furious Chicken | Reykjavik United | 18 | D (RL), AM (L) | 92.70 | 0.64 | 0.50 | 5.07 | 1.29 | 7.86 | 84.18 |
| 5 | Kimi Häkkinen | Schwarzwälder FV | 15 | AM (RLC) | 92.50 | 0.60 | 0.62 | 4.74 | 1.96 | 3.39 | 87.52 |
| 6 | Marco Tentacles | Reykjavik United | 20 | D/M/AM (L) | 92.30 | 0.83 | 0.68 | 6.83 | 1.56 | 6.28 | 82.68 |
| 7 | Marco Tentacles | Reykjavik United | 21 | D/M/AM (L) | 92.10 | 0.61 | 0.58 | 6.78 | 1.06 | 8.17 | 83.58 |
| 8 | Furious Chicken | Reykjavik United | 17 | D (RL), AM (L) | 91.90 | 0.50 | 0.38 | 4.50 | 1.14 | 9.43 | 85.32 |
| 9 | Fara Dian | Schwarzwälder FV | 16 | M ( C ), AM (RC) | 91.80 | 0.36 | 0.60 | 6.50 | 1.43 | 5.21 | 87.07 |
| 10 | Roquefort Cotswold | União São Paulo | 22 | D ( R ), WB (RL) | 91.70 | 0.61 | 0.66 | 4.98 | 1.79 | 7.80 | 82.32 |
The Playmaker rankings ended up being one of the most concentrated categories in the entire project. Of the ten spots available, six belong to just two players: Marco Tentacles and Furious Chicken. When one player appears three times in the top ten, that's impressive. When two players combine for more than half of the list, it tells you they were operating on a completely different level from their peers. Remember, this is over all seasons, so not only were they able to better their own group of same season players, but everyone before and after them as well.
At the top sits Marco Tentacles' s22 season for Reykjavik United, narrowly edging out Furious Chicken's s16 campaign by just 0.1 points. Looking at the raw numbers, there isn't much separating them. Chicken produced more assists, more key passes, and more chances created, while Tentacles held advantages in expected assists, progressive passes, and passing percentage. Since the weighting favors assists, expected assists, and key passes most heavily, both players end up scoring exceptionally well across the board, leaving only tiny percentile differences to determine first place.
That theme continues throughout the rankings. These aren't players winning because they dominate a single category. They're winning because they're elite in all of them. The Playmaker score rewards players who consistently create opportunities through multiple methods, whether that's directly providing assists, generating quality chances, progressing the ball into dangerous areas, or simply retaining possession efficiently enough to keep attacks flowing.
The player who manages to break up the Reykjavik monopoly is Jude Greer's s18 season for Tokyo S.C. Greer actually matches Furious Chicken's assist rate and posts the highest progressive passing figure among the top three. However, a noticeably lower key pass total and slightly weaker passing efficiency leave them just behind the two Reykjavik legends. It's a perfect example of how the weighted percentile system works. Being the best in one category is helpful, but the rankings heavily reward players who can maintain elite percentile rankings across every category.
One of the more interesting observations from this list is how frequently the same players reappear. Tentacles occupies first, sixth, and seventh. Furious Chicken claims second, fourth, and eighth. Rather than identifying one-off peak seasons, the rankings are highlighting players who sustained elite creative production year after year. That's arguably more impressive than a single dominant campaign.
The margins are also incredibly thin. Only 1.6 points separate first place from tenth, meaning virtually every player on this list has a legitimate argument for belonging among the greatest creative seasons in league history. The order matters, but the real takeaway is just how small the gap is between these performances once they are converted into weighted percentile scores.
Passer Role
| Rank | Name | Club | Season | Position | Score | Successful Passes p90 | Pass % | Progressive Passes p90 | Key Passes p90 | Attempted Passes p90 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Karl Schenkinger | Schwarzwälder FV | 16 | D ( C ), DM, M ( C ) | 86.70 | 68.21 | 92.90 | 5.79 | 0.79 | 73.43 |
| 2 | David Brukspatron | CA Buenos Aires | 18 | D ( C ), DM, M ( C ) | 84.80 | 62.71 | 93.60 | 4.64 | 1.00 | 67.00 |
| 3 | Momo Adamu | Tokyo S.C. | 17 | D (LC), WB (RL) | 84.50 | 69.93 | 91.92 | 5.86 | 0.29 | 76.07 |
| 4 | Nicolás Muñoz | CA Buenos Aires | 20 | D ( C ), DM, M ( C ) | 84.40 | 53.94 | 93.91 | 4.39 | 2.56 | 57.44 |
| 5 | Sami Zerhouni | Schwarzwälder FV | 13 | D ( C ), DM, M ( C ) | 84.00 | 59.75 | 92.76 | 4.00 | 1.67 | 64.42 |
| 6 | Karl Schenkinger | Schwarzwälder FV | 13 | D ( C ), DM, M ( C ) | 83.40 | 70.71 | 91.84 | 4.64 | 0.50 | 77.00 |
| 7 | Dombrofski Maximiliano | Schwarzwälder FV | 20 | D ( R ), DM, M ( C ) | 82.90 | 60.72 | 89.37 | 5.94 | 1.44 | 67.94 |
| 8 | Henrik Lind | CA Buenos Aires | 13 | D ( C ), DM | 82.80 | 71.36 | 91.57 | 5.21 | 0.14 | 77.93 |
| 9 | Beaklie Eilish | União São Paulo | 19 | D (RC), DM | 82.40 | 62.00 | 90.89 | 5.71 | 0.71 | 68.21 |
| 10 | Jude Greer | Tokyo S.C. | 14 | D/WB/AM (L) | 82.20 | 51.07 | 91.08 | 5.50 | 3.21 | 56.07 |
Unlike the Scorer and Playmaker roles, the Passer rankings are dominated by deeper-lying players. Successful Passes p90 and Pass % account for 60% of the score by themselves, so this category is heavily geared toward players who can move the ball efficiently and consistently over large volumes.
At the top of the list sits s16 Karl Schenkinger of Schwarzwälder FV. Looking at the raw numbers, it's easy to see why. He combines an elite 68.21 successful passes per 90 with a 92.90% completion rate while also maintaining strong progressive passing numbers. What separates them from the rest of the field is that they have no obvious weakness. Some players have higher passing volume, some have slightly higher completion percentages, and some offer more key passes, but nobody matches their combination across all five categories.
David Brukspatron's s18 season finishes second, just 1.9 points behind. Their passing accuracy is actually higher than Schenkinger's, but the gap in successful passes per 90 and progressive passes is enough to keep them from the top spot. Momo Adamu rounds out the podium with perhaps the most unusual profile in the top three. Despite playing primarily as a wide defender (compared to center like everyone else), they posted the highest successful passes per 90 among the top three and nearly matched Schenkinger's progressive passing output. What ultimately costs them is a much lower key pass rate, which carries a 10% weight in the formula.
One of the more interesting patterns here is how little key passes seem to matter relative to the playmaker rankings. Jude Greer's s14 season, for example, leads the entire top ten in key passes p90 at 3.21, yet only finishes tenth overall. The reason is simple: key passes only account for 10% of the Passer score, while successful passes and pass percentage account for 60%. A player can create chances, but if they aren't also completing huge numbers of passes efficiently, they will struggle to climb this list.
The clubs represented here also tell an interesting story. Schwarzwälder FV places four seasons in the top ten, including the top spot and two appearances from Schenkinger. CA Buenos Aires contributes three entries, while Tokyo S.C. places two. Unlike the Playmaker rankings, which were largely a battle between Marco Tentacles and Furious Chicken, the Passer category is spread across several clubs and player archetypes.
Perhaps the biggest takeaway from this list is how compressed the scores are. Schenkinger's league-leading 86.7 is only 4.5 points clear of Jude Greer's tenth-place finish at 82.2. While the names are different from the Playmaker rankings, the same trend appears again: once a player reaches an elite level in the weighted categories, small percentile differences become the deciding factor.
Defender Role
| Rank | Name | Club | Season | Position | Score | Tackles Won p90 | Tackle % | Interceptions p90 | Clearances p90 | Blocks p90 | Successful Presses p90 | Header % |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ilya Prusikin | CF Catalunya | 16 | D/WB ( R ), DM | 87.20 | 3.17 | 86.36 | 5.33 | 1.83 | 1.00 | 3.83 | 22.22 |
| 2 | Matthew Mayhem | União São Paulo | 14 | D (RLC) | 86.80 | 4.93 | 82.14 | 3.79 | 1.43 | 1.50 | 4.57 | 53.70 |
| 3 | Dina Skovgaard | Schwarzwälder FV | 23 | D (RL), WB ( R ) | 86.70 | 3.18 | 89.74 | 3.68 | 1.68 | 0.73 | 3.45 | 46.15 |
| 4 | Jude Greer | Tokyo S.C. | 19 | D/WB/AM (L) | 86.10 | 3.93 | 84.62 | 4.29 | 1.50 | 1.07 | 2.93 | 50.00 |
| 5 | Kofi Anshah | União São Paulo | 13 | D/WB/AM (L) | 85.70 | 3.43 | 82.76 | 4.07 | 1.50 | 1.43 | 3.64 | 42.25 |
| 6 | Milton Núñez | A.C. Romana | 21 | D (LC), WB (L) | 85.70 | 3.83 | 84.15 | 3.33 | 1.67 | 1.44 | 3.33 | 46.97 |
| 7 | Hippity Hoppity | A.C. Romana | 19 | D (RL), WB ( R ) | 85.50 | 3.43 | 84.21 | 2.86 | 3.00 | 1.57 | 2.43 | 76.83 |
| 8 | Felix Arasov | Reykjavik United | 18 | D (L), AM (RL) | 85.00 | 3.39 | 90.38 | 3.90 | 0.87 | 0.65 | 4.33 | 44.55 |
| 9 | Hippity Hoppity | A.C. Romana | 22 | D (RL), WB ( R ) | 84.50 | 3.73 | 80.39 | 3.27 | 1.50 | 0.95 | 4.32 | 69.35 |
| 10 | Milton Núñez | A.C. Romana | 19 | D (LC), WB (L) | 84.30 | 3.21 | 78.95 | 3.50 | 2.57 | 2.21 | 3.29 | 59.72 |
The Defender rankings produced one of the most balanced top tens of any category. Unlike Playmaker, which was dominated by a handful of names, or Scorer, where a few elite attacking seasons separated themselves from the pack, the Defender list is filled with players who arrived at elite scores in very different ways.
Sitting at the top is Ilya Prusikin's s16 season for CF Catalunya. What's interesting about Prusikin is that they don't actually lead many of the raw categories. Matthew Mayhem wins comfortably in tackles won, Hippity Hoppity dominates the aerial game, and several players post similar pressing numbers. What Prusikin does have is perhaps the most complete defensive profile in the group. Their combination of elite interception numbers, strong tackling efficiency, and solid production across every weighted category allows them to narrowly claim first place.
The margins at the top are incredibly thin. Just 0.5 points separate first place Prusikin from third place Dina Skovgaard. In fact, only 2.2 points separate the entire top ten. That is the smallest spread we've seen so far and highlights how difficult it is to distinguish between elite defensive seasons once they are converted into weighted percentile scores.
Matthew Mayhem's s14 campaign is a good example of how the weighting influences the results. Their 4.93 tackles won per 90 is comfortably the best figure in the top ten and one of the strongest individual numbers in any category we've examined. However, tackles won only account for 25% of the Defender score. Prusikin's advantages in interceptions and tackling efficiency are enough to overcome Mayhem's massive lead in raw tackling volume and secure the top spot.
A.C. Romana is perhaps the biggest winner here, placing four seasons in the top ten. Hippity Hoppity appears twice, while Milton Núñez also claims two spots.
What stands out most from this list is the absence of a truly dominant defensive season. No player separates from the field. Instead, the Defender rankings are defined by consistency. Every player in the top ten excelled in multiple defensive categories, and the final order ultimately comes down to tiny percentile differences spread across seven separate metrics.
Presser Role
| Rank | Name | Club | Season | Position | Score | Successful Presses p90 | Press % | Distance Run p90 | Tackles Won p90 | Interceptions p90 | Fouls Against p90 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Tamanna Whittington | Schwarzwälder FV | 16 | D ( C ), WB (RL) | 94.40 | 5.01 | 38.18 | 15.11 | 3.58 | 3.74 | 1.11 |
| 2 | Shinji Kaido | Tokyo S.C. | 15 | M (L), AM (LC) | 94.30 | 5.36 | 36.23 | 15.03 | 3.00 | 4.21 | 1.79 |
| 3 | Tom Pedersen | Schwarzwälder FV | 15 | D (RL), WB (L) | 93.40 | 5.00 | 36.65 | 15.24 | 3.00 | 3.86 | 0.93 |
| 4 | Puma Superhoops | Tokyo S.C. | 21 | D/WB/M ( R ) | 93.30 | 5.22 | 36.29 | 15.01 | 2.72 | 3.50 | 1.83 |
| 5 | Puma Superhoops | Tokyo S.C. | 22 | D/WB/M ( R ) | 92.60 | 5.29 | 33.33 | 15.18 | 2.85 | 4.47 | 1.57 |
| 6 | Puma Superhoops | Tokyo S.C. | 20 | D/WB/M ( R ) | 91.80 | 5.68 | 34.23 | 14.80 | 3.51 | 3.68 | 1.23 |
| 7 | Felix Arasov | Reykjavik United | 14 | D (RL), AM ( R ) | 91.70 | 4.50 | 38.41 | 14.68 | 3.14 | 4.21 | 0.93 |
| 8 | Tom Pedersen | Schwarzwälder FV | 16 | D (RL), WB (L) | 91.70 | 5.00 | 40.23 | 15.02 | 2.29 | 3.14 | 1.07 |
| 9 | Hun Possible | Reykjavik United | 13 | D/WB/M ( R ) | 91.60 | 4.50 | 42.00 | 14.89 | 2.29 | 3.57 | 1.07 |
| 10 | Tamanna Whittington | Schwarzwälder FV | 17 | D (LC), WB (RL) | 91.20 | 4.57 | 34.41 | 14.84 | 3.14 | 3.71 | 1.36 |
If the Defender rankings rewarded complete defensive profiles, the Presser rankings reward pure work rate. Successful Presses p90 alone accounts for 35% of the score, while Press %, Distance Run, Tackles Won, and Interceptions make up another 60%. The result is a list dominated by players who seemed determined to make life miserable for anyone trying to keep possession.
At the top sits Tamanna Whittington's s16 season for Schwarzwälder FV, edging out Shinji Kaido's s15 campaign by the slimmest of margins. Just 0.1 points separate the two. Looking at the numbers, it's easy to see why. Kaido actually records more successful presses and interceptions, while Whittington holds advantages in pressing efficiency, distance covered, and tackles won. Neither player has a meaningful weakness, and the final ranking comes down to tiny percentile differences across six different categories.
What immediately stands out from this list is how concentrated it is around a specific player profile. Nearly every player here has the defender or wing back position. That isn't a coincidence. These positions naturally favor players operating in larger spaces and covering greater distances, and the metrics used in this category heavily reward those responsibilities.
No player embodies that more than Puma Superhoops. Tokyo's relentless right-sided engine appears three times in the top six, including fourth, fifth, and sixth place. What's remarkable is how similar the three seasons are. Each year produced elite pressing numbers, excellent work rate, and strong defensive contributions. Rather than a single standout campaign, the rankings suggest Superhoops maintained an elite pressing standard for several consecutive seasons.
Distance covered also plays a larger role here than it has in any previous category. Hun Possible's s13 season provides a great example. Their 42.00 distance run per 90 is the highest figure in the entire top ten, helping them secure a place despite more modest tackling and pressing numbers than some of the players above them. Likewise, Tom Pedersen's s16 season posts the second-highest pressing efficiency in the group at 40.23%, allowing them to remain competitive despite lower tackle numbers.
The club distribution is also noteworthy. Schwarzwälder FV places four seasons in the top ten, while Tokyo S.C. claims another four. Between Whittington, Pedersen, Kaido, and Superhoops, these two clubs essentially define the category. Whatever tactical systems they employed, they consistently produced players capable of sustaining elite defensive pressure over an entire season.
Perhaps the most impressive takeaway is just how high the overall standard is. First place sits at 94.4, while tenth still clears 91.0. Unlike the Defender rankings, where the field was tightly packed but varied in style, the Presser rankings are packed with specialists who excelled at the exact same job.
Aerial Role
| Rank | Name | Club | Season | Position | Score | Successful Headers p90 | Header % | Key Headers p90 | Clearances p90 |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Henrik Lind | Athênai F.C. | 10 | D ( C ), DM | 99.70 | 23.07 | 92.29 | 4.64 | 19.36 |
| 2 | Bob Divesinalot | Inter London | 9 | D ( C ), DM | 99.60 | 18.00 | 92.31 | 3.57 | 17.71 |
| 3 | Cole Mertz | Seoul MFC | 9 | D ( C ), DM, M (L) | 99.60 | 17.64 | 92.86 | 3.93 | 15.86 |
| 4 | Henrik Lind | Athênai F.C. | 9 | D ( C ), DM | 99.50 | 21.43 | 91.19 | 3.43 | 16.64 |
| 5 | Henrik Lind | AS Paris | 11 | D ( C ), DM | 99.40 | 18.50 | 91.52 | 3.21 | 15.29 |
| 6 | Pierre Houde | Cairo City | 8 | D (LC), DM | 99.30 | 17.14 | 92.66 | 2.57 | 15.29 |
| 7 | Henrik Lind | Athênai F.C. | 8 | D ( C ), DM | 99.10 | 15.79 | 93.25 | 3.86 | 13.07 |
| 8 | Jökull Júlíusson | CF Catalunya | 8 | D (RLC) | 99.10 | 15.73 | 92.80 | 2.87 | 14.94 |
| 9 | Spack Jarrow | Hollywood FC | 12 | D (RLC) | 99.10 | 17.00 | 90.49 | 3.57 | 16.64 |
| 10 | Sator Freddy | Sydney City | 5 | D ( C ), DM, M ( C ) | 99.00 | 17.71 | 91.18 | 2.00 | 16.29 |
The Aerial rankings may be the most lopsided category in the entire project. While other roles featured players from across multiple eras and clubs competing on relatively even footing, the Aerial list is almost entirely dominated by a small group of early league central defenders who appear to have been playing a different sport from everyone else.
At the center of it all is Henrik Lind. Four of the top ten spots belong to them, including the number one overall ranking from their s10 campaign at Athênai F.C. Their numbers are absurd across the board. More than 23 successful headers per 90, a 92.29% success rate, 4.64 key headers per 90, and over 19 clearances per match. Those figures aren't just elite within this ranking, they're outliers even compared to the rest of an already aerially dominant top ten.
In fact, Henrik's greatest competition comes from players who posted nearly identical profiles. Bob Divesinalot, Cole Mertz, Pierre Houde, Jökull Júlíusson, and Spack Jarrow all combine exceptional heading volume with success rates above 90%. The result is one of the closest finishes we've seen so far. Only 0.7 points separate first place from tenth, despite the category spanning multiple clubs and seasons.
What's particularly interesting is how compressed the scores become when a category is built around only four metrics. Successful Headers p90 and Header % account for 70% of the score by themselves, meaning players who dominate aerial duels start with a massive advantage. Key Headers and Clearances then serve primarily as tiebreakers between players who are already elite in the air. Once a player reaches the low 90s in Header % while maintaining enormous volume, there simply isn't much room left to separate them from their peers.
The recurring names tell the story. Henrik Lind appears four times while several other defenders appear with seasons that would have comfortably topped later eras. Rather than highlighting a single dominant year, the rankings suggest that a specific generation of defenders consistently produced aerial numbers that have rarely been matched since.
The other thing that jumps out is how traditional these players are. Unlike the Defender or Presser rankings, where wing backs and wide players frequently appeared, the Aerial category belongs almost exclusively to center-backs and defensive midfielders. That's not surprising given the weighting. Winning headers, winning them efficiently, and then turning those wins into defensive actions are exactly the traits the formula is designed to reward.
More than any other role so far, the Aerial rankings feel like a snapshot of a particular era. Whether that reflects tactical differences, league evolution, or simply a generation of great players roaming the back lines is up for debate. What isn't debatable is Henrik Lind's place at the top. When one player claims four of the top ten spots, including the best score ever recorded in the category, the discussion begins with them and works downward from there.
Wide Creator Role
| Rank | Name | Club | Season | Position | Score | Successful Crosses p90 | Cross % | Chances Created p90 | Open Play Key Passes p90 | Assists p90 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Furious Chicken | Reykjavik United | 19 | D (RL), AM ( R ) | 96.30 | 4.50 | 34.43 | 1.29 | 2.29 | 0.86 |
| 2 | Yoma Hashimoto | União São Paulo | 22 | D/WB (L), DM | 95.90 | 4.73 | 31.52 | 1.45 | 2.91 | 0.68 |
| 3 | Henry Andrews | Reykjavik United | 17 | WB/M/AM ( R ) | 95.20 | 2.64 | 32.74 | 2.57 | 4.93 | 1.36 |
| 4 | Furious Chicken | Reykjavik United | 16 | D (RL), AM (L) | 94.90 | 4.07 | 30.48 | 1.50 | 2.14 | 0.71 |
| 5 | Jude Greer | Tokyo S.C. | 21 | D/WB/AM (L) | 94.80 | 4.11 | 29.48 | 1.33 | 2.56 | 0.72 |
| 6 | Marco Tentacles | Reykjavik United | 20 | D/M/AM (L) | 94.70 | 3.89 | 28.57 | 1.56 | 2.89 | 0.83 |
| 7 | Yoma Hashimoto | União São Paulo | 21 | D/WB (L), DM | 94.60 | 3.94 | 28.98 | 1.50 | 2.44 | 0.83 |
| 8 | Fara Dian | Schwarzwälder FV | 17 | M ( C ), AM (RC) | 94.40 | 4.00 | 30.27 | 1.36 | 2.21 | 0.50 |
| 9 | Evan Hunt | Hollywood FC | 23 | WB (RL), DM, AM ( C ) | 94.40 | 2.68 | 40.97 | 1.09 | 1.68 | 0.64 |
| 10 | Yoma Hashimoto | União São Paulo | 23 | D/WB (L), DM | 94.40 | 3.76 | 28.03 | 1.85 | 3.76 | 1.02 |
The Wide Creator rankings feel like the natural mix between the Playmaker and Passer categories. Crossing volume and crossing efficiency account for 60% of the score, but players are also rewarded for creating chances, generating open-play key passes, and producing assists. The result is a category that favors players who consistently create attacking opportunities from wide areas, whether through traditional crossing or more modern creative play.
At the top sits Furious Chicken's s19 season for Reykjavik United. While several players on this list post stronger numbers in individual categories, Chicken's profile is remarkably complete. They combine elite crossing volume with strong accuracy, solid chance creation, and the highest assist rate among the top two finishers. As we've seen throughout these rankings, having no weaknesses often matters more than dominating a single category.
The battle for first is surprisingly close. Yoma Hashimoto's s22 season finishes just 0.4 points behind despite posting more successful crosses and creating more chances. The difference ultimately comes down to crossing efficiency and assists, where Chicken maintains a small edge. Given how heavily crossing metrics are weighted, even minor percentile advantages can make the difference between first and second place.
The most fascinating player on the list may be Henry Andrews. Unlike the players around them, Andrews doesn't rely on crossing volume at all. Their 2.64 successful crosses per 90 is nearly two full crosses behind the leaders. Instead, they compensated with extraordinary creative numbers: 2.57 chances created, 4.93 open-play key passes, and 1.36 assists per 90. Those figures are among the best in the entire table and demonstrate that there is more than one path to success in this category.
Much like the Playmaker rankings, certain names appear repeatedly. Furious Chicken claims two spots in the top four, while Yoma Hashimoto appears three times in the top ten. Marco Tentacles, Jude Greer, and Fara Dian also make appearances after already showing up in earlier creative categories. This suggests that many of the league's best creators were capable of producing chances in multiple ways rather than fitting neatly into a single role.
One player who stands out from the rest is Evan Hunt's s23 season. Their crossing accuracy of 40.97% is comfortably the highest in the top ten, yet they only ranks ninth overall. That's a perfect illustration of how the weighting system works. Cross percentage is important, but it only accounts for 25% of the score. Lower crossing volume and fewer chance creation numbers prevent Hunt from climbing higher despite his exceptional efficiency.
The club distribution also tells an interesting story. Reykjavik United claims four of the top ten spots, while União São Paulo contributes three. Together they account for seven of the ten best wide-creator seasons ever recorded.
Unlike the Aerial rankings, where a handful of historical outliers separated themselves from the field, the Wide Creator category remains remarkably competitive. Only 1.9 points separate first place from tenth, and nearly every player on the list has a compelling argument built around a different strength. Some overwhelmed opponents with crossing volume, others with efficiency, and a few through pure creativity. The rankings ultimately reward the players who managed to do all three at once.
Dribbler Role
| Rank | Name | Club | Season | Position | Score | Dribbles p90 | Chances Created p90 | Key Passes p90 | Progressive Passes p90 | Assists p90 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Yoma Hashimoto | União São Paulo | 21 | D/WB (L), DM | 98.00 | 3.72 | 1.50 | 5.06 | 7.67 | 0.83 |
| 2 | Furious Chicken | Reykjavik United | 16 | D (RL), AM (L) | 97.80 | 4.43 | 1.50 | 5.64 | 5.57 | 0.71 |
| 3 | Roquefort Cotswold | União São Paulo | 22 | D ( R ), WB (RL) | 97.80 | 3.67 | 1.79 | 4.98 | 7.80 | 0.61 |
| 4 | Jude Greer | Tokyo S.C. | 18 | D/WB/AM (L) | 97.60 | 3.71 | 1.50 | 4.43 | 7.21 | 0.71 |
| 5 | Yoma Hashimoto | União São Paulo | 19 | D/WB (L), DM | 97.50 | 3.70 | 1.56 | 4.44 | 7.48 | 0.52 |
| 6 | Yoma Hashimoto | União São Paulo | 23 | D/WB (L), DM | 97.20 | 3.29 | 1.85 | 6.31 | 7.37 | 1.02 |
| 7 | Roquefort Cotswold | União São Paulo | 23 | D ( R ), WB (RL) | 96.70 | 3.18 | 1.61 | 6.58 | 9.12 | 0.60 |
| 8 | Hun Possible | Reykjavik United | 13 | D/WB/M ( R ) | 96.60 | 5.14 | 1.36 | 2.43 | 7.93 | 0.43 |
| 9 | Yoma Hashimoto | União São Paulo | 22 | D/WB (L), DM | 96.60 | 3.55 | 1.45 | 5.73 | 5.82 | 0.68 |
| 10 | Felix Arasov | Reykjavik United | 16 | D (RL), AM ( R ) | 96.30 | 3.86 | 1.29 | 3.14 | 9.07 | 0.36 |
The Dribbler rankings may have the most misleading name of any category in the project. Dribbles account for 40% of the score, making them the single most important metric, but the remaining 60% comes from chance creation, key passes, progressive passes, and assists. The result isn't a ranking of players who simply beat opponents off the dribble. It's a ranking of players who turn ball progression into attacking production.
At the top sits Yoma Hashimoto's s21 season for União São Paulo. What makes Hashimoto's performance so impressive is how balanced it is. They don’t lead the table in dribbles, assists, or chance creation, but they rank near the top in every category. As we've seen repeatedly throughout these rankings, the weighted percentile system heavily rewards players who avoid weaknesses. Being elite everywhere is often more valuable than being historically great in a single area.
The race for first is exceptionally close. Furious Chicken's s16 campaign finishes just 0.2 points behind despite posting the highest dribble rate among the top contenders. In many ways, Chicken's season is exactly what people imagine when they hear "dribbler." They attack defenders relentlessly, create chances, and generate key passes at an elite rate. What ultimately costs them is a lower progressive passing contribution compared to Hashimoto, whose ability to consistently move the ball into dangerous areas boosts their overall profile.
One of the biggest surprises is how heavily the rankings are dominated by defenders and wing backs. Nine of the ten entries come from players whose primary positions are in defense or wide areas. Rather than traditional wingers or attacking midfielders, the formula consistently rewards players who can carry the ball forward from deeper positions while also contributing creatively.
No player embodies that better than Yoma Hashimoto. They appeared four times in the top ten, including the number one spot. Roquefort Cotswold adds two more appearances for União São Paulo, meaning the club claims six of the ten positions outright. For a category built around ball progression and creativity, that's a staggering level of dominance by a single club.
Roquefort's two appearances are particularly interesting because they demonstrate a different route to success. Their s23 season records the highest progressive passing figure in the entire top ten at 9.12 per 90 while also leading the table in key passes. The dribbling numbers themselves are relatively modest compared to some of the players around them, but their ability to advance attacks through passing compensates for that and keeps them firmly among the elite.
Hun Possible's s13 campaign provides another example of the formula at work. They lead the entire top ten in dribbles per 90 with an incredible 5.14, yet only finish eighth overall. The reason is simple: dribbling may be the largest individual category, but it is still only worth 40% of the score. Lower creative output in the remaining metrics prevents them from climbing higher despite posting the category's most eye-catching headline statistic.
Perhaps the biggest takeaway from the Dribbler rankings is how closely intertwined dribbling and creativity have become. The players at the top aren't simply carrying the ball. They're using those carries to create chances, generate assists, and progress attacks. That's why the list feels less like a ranking of flashy dribblers and more like a ranking of the league's most effective ball progressors.
The margins reflect that quality. Only 1.7 points separate first place from tenth, and several players are separated by just a few tenths of a point. As with many of the creative categories, the difference between the best and the tenth-best season is remarkably small once everything is converted into weighted percentile scores.
Goalkeeper Role
| Rank | Name | Club | Season | Position | Score | Average Rating | Save % | xSave % | xG Prevented p90 | Clean Sheets p90 | Penalties Saved p90 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Bartholomew Twinkletoes | CF Catalunya | 24 | 91.10 | 7.24 | 84.62 | 84.95 | 0.96 | 0.57 | 0.14 | |
| 2 | Shingo Takechi | Shanghai Dragons FC | 24 | 88.80 | 7.27 | 82.28 | 86.33 | 0.66 | 0.30 | 0.00 | |
| 3 | Denis Mobek | Xelajú Cósmico FC | 24 | 88.20 | 7.30 | 81.25 | 85.71 | 0.48 | 0.40 | 0.00 | |
| 4 | Elmis The Heretic | A.C. Romana | 13 | 87.50 | 8.29 | 77.65 | 107.27 | 0.68 | 0.14 | 0.07 | |
| 5 | Duncan Walrusson | Hollywood FC | 25 | 86.40 | 7.45 | 83.12 | 82.91 | 0.68 | 0.60 | 0.00 | |
| 6 | Muunokhoi Sarantsatsral | Schwarzwälder FV | 15 | 84.50 | 7.27 | 81.97 | 84.81 | 0.38 | 0.36 | 0.00 | |
| 7 | Certified Problem | Sydney City | 6 | 84.00 | 7.87 | 91.18 | 87.74 | 0.00 | 0.43 | 0.00 | |
| 8 | Duncan Walrusson | Hollywood FC | 23 | 83.20 | 7.25 | 82.11 | 83.19 | 0.46 | 0.56 | 0.00 | |
| 9 | Dexter Hall | Hollywood FC | 18 | 81.50 | 7.30 | 78.81 | 83.73 | 0.52 | 0.21 | 0.14 | |
| 10 | Jannik Andersen | Athênai F.C. | 9 | 80.40 | 7.27 | 83.96 | 86.82 | 0.00 | 0.36 | 0.00 |
The Goalkeeper rankings produced one of the most interesting results in the entire project because they highlight a fundamental difference between evaluating outfield players and evaluating goalkeepers. While most of the other categories are built around volume statistics, the Goalkeeper score is much more heavily influenced by efficiency. Average Rating and Save Percentage alone account for half of the total score, while xSave%, xG Prevented, Clean Sheets, and Penalties Saved fill out the remainder.
At the top of the rankings is Bartholomew Twinkletoes' s24 season for CF Catalunya. Unlike some of the runaway winners we've seen in other categories, Twinkletoes doesn't dominate every individual statistic. Shingo Takechi, Denis Mobek, and even several lower-ranked goalkeepers hold advantages in specific categories. What separates Twinkletoes is their combination of elite shot-stopping, strong xG prevention, and an outstanding clean sheet rate. they don't simply excel in one area, they rank near the top across almost all of them.
The most striking aspect of the list is how heavily it favors recent seasons. Three of the top four spots belong to season 24 goalkeepers, and four of the top five come from seasons 24 and 25. Whether that reflects improved goalkeeping quality, tactical evolution, or changes in the way teams defend is difficult to say, but the rankings clearly suggest modern keepers are producing stronger all-around statistical profiles than many of their predecessors.
The biggest outlier on the list is undoubtedly Elmis The Heretic's s13 season for A.C. Romana. While most goalkeepers cluster in the mid-80s for xSave%, Elmis posts an astonishing 107.27%. That number immediately jumps off the page because it implies they saved substantially more goals than expected over the course of the season. Despite posting by far the most eye-catching xSave% figure in the rankings, he only finishes fourth overall. That's a perfect illustration of how the weighting works. xSave% is important, but it only accounts for 15% of the score. Lower save percentage and clean sheet numbers prevent them from challenging for first place.
Another interesting case is Certified Problem's s6 campaign for Sydney City. They own the highest save percentage in the top ten at 91.18%, an absurd figure by any standard. Yet he only finishes seventh overall because their xG prevented value is zero and their clean sheet rate trails several of the players above them. Once again, the rankings reward complete goalkeeping profiles rather than singular standout statistics.
Hollywood FC quietly places three separate seasons in the top ten, led by Duncan Walrusson's s25 and s23 campaigns and Dexter Hall's s18 season. No other club matches that level of representation, suggesting Hollywood enjoyed sustained excellence in goal rather than relying on a single elite season.
Perhaps the most surprising takeaway is the gap at the top. Unlike many of the outfield categories where a point or two separated the entire top ten, Twinkletoes finished 2.3 points ahead of second place. In weighted percentile terms, that's a significant margin. While the rest of the rankings are tightly contested, the numbers suggest that their s24 season stands alone as the strongest goalkeeping campaign on record.
Whether that makes Bartholomew Twinkletoes the greatest goalkeeper in league history is a debate for another day. What this ranking does show is that no other season combined elite shot-stopping, goal prevention, and team success quite as effectively as they did.
Conclusion
If you made it to this point, then I hope you enjoyed the deep statistics dump. There was a lot more to look at here than I thought there would be, but similar to when I made scouting profiles for the Academy class, I really enjoy looking at players more by the type of role they are playing rather than trying to map into raw positions.
Given how many different tactics setups I have seen examples of, I'm not sure positions are ever consistent enough to be useful in actually categorizing.
More importantly, I'm still extremely happy with the index and the ability to get it via API. I have a massive Google sheet for this and it wouldn't be possible to write articles like this if I could get table summary data that I can then visually determine what I think is worth exploring further.
If the s25 Academy Index gets fixed, I might do this article again for the Academy class so that my class can see where we rank in terms of the all time Academy classes.
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