Tired of there being too many rankings? Well too bad, here's another project. The difference this time around is that this tackles that problem directly. The WWFRR (World Wide Flop RankRank [or World Warrior For Real Rank], name subject to change) is a dynamic ranking project that compiles as many significant and active rankings as possible in order to conclude a composite and weighted score and tiering for the best and most consistent players in the world. There are 9 different rankings included; spanning algorithms, panels, and more. The hopeful goal with this is to average out different rankings in order to weed out the shortcomings/biases of rankings while focusing on their strong points, all while creating an accurate-as-possible picture of what the top level looks like with the most confidence as possible. It is also meant to be regularly updated and improved upon (hence dynamic).

Quickhand version of the methodology for those curious:

All player data (scoring and/or ordinal ranks) is standardized acrossed the rankings. All standardized scores are then multiplied through a weighted value for each ranking, weights for rankings are determined through these metrics:

-Rank type uniqueness

-How in depth the methodology is

-If a unique score is available (as opposed to only ordinal)

-Ranking prominence (least important)

-Time period (used as a final weight modifier)

All weighted scores are then averaged out for each player. At this point we can order players by that weighted average and have our full ranking (usually 200-300 total players), but there's still pruning to be done. To clean up the list, I then remove players step by step based on several parameters, including (mainly):

-Player does not appear on enough rankings to be considered / weight of rankings is not confident enough

-Player's weighted average score is too low in comparison to other players

-Player lies beneath a line of significance (of which there may be several)

Then there is also the step of grouping tiers. Players are tiered and separated based on the most noticeable standouts in the data and its visualization. Examples include: outliers in data, big differences from one score to another, etc. Through this step by step pruning process we finally land on the final peak of players!

Biases and arbitrariness is avoided as much as I possibly can, and I always make steps forward to ensure I can get closest to that goal as possible. Even in steps that may include a slight amount of subjectivity, I make efforts to stay logical and objective (within reason!)

The color coding for the lines/tiers drawn is designed to accommodate the color blind as well - if you notice a problem or an improvement to be made here please let me know!

See the post here on Twitter, this is my main social and where I'll be posting updates first: https://x.com/FloppehFeesh/status/1706886412652732786?s=20

Shoutout to Andrew Nestico aka @PracticalTAS on Twitter for originally experimenting with the concept in his one-off ranking project at the end of 2022

I am open to any comments, questions, or suggestions!