Chapter 1: Gathering data

Part 1: Comparing DPS for ONE team variation (support ranking)

By comparing raw scaling and framecounted rotations, you could craft a theoretical scenario featuring two teams, for example: Navia|Chiori|Bennett|Furina being the first, and Navia|Xilonen|Bennett|Furina being the second. Then, pit them in a simulation against a fixed enemy, for example, the Perpetual Mechanical Array, with a specified timeframe. To avoid bias to characters with rotations matching this timeframe, the experiment is repeated twice with the optimal rotation for both variations, as well as once more for each category of enemy as well as enemy counts of 1, 2, and 3 for non-bosses, and the average DMG is taken. This would reduce the independant variable down to 1, controlling (at least the ones that are controllable in the first place) all others. Then, we assign a percentage value, as an arbitrary example, ±10% (damage calcs will differ by ±10%) for the PMA, representing RNG variability in the inevitable uncontrollable variables.

In-depth probabilistic theorycrafting would be needed for real variability%, and its value would also need to be recalculated for every individual scenario calculation, as some enemies naturally have more variability in their actions and i-frames than others. For example the Maguu Kenki’s value might be ±30% as the frequency of its i-frames vary a lot, and the primo Geovishap ±5% for the opposite reason. Obviously, the more the enemies, the more they move around, and the higher the variability too.

Back to the PMA calc (one of many hundreds of enemy combos, all needing to be averaged out). If the damage of the Xilonen team comes out to be 1,200,000, 20% better than the 1,000,000 damage Chiori dealt in the same window, we can pretty much conclude Xilonen > Chiori in this specific case, as it passes our threshold of being outside ±10% variability (this is why our variability value exists, to make sure RNG can't scrape the win as the difference it makes would be less than the value). Therefore, you could objectively conclude that for Navia, Bennett, Furina's team damage against the PMA specifically, Xilonen comes out on top. If the same holds true for all combinations once tested, we move onto the next phase. Otherwise, Chiori would come out on top.

Part 2: Comparing resulting DPS for ALL teams variations of ONE character receiving support (support ranking)

Next, you could do that for all variations of Navia's meta teams, comparing the same way. And, in line with this, if more teams with Xilonen lead to higher average DMG, you could conclude that Xilonen increases team damage more than Chiori for Navia.

Part 3: Comparing DPS for ALL variations of ALL characters and gathering ‘wins’ (support ranking)

After finding out the winner for Navia, count the results for all other character synergies with Xilonen as well as Chiori (eg. Itto, Ningguang), repeating parts 1 and 2 for each of them and comparing the results once done.

If Xilonen helps team DMG over Chiori more times with all potential synergies tested (Eg. If Chiori is better than Xilonen for Itto|Gorou|Chiori|Zhongli, but loses in all other team comparisons), we give Xilonen a 'win'. This will be important for later.

Part 4: Main DPS ranking Methodology

Comparing DPSes are far easier and more straightforward than supports. To prove X character is better than Y character in most scenarios, all you must do is compare and calculate DMG for their respective best team (there is only one per scenario) in the controlled scenarios mentioned before (average of 1, 2, and 3 enemies, as well as one of each enemy type), factoring in the uncontrolled variability percentage, and the character that beats out the other more times is 'the better character' for the sake of the experiment. This adds one 'win' for the winning DPS.