Name history
| mmmll | |
| KNEE | |
| JPMonkey killer | |
| 일본숭이 머리 부수기 | |
| LOllll | |
| hi ccccc | |
| 뉴비 부수기 | |
| 뭘쳐다보노 | |
| BYungsin game | |
| ISLAM | |
| 뉴비 분쇄기 | |
| lgbt | |
| ㅋㅋ쉽노 | |
| 부캐 브러지 줘패기 | |
| Japan = monkey | |
| 녹단 분쇄기 |
| mmmll vs Dragunov | 70–6 | 92.11% |
| mmmll vs Kazuya | 71–1 | 98.61% |
| mmmll vs Reina | 60–5 | 92.31% |
| mmmll vs Jin | 54–8 | 87.10% |
| mmmll vs Paul | 57–4 | 93.44% |
| mmmll vs Bryan | 56–1 | 98.25% |
| mmmll vs Victor | 51–4 | 92.73% |
| mmmll vs King | 51–3 | 94.44% |
| mmmll vs Hwoarang | 52–2 | 96.30% |
| mmmll vs Steve | 46–1 | 97.87% |
| mmmll vs Asuka | 38–5 | 88.37% |
| mmmll vs Lili | 41–0 | 100.00% |
| mmmll vs Yoshimitsu | 38–2 | 95.00% |
| mmmll vs Azucena | 29–1 | 96.67% |
| mmmll vs Lars | 26–2 | 92.86% |
| mmmll vs Eddy | 13–13 | 50.00% |
| mmmll vs Lee | 21–4 | 84.00% |
| mmmll vs Devil Jin | 24–0 | 100.00% |
| mmmll vs Nina | 21–2 | 91.30% |
| mmmll vs Claudio | 21–1 | 95.45% |
| mmmll vs Jun | 21–1 | 95.45% |
| mmmll vs Lidia | 21–1 | 95.45% |
| mmmll vs Kuma | 18–2 | 90.00% |
| mmmll vs Alisa | 17–1 | 94.44% |
| mmmll vs Leroy | 17–0 | 100.00% |
| mmmll vs Feng | 13–3 | 81.25% |
| mmmll vs Law | 15–0 | 100.00% |
| mmmll vs Jack-8 | 12–1 | 92.31% |
| mmmll vs Zafina | 10–1 | 90.91% |
| mmmll vs Raven | 8–2 | 80.00% |
| mmmll vs Shaheen | 6–3 | 66.67% |
| mmmll vs Xiaoyu | 7–1 | 87.50% |
| mmmll vs Leo | 7–1 | 87.50% |
| mmmll vs Clive | 6–0 | 100.00% |
| mmmll vs Panda | 3–0 | 100.00% |
| mmmll vs Heihachi | 3–0 | 100.00% |
| mmmll vs Anna | 1–0 | 100.00% |
Limitations
This data is often requested to give insight into which characters you have more trouble with than others, but it is not particularly helpful for that. The main issue is that it is heavily skewed by how strong the opponents you play are.
For example, this data suggests my worst matchup is clearly vs Reina, but that's just because most of those games are vs Yagami.
There is a way to account for this being worked on. The central idea is to assign each matchup a rating vs you which adjusts based on the result, much like the regular rating but also based on the rating of each player. With this, it would give a better summary of how well you perform vs each character.
In the meantime, this page is here to present the data as requested.