| M!lf Lover vs Kazuya | 49–55 | 47.12% |
| M!lf Lover vs Hwoarang | 48–36 | 57.14% |
| M!lf Lover vs Jin | 52–30 | 63.41% |
| M!lf Lover vs Law | 29–33 | 46.77% |
| M!lf Lover vs Steve | 38–23 | 62.30% |
| M!lf Lover vs King | 30–27 | 52.63% |
| M!lf Lover vs Bryan | 24–29 | 45.28% |
| M!lf Lover vs Fahkumram | 31–19 | 62.00% |
| M!lf Lover vs Paul | 28–20 | 58.33% |
| M!lf Lover vs Eddy | 32–14 | 69.57% |
| M!lf Lover vs Jun | 25–16 | 60.98% |
| M!lf Lover vs Clive | 22–19 | 53.66% |
| M!lf Lover vs Lee | 20–15 | 57.14% |
| M!lf Lover vs Heihachi | 12–22 | 35.29% |
| M!lf Lover vs Victor | 20–12 | 62.50% |
| M!lf Lover vs Yoshimitsu | 15–16 | 48.39% |
| M!lf Lover vs Lars | 17–14 | 54.84% |
| M!lf Lover vs Dragunov | 17–13 | 56.67% |
| M!lf Lover vs Azucena | 20–10 | 66.67% |
| M!lf Lover vs Devil Jin | 17–12 | 58.62% |
| M!lf Lover vs Feng | 18–11 | 62.07% |
| M!lf Lover vs Lili | 17–11 | 60.71% |
| M!lf Lover vs Reina | 14–14 | 50.00% |
| M!lf Lover vs Nina | 10–14 | 41.67% |
| M!lf Lover vs Asuka | 14–9 | 60.87% |
| M!lf Lover vs Leroy | 11–11 | 50.00% |
| M!lf Lover vs Raven | 13–6 | 68.42% |
| M!lf Lover vs Xiaoyu | 6–9 | 40.00% |
| M!lf Lover vs Alisa | 7–7 | 50.00% |
| M!lf Lover vs Jack-8 | 8–5 | 61.54% |
| M!lf Lover vs Anna | 4–8 | 33.33% |
| M!lf Lover vs Shaheen | 6–5 | 54.55% |
| M!lf Lover vs Leo | 3–6 | 33.33% |
| M!lf Lover vs Kuma | 6–2 | 75.00% |
| M!lf Lover vs Lidia | 1–6 | 14.29% |
| M!lf Lover vs Armor King | 5–1 | 83.33% |
| M!lf Lover vs Claudio | 0–5 | 0.00% |
| M!lf Lover vs Zafina | 3–2 | 60.00% |
| M!lf Lover vs Miary Zo | 1–2 | 33.33% |
| M!lf Lover vs Panda | 0–1 | 0.00% |
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.