| TrashyGeek vs Kazuya | 53–59 | 47.32% |
| TrashyGeek vs Bryan | 32–40 | 44.44% |
| TrashyGeek vs Hwoarang | 27–37 | 42.19% |
| TrashyGeek vs Jin | 26–36 | 41.94% |
| TrashyGeek vs Reina | 38–21 | 64.41% |
| TrashyGeek vs Heihachi | 23–34 | 40.35% |
| TrashyGeek vs Lili | 23–31 | 42.59% |
| TrashyGeek vs King | 25–28 | 47.17% |
| TrashyGeek vs Yoshimitsu | 22–26 | 45.83% |
| TrashyGeek vs Law | 20–21 | 48.78% |
| TrashyGeek vs Dragunov | 20–21 | 48.78% |
| TrashyGeek vs Asuka | 20–16 | 55.56% |
| TrashyGeek vs Paul | 13–22 | 37.14% |
| TrashyGeek vs Steve | 15–19 | 44.12% |
| TrashyGeek vs Lidia | 18–16 | 52.94% |
| TrashyGeek vs Eddy | 21–11 | 65.62% |
| TrashyGeek vs Alisa | 16–15 | 51.61% |
| TrashyGeek vs Azucena | 17–12 | 58.62% |
| TrashyGeek vs Xiaoyu | 12–14 | 46.15% |
| TrashyGeek vs Lee | 10–15 | 40.00% |
| TrashyGeek vs Victor | 15–10 | 60.00% |
| TrashyGeek vs Armor King | 16–9 | 64.00% |
| TrashyGeek vs Devil Jin | 15–7 | 68.18% |
| TrashyGeek vs Feng | 10–12 | 45.45% |
| TrashyGeek vs Lars | 11–11 | 50.00% |
| TrashyGeek vs Jun | 6–14 | 30.00% |
| TrashyGeek vs Nina | 10–9 | 52.63% |
| TrashyGeek vs Raven | 9–8 | 52.94% |
| TrashyGeek vs Clive | 9–8 | 52.94% |
| TrashyGeek vs Jack-8 | 10–6 | 62.50% |
| TrashyGeek vs Leo | 6–6 | 50.00% |
| TrashyGeek vs Shaheen | 7–5 | 58.33% |
| TrashyGeek vs Leroy | 8–4 | 66.67% |
| TrashyGeek vs Anna | 6–5 | 54.55% |
| TrashyGeek vs Claudio | 5–5 | 50.00% |
| TrashyGeek vs Miary Zo | 6–4 | 60.00% |
| TrashyGeek vs Fahkumram | 4–5 | 44.44% |
| TrashyGeek vs Panda | 3–2 | 60.00% |
| TrashyGeek vs Zafina | 2–2 | 50.00% |
| TrashyGeek vs Kuma | 0–3 | 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.