| Danggit Smee vs Heihachi | 38–22 | 63.33% |
| Danggit Smee vs Kazuya | 22–16 | 57.89% |
| Danggit Smee vs Armor King | 20–15 | 57.14% |
| Danggit Smee vs Reina | 22–8 | 73.33% |
| Danggit Smee vs Bryan | 15–13 | 53.57% |
| Danggit Smee vs Clive | 17–11 | 60.71% |
| Danggit Smee vs Jin | 15–11 | 57.69% |
| Danggit Smee vs King | 16–7 | 69.57% |
| Danggit Smee vs Lee | 14–9 | 60.87% |
| Danggit Smee vs Lars | 11–10 | 52.38% |
| Danggit Smee vs Azucena | 13–6 | 68.42% |
| Danggit Smee vs Lidia | 11–7 | 61.11% |
| Danggit Smee vs Hwoarang | 10–7 | 58.82% |
| Danggit Smee vs Steve | 13–4 | 76.47% |
| Danggit Smee vs Paul | 12–3 | 80.00% |
| Danggit Smee vs Leo | 9–6 | 60.00% |
| Danggit Smee vs Law | 10–4 | 71.43% |
| Danggit Smee vs Yoshimitsu | 13–1 | 92.86% |
| Danggit Smee vs Lili | 7–7 | 50.00% |
| Danggit Smee vs Asuka | 7–6 | 53.85% |
| Danggit Smee vs Jun | 10–3 | 76.92% |
| Danggit Smee vs Victor | 8–3 | 72.73% |
| Danggit Smee vs Miary Zo | 7–4 | 63.64% |
| Danggit Smee vs Xiaoyu | 7–3 | 70.00% |
| Danggit Smee vs Fahkumram | 1–9 | 10.00% |
| Danggit Smee vs Dragunov | 5–4 | 55.56% |
| Danggit Smee vs Devil Jin | 2–6 | 25.00% |
| Danggit Smee vs Alisa | 6–2 | 75.00% |
| Danggit Smee vs Claudio | 2–6 | 25.00% |
| Danggit Smee vs Kuma | 6–1 | 85.71% |
| Danggit Smee vs Jack-8 | 3–3 | 50.00% |
| Danggit Smee vs Raven | 2–4 | 33.33% |
| Danggit Smee vs Nina | 2–2 | 50.00% |
| Danggit Smee vs Feng | 3–0 | 100.00% |
| Danggit Smee vs Panda | 1–2 | 33.33% |
| Danggit Smee vs Zafina | 2–0 | 100.00% |
| Danggit Smee vs Eddy | 0–2 | 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.