| nps_ vs King | 27–20 | 57.45% |
| nps_ vs Jin | 24–15 | 61.54% |
| nps_ vs Dragunov | 21–14 | 60.00% |
| nps_ vs Reina | 19–14 | 57.58% |
| nps_ vs Hwoarang | 19–13 | 59.38% |
| nps_ vs Bryan | 21–9 | 70.00% |
| nps_ vs Yoshimitsu | 12–16 | 42.86% |
| nps_ vs Law | 14–10 | 58.33% |
| nps_ vs Jun | 7–15–1 | 31.82% |
| nps_ vs Lili | 17–5 | 77.27% |
| nps_ vs Lee | 14–8 | 63.64% |
| nps_ vs Asuka | 13–8 | 61.90% |
| nps_ vs Feng | 6–15 | 28.57% |
| nps_ vs Lars | 15–6 | 71.43% |
| nps_ vs Eddy | 10–11 | 47.62% |
| nps_ vs Kazuya | 16–4 | 80.00% |
| nps_ vs Zafina | 13–6 | 68.42% |
| nps_ vs Azucena | 11–8 | 57.89% |
| nps_ vs Alisa | 13–5 | 72.22% |
| nps_ vs Claudio | 13–5 | 72.22% |
| nps_ vs Steve | 10–7 | 58.82% |
| nps_ vs Victor | 11–6 | 64.71% |
| nps_ vs Paul | 9–7 | 56.25% |
| nps_ vs Devil Jin | 6–9 | 40.00% |
| nps_ vs Leroy | 7–8 | 46.67% |
| nps_ vs Jack-8 | 10–4 | 71.43% |
| nps_ vs Raven | 4–10 | 28.57% |
| nps_ vs Heihachi | 7–4 | 63.64% |
| nps_ vs Leo | 4–6 | 40.00% |
| nps_ vs Nina | 6–4 | 60.00% |
| nps_ vs Xiaoyu | 6–3 | 66.67% |
| nps_ vs Anna | 5–3 | 62.50% |
| nps_ vs Panda | 1–3 | 25.00% |
| nps_ vs Lidia | 3–1 | 75.00% |
| nps_ vs Clive | 1–3 | 25.00% |
| nps_ vs Shaheen | 2–1 | 66.67% |
| nps_ vs Kuma | 0–1 | 0.00% |
| nps_ vs Fahkumram | 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.