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VictoryRanked
CH
M.CHUpdated 5 hours ago
Steam
OtherClub · KungFuAcademyC100%
SteamOtherClub · KungFuAcademyC100%あらやしき
NEW GENERATION
MR 1,604#84,326Last 106–4+21Last 2013–7+47
Winrate
53%
47W · 41L · 88 stored
Ranked matches
88
selected mode
Wins
47
Ranked
Losses
41
Ranked
Peak MR
1,604
Chun-Li
Ranked MR · Chun-Li
88 local · 0 imported · 88 on chart · 136 battles stored
Replay not requested
CHM.Playstyle reference
Habits vs top 30 Chun-Li
Benchmark refreshed Sep 3, 2026, 7:25 AM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
CHLargest absolute differences first23 habits
| Habit | あらやしき | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA2 | 9.6% | 31.7% | -22.1% | |
| SA3 | 48.1% | 26.2% | +21.9% | |
| CA | 17.3% | 7.6% | +9.7% | |
| SA1 | 25.0% | 34.5% | -9.5% | |
| Drive Arts | 11.7% | 16.9% | -5.2% | |
| Drive Rush (cancel) | 43.1% | 40.1% | +3.0% | |
| Corner pressure | 10.40 | 11.27 | -0.87 | |
| Throws | 1.80 | 2.39 | -0.59 | |
| Drive Guard | 9.7% | 10.2% | -0.5% | |
| Drive Impact gauge | 1.9% | 1.4% | +0.5% | |
| Throw techs | 0.80 | 0.40 | +0.40 | |
| Time cornered | 9.90 | 9.57 | +0.33 | |
| Perfect Parry | 0.30 | 0.57 | -0.27 | |
| Drive Rush (parry) | 4.8% | 5.0% | -0.2% | |
| Drive Parry | 1.80 | 1.64 | +0.16 | |
| Got thrown | 2.20 | 2.08 | +0.12 | |
| Punish Counters landed | 0.20 | 0.10 | +0.10 | |
| Drive Reversal | 0.10 | 0.17 | -0.07 | |
| Stuns landed | 0.10 | 0.04 | +0.06 | |
| Got stunned | 0.10 | 0.05 | +0.05 | |
| Drive Impact | 0.20 | 0.16 | +0.04 | |
| Throw parries | 0.20 | 0.22 | -0.02 | |
| Got Punish Countered | 0.10 | 0.09 | +0.01 |
FROM STATS TO PRACTICE
Latest 100Match plan
Turn this profile’s habits and results into ideas to test. Habits cover all characters; the selected character sets the comparison group.
Checking for a saved match plan…
Uses public profile stats with OpenRouter’s free AI models. Plans are shared and saved for reuse. Suggestions are hypotheses, not replay analysis.Saved match plans
Previously generated AI plans, with their original supporting stats and data dates. Expand a plan to read both perspectives.
Chun-Li · Latest 100
What to improve
Find the cause of punish events
Punish Counters landed: profile 0.2 · reference 0.1
Review five Punish Counters received and five landed. Separate whiffed attacks from unsafe actions on block before choosing what to practice.
Try thisRecreate one repeated situation in training. Check the spacing and frame meter, then practice a safer choice or a punish that actually connects.
Supporting stats
- Punish Counters landed: profile 0.2 · reference 0.1
- Got Punish Countered: profile 0.1 · reference 0.09
Review close-range choices
Throw techs: profile 0.8 · reference 0.4
Look at five close-range exchanges. Separate throw opportunities, throw attempts and defensive choices; the aggregate counts cannot show which option was correct.
Try thisReplay one recurring situation in training with both a strike and a throw recorded. Practice your chosen responses, then check how often you make that choice deliberately in matches.
Supporting stats
- Throw techs: profile 0.8 · reference 0.4
- Throws: profile 1.8 · reference 2.39
- Got thrown: profile 2.2 · reference 2.08
Give each meter spend a purpose
SA3: profile 48.1% · reference 26.2%
Review five resource decisions with the actual gauge visible. Note what the spend gained and what options remained; usage shares alone cannot measure efficiency.
Try thisPractice a familiar sequence with a resource spend and a conservation alternative. Compare the outcome and remaining gauge before deciding which situation calls for each.
Supporting stats
- SA3: profile 48.1% · reference 26.2%
- CA: profile 17.3% · reference 7.6%
- SA1: profile 25% · reference 34.5%
AI selects and prioritizes reviewed practice plans from the cited stats. Habits cover the whole profile; character records and recent samples have separate scopes. These are ideas to test, not confirmed weaknesses or video analysis. Latest reported trends across the last 100 matches, not lifetime habit averages.
Data saved Sep 6, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Use matchup records as scouting leads
vs Jamie: 14 wins in 20 games
A lower win rate suggests footage to examine, not a guaranteed counterpick. Check whether the same difficult situation appears in multiple matches.
Try thisIf you play the cited character, test one familiar situation from that footage. If the player handles it well, abandon the assumption and adapt to what they show.
Supporting stats
- vs Jamie: 14 wins in 20 games
- vs Ryu: 10 wins in 19 games
- vs Yasmine: 7 wins in 10 games
AI selects and prioritizes reviewed practice plans from the cited stats. Habits cover the whole profile; character records and recent samples have separate scopes. These are ideas to test, not confirmed weaknesses or video analysis. Latest reported trends across the last 100 matches, not lifetime habit averages.
Data saved Sep 6, 2026 · AI: dots-studio/dots-3-note-preview:free