Latest game
DefeatRanked
CH
LUCHUpdated 6 days ago
CrossPlatform
JapanC100%
CrossPlatformJapanC100%てつぱん
Meow
MR 1,627#54,348Last 106–4+16Last 207–13-44
Winrate
53%
102W · 89L · 191 stored
Ranked matches
191
selected mode
Wins
102
Ranked
Losses
89
Ranked
Peak MR
1,731
Chun-Li
Ranked MR · Chun-Li
191 local · 0 imported · 191 on chart · 195 battles stored
Replay not requested
CHLUPlaystyle 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 | てつぱん | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA2 | 45.1% | 31.7% | +13.4% | |
| Drive Rush (cancel) | 51.0% | 40.1% | +10.9% | |
| SA1 | 26.5% | 34.5% | -7.9% | |
| Drive Arts | 10.3% | 16.9% | -6.5% | |
| SA3 | 20.4% | 26.2% | -5.9% | |
| Drive Guard | 7.1% | 10.2% | -3.1% | |
| Corner pressure | 8.60 | 11.27 | -2.67 | |
| Drive Rush (parry) | 3.1% | 5.0% | -2.0% | |
| Time cornered | 10.20 | 9.57 | +0.63 | |
| Drive Parry | 1.20 | 1.64 | -0.44 | |
| CA | 8.0% | 7.6% | +0.4% | |
| Throw parries | 0.50 | 0.22 | +0.28 | |
| Throw techs | 0.20 | 0.40 | -0.20 | |
| Drive Reversal | 0.00 | 0.17 | -0.17 | |
| Perfect Parry | 0.40 | 0.57 | -0.17 | |
| Drive Impact gauge | 1.6% | 1.4% | +0.2% | |
| Got stunned | 0.20 | 0.05 | +0.15 | |
| Got thrown | 2.20 | 2.08 | +0.12 | |
| Throws | 2.50 | 2.39 | +0.11 | |
| Got Punish Countered | 0.20 | 0.09 | +0.11 | |
| Drive Impact | 0.10 | 0.16 | -0.06 | |
| Stuns landed | 0.10 | 0.04 | +0.06 | |
| Punish Counters landed | 0.10 | 0.10 | -0.00 |
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
Review close-range choices
Throw techs: profile 0.2 · 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 0.2 · reference 0.4
- Got thrown: profile 2.2 · reference 2.08
Make Drive Impact a deliberate choice
Drive Impact: profile 0.1 · reference 0.16
Review five Drive Impacts and record the situation and outcome. Keep effective uses; investigate repeated failures rather than aiming for the reference frequency.
Try thisRecord Drive Impact and an ordinary attack in separate dummy slots. Mix playback and practice recognizing the difference with a response available in your current gauge state.
Supporting stats
- Drive Impact: profile 0.1 · reference 0.16
- Drive Impact gauge: profile 1.6% · reference 1.4%
Trace how you lose or gain space
Corner pressure: profile 8.6 · reference 11.27
Review five trips into the corner. Identify the earlier decision that gave up space, not just the final escape attempt. Check successful corner pressure too.
Try thisRecreate one recurring position in training. Compare your usual response with a less committal alternative, then track whether you reach the corner less often over the next set.
Supporting stats
- Corner pressure: profile 8.6 · reference 11.27
- Time cornered: profile 10.2 · reference 9.57
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 8, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Use matchup records as scouting leads
vs Jamie: 52 wins in 110 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: 52 wins in 110 games
- vs Akuma: 61 wins in 109 games
- vs JP: 51 wins in 96 games
Separate a tendency from a one-off event
Recent stored ranked sample: 55 wins in 100 games
Scout several recent matches and compare wins with losses. Prepare around a repeated situation rather than one memorable mistake.
Try thisUse the first round to check one scouting hypothesis. If it repeats, apply your prepared adjustment; if it does not, discard it and observe a new pattern.
Supporting stats
- Recent stored ranked sample: 55 wins in 100 games
Look for a repeatable punish opportunity
Got Punish Countered: profile 0.2 · reference 0.09
Use the stats as a scouting lead. Watch for an action you have already verified as punishable at that spacing; do not assume every apparent opening is unsafe.
Try thisTest one confirmed response when that action appears. If it stops appearing, return to your normal spacing instead of forcing the punish.
Supporting stats
- Got Punish Countered: profile 0.2 · reference 0.09
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 8, 2026 · AI: dots-studio/dots-3-note-preview:free