Chun-Li
Updated 16 hours agoSteamSteamJapanJapanClub · KungFuAcademyM100%

Chise

Team REJECT
Rank 41
MR 1,718#24,074
Last 109–1+29Last 2014–6+14
Winrate
57%
119W · 90L · 209 stored
Ranked matches
209
selected mode
Wins
119
Ranked
Losses
90
Ranked
Peak MR
1,718
Chun-Li

Ranked MR · Chun-Li

174 local · 0 imported · 174 on chart · 399 battles stored

Latest game

VictoryCustom Room

Replay not requested
Chun-Li
Chise · Chun-Li
Zangief
Opponent · Zangiefkanji

Playstyle reference

Habits vs top 30 Chun-Li

Benchmark refreshed Sep 14, 2026, 10:23 PM

Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details

Chun-Li
Chise’s Chun-Li benchmark

Compared with the average habits of the top 30 sampled Chun-Li players.

HAITANI 2263 · Seiya 2244 · ドドガマル_pc 2209 · YOKOHAMA_TARO 2152 · leith 2147 · +25 others

Largest absolute differences first23 habits
HabitChiseLegendsDifference LessLegendsMore
SA339.2%25.9%+13.4%
SA122.8%33.0%-10.2%
Drive Rush (cancel)33.1%43.1%-10.1%
SA222.8%32.6%-9.8%
CA15.2%8.5%+6.6%
Drive Rush (parry)7.4%4.5%+3.0%
Corner pressure9.1012.05-2.95
Drive Guard11.8%9.6%+2.2%
Throws4.202.51+1.69
Time cornered10.008.40+1.60
Drive Arts16.7%15.6%+1.1%
Throw techs0.900.35+0.55
Drive Impact gauge2.0%1.5%+0.5%
Got thrown2.401.97+0.43
Perfect Parry0.300.57-0.27
Drive Impact0.400.17+0.23
Punish Counters landed0.300.10+0.20
Drive Parry1.501.61-0.11
Drive Reversal0.100.18-0.08
Throw parries0.300.22+0.08
Stuns landed0.000.05-0.05
Got stunned0.100.05+0.05
Got Punish Countered0.100.07+0.03
FROM STATS TO PRACTICE

Match plan

Latest 100

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

  1. 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 this

    Recreate 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 0.3 · reference 0.09
  2. Make Drive Impact a deliberate choice

    Drive Impact: profile 0.2 · 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 this

    Record 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 0.2 · reference 0.16
    • Drive Impact gauge: profile 1.5% · reference 1.4%
  3. Trace how you lose or gain space

    Corner pressure: profile 8.1 · 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 this

    Recreate 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 8.1 · reference 11.27
    • Time cornered: profile 11.6 · 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 6, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

  1. Use matchup records as scouting leads

    vs Alex: 54 wins in 98 games

    A lower win rate suggests footage to examine, not a guaranteed counterpick. Check whether the same difficult situation appears in multiple matches.

    Try this

    If 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 Alex: 54 wins in 98 games
    • vs C. Viper: 47 wins in 93 games
    • vs Luke: 52 wins in 87 games
  2. Separate a tendency from a one-off event

    Recent stored ranked sample: 44 wins in 83 games

    Scout several recent matches and compare wins with losses. Prepare around a repeated situation rather than one memorable mistake.

    Try this

    Use 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: 44 wins in 83 games
  3. Look for a repeatable punish opportunity

    Got Punish Countered: profile 0.3 · 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 this

    Test 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 0.3 · 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 6, 2026 · AI: dots-studio/dots-3-note-preview:free

Public data

Public statistics and datasets

Results from 399 of 399 stored matches. Explore the datasets for the full saved history.

Results in stored match sample

1 row
Wins
233games
Losses
166games
Draws
0games
Games
399games
Win rate (draw = half win)
58.4%
Field definitions 5 fields
Wins
Wins · games. wins
Losses
Losses · games. losses
Draws
Draws · games. draws
Games
Games · games. total
Win rate (draw = half win)
Win rate (draw = half win) · percent. winRate Displayed to two decimal places; JSON retains full precision.

Current saved character ratings

10 rows

Scroll if needed to compare columns. Expand Details for the remaining fields.

Current saved character ratings. 10 rows on page 1; 10 rows total.
CharacterMRMR pointsLPLP pointsDetails
Chun-Li1,718385,674
Details for Current saved character ratings, row 1
Character ID
4
Manon0-1
Details for Current saved character ratings, row 2
Character ID
5
Cammy0-1
Details for Current saved character ratings, row 3
Character ID
9
Lily0-1
Details for Current saved character ratings, row 4
Character ID
12
Juri023,992
Details for Current saved character ratings, row 5
Character ID
16
Marisa0-1
Details for Current saved character ratings, row 6
Character ID
17
Guile0-1
Details for Current saved character ratings, row 7
Character ID
18
Akuma0-1
Details for Current saved character ratings, row 8
Character ID
22
M. Bison0-1
Details for Current saved character ratings, row 9
Character ID
26
Mai0-1
Details for Current saved character ratings, row 10
Character ID
28
Field definitions 4 fields
Character ID
Character ID · identifier. characterId
Character
Character · text. character
MR
Master Rating · MR points. mr
LP
League Points · LP points. lp

Last saved update: .

Latest saved profile; local results cover at most the newest 10,000 stored matches across all modes.

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