Chun-Li
Updated 1 week agoSteamSteamOtherOtherClub · HONDAclubC100%

Glow_Aggro

Just a Young, Single Girl
Rank 36MR 1,506#217,647
Last 108–2+51Last 2012–8+36
Winrate
56%
34W · 27L · 61 stored
Ranked matches
61
selected mode
Wins
34
Ranked
Losses
27
Ranked
Peak MR
1,510
Chun-Li

Ranked MR · Chun-Li

61 local · 0 imported · 61 on chart · 120 battles stored

Latest game

VictoryRanked

Replay not requested
Chun-Li
Glow_Aggro · Chun-Li
Manon
Opponent · ManonPurifier

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

Chun-Li
Glow_Aggro’s Chun-Li benchmark

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

Seiya 2215 · HAITANI 2172 · edamakotoTwitch 2139 · ドドガマル_pc 2136 · YOKOHAMA_TARO 2105 · +25 others

Largest absolute differences first23 habits
HabitGlow_AggroLegendsDifference LessLegendsMore
SA266.7%31.7%+34.9%
SA115.2%34.5%-19.3%
SA39.1%26.2%-17.1%
Drive Rush (cancel)31.6%40.1%-8.6%
Time cornered13.209.57+3.63
Corner pressure7.7011.27-3.57
Drive Impact gauge3.8%1.4%+2.4%
Got thrown3.902.08+1.82
Drive Rush (parry)3.5%5.0%-1.5%
CA9.1%7.6%+1.5%
Throw techs1.200.40+0.80
Drive Guard9.5%10.2%-0.7%
Drive Arts16.5%16.9%-0.4%
Drive Parry1.901.64+0.26
Perfect Parry0.400.57-0.17
Drive Impact0.300.16+0.14
Throw parries0.100.22-0.12
Got Punish Countered0.200.09+0.11
Punish Counters landed0.200.10+0.10
Throws2.302.39-0.09
Got stunned0.100.05+0.05
Stuns landed0.000.04-0.04
Drive Reversal0.200.17+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. Review close-range choices

    Throw techs: profile 1.2 · 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 this

    Replay 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 1.2 · reference 0.4
    • Got thrown: profile 3.9 · reference 2.08
    • Throws: profile 2.3 · reference 2.39
  2. Trace how you lose or gain space

    Corner pressure: profile 7.3 · 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 7.3 · reference 11.27
    • Time cornered: profile 13.4 · reference 9.57
  3. Give each meter spend a purpose

    SA2: profile 66.7% · reference 31.7%

    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 this

    Practice 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
    • SA2: profile 66.7% · reference 31.7%
    • SA3: profile 9.1% · reference 26.2%
    • SA1: profile 15.2% · 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

  1. 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 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 0.2 · reference 0.09
  2. Test the Drive Impact tendency

    Drive Impact: profile 0.2 · reference 0.16

    Observe whether Drive Impact repeats in a particular situation. Prepare a response you have verified in training, while respecting range, recovery and available gauge.

    Try this

    Revisit that situation with a less committal approach. If the Impact appears, use your practiced response; if it does not, resume your usual game plan.

    Supporting stats
    • Drive Impact: profile 0.2 · reference 0.16
    • Drive Impact gauge: profile 3.5% · reference 1.4%
  3. Probe defense before committing

    Drive Parry: profile 1.9 · reference 1.64

    Test one familiar pressure situation and observe the actual response. Use that observation to vary your next choice; do not infer a success rate from event counts.

    Try this

    Try your usual strike/throw decision in a situation you understand. Repeat only if the response is consistent; if they adapt, vary your timing or reset to neutral.

    Supporting stats
    • Drive Parry: profile 1.9 · reference 1.64
    • Throw techs: profile 1.2 · reference 0.4
    • Got thrown: profile 3.9 · reference 2.08

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

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