Updated 1 week agoSteamSteamJapanJapanClub · NyajiGameC100%

昇竜対空頑張るマン@わきを君

Steady and Relentless
Winrate
0W · 0L · 0 stored
Ranked matches
0
selected mode
Wins
0
Ranked
Losses
0
Ranked
Peak MR

Ranked MR

0 local · 0 imported · 0 on chart · 92 battles stored

Not enough ranked MR points in this range yet.

Latest game

DefeatBattle Hub

Replay not requested
Akuma
昇竜対空頑張るマン@わきを君 · Akuma
Kimberly
Opponent · KimberlyKARABO

Playstyle reference

Habits vs top 30 Akuma

Benchmark refreshed Sep 13, 2026, 7:27 AM

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

Akuma
昇竜対空頑張るマン@わきを君’s Akuma benchmark

Compared with the average habits of the top 30 sampled Akuma players.

Naooonn 2213 · DaigoTheBeasTV 2204 · 一瞬一瞬 2158 · touwa 2114 · ドスバギイ 2113 · +25 others

Largest absolute differences first23 habits
Habit昇竜対空頑張るマン@わきを君LegendsDifference LessLegendsMore
SA331.6%38.7%-7.1%
SA133.3%28.6%+4.7%
SA226.3%22.8%+3.6%
Time cornered5.308.55-3.25
Drive Rush (cancel)34.6%32.7%+1.9%
CA8.8%9.9%-1.1%
Drive Arts20.3%19.2%+1.1%
Drive Impact gauge2.4%1.5%+0.9%
Throws1.902.74-0.84
Drive Rush (parry)6.7%7.4%-0.8%
Corner pressure10.7011.47-0.77
Drive Guard12.6%13.1%-0.5%
Drive Impact0.400.20+0.20
Got thrown1.902.09-0.19
Punish Counters landed0.300.13+0.17
Perfect Parry0.600.77-0.17
Drive Reversal0.100.20-0.10
Drive Parry1.701.65+0.05
Throw techs0.500.45+0.05
Got stunned0.000.04-0.04
Stuns landed0.000.03-0.03
Throw parries0.300.27+0.03
Got Punish Countered0.100.09+0.01
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.

Akuma · Latest 100

What to improve

  1. Find the cause of punish events

    Punish Counters landed: profile 0.3 · reference 0.13

    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 0.3 · reference 0.13
    • Got Punish Countered: profile 0.1 · reference 0.08
  2. Make Drive Impact a deliberate choice

    Drive Impact: profile 0.4 · reference 0.21

    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 0.4 · reference 0.21
    • Drive Impact gauge: profile 2.4% · reference 1.6%
  3. Trace how you lose or gain space

    Corner pressure: profile 10.7 · reference 12.38

    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 10.7 · reference 12.38
    • Time cornered: profile 5.3 · reference 8.05

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 7, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

  1. Use matchup records as scouting leads

    vs Ryu: 85 wins in 106 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 Ryu: 85 wins in 106 games
    • vs Luke: 84 wins in 100 games
    • vs Ingrid: 61 wins in 84 games
  2. Test the Drive Impact tendency

    Drive Impact: profile 0.4 · reference 0.21

    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 0.4 · reference 0.21
    • Drive Impact gauge: profile 2.4% · reference 1.6%
  3. Probe defense before committing

    Drive Parry: profile 1.7 · reference 1.8

    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 1.7 · reference 1.8
    • Perfect Parry: profile 0.6 · reference 0.85
    • Drive Reversal: profile 0.1 · reference 0.22

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 7, 2026 · AI: dots-studio/dots-3-note-preview:free

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