A.K.I.
Updated 6 days agoCrossPlatformCrossPlatformJapanJapanC100%

アホロートル

EM Wave Change! On the Air!!
Rank 36MR 1,632#51,723
Last 108–2+56Last 2013–7+61
Winrate
58%
54W · 39L · 93 stored
Ranked matches
93
selected mode
Wins
54
Ranked
Losses
39
Ranked
Peak MR
1,632
A.K.I.

Ranked MR · A.K.I.

93 local · 0 imported · 93 on chart · 93 battles stored

Latest game

VictoryRanked

Replay not requested
A.K.I.
アホロートル · A.K.I.
M. Bison
Opponent · M. Bisonbis0n

Playstyle reference

Habits vs top 30 A.K.I.

Benchmark refreshed Sep 12, 2026, 4:20 AM

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

A.K.I.
アホロートル’s A.K.I. benchmark

Compared with the average habits of the top 30 sampled A.K.I. players.

hikaru_shiftne 2249 · ネコと和解せよ 2211 · マーノ/ギコギコはしません 2205 · Motchan 2204 · M.M. 2181 · +25 others

Largest absolute differences first23 habits
HabitアホロートルLegendsDifference LessLegendsMore
SA367.6%30.6%+37.0%
Drive Rush (cancel)1.2%23.3%-22.1%
SA116.2%37.8%-21.6%
SA28.1%22.0%-13.9%
Drive Arts33.4%21.0%+12.4%
Drive Guard21.2%17.0%+4.2%
Corner pressure9.4011.29-1.89
CA8.1%9.6%-1.5%
Drive Impact gauge3.2%1.8%+1.5%
Drive Rush (parry)12.0%11.0%+1.0%
Throws3.202.34+0.86
Drive Parry2.701.93+0.77
Throw techs0.100.52-0.42
Got thrown2.002.30-0.30
Time cornered10.109.83+0.27
Perfect Parry0.400.60-0.20
Punish Counters landed0.300.12+0.18
Drive Impact0.400.24+0.16
Got Punish Countered0.200.11+0.09
Throw parries0.300.22+0.08
Got stunned0.000.04-0.04
Stuns landed0.100.08+0.02
Drive Reversal0.400.400.00
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.

A.K.I. · Latest 100

What to improve

  1. Find the cause of punish events

    Punish Counters landed: profile 0.3 · 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 0.3 · reference 0.1
    • Got Punish Countered: profile 0.2 · reference 0.1
  2. Review close-range choices

    Throw techs: profile 0.2 · reference 0.53

    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 0.2 · reference 0.53
    • Got thrown: profile 2.6 · reference 2.33
    • Throws: profile 2.4 · reference 2.31
  3. Give each meter spend a purpose

    Drive Arts: profile 36.1% · reference 20.5%

    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
    • Drive Arts: profile 36.1% · reference 20.5%
    • SA3: profile 41.2% · reference 31.9%
    • Drive Rush (cancel): profile 1.8% · reference 23%

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 Aug 30, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

  1. Use matchup records as scouting leads

    vs Ryu: 26 wins in 53 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: 26 wins in 53 games
    • vs Ken: 18 wins in 29 games
    • vs Luke: 13 wins in 23 games
  2. Separate a tendency from a one-off event

    Recent stored ranked sample: 14 wins in 24 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: 14 wins in 24 games
  3. 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 4.9% · reference 1.6%

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 Aug 30, 2026 · AI: dots-studio/dots-3-note-preview:free

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