Dee Jay
Updated 1 day agoCrossPlatformCrossPlatformJapanJapanM100%

ちぐう。

Let's Do This
Rank 36MR 1,477#385,267
Last 105–5-7Last 2013–7+24
Winrate
53%
55W · 48L · 103 stored
Ranked matches
103
selected mode
Wins
55
Ranked
Losses
48
Ranked
Peak MR
1,502
Dee Jay

Ranked MR · Dee Jay

103 local · 0 imported · 103 on chart · 103 battles stored

Latest game

DefeatRanked

Replay not requested
Dee Jay
ちぐう。 · Dee Jay
Ryu
Opponent · Ryuいおたい1048

Playstyle reference

Habits vs top 30 Dee Jay

Benchmark refreshed Sep 11, 2026, 10:15 PM

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

Dee Jay
ちぐう。’s Dee Jay benchmark

Compared with the average habits of the top 30 sampled Dee Jay players.

tomorock21 2244 · @Sycom|zabutonn 2228 · Naza 2221 · ネコ派です_ 2214 · Asuka 2162 · +25 others

Largest absolute differences first23 habits
Habitちぐう。LegendsDifference LessLegendsMore
SA241.2%21.3%+19.8%
Drive Rush (cancel)7.6%23.0%-15.4%
SA318.8%33.8%-14.9%
Drive Arts34.9%24.7%+10.2%
SA127.1%36.2%-9.1%
Corner pressure16.5012.23+4.27
CA12.9%8.7%+4.2%
Drive Impact gauge5.1%1.8%+3.3%
Drive Rush (parry)12.1%8.9%+3.2%
Time cornered6.109.11-3.01
Throws3.802.38+1.42
Drive Guard16.0%15.1%+1.0%
Drive Parry0.901.76-0.86
Perfect Parry0.000.57-0.57
Got thrown1.802.20-0.40
Drive Impact0.600.24+0.36
Throw techs0.200.43-0.23
Got Punish Countered0.300.09+0.21
Drive Reversal0.000.19-0.19
Punish Counters landed0.300.13+0.17
Stuns landed0.000.07-0.07
Got stunned0.000.07-0.07
Throw parries0.200.25-0.05
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.

Dee Jay · Latest 100

What to improve

  1. Review defensive decisions

    Drive Reversal: profile 0 · reference 0.19

    Compare five defensive situations: what did the opponent do, what did you choose, and what happened? Low parry or throw-tech counts alone do not establish a weakness.

    Try this

    Record two different options from one troublesome situation. Practice identifying them and choosing a response, then review whether the same decision improves in your next set.

    Supporting stats
    • Drive Reversal: profile 0 · reference 0.19
    • Perfect Parry: profile 0.1 · reference 0.55
    • Drive Parry: profile 0.9 · reference 1.54
  2. Find the cause of punish events

    Punish Counters landed: profile 0.2 · reference 0.12

    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 0.12
    • Got Punish Countered: profile 0.2 · reference 0.12
  3. Give each meter spend a purpose

    SA2: profile 46.2% · reference 22%

    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 46.2% · reference 22%
    • SA1: profile 20% · reference 35.1%
    • CA: profile 13.8% · reference 9.2%

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

  1. Use matchup records as scouting leads

    vs Ryu: 31 wins in 64 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: 31 wins in 64 games
    • vs Luke: 19 wins in 38 games
    • vs Zangief: 14 wins in 19 games
  2. Test the close-range pattern

    Throws: profile 3.8 · reference 2.49

    Observe actual strike, throw and defensive choices at close range. Change one part of your familiar mix only after a tendency appears.

    Try this

    If the same response repeats twice, try a different option in that situation. If the player changes their response, return to varied choices instead of assuming the pattern is permanent.

    Supporting stats
    • Throws: profile 3.8 · reference 2.49
    • Got thrown: profile 1.9 · reference 2.12
    • Throw techs: profile 0.2 · reference 0.44
  3. Test the Drive Impact tendency

    Drive Impact: profile 0.2 · reference 0.22

    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 0.22
    • Drive Impact gauge: profile 2.9% · reference 1.8%

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

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