えんがわ

What to improve

1. 01

   Find the cause of punish events

   Got Punish Countered: profile 0.2 · reference 0.07

   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

   • Got Punish Countered: profile 0.2 · reference 0.07
   • Punish Counters landed: profile 0.1 · reference 0.09
2. 02

   Review close-range choices

   Throw techs: profile 0.7 · reference 0.47

   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 0.7 · reference 0.47
   • Throws: profile 2.3 · reference 2.55
   • Got thrown: profile 3 · reference 2.48
3. 03

   Give each meter spend a purpose

   SA3: profile 30.3% · reference 40.4%

   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

   • SA3: profile 30.3% · reference 40.4%
   • SA1: profile 34.8% · reference 30.3%
   • SA2: profile 24.2% · reference 18.6%

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 Sep 8, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

1. 01

   Probe defense before committing

   Drive Reversal: profile 0.1 · reference 0.3

   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 Reversal: profile 0.1 · reference 0.3
   • Perfect Parry: profile 0.3 · reference 0.68
2. 02

   Test the close-range pattern

   Throw techs: profile 0.7 · reference 0.47

   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

   • Throw techs: profile 0.7 · reference 0.47
   • Throws: profile 2.3 · reference 2.55
   • Got thrown: profile 3 · reference 2.48
3. 03

   Use matchup records as scouting leads

   vs Char 253: 164 wins in 306 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 Char 253: 164 wins in 306 games
   • vs Luke: 13 wins in 26 games
   • vs Mai: 17 wins in 26 games

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