What to improve

1. 01

   Find the cause of punish events

   Got Punish Countered: profile 0.1 · 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 0.1 · reference 0.07
   • Punish Counters landed: profile 0.2 · reference 0.11
2. 02

   Review close-range choices

   Throw techs: profile 1.1 · 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 1.1 · reference 0.4
   • Throws: profile 1.3 · reference 2.8
   • Got thrown: profile 2.9 · reference 1.99
3. 03

   Give each meter spend a purpose

   SA1: profile 16.2% · reference 28.6%

   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

   • SA1: profile 16.2% · reference 28.6%
   • SA2: profile 30.9% · reference 24.7%
   • SA3: profile 39.7% · reference 36.7%

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 9, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

1. 01

   Probe defense before committing

   Throw techs: profile 1.1 · reference 0.4

   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

   • Throw techs: profile 1.1 · reference 0.4
   • Got thrown: profile 2.9 · reference 1.99
   • Perfect Parry: profile 0.4 · reference 0.8
2. 02

   Test the close-range pattern

   Throws: profile 1.3 · reference 2.8

   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 1.3 · reference 2.8
   • Got thrown: profile 2.9 · reference 1.99
   • Throw techs: profile 1.1 · reference 0.4
3. 03

   Use matchup records as scouting leads

   vs Rashid: 7 wins in 15 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 Rashid: 7 wins in 15 games
   • vs Juri: 9 wins in 25 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 9, 2026 · AI: dots-studio/dots-3-note-preview:free