monolith

What to improve

1. 01

   Review defensive decisions

   Perfect Parry: profile 0.6 · reference 0.74

   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

   • Perfect Parry: profile 0.6 · reference 0.74
   • Drive Parry: profile 2.9 · reference 2.09
2. 02

   Review close-range choices

   Throw techs: profile 0.3 · reference 0.43

   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 0.3 · reference 0.43
   • Got thrown: profile 1.9 · reference 2.11
   • Throws: profile 2.7 · reference 2.67
3. 03

   Give each meter spend a purpose

   SA2: profile 0% · reference 20.4%

   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

   • SA2: profile 0% · reference 20.4%
   • SA3: profile 48.6% · reference 33.5%
   • CA: profile 20.8% · 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 9, 2026 · AI: dots-studio/dots-3-note-preview:free

How to beat this player

1. 01

   Use matchup records as scouting leads

   vs Char 253: 299 wins in 611 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: 299 wins in 611 games
   • vs Ken: 40 wins in 73 games
   • vs Akuma: 40 wins in 69 games
2. 02

   Test the Drive Impact tendency

   Drive Impact: profile 0.3 · reference 0.25

   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 0.3 · reference 0.25
   • Drive Impact gauge: profile 2.2% · reference 1.8%
3. 03

   Build pressure without overextending

   Corner pressure: profile 9.3 · reference 13.05

   Look for chances to gain space with familiar options. Once the opponent is cornered, observe how they actually try to leave before selecting an adjustment.

   Try this

   Test one pressure sequence you know well. If an escape repeats, rehearse an answer to that exact option; if they vary their defense, avoid committing to a single read.

   Supporting stats

   • Corner pressure: profile 9.3 · reference 13.05
   • Time cornered: profile 10.8 · reference 8.63

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