Latest game
DefeatRanked
MA
YAMAUpdated 5 days ago
CrossPlatform
JapanClub · PongiChannelM85%
CrossPlatformJapanClub · PongiChannelM85%ぽんぎc
Day Off Means Game On!
MR 1,753#15,249Last 104–6-8Last 208–12-19
Winrate
52%
44W · 40L · 84 stored
Ranked matches
84
selected mode
Wins
44
Ranked
Losses
40
Ranked
Peak MR
1,799
Marisa
Ranked MR · Marisa
84 local · 0 imported · 84 on chart · 163 battles stored
Replay not requested
MAYAPlaystyle reference
Habits vs top 30 Marisa
Benchmark refreshed Sep 12, 2026, 4:25 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
MALargest absolute differences first23 habits
| Habit | ぽんぎc | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 18.6% | 41.8% | -23.2% | |
| Drive Rush (cancel) | 15.5% | 29.3% | -13.8% | |
| SA2 | 27.1% | 18.1% | +9.0% | |
| Drive Arts | 27.4% | 19.0% | +8.5% | |
| CA | 15.7% | 7.3% | +8.4% | |
| SA3 | 38.6% | 32.8% | +5.8% | |
| Drive Guard | 16.1% | 14.5% | +1.6% | |
| Got thrown | 3.00 | 2.20 | +0.80 | |
| Drive Rush (parry) | 9.7% | 8.9% | +0.7% | |
| Time cornered | 8.60 | 7.86 | +0.74 | |
| Corner pressure | 9.30 | 10.02 | -0.72 | |
| Throw techs | 0.70 | 0.54 | +0.16 | |
| Drive Parry | 1.70 | 1.56 | +0.14 | |
| Perfect Parry | 0.70 | 0.59 | +0.11 | |
| Drive Impact | 0.10 | 0.20 | -0.10 | |
| Got Punish Countered | 0.20 | 0.11 | +0.09 | |
| Drive Reversal | 0.40 | 0.33 | +0.07 | |
| Drive Impact gauge | 1.8% | 1.8% | -0.0% | |
| Throws | 2.00 | 2.03 | -0.03 | |
| Got stunned | 0.00 | 0.03 | -0.03 | |
| Throw parries | 0.30 | 0.28 | +0.02 | |
| Punish Counters landed | 0.10 | 0.11 | -0.01 | |
| Stuns landed | 0.10 | 0.09 | +0.01 |
FROM STATS TO PRACTICE
Latest 100Match plan
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.
Marisa · Latest 100
What to improve
Find the cause of punish events
Got Punish Countered: profile 0.2 · reference 0.11
Review five Punish Counters received and five landed. Separate whiffed attacks from unsafe actions on block before choosing what to practice.
Try thisRecreate 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 0.11
- Punish Counters landed: profile 0.1 · reference 0.12
Review close-range choices
Throws: profile 2 · reference 2.13
Look at five close-range exchanges. Separate throw opportunities, throw attempts and defensive choices; the aggregate counts cannot show which option was correct.
Try thisReplay 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
- Throws: profile 2 · reference 2.13
- Throw techs: profile 0.7 · reference 0.52
- Got thrown: profile 3 · reference 2.3
Give each meter spend a purpose
Drive Rush (cancel): profile 15.5% · reference 28.2%
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 thisPractice 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 Rush (cancel): profile 15.5% · reference 28.2%
- Drive Arts: profile 27.4% · reference 18.5%
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: nex-agi/nex-n2.5-mini:free
How to beat this player
Use matchup records as scouting leads
vs JP: 36 wins in 99 games
A lower win rate suggests footage to examine, not a guaranteed counterpick. Check whether the same difficult situation appears in multiple matches.
Try thisIf 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 JP: 36 wins in 99 games
Look for a repeatable punish opportunity
Got Punish Countered: profile 0.2 · reference 0.11
Use the stats as a scouting lead. Watch for an action you have already verified as punishable at that spacing; do not assume every apparent opening is unsafe.
Try thisTest one confirmed response when that action appears. If it stops appearing, return to your normal spacing instead of forcing the punish.
Supporting stats
- Got Punish Countered: profile 0.2 · reference 0.11
Separate a tendency from a one-off event
Recent stored ranked sample: 38 wins in 72 games
Scout several recent matches and compare wins with losses. Prepare around a repeated situation rather than one memorable mistake.
Try thisUse 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: 38 wins in 72 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: nex-agi/nex-n2.5-mini:free