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MA?
Opponent · RandomつよオスMAUpdated 5 days ago
Steam
JapanM100%
SteamJapanM100%Rans_OE
Self-Styled Martial Artist
MR 1,601#88,440Last 108–2+52Last 2012–8+33
Winrate
55%
27W · 22L · 49 stored
Ranked matches
49
selected mode
Wins
27
Ranked
Losses
22
Ranked
Peak MR
1,601
Marisa
Ranked MR · Marisa
49 local · 0 imported · 49 on chart · 59 battles stored
Replay not requested
MAPlaystyle 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 | Rans_OE | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 22.7% | 41.8% | -19.1% | |
| SA3 | 48.5% | 32.8% | +15.7% | |
| CA | 22.7% | 7.3% | +15.4% | |
| SA2 | 6.1% | 18.1% | -12.0% | |
| Drive Arts | 11.8% | 19.0% | -7.1% | |
| Drive Rush (cancel) | 34.2% | 29.3% | +4.9% | |
| Drive Rush (parry) | 13.7% | 8.9% | +4.8% | |
| Drive Impact gauge | 4.5% | 1.8% | +2.7% | |
| Corner pressure | 11.30 | 10.02 | +1.28 | |
| Drive Parry | 0.40 | 1.56 | -1.16 | |
| Drive Guard | 15.2% | 14.5% | +0.7% | |
| Time cornered | 7.20 | 7.86 | -0.66 | |
| Perfect Parry | 0.00 | 0.59 | -0.59 | |
| Got thrown | 2.00 | 2.20 | -0.20 | |
| Throws | 1.90 | 2.03 | -0.13 | |
| Drive Impact | 0.30 | 0.20 | +0.10 | |
| Got Punish Countered | 0.20 | 0.11 | +0.09 | |
| Throw parries | 0.20 | 0.28 | -0.08 | |
| Got stunned | 0.10 | 0.03 | +0.07 | |
| Drive Reversal | 0.40 | 0.33 | +0.07 | |
| Throw techs | 0.50 | 0.54 | -0.04 | |
| 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
Review defensive decisions
Perfect Parry: profile 0 · reference 0.6
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 thisRecord 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 · reference 0.6
- Drive Parry: profile 0.4 · reference 1.59
- Drive Reversal: profile 0.5 · reference 0.32
Give each meter spend a purpose
SA3: profile 50.7% · reference 34.5%
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
- SA3: profile 50.7% · reference 34.5%
- CA: profile 21.1% · reference 9%
- SA1: profile 21.1% · reference 35.7%
Make Drive Impact a deliberate choice
Drive Impact: profile 0.3 · reference 0.2
Review five Drive Impacts and record the situation and outcome. Keep effective uses; investigate repeated failures rather than aiming for the reference frequency.
Try thisRecord Drive Impact and an ordinary attack in separate dummy slots. Mix playback and practice recognizing the difference with a response available in your current gauge state.
Supporting stats
- Drive Impact: profile 0.3 · reference 0.2
- Drive Impact gauge: profile 3.8% · reference 1.9%
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 7, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Use matchup records as scouting leads
vs Luke: 52 wins in 108 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 Luke: 52 wins in 108 games
- vs Akuma: 52 wins in 104 games
- vs Ryu: 50 wins in 98 games
Separate a tendency from a one-off event
Recent stored ranked sample: 16 wins in 30 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: 16 wins in 30 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
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 7, 2026 · AI: dots-studio/dots-3-note-preview:free