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M.
ALM.Updated 6 days ago
CrossPlatform
JapanClub · HiruneKingdomM100%
CrossPlatformJapanClub · HiruneKingdomM100%昼寝大王
The Emperor of Evil Is Eternal
MR 1,315#563,912Last 104–6-22Last 208–12-45
Winrate
45%
38W · 46L · 84 stored
Ranked matches
84
selected mode
Wins
38
Ranked
Losses
46
Ranked
Peak MR
1,378
M. Bison
Ranked MR · M. Bison
41 local · 0 imported · 41 on chart · 127 battles stored
Replay not requested
M.ALPlaystyle reference
Habits vs top 30 M. Bison
Benchmark refreshed Sep 13, 2026, 1:26 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
M.Largest absolute differences first23 habits
| Habit | 昼寝大王 | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA2 | 40.8% | 21.0% | +19.9% | |
| Drive Impact gauge | 15.2% | 1.5% | +13.7% | |
| SA3 | 24.5% | 35.2% | -10.7% | |
| Drive Rush (cancel) | 22.0% | 30.0% | -8.0% | |
| SA1 | 27.6% | 33.7% | -6.1% | |
| CA | 7.1% | 10.2% | -3.1% | |
| Time cornered | 6.80 | 9.85 | -3.05 | |
| Corner pressure | 8.90 | 11.91 | -3.01 | |
| Drive Rush (parry) | 4.3% | 7.2% | -2.9% | |
| Drive Guard | 10.8% | 13.4% | -2.5% | |
| Drive Arts | 16.6% | 18.7% | -2.2% | |
| Drive Impact | 1.10 | 0.19 | +0.91 | |
| Punish Counters landed | 0.70 | 0.11 | +0.59 | |
| Perfect Parry | 0.20 | 0.74 | -0.54 | |
| Got Punish Countered | 0.60 | 0.09 | +0.51 | |
| Throw techs | 0.30 | 0.57 | -0.27 | |
| Throws | 2.20 | 2.42 | -0.22 | |
| Got thrown | 2.40 | 2.59 | -0.19 | |
| Drive Reversal | 0.50 | 0.33 | +0.17 | |
| Stuns landed | 0.00 | 0.07 | -0.07 | |
| Throw parries | 0.20 | 0.26 | -0.06 | |
| Drive Parry | 1.70 | 1.75 | -0.05 | |
| Got stunned | 0.00 | 0.04 | -0.04 |
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.
M. Bison · Latest 100
What to improve
Find the cause of punish events
Got Punish Countered: profile 0.6 · 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 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 0.6 · reference 0.07
- Punish Counters landed: profile 0.7 · reference 0.09
Review defensive decisions
Perfect Parry: profile 0.2 · reference 0.68
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 0.2 · reference 0.68
- Drive Parry: profile 1.7 · reference 1.76
- Drive Reversal: profile 0.5 · reference 0.3
Give each meter spend a purpose
SA2: profile 40.8% · reference 18.6%
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 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
- SA2: profile 40.8% · reference 18.6%
- SA3: profile 24.5% · reference 40.4%
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
Test the Drive Impact tendency
Drive Impact: profile 1.1 · reference 0.18
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 thisRevisit 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 1.1 · reference 0.18
- Drive Impact gauge: profile 15.2% · reference 1.4%
Use matchup records as scouting leads
vs Yasmine: 38 wins in 100 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 Yasmine: 38 wins in 100 games
- vs Akuma: 26 wins in 48 games
- vs Ryu: 20 wins in 40 games
Separate a tendency from a one-off event
Recent stored ranked sample: 22 wins in 43 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: 22 wins in 43 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