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DefeatRanked
JU
TE
MAUpdated 2 days ago
CrossPlatform
JapanClub · UsagiyaM100%
CrossPlatformJapanClub · UsagiyaM100%実にエレガント
Let's Do This
MR 1,808#8,795Last 105–5+3Last 208–12-26
Winrate
44%
44W · 56L · 100 stored
Ranked matches
100
selected mode
Wins
44
Ranked
Losses
56
Ranked
Peak MR
1,837
Manon
Ranked MR · Manon
99 local · 0 imported · 99 on chart · 135 battles stored
Replay not requested
JUTEPlaystyle reference
Habits vs top 30 Manon
Benchmark refreshed Sep 7, 2026, 7:24 AM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
MALargest absolute differences first23 habits
| Habit | 実にエレガント | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 67.6% | 25.4% | +42.2% | |
| SA3 | 11.0% | 32.9% | -21.8% | |
| SA2 | 14.5% | 32.1% | -17.6% | |
| Drive Rush (cancel) | 37.2% | 43.0% | -5.8% | |
| Drive Arts | 16.1% | 11.3% | +4.8% | |
| Time cornered | 12.20 | 8.72 | +3.48 | |
| CA | 6.9% | 9.7% | -2.8% | |
| Corner pressure | 5.80 | 8.16 | -2.36 | |
| Got thrown | 4.00 | 2.28 | +1.72 | |
| Throws | 3.40 | 2.53 | +0.87 | |
| Drive Impact gauge | 2.4% | 1.8% | +0.6% | |
| Drive Guard | 10.8% | 11.3% | -0.5% | |
| Drive Reversal | 0.50 | 0.30 | +0.20 | |
| Throw techs | 0.70 | 0.56 | +0.14 | |
| Got stunned | 0.20 | 0.06 | +0.14 | |
| Throw parries | 0.10 | 0.20 | -0.10 | |
| Punish Counters landed | 0.20 | 0.14 | +0.06 | |
| Perfect Parry | 0.60 | 0.64 | -0.04 | |
| Stuns landed | 0.10 | 0.07 | +0.03 | |
| Drive Impact | 0.20 | 0.22 | -0.02 | |
| Drive Rush (parry) | 5.0% | 5.0% | -0.0% | |
| Got Punish Countered | 0.10 | 0.11 | -0.01 | |
| Drive Parry | 1.80 | 1.80 | -0.00 |
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.
Manon · Latest 100
What to improve
Review close-range choices
Got thrown: profile 4 · reference 2.28
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
- Got thrown: profile 4 · reference 2.28
- Throws: profile 3.4 · reference 2.53
- Throw techs: profile 0.7 · reference 0.56
Trace how you lose or gain space
Corner pressure: profile 5.8 · reference 8.16
Review five trips into the corner. Identify the earlier decision that gave up space, not just the final escape attempt. Check successful corner pressure too.
Try thisRecreate one recurring position in training. Compare your usual response with a less committal alternative, then track whether you reach the corner less often over the next set.
Supporting stats
- Corner pressure: profile 5.8 · reference 8.16
- Time cornered: profile 12.2 · reference 8.72
Give each meter spend a purpose
SA1: profile 67.6% · reference 25.4%
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 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
- SA1: profile 67.6% · reference 25.4%
- SA3: profile 11% · reference 32.9%
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 5, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Probe defense before committing
Drive Reversal: profile 0.5 · reference 0.3
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 thisTry 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
- Drive Reversal: profile 0.5 · reference 0.3
- Got thrown: profile 4 · reference 2.28
Use matchup records as scouting leads
vs C. Viper: 48 wins in 86 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 C. Viper: 48 wins in 86 games
- vs Kimberly: 39 wins in 77 games
- vs Luke: 34 wins in 68 games
Separate a tendency from a one-off event
Recent stored ranked sample: 31 wins in 70 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: 31 wins in 70 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 5, 2026 · AI: dots-studio/dots-3-note-preview:free