Latest game
VictoryRanked
MA
C.MAUpdated 3 hours ago
Steam
JapanC100%
SteamJapanC100%Ribot
Let's Do This
MR 1,547#146,787Last 108–2+47Last 2015–5+84
Winrate
52%
77W · 71L · 148 stored
Ranked matches
148
selected mode
Wins
77
Ranked
Losses
71
Ranked
Peak MR
1,552
Manon
Ranked MR · Manon
148 local · 0 imported · 148 on chart · 191 battles stored
Replay not requested
MAC.Playstyle 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 | Ribot | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 36.4% | 25.4% | +10.9% | |
| Drive Rush (cancel) | 52.5% | 43.0% | +9.5% | |
| SA3 | 26.1% | 32.9% | -6.7% | |
| SA2 | 27.3% | 32.1% | -4.8% | |
| Drive Arts | 6.6% | 11.3% | -4.8% | |
| Corner pressure | 4.60 | 8.16 | -3.56 | |
| Drive Guard | 9.4% | 11.3% | -1.9% | |
| Drive Rush (parry) | 3.5% | 5.0% | -1.5% | |
| Time cornered | 9.80 | 8.72 | +1.08 | |
| CA | 10.2% | 9.7% | +0.6% | |
| Drive Reversal | 0.60 | 0.30 | +0.30 | |
| Drive Parry | 1.50 | 1.80 | -0.30 | |
| Got thrown | 2.00 | 2.28 | -0.28 | |
| Perfect Parry | 0.40 | 0.64 | -0.24 | |
| Drive Impact gauge | 1.7% | 1.8% | -0.1% | |
| Throws | 2.40 | 2.53 | -0.13 | |
| Throw parries | 0.10 | 0.20 | -0.10 | |
| Got Punish Countered | 0.20 | 0.11 | +0.09 | |
| Stuns landed | 0.00 | 0.07 | -0.07 | |
| Throw techs | 0.50 | 0.56 | -0.06 | |
| Punish Counters landed | 0.10 | 0.14 | -0.04 | |
| Got stunned | 0.10 | 0.06 | +0.04 | |
| Drive Impact | 0.20 | 0.22 | -0.02 |
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
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 0.14
Trace how you lose or gain space
Time cornered: profile 9.5 · reference 8.72
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
- Time cornered: profile 9.5 · reference 8.72
Review defensive decisions
Got thrown: profile 2.3 · reference 2.28
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
- Got thrown: profile 2.3 · reference 2.28
- Throw techs: profile 0.5 · reference 0.56
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 Random: 25 wins in 56 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 Random: 25 wins in 56 games
- vs C. Viper: 14 wins in 50 games
- vs Akuma: 24 wins in 44 games
Separate a tendency from a one-off event
Recent stored ranked sample: 48 wins in 100 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: 48 wins in 100 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