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DefeatCustom Room
EDEDEDUpdated 3 hours ago
CrossPlatform
JapanClub · ITAMAEM92%
CrossPlatformJapanClub · ITAMAEM92%P_YAMA_K
Ever Improving
MR 1,755#16,667Last 103–7-28Last 208–12-26
Winrate
49%
70W · 73L · 143 stored
Ranked matches
143
selected mode
Wins
70
Ranked
Losses
73
Ranked
Peak MR
1,773
Ed
Ranked MR · Ed
139 local · 0 imported · 139 on chart · 218 battles stored
Replay not requested
EDEDPlaystyle reference
Habits vs top 30 Ed
Benchmark refreshed Sep 12, 2026, 10:17 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
EDLargest absolute differences first23 habits
| Habit | P_YAMA_K | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA3 | 42.7% | 33.7% | +9.0% | |
| SA2 | 17.3% | 23.2% | -5.8% | |
| SA1 | 30.7% | 33.3% | -2.7% | |
| Corner pressure | 9.30 | 11.75 | -2.45 | |
| Drive Rush (cancel) | 37.0% | 35.3% | +1.7% | |
| Drive Impact gauge | 3.1% | 1.7% | +1.4% | |
| Drive Guard | 11.7% | 12.9% | -1.3% | |
| Drive Arts | 16.9% | 18.1% | -1.1% | |
| Drive Rush (parry) | 5.9% | 6.8% | -0.9% | |
| Time cornered | 8.20 | 8.93 | -0.73 | |
| CA | 9.3% | 9.8% | -0.5% | |
| Got thrown | 2.60 | 2.20 | +0.40 | |
| Throws | 2.20 | 2.53 | -0.33 | |
| Perfect Parry | 0.50 | 0.79 | -0.29 | |
| Drive Parry | 1.70 | 1.92 | -0.22 | |
| Drive Reversal | 0.10 | 0.22 | -0.12 | |
| Got Punish Countered | 0.20 | 0.10 | +0.10 | |
| Drive Impact | 0.30 | 0.22 | +0.08 | |
| Stuns landed | 0.00 | 0.06 | -0.06 | |
| Got stunned | 0.00 | 0.04 | -0.04 | |
| Punish Counters landed | 0.10 | 0.14 | -0.04 | |
| Throw techs | 0.50 | 0.53 | -0.03 | |
| Throw parries | 0.20 | 0.21 | -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.
Ed · Latest 100
What to improve
Compare decisions across wins and losses
Recent stored ranked sample: 49 wins in 100 games
Choose two wins and two losses from the recent stored sample. Find one decision that changes between them; the record alone cannot explain why results changed.
Try thisWrite down one specific decision to test in your next five games. Review whether you executed it and what happened, rather than judging the drill only by wins.
Supporting stats
- Recent stored ranked sample: 49 wins in 100 games
Review close-range choices
Throws: profile 1.4 · reference 2.6
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 1.4 · reference 2.6
- Throw techs: profile 0.4 · reference 0.47
- Got thrown: profile 2.4 · reference 2.18
Trace how you lose or gain space
Time cornered: profile 10.6 · reference 9.53
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 10.6 · reference 9.53
- Corner pressure: profile 8.4 · reference 11.8
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 12, 2026 · AI: nex-agi/nex-n2.5-mini:free
How to beat this player
Use matchup records as scouting leads
vs Luke: 64 wins in 101 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: 64 wins in 101 games
- vs Yasmine: 53 wins in 85 games
Separate a tendency from a one-off event
Recent stored ranked sample: 49 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: 49 wins in 100 games
Probe defense before committing
Throw techs: profile 0.4 · reference 0.47
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
- Throw techs: profile 0.4 · reference 0.47
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 12, 2026 · AI: nex-agi/nex-n2.5-mini:free