Latest game
DefeatRanked
KE
MAKEUpdated 1 day ago
Steam
South KoreaC100%
SteamSouth KoreaC100%Run44
Curry, Three Meals a Day
MR 1,556#132,650Last 106–4+13Last 2011–9+14
Winrate
51%
148W · 144L · 292 stored
Ranked matches
292
selected mode
Wins
148
Ranked
Losses
144
Ranked
Peak MR
1,583
Ken
Ranked MR · Ken
292 local · 237 imported · 493 on chart · 292 battles stored
Replay not requested
KEMAPlaystyle reference
Habits vs top 30 Ken
Benchmark refreshed Sep 11, 2026, 7:22 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
KELargest absolute differences first23 habits
| Habit | Run44 | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 45.5% | 27.3% | +18.3% | |
| SA2 | 13.9% | 25.9% | -12.0% | |
| SA3 | 30.7% | 35.8% | -5.1% | |
| Drive Rush (cancel) | 43.2% | 38.2% | +5.0% | |
| Drive Arts | 19.0% | 14.9% | +4.0% | |
| Drive Guard | 9.3% | 12.7% | -3.4% | |
| Corner pressure | 10.20 | 13.12 | -2.92 | |
| Drive Rush (parry) | 5.4% | 6.8% | -1.4% | |
| CA | 9.9% | 11.0% | -1.1% | |
| Time cornered | 9.60 | 8.95 | +0.65 | |
| Drive Impact gauge | 2.0% | 1.4% | +0.6% | |
| Perfect Parry | 0.20 | 0.84 | -0.64 | |
| Throws | 2.10 | 2.57 | -0.47 | |
| Got thrown | 1.80 | 2.26 | -0.46 | |
| Drive Parry | 1.60 | 1.89 | -0.29 | |
| Drive Reversal | 0.10 | 0.23 | -0.13 | |
| Got Punish Countered | 0.20 | 0.08 | +0.12 | |
| Punish Counters landed | 0.20 | 0.10 | +0.10 | |
| Stuns landed | 0.00 | 0.07 | -0.07 | |
| Got stunned | 0.10 | 0.05 | +0.05 | |
| Throw techs | 0.40 | 0.44 | -0.04 | |
| Throw parries | 0.20 | 0.21 | -0.01 | |
| Drive Impact | 0.20 | 0.19 | +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.
Ken · Latest 100
What to improve
Find the cause of punish events
Punish Counters landed: profile 0.2 · reference 0.1
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
- Punish Counters landed: profile 0.2 · reference 0.1
- Got Punish Countered: profile 0.2 · reference 0.08
Give each meter spend a purpose
SA1: profile 47.8% · reference 29.1%
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 47.8% · reference 29.1%
- SA2: profile 10.6% · reference 23.7%
- Drive Rush (cancel): profile 47.6% · reference 38.4%
Review close-range choices
Throws: profile 2.5 · reference 2.62
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 2.5 · reference 2.62
- Throw techs: profile 0.6 · reference 0.45
- Got thrown: profile 2.2 · reference 2.26
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 10, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Use matchup records as scouting leads
vs Akuma: 51 wins in 90 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 Akuma: 51 wins in 90 games
- vs Ken: 41 wins in 77 games
- vs Alex: 33 wins in 69 games
Separate a tendency from a one-off event
Recent stored ranked sample: 51 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: 51 wins in 100 games
Test the Drive Impact tendency
Drive Impact: profile 0.2 · reference 0.17
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 0.2 · reference 0.17
- Drive Impact gauge: profile 1.8% · reference 1.3%
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 10, 2026 · AI: dots-studio/dots-3-note-preview:free