Latest game
VictoryRanked
KEKE
LUUpdated 5 days ago
Steam
JapanClub · cacao15percentC100%
SteamJapanClub · cacao15percentC100%nenekiri
Hit 'Em With The Jabs!
MR 1,481#357,357Last 105–5-1Last 206–14-62
Winrate
35%
16W · 30L · 46 stored
Ranked matches
46
selected mode
Wins
16
Ranked
Losses
30
Ranked
Peak MR
1,481
Luke
Ranked MR · Luke
46 local · 0 imported · 46 on chart · 100 battles stored
Not enough ranked MR points in this range yet.
Replay not requested
KEKEPlaystyle reference
Habits vs top 30 Luke
Benchmark refreshed Sep 6, 2026, 10:21 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
LULargest absolute differences first23 habits
| Habit | nenekiri | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 61.3% | 30.6% | +30.7% | |
| SA2 | 3.8% | 26.0% | -22.2% | |
| Drive Rush (cancel) | 51.1% | 37.3% | +13.8% | |
| SA3 | 24.5% | 34.2% | -9.6% | |
| Drive Guard | 6.7% | 13.2% | -6.5% | |
| Corner pressure | 8.60 | 13.55 | -4.95 | |
| Drive Rush (parry) | 3.7% | 7.6% | -3.9% | |
| Drive Impact gauge | 3.3% | 1.4% | +1.9% | |
| Time cornered | 6.90 | 8.74 | -1.84 | |
| CA | 10.4% | 9.2% | +1.2% | |
| Drive Parry | 1.10 | 1.80 | -0.70 | |
| Got thrown | 1.40 | 2.01 | -0.61 | |
| Perfect Parry | 0.20 | 0.69 | -0.49 | |
| Throws | 2.00 | 2.43 | -0.43 | |
| Got Punish Countered | 0.40 | 0.06 | +0.34 | |
| Throw techs | 0.10 | 0.37 | -0.27 | |
| Drive Arts | 16.9% | 16.7% | +0.2% | |
| Throw parries | 0.10 | 0.22 | -0.12 | |
| Punish Counters landed | 0.20 | 0.08 | +0.12 | |
| Got stunned | 0.10 | 0.06 | +0.04 | |
| Stuns landed | 0.10 | 0.07 | +0.03 | |
| Drive Impact | 0.20 | 0.19 | +0.01 | |
| Drive Reversal | 0.20 | 0.20 | +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.
Ken · Latest 100
What to improve
Find the cause of punish events
Got Punish Countered: profile 0.4 · reference 0.08
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 0.4 · reference 0.08
Give each meter spend a purpose
SA1: profile 61.3% · 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 61.3% · reference 29.1%
- SA2: profile 3.8% · reference 23.7%
- SA3: profile 24.5% · reference 36.3%
Review defensive decisions
Drive Parry: profile 1.1 · reference 1.93
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
- Drive Parry: profile 1.1 · reference 1.93
- Perfect Parry: profile 0.2 · reference 0.8
- Drive Reversal: profile 0.2 · reference 0.19
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
Use matchup records as scouting leads
vs Char 253: 40 wins in 106 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 Char 253: 40 wins in 106 games
- vs Ryu: 4 wins in 13 games
- vs Alex: 6 wins in 12 games
Separate a tendency from a one-off event
Recent stored ranked sample: 16 wins in 46 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: 16 wins in 46 games
Look for a repeatable punish opportunity
Got Punish Countered: profile 0.4 · reference 0.08
Use the stats as a scouting lead. Watch for an action you have already verified as punishable at that spacing; do not assume every apparent opening is unsafe.
Try thisTest one confirmed response when that action appears. If it stops appearing, return to your normal spacing instead of forcing the punish.
Supporting stats
- Got Punish Countered: profile 0.4 · reference 0.08
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