Latest game
VictoryRanked
EL
CAELUpdated 1 week ago
Steam
JapanC100%
SteamJapanC100%Calpis
It's a Pweasure to Meet You
MR 1,570#111,948Last 106–4+13Last 2011–9+18
Winrate
59%
23W · 16L · 39 stored
Ranked matches
39
selected mode
Wins
23
Ranked
Losses
16
Ranked
Peak MR
1,597
Elena
Ranked MR · Elena
39 local · 0 imported · 39 on chart · 39 battles stored
Replay not requested
ELCAPlaystyle reference
Habits vs top 30 Elena
Benchmark refreshed Sep 13, 2026, 10:21 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
ELLargest absolute differences first23 habits
| Habit | Calpis | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| SA1 | 8.2% | 31.4% | -23.2% | |
| SA3 | 47.5% | 32.8% | +14.8% | |
| SA2 | 39.3% | 26.3% | +13.0% | |
| Drive Arts | 12.7% | 18.1% | -5.4% | |
| CA | 4.9% | 9.5% | -4.6% | |
| Drive Rush (cancel) | 34.6% | 31.0% | +3.6% | |
| Drive Rush (parry) | 10.5% | 7.5% | +3.1% | |
| Time cornered | 7.30 | 9.26 | -1.96 | |
| Drive Parry | 0.80 | 1.85 | -1.05 | |
| Throws | 1.40 | 2.14 | -0.74 | |
| Perfect Parry | 0.10 | 0.80 | -0.70 | |
| Drive Impact gauge | 1.8% | 1.2% | +0.6% | |
| Throw techs | 0.00 | 0.54 | -0.54 | |
| Got thrown | 1.70 | 2.24 | -0.54 | |
| Corner pressure | 13.50 | 13.06 | +0.44 | |
| Drive Reversal | 0.00 | 0.22 | -0.22 | |
| Got Punish Countered | 0.20 | 0.07 | +0.13 | |
| Throw parries | 0.30 | 0.19 | +0.11 | |
| Stuns landed | 0.00 | 0.06 | -0.06 | |
| Drive Impact | 0.20 | 0.15 | +0.05 | |
| Drive Guard | 13.9% | 13.8% | +0.0% | |
| Got stunned | 0.00 | 0.04 | -0.04 | |
| Punish Counters landed | 0.10 | 0.08 | +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.
Elena · Latest 100
What to improve
Find the cause of punish events
Got Punish Countered: profile 0.2 · reference 0.07
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 0.07
- Punish Counters landed: profile 0.1 · reference 0.08
Give each meter spend a purpose
SA1: profile 9.8% · reference 30.5%
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 9.8% · reference 30.5%
- SA2: profile 41% · reference 25.9%
- SA3: profile 44.3% · reference 34.1%
Make Drive Impact a deliberate choice
Drive Impact: profile 0.2 · reference 0.17
Review five Drive Impacts and record the situation and outcome. Keep effective uses; investigate repeated failures rather than aiming for the reference frequency.
Try thisRecord Drive Impact and an ordinary attack in separate dummy slots. Mix playback and practice recognizing the difference with a response available in your current gauge state.
Supporting stats
- Drive Impact: profile 0.2 · reference 0.17
- Drive Impact gauge: profile 1.9% · reference 1.4%
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 7, 2026 · AI: dots-studio/dots-3-note-preview:free
How to beat this player
Use matchup records as scouting leads
vs Luke: 33 wins in 65 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: 33 wins in 65 games
- vs Yasmine: 33 wins in 61 games
- vs Ryu: 28 wins in 48 games
Separate a tendency from a one-off event
Recent stored ranked sample: 12 wins in 22 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: 12 wins in 22 games
Probe defense before committing
Drive Reversal: profile 0.1 · reference 0.2
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 0.1 · reference 0.2
- Throw techs: profile 0 · reference 0.46
- Perfect Parry: profile 0.2 · reference 0.72
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 7, 2026 · AI: dots-studio/dots-3-note-preview:free