Latest game
VictoryRanked
BL
ZABLUpdated 5 days ago
Steam
South KoreaC100%
SteamSouth KoreaC100%Nonname835
Beasting on Fools
MR 1,506#223,810Last 106–4+15Last 2013–7+47
Winrate
49%
128W · 132L · 260 stored
Ranked matches
260
selected mode
Wins
128
Ranked
Losses
132
Ranked
Peak MR
1,519
Blanka
Ranked MR · Blanka
184 local · 0 imported · 184 on chart · 260 battles stored
Replay not requested
BLZAPlaystyle reference
Habits vs top 30 Blanka
Benchmark refreshed Sep 12, 2026, 10:24 AM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
BLLargest absolute differences first23 habits
| Habit | Nonname835 | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| Drive Arts | 46.2% | 24.9% | +21.3% | |
| Drive Guard | 3.0% | 12.7% | -9.7% | |
| SA3 | 6.9% | 14.8% | -7.9% | |
| SA2 | 62.6% | 54.8% | +7.8% | |
| Drive Rush (cancel) | 19.2% | 26.9% | -7.7% | |
| Corner pressure | 5.50 | 12.16 | -6.66 | |
| Drive Rush (parry) | 1.6% | 7.2% | -5.6% | |
| SA1 | 28.2% | 25.2% | +3.0% | |
| CA | 2.3% | 5.2% | -2.9% | |
| Time cornered | 11.30 | 9.41 | +1.89 | |
| Drive Impact gauge | 3.0% | 1.7% | +1.3% | |
| Drive Parry | 0.40 | 1.61 | -1.21 | |
| Perfect Parry | 0.10 | 0.52 | -0.42 | |
| Throws | 2.90 | 2.59 | +0.31 | |
| Got stunned | 0.30 | 0.06 | +0.24 | |
| Punish Counters landed | 0.30 | 0.09 | +0.21 | |
| Drive Impact | 0.40 | 0.21 | +0.19 | |
| Drive Reversal | 0.60 | 0.44 | +0.16 | |
| Throw techs | 0.20 | 0.32 | -0.12 | |
| Got Punish Countered | 0.20 | 0.10 | +0.10 | |
| Got thrown | 2.10 | 2.01 | +0.09 | |
| Stuns landed | 0.00 | 0.09 | -0.09 | |
| Throw parries | 0.30 | 0.33 | -0.03 |
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.
Blanka · Latest 100
What to improve
Find the cause of punish events
Got Punish Countered: profile 0.2 · reference 0.09
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 0.09
Trace how you lose or gain space
Time cornered: profile 11.3 · reference 9.41
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 11.3 · reference 9.41
- Corner pressure: profile 5.5 · reference 12.38
Compare decisions across wins and losses
Recent stored ranked sample: 52 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: 52 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
How to beat this player
Separate a tendency from a one-off event
Recent stored ranked sample: 52 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: 52 wins in 100 games
Test the Drive Impact tendency
Drive Impact: profile 0.4 · reference 0.23
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 0.4 · reference 0.23
Use matchup records as scouting leads
vs Jamie: 17 wins in 28 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 Jamie: 17 wins in 28 games
- vs Akuma: 9 wins in 21 games
- vs Mai: 8 wins in 18 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