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VictoryRanked
ED
DETTV/GareDaBear — Street Fighter 6 Stats
Ranked analytics on SF6 Stats · main Kimberly · peak MR 1,667. MR and LP charts, match history, matchups, rivals, and habits vs Legends.
KIUpdated 1 week ago
Steam
JapanM55%
SteamJapanM55%TTV/GareDaBear
Let's Do This
MR 1,667#35,467Last 106–4+6Last 2013–7+43
Winrate
50%
60W · 61L · 121 stored
Ranked matches
121
selected mode
Wins
60
Ranked
Losses
61
Ranked
Peak MR
1,730
Kimberly
Ranked MR · Kimberly
119 local · 0 imported · 119 on chart · 140 battles stored
Replay not requested
EDDEPlaystyle reference
Habits vs top 30 Kimberly
Benchmark refreshed Sep 14, 2026, 7:25 PM
Latest reported trends across the last 100 matches, not lifetime habit averages. · Data details
KILargest absolute differences first23 habits
| Habit | TTV/GareDaBear | Legends | Difference | LessLegendsMore |
|---|---|---|---|---|
| Drive Arts | 8.1% | 23.3% | -15.1% | |
| Drive Rush (cancel) | 42.0% | 28.7% | +13.3% | |
| SA3 | 32.4% | 41.7% | -9.3% | |
| SA1 | 40.7% | 34.1% | +6.7% | |
| Corner pressure | 10.20 | 15.86 | -5.66 | |
| CA | 12.0% | 8.7% | +3.4% | |
| Drive Guard | 11.8% | 13.5% | -1.7% | |
| Drive Rush (parry) | 6.2% | 7.7% | -1.5% | |
| Throws | 2.50 | 3.66 | -1.16 | |
| SA2 | 14.8% | 15.5% | -0.7% | |
| Time cornered | 8.20 | 8.87 | -0.67 | |
| Drive Impact gauge | 1.5% | 2.1% | -0.6% | |
| Drive Reversal | 0.70 | 0.32 | +0.38 | |
| Throw techs | 0.30 | 0.52 | -0.22 | |
| Throw parries | 0.20 | 0.35 | -0.15 | |
| Drive Parry | 1.80 | 1.68 | +0.12 | |
| Perfect Parry | 0.40 | 0.49 | -0.09 | |
| Got thrown | 2.40 | 2.31 | +0.09 | |
| Got stunned | 0.10 | 0.03 | +0.07 | |
| Drive Impact | 0.20 | 0.23 | -0.03 | |
| Got Punish Countered | 0.10 | 0.12 | -0.02 | |
| Punish Counters landed | 0.10 | 0.11 | -0.01 | |
| Stuns landed | 0.10 | 0.11 | -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.
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Uses public profile stats with OpenRouter’s free AI models. Plans are shared and saved for reuse. Suggestions are hypotheses, not replay analysis.