Data Doll MKII at Showdown: San Diego 2026
Data Doll MKII
1 players • Match results by round
Tip: Click any player row to see detailed round-by-round calculations
dropped • 1-4-0
100%
Meta
-1.5
Perf
2.5
Surp
1.0
Diff
R1
R1 SAGE
Round 1: Loss vs Andrea Biaggi (Fai) - Expected: Not enough data (1 matches)
R2
R2 SAGE
Round 2: Loss vs Colin Marshall (Dash) - Expected: Not enough data (1 matches)
R3
R3 SAGE
Round 3: Loss vs Dominic Perez (Lexi) - Expected: Not enough data (2 matches)
R4
R4 SAGE
Round 4: Loss vs Bryce Erickson (Oldhim) - Expected: Not enough data (1 matches)
R5
R5 SAGE
Round 5: Win vs None ()
Meta Fit
100% (5/5 favorable matchups)
Performance
-1.5 Wins (2.5 expected, 1 actual)
Surprise
2.5 total deviation
Difficulty
1.0 difficulty points
Round-by-Round Breakdown
Win probabilities rounded to nearest 5% for consistency with match display
Swipe left to see all columns →
| Round | Opponent | Expected | Result |
|---|---|---|---|
| R1 | Fai | 50% | Loss |
| R2 | Dash | 50% | Loss |
| R3 | Lexi | 50% | Loss |
| R4 | Oldhim | 50% | Loss |
| R5 | 50% | Win |
Legend
Win
Loss
Draw
Unknown Opponent
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Performance Metrics
Meta
% of rounds where the historical win rate favored you (≥50%). Higher means you faced a softer field.
Perf
Actual wins minus expected wins based on matchup odds. Positive = outperformed; negative = underperformed.
Surp
Sum of |result − win probability| per round. Near 0 means results matched expectations; higher means consistently surprising outcomes in either direction.
Diff
Win points weighted by matchup difficulty and round number (−log₂(prob) × round/total). Rewards beating long-shot matchups in late rounds.