Suffern, NY - Suffern, NY, USA
Explore the pool victory probability density for each fencer, with their actual victories highlighted in a box. Learn more.
| # | Name | Number of victories | ||||||
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | ||
| 1 | PENG Bryan | - | - | 4% | 19% | 37% | 31% | 9% |
| 2 | OH Triton | - | - | 1% | 6% | 25% | 43% | 25% |
| 3 | HUANG Alexander C. | - | - | 2% | 14% | 33% | 36% | 14% |
| 3 | HUANG Maxwell H. | - | - | 2% | 13% | 32% | 37% | 15% |
| 5 | SHOMAN Zachary | - | - | - | 3% | 19% | 43% | 35% |
| 6 | SHOMAN Noah | - | - | - | 2% | 13% | 39% | 46% |
| 7 | KUSHKOV Daniel | - | - | 1% | 8% | 28% | 41% | 21% |
| 8 | VARUKATTY-GAFOOR Sohil | - | - | 1% | 8% | 26% | 42% | 23% |
| 9 | WANG Robert | - | - | 1% | 5% | 21% | 41% | 32% |
| 10 | BABAYEV Gabriel A. | - | - | - | 4% | 22% | 45% | 28% |
| 11 | MICLAUS Justin | - | - | 3% | 16% | 35% | 34% | 12% |
| 12 | SAHAY Kenji | - | 5% | 19% | 35% | 29% | 10% | 1% |
| 13 | GRIGORESCU Alexander | - | - | 2% | 12% | 33% | 40% | 13% |
| 14 | STURN Oliver | - | - | 1% | 11% | 43% | 45% | |
| 15 | CLARK Gabriel | - | - | 2% | 12% | 32% | 38% | 15% |
| 16 | ALLARDYCE Lachlan | - | 3% | 16% | 35% | 32% | 12% | 1% |
| 17 | ALAVE Kyle | - | - | 1% | 7% | 28% | 47% | 17% |
| 18 | SHIPITSIN Alexander | - | - | 3% | 24% | 51% | 23% | |
| 19 | KWALWASSER Eric | 3% | 15% | 31% | 31% | 16% | 4% | - |
| 20 | KESSLER Nathan | - | 4% | 18% | 35% | 30% | 11% | 1% |
| 21 | SHINCHUK Daniel | - | 2% | 10% | 26% | 36% | 22% | 4% |
| 22 | SUBRAMANIAM Oliver C. | - | 1% | 9% | 26% | 37% | 22% | 5% |
| 23 | LIN Philip T. | - | 2% | 17% | 36% | 31% | 12% | 2% |
| 24 | MERCHANT Milan | - | 4% | 25% | 45% | 22% | 3% | |
| 25 | ALLARDYCE Graham | - | 2% | 13% | 32% | 35% | 16% | 2% |
| 26 | PENG Victor | 1% | 9% | 28% | 38% | 20% | 4% | - |
| 27 | HWANG Nathaniel | 3% | 20% | 36% | 28% | 11% | 2% | - |
| 28 | BOULAIS Andrew D. | - | 7% | 28% | 37% | 21% | 5% | 1% |
| 29 | MARGULIS Jared | 10% | 35% | 36% | 16% | 3% | - | - |
| 30 | MEDVINSKY Daniel | - | - | 4% | 17% | 36% | 32% | 11% |
| 31 | TVERSKOY Sam | 1% | 15% | 36% | 32% | 13% | 2% | - |
| 32 | KAZIMIR Harrison | - | 4% | 17% | 35% | 31% | 11% | 1% |
| 33 | MANGAN Daniel Marsh | 1% | 10% | 26% | 34% | 22% | 7% | 1% |
| 34 | FRANCOIS Alexander C. | 2% | 17% | 39% | 30% | 10% | 1% | - |
| 35 | KLETTER Max | 12% | 33% | 34% | 16% | 4% | - | - |
| 36 | SINGER Marcus | 29% | 42% | 23% | 6% | 1% | - | - |
| 37 | VORONOVICH Aleksei | 22% | 39% | 28% | 10% | 2% | - | - |
| 38 | WONG Caleb W. | 6% | 29% | 38% | 21% | 5% | 1% | - |
| 39 | NAYAK Surin K. | - | 6% | 21% | 35% | 28% | 10% | 1% |
| 40 | EYBELMAN Ariel | 1% | 8% | 24% | 34% | 24% | 8% | 1% |
| 41 | LIU Ryan | 1% | 22% | 49% | 24% | 3% | - | |
| 42 | LEE Andrew | 2% | 16% | 33% | 31% | 15% | 3% | - |
| 43 | SUGIURA Samuel | 1% | 6% | 20% | 33% | 28% | 11% | 1% |
| 44 | SEN Christian | 1% | 12% | 34% | 36% | 15% | 2% | - |
| 45 | GONG Jerry | 4% | 18% | 33% | 29% | 13% | 3% | - |
| 45 | TIAGI George | 17% | 40% | 31% | 10% | 2% | - | - |
| 47 | CARROLL Charles | 2% | 12% | 30% | 35% | 18% | 3% | - |
| 48 | NARDINI Nathanael P. | - | 1% | 7% | 25% | 38% | 23% | 5% |
| 49 | ADAMS Rhys | 6% | 25% | 38% | 24% | 7% | 1% | - |
| 50 | MUNGOVAN Matthew | 7% | 47% | 38% | 9% | 1% | - | |
| 51 | LIM Brandon | 4% | 20% | 35% | 28% | 11% | 2% | - |
| 52 | JUNG Ethan | 38% | 46% | 14% | 2% | - | - | - |
| 53 | WITCZAK Mateus | 1% | 9% | 25% | 34% | 24% | 8% | 1% |
| 54 | DOCIL Jack | 5% | 22% | 34% | 26% | 10% | 2% | - |
| 55 | BARATY Teagan | 58% | 35% | 7% | 1% | - | - | - |
| 56 | TAI Roebling | 77% | 21% | 2% | - | - | - | |
| 57 | BHERWANI Ishaan | 7% | 36% | 39% | 15% | 2% | - | - |
| 58 | MIRKIS Levi | 45% | 41% | 12% | 2% | - | - | - |
| 59 | GONZALEZ Kal-El | 7% | 33% | 38% | 18% | 4% | - | - |
| 60 | MENDOZA Diwa | 29% | 49% | 19% | 3% | - | - | - |
| 61 | LIU Alexander | 24% | 43% | 26% | 6% | 1% | - | - |
| 62 | DAVIDSON Dean | 4% | 21% | 37% | 27% | 9% | 1% | - |
The heatmap in this table provides a visual representation of the victory probability distribution for each fencer in their respective pools:
This heatmap visualization offers an immediate understanding of each fencer's expected performance compared to their actual results.