Milwaukee, WI - Milwaukee, WI, 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 | YANG Ziyi | - | - | 1% | 6% | 23% | 41% | 29% |
| 2 | FRISHMAN Ethan J. | - | - | - | 1% | 7% | 34% | 58% |
| 3 | DHINGRA Gian K. | - | - | 2% | 12% | 31% | 38% | 17% |
| 3 | TANG Alex Y. | - | 1% | 6% | 22% | 39% | 27% | 5% |
| 5 | CHANG Colin S. | - | 1% | 9% | 26% | 36% | 23% | 5% |
| 6 | LUKASHENKO Darii | - | - | 1% | 8% | 26% | 41% | 24% |
| 7 | SO Hananiah | - | - | - | 2% | 12% | 39% | 47% |
| 8 | CHAN Matthew | - | 1% | 6% | 19% | 34% | 30% | 10% |
| 9 | SILBERZWEIG Jordan H. | - | - | - | 2% | 13% | 41% | 45% |
| 10 | WU MENGKE | - | - | 2% | 12% | 29% | 37% | 19% |
| 11 | LINSKY Matthew | - | - | - | 3% | 17% | 42% | 37% |
| 12 | LE Hayden | - | - | 4% | 18% | 35% | 32% | 11% |
| 13 | YANG Duncan | - | 4% | 16% | 30% | 31% | 16% | 3% |
| 14 | BERMAN Luca | - | 1% | 10% | 28% | 35% | 21% | 5% |
| 15 | KOGAN Benjamin | 2% | 11% | 27% | 32% | 20% | 7% | 1% |
| 16 | GINIS Nathan | - | - | 4% | 19% | 42% | 35% | |
| 17 | BERRIO Carter E. | - | 1% | 5% | 18% | 34% | 31% | 11% |
| 17 | CHEN Oscar | - | 1% | 5% | 18% | 33% | 31% | 12% |
| 19 | GOERING Ashton H. | 1% | 7% | 24% | 36% | 25% | 7% | 1% |
| 20 | CHEONG Heonjae | - | - | 5% | 20% | 37% | 29% | 8% |
| 21 | SKINNER Graham B. | - | - | - | 3% | 18% | 44% | 35% |
| 22 | LINDHOLM Oliver S. | - | 2% | 13% | 33% | 34% | 16% | 3% |
| 23 | GUZZO Vito | 1% | 11% | 30% | 35% | 19% | 4% | - |
| 24 | WOODWARD Connor | - | 4% | 20% | 38% | 30% | 8% | |
| 25 | ZUBATIY Samuel | - | 5% | 19% | 33% | 29% | 12% | 2% |
| 26 | TONG ZACHARY | - | - | 4% | 19% | 38% | 31% | 8% |
| 27 | LICHT Aaron H. | 2% | 12% | 27% | 32% | 20% | 6% | 1% |
| 28 | CHON Taylor A. | - | 2% | 12% | 29% | 34% | 18% | 3% |
| 29 | LUO George F. | - | 1% | 6% | 22% | 37% | 27% | 7% |
| 30 | XU Andrew | 3% | 14% | 29% | 31% | 17% | 5% | 1% |
| 31 | BEAULAC Stephane | - | 3% | 14% | 29% | 32% | 17% | 4% |
| 32 | BUCHMANN Finn D. | - | 4% | 16% | 30% | 31% | 16% | 3% |
| 33 | POPE Nico | - | 1% | 5% | 17% | 33% | 32% | 12% |
| 33 | MORRILL William | - | 4% | 15% | 30% | 31% | 16% | 3% |
| 35 | FERNANDEZ Rodrigo | - | - | 4% | 19% | 42% | 35% | |
| 35 | WILSON Jude | - | - | 2% | 12% | 40% | 46% | |
| 37 | GREENBAUM Ian L. | 2% | 15% | 33% | 33% | 14% | 2% | |
| 38 | KIM Alexander M. | - | - | 3% | 16% | 35% | 33% | 11% |
| 39 | TANN Justin | - | - | 1% | 8% | 27% | 44% | 20% |
| 40 | JI Cody Walter | - | - | 3% | 12% | 29% | 37% | 19% |
| 40 | HONG Steven | - | 3% | 15% | 34% | 33% | 13% | 2% |
| 42 | COLE Alexander | - | 3% | 16% | 34% | 32% | 13% | 2% |
| 43 | HONG Vincent Q. | - | 2% | 11% | 29% | 37% | 19% | 2% |
| 44 | MAKLIN Edward P. | - | 3% | 15% | 31% | 32% | 16% | 3% |
| 45 | KIM-COGAN Ryan | - | 1% | 5% | 22% | 39% | 28% | 6% |
| 46 | NAZLYMOV Andrei | - | 2% | 11% | 29% | 36% | 19% | 4% |
| 47 | CHAVES Matthew J. | 1% | 9% | 25% | 33% | 23% | 8% | 1% |
| 48 | FENG Leo | - | 1% | 7% | 24% | 38% | 25% | 5% |
| 49 | KEEFE Duncan | - | 2% | 11% | 26% | 34% | 22% | 5% |
| 50 | JAWOROWSKI Matthew | - | 2% | 12% | 32% | 36% | 16% | 2% |
| 51 | WANG Charles | 1% | 10% | 30% | 36% | 19% | 4% | - |
| 52 | XU William | 2% | 15% | 36% | 33% | 12% | 2% | |
| 53 | ERMAKOV Lev | - | 1% | 6% | 24% | 43% | 27% | |
| 54 | GHENEA George Philipe | 2% | 19% | 40% | 30% | 9% | 1% | |
| 55 | LIU Christopher X. | - | 2% | 12% | 28% | 34% | 19% | 4% |
| 56 | YUN Jake | 1% | 9% | 24% | 33% | 23% | 8% | 1% |
| 57 | ERACHSHAW Cyrus P. | 1% | 5% | 18% | 33% | 29% | 12% | 2% |
| 58 | CHAN Aidan | 8% | 28% | 36% | 21% | 6% | 1% | - |
| 59 | AVAKIAN Alec | - | 6% | 26% | 40% | 24% | 4% | |
| 60 | OWENS Harrison J. | 3% | 28% | 42% | 23% | 5% | - | |
| 61 | BONSELL Vance | 2% | 18% | 40% | 29% | 9% | 1% | - |
| 62 | EICHHORN Lukas H. | - | 1% | 6% | 22% | 36% | 28% | 8% |
| 63 | DU Gavin J. | 1% | 7% | 22% | 34% | 26% | 10% | 1% |
| 64 | CHAUDHURI Eeshaan A. | 4% | 26% | 40% | 23% | 6% | 1% | - |
| 65 | LEE Justin | - | 6% | 24% | 39% | 25% | 5% | |
| 66 | ZHOU Miles | 1% | 10% | 27% | 34% | 21% | 7% | 1% |
| 67 | HOUTZ Jackson | - | 4% | 17% | 36% | 34% | 10% | |
| 68 | SIMAK Joseph P. | - | 1% | 5% | 19% | 35% | 30% | 10% |
| 69 | SHTEYN Mark | 1% | 10% | 26% | 33% | 22% | 7% | 1% |
| 70 | CONINE Tanner C. | - | 2% | 12% | 30% | 36% | 17% | 2% |
| 71 | OVERDECK Andrew | 3% | 17% | 34% | 31% | 13% | 2% | - |
| 72 | HUANG Alexander C. | - | 2% | 14% | 33% | 34% | 15% | 2% |
| 73 | BULL Anderson | 1% | 6% | 21% | 33% | 26% | 10% | 2% |
| 74 | SHERWOOD Hayden F. | 1% | 9% | 26% | 34% | 22% | 7% | 1% |
| 75 | DENG Andrew | - | 4% | 18% | 32% | 30% | 13% | 2% |
| 76 | ZHANG Jeffrey | 3% | 16% | 31% | 30% | 16% | 4% | - |
| 77 | DENNER Maximilian P. | - | - | 2% | 12% | 32% | 38% | 16% |
| 78 | LUTHRA Arjun | - | 4% | 17% | 31% | 30% | 14% | 3% |
| 79 | LIU Mingyang Ryan | 6% | 35% | 39% | 17% | 3% | - | - |
| 80 | KIM Shaun M. | 1% | 9% | 30% | 38% | 19% | 3% | |
| 81 | HUANG Ethan F. | 1% | 9% | 29% | 38% | 20% | 3% | |
| 82 | GHAYALOD ansh | - | 3% | 19% | 40% | 31% | 7% | |
| 83 | CHENG Kyle | 7% | 27% | 37% | 23% | 6% | 1% | |
| 84 | PIWOWAR Alex | 15% | 36% | 33% | 14% | 2% | - | |
| 85 | SPRINGER Patrick | 50% | 38% | 11% | 1% | - | - | |
| 86 | ROSBERG Dashiell W. | - | 3% | 16% | 36% | 34% | 11% | |
| 87 | YOO Joshua H. | 18% | 43% | 29% | 8% | 1% | - | |
| 88 | LEE Aydan J. | - | 1% | 10% | 31% | 37% | 18% | 3% |
| 89 | ZHANG Derek | 16% | 41% | 31% | 10% | 2% | - | - |
| 90 | POSY Daniel | 7% | 48% | 34% | 10% | 1% | - | - |
| 91 | CHEN Leo | 1% | 7% | 20% | 32% | 27% | 11% | 2% |
| 92 | SANDERS Samuel B. | 4% | 23% | 38% | 26% | 8% | 1% | - |
| 93 | HAO Anwen | - | 5% | 27% | 38% | 22% | 6% | 1% |
| 94 | HAN Edward | 3% | 20% | 38% | 28% | 10% | 1% | - |
| 95 | NOBLE Colin | 1% | 8% | 28% | 36% | 21% | 6% | 1% |
| 96 | ZENG Noah | 8% | 25% | 34% | 23% | 8% | 2% | - |
| 97 | FERNANDEZ Liam | 18% | 38% | 30% | 11% | 2% | - | - |
| 98 | JEON Daniel | 8% | 27% | 34% | 22% | 8% | 1% | - |
| 99 | LEUNG Nathan | 15% | 37% | 32% | 13% | 3% | - | - |
| 100 | MYLEK Peter | 2% | 11% | 28% | 33% | 20% | 6% | 1% |
| 101 | WANG Andy | 6% | 25% | 37% | 24% | 7% | 1% | - |
| 102 | HOLZ William A. | 2% | 13% | 28% | 32% | 19% | 6% | 1% |
| 103 | LUHMAN Gabriel | 60% | 33% | 7% | 1% | - | - | - |
| 104 | RHEE Ethan N. | 2% | 13% | 34% | 35% | 15% | 2% | - |
| 105 | SMITH Vaughn | 18% | 39% | 30% | 10% | 2% | - | - |
| 106 | BEITEL Noah | 75% | 23% | 2% | - | - | - | |
| 107 | PANDEY Neil | 11% | 33% | 35% | 17% | 3% | - | |
| 108 | KILGALLEN William | 1% | 9% | 28% | 35% | 21% | 5% | - |
| 109 | WANG Nicolas | 2% | 14% | 31% | 32% | 16% | 4% | - |
| 110 | LOPEZ Lucas M. | 30% | 42% | 22% | 6% | 1% | - | - |
| 111 | WU Richard | 4% | 26% | 39% | 24% | 7% | 1% | - |
| 112 | LU Caleb Q. | 12% | 32% | 34% | 17% | 5% | 1% | - |
| 113 | CHEONG Heonjun | 2% | 12% | 29% | 32% | 18% | 5% | 1% |
| 114 | MORALES Jonathan | 13% | 42% | 32% | 11% | 2% | - | - |
| 115 | RYAN Will | 48% | 40% | 11% | 1% | - | - | - |
| 115 | MOULTON Ian | 27% | 41% | 24% | 6% | 1% | - | - |
| 117 | STONE Esmond A. | 13% | 32% | 32% | 17% | 5% | 1% | - |
| 118 | MALONE Gregory D. | 2% | 12% | 30% | 34% | 18% | 4% | - |
| 119 | RASMUSSEN Oliver | 20% | 41% | 28% | 9% | 1% | - | - |
| 120 | BLEYMAN David | 39% | 40% | 17% | 4% | - | - | - |
| 120 | ALTIRS Giorgio | 33% | 43% | 19% | 4% | - | - | - |
| 122 | ZHANG Michael (Jiayuan) | 6% | 37% | 38% | 15% | 3% | - | |
| 123 | KUMAR Sachit | 69% | 27% | 4% | - | - | - | - |
| 124 | AKS Ari | 2% | 13% | 32% | 34% | 16% | 3% | - |
| 125 | DILLE Jackson K. | 34% | 44% | 19% | 4% | - | - | |
| 126 | WANG YiLe(Justin) | 37% | 40% | 18% | 4% | - | - | - |
| 126 | GIANETTO Ethan K. | 9% | 27% | 34% | 22% | 8% | 1% | - |
| 128 | WEIL Asher D. | 53% | 37% | 9% | 1% | - | - | - |
| 129 | SUSTAR Mir | 83% | 17% | 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.