Meadowlands Expo Center - Secaucus, NJ, 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 | MUND Ruth | - | - | - | 4% | 18% | 41% | 37% |
| 2 | CHOI Charlotte | - | 1% | 8% | 24% | 36% | 25% | 6% |
| 3 | GUGALA Hanna | - | - | - | 3% | 15% | 41% | 41% |
| 3 | MAK Kaitlin | - | 2% | 9% | 24% | 34% | 24% | 6% |
| 5 | LIU Hannah | - | - | 3% | 18% | 43% | 35% | |
| 6 | YUEN Nicole | - | 3% | 17% | 36% | 33% | 11% | |
| 7 | SHMULER Fiona | - | - | 3% | 19% | 38% | 31% | 9% |
| 8 | SPEARS Mya B | - | - | 4% | 18% | 42% | 35% | |
| 9 | ZHU Avril | - | 2% | 14% | 33% | 34% | 15% | 2% |
| 10 | CASTELO Soleil | - | 2% | 12% | 32% | 36% | 16% | 2% |
| 11 | CHANG Norah | - | 3% | 18% | 36% | 31% | 11% | 1% |
| 12 | BARNES Sarah | 3% | 24% | 39% | 25% | 8% | 1% | - |
| 13 | TA-ZHOU Emma | - | 2% | 14% | 35% | 37% | 12% | |
| 14 | XU Elaine | 1% | 10% | 30% | 37% | 18% | 3% | |
| 15 | BROWN Aria | - | - | 3% | 16% | 36% | 35% | 10% |
| 16 | FOSS Persephone | - | 5% | 23% | 41% | 26% | 4% | |
| 17 | DAVIDOVA Kira | - | - | 3% | 13% | 31% | 37% | 16% |
| 18 | SEVASTOPULO Sahra | 3% | 15% | 30% | 30% | 16% | 4% | < 1% |
| 19 | CAI Xinyi | - | - | 1% | 8% | 35% | 57% | |
| 20 | HU Anna | - | - | - | 3% | 17% | 42% | 37% |
| 21 | GONG Joy | - | - | 3% | 14% | 34% | 36% | 13% |
| 22 | AWAD Royce | - | 1% | 10% | 31% | 40% | 18% | |
| 23 | SCHAIBLE Sofia L. | - | - | 3% | 16% | 42% | 39% | |
| 24 | LAFFY Lily | 3% | 18% | 38% | 30% | 10% | 1% | |
| 25 | KIM Audrey | - | 5% | 22% | 37% | 26% | 8% | 1% |
| 26 | BUSH Bethany | - | - | 3% | 13% | 31% | 37% | 16% |
| 26 | MERMEGAS Olivia | - | 1% | 6% | 21% | 38% | 29% | 6% |
| 28 | RANJAN Diya | 1% | 12% | 33% | 35% | 17% | 3% | |
| 29 | REN Katherine | - | 5% | 26% | 41% | 24% | 4% | |
| 30 | HAMMERSTROM Aria | - | - | 1% | 6% | 23% | 43% | 27% |
| 31 | PASUPULETI Laya | 9% | 28% | 34% | 21% | 7% | 1% | - |
| 32 | LOURENCO Alexandra | 3% | 17% | 36% | 32% | 12% | 1% | |
| 33 | YOUNG Charlotte G. | - | - | - | 2% | 12% | 39% | 47% |
| 34 | MERCHANT Aishwarya | - | - | 1% | 11% | 32% | 39% | 17% |
| 35 | MYAT Chloe | - | 1% | 5% | 20% | 36% | 30% | 8% |
| 36 | HALPERIN Elizabeth H. | - | - | 2% | 11% | 28% | 38% | 21% |
| 37 | PROBASCO Leila | 1% | 7% | 26% | 39% | 23% | 4% | |
| 38 | YANNOPOULOS Pompie | - | - | 3% | 16% | 36% | 34% | 11% |
| 39 | OSMINKINA-JONES Kai | - | 1% | 8% | 25% | 36% | 24% | 6% |
| 40 | DAMBAL Sasha | - | - | 2% | 9% | 28% | 40% | 21% |
| 40 | BERRIOS Catalina | - | 4% | 20% | 37% | 29% | 9% | 1% |
| 42 | GENTILE Vittoria | - | 3% | 14% | 32% | 34% | 15% | 2% |
| 43 | RANDALL-COLLINS Shea M. | - | 3% | 16% | 35% | 32% | 12% | 1% |
| 44 | WANG JiaQi | - | 2% | 17% | 39% | 33% | 8% | |
| 45 | VISWANATHAN Nishka | 1% | 16% | 36% | 32% | 13% | 2% | |
| 46 | GONZALEZ Veronika | - | 3% | 15% | 35% | 34% | 13% | |
| 47 | CHI Claire | - | 3% | 17% | 37% | 34% | 9% | |
| 48 | LOO Kaitlyn | - | 1% | 7% | 26% | 42% | 23% | |
| 49 | IANNUZZI Lucy | - | 1% | 11% | 34% | 36% | 16% | 3% |
| 50 | FUNG Iris | - | 1% | 6% | 23% | 38% | 27% | 5% |
| 50 | VATS Ishita | 1% | 11% | 32% | 35% | 17% | 4% | - |
| 52 | MULLER Inara | 1% | 9% | 26% | 35% | 23% | 7% | 1% |
| 53 | ILAGAN Ava | 2% | 10% | 27% | 34% | 21% | 6% | 1% |
| 54 | LEIGH Adalene | 2% | 12% | 30% | 34% | 18% | 5% | - |
| 55 | KIM Grace | 28% | 43% | 23% | 5% | 1% | - | |
| 56 | DESAUTELS Alexandra | 4% | 21% | 37% | 28% | 8% | 1% | |
| 56 | ROHATGI Saanvi | 42% | 42% | 14% | 2% | - | - | |
| 58 | KWON Ava | - | 4% | 20% | 41% | 30% | 5% | |
| 58 | ZHAO Selena | 5% | 27% | 41% | 22% | 5% | - | |
| 60 | HUANG Pierra | 5% | 21% | 35% | 27% | 10% | 2% | - |
| 61 | KWON Ava | 7% | 30% | 37% | 20% | 5% | 1% | - |
| 62 | WONG Charlene | 4% | 18% | 33% | 29% | 13% | 3% | - |
| 63 | RIFKIN Lielle | 16% | 40% | 32% | 11% | 2% | - | |
| 64 | LEE Grace | 3% | 16% | 31% | 30% | 15% | 4% | - |
| 65 | KHOST Maeve | - | 5% | 26% | 41% | 23% | 4% | |
| 66 | LEE Kaitlin | 1% | 9% | 25% | 33% | 23% | 8% | 1% |
| 67 | JOHN Sophia | - | 4% | 31% | 40% | 20% | 5% | - |
| 68 | ZHANG Ashley | - | 4% | 17% | 33% | 31% | 13% | 2% |
| 69 | BANDHU Saahiti | 9% | 34% | 38% | 16% | 3% | - | |
| 70 | GUHA Surabhi | 3% | 17% | 36% | 32% | 11% | 1% | |
| 71 | YOON Sela | 15% | 38% | 33% | 12% | 2% | - | |
| 72 | DUTA Lyra | 2% | 11% | 27% | 33% | 21% | 6% | 1% |
| 73 | KIM isabel | 13% | 34% | 33% | 15% | 4% | - | - |
| 74 | NG Sophia | 5% | 21% | 34% | 27% | 11% | 2% | - |
| 75 | NANDA Maanika | 1% | 8% | 31% | 37% | 19% | 4% | - |
| 76 | SU JOSERINE | 32% | 42% | 21% | 5% | 1% | - | - |
| 77 | FABRICANT Kioka R. | - | 8% | 27% | 36% | 22% | 6% | - |
| 78 | LIM Alexa J. | 2% | 13% | 29% | 32% | 18% | 5% | - |
| 79 | HO Sophia | 18% | 39% | 31% | 10% | 1% | - | |
| 80 | STADNIK Emilia | - | 7% | 26% | 38% | 24% | 6% | |
| 81 | LIANG Claire | 12% | 38% | 35% | 13% | 2% | - | |
| 82 | BHARDWAJ Riya | 23% | 50% | 23% | 4% | - | - | |
| 83 | GOEL Lineysha | 8% | 26% | 34% | 23% | 8% | 1% | - |
| 84 | PARK Haylie | - | 5% | 24% | 38% | 25% | 7% | 1% |
| 85 | CHOWDHERY Myra | 1% | 9% | 29% | 36% | 20% | 5% | - |
| 86 | KIM Grace | 4% | 17% | 31% | 30% | 15% | 4% | - |
| 87 | LEE Janice | 24% | 47% | 23% | 5% | 1% | - | - |
| 88 | ROBERTSON Szilvia | 19% | 63% | 16% | 1% | - | - | - |
| 89 | VATSA Shradha | 37% | 45% | 16% | 2% | - | - | - |
| 90 | HUANG Zoe | 13% | 44% | 32% | 9% | 1% | - | - |
| 91 | LEUNG Ella | 57% | 36% | 6% | - | - | - | - |
| 92 | GALLAGHER Isabella | 36% | 43% | 18% | 3% | - | - | |
| 93 | KESSLER Amelia | 47% | 42% | 9% | 1% | - | - | |
| 94 | VALENCIA Sadie | 11% | 32% | 35% | 18% | 4% | 1% | - |
| 95 | YUN Kailyn | 76% | 22% | 2% | - | - | - | |
| 96 | LIU Teresa | 35% | 42% | 19% | 4% | - | - | - |
| 97 | NAKATA Gwyneth | 69% | 27% | 4% | - | - | - | - |
| 98 | CAFAGNA Sofia | 16% | 42% | 31% | 10% | 1% | - | - |
| 99 | REYNOLDS CHANG Anna | 72% | 27% | 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.