Secaucus, NJ - 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 | BOIS Adele | - | - | - | 1% | 10% | 36% | 52% |
| 2 | PRIEUR Lauren | - | 3% | 17% | 36% | 33% | 11% | |
| 3 | GORMAN Victoria M. | - | - | 3% | 22% | 48% | 27% | |
| 3 | SHOMAN Jenna | - | - | - | 1% | 9% | 38% | 53% |
| 5 | ENGELMAN Madeline A. | - | - | - | 1% | 8% | 34% | 57% |
| 6 | HILD Nisha | - | - | 1% | 9% | 30% | 41% | 19% |
| 7 | MIKA Veronica | - | - | - | 4% | 23% | 46% | 26% |
| 8 | YANG Ashley M. | - | - | 1% | 4% | 19% | 41% | 36% |
| 9 | NATHANSON Sammy E. | - | 2% | 11% | 31% | 39% | 18% | |
| 9 | BALAKUMARAN Maya | - | 1% | 7% | 29% | 43% | 20% | |
| 11 | SOURIMTO Valeria | - | 4% | 17% | 36% | 32% | 10% | |
| 12 | ROMAGNOLI Isabella | - | - | 2% | 11% | 34% | 42% | 12% |
| 13 | LU Amy | - | 1% | 8% | 25% | 37% | 24% | 6% |
| 14 | CHEN Xiaohan | 4% | 19% | 34% | 29% | 12% | 2% | - |
| 15 | PAUL Lila | - | - | 1% | 6% | 23% | 42% | 29% |
| 16 | ANTHONY Alexia B. | - | 2% | 18% | 47% | 29% | 5% | |
| 17 | SUBRAMANIAN Nitika | - | - | 1% | 8% | 26% | 41% | 24% |
| 18 | WEI Vivian W. | - | - | 5% | 21% | 38% | 28% | 7% |
| 19 | WIGGERS Susan Q. | - | - | - | 1% | 12% | 46% | 41% |
| 20 | MAGLIATO Julia | 3% | 16% | 32% | 30% | 15% | 3% | - |
| 21 | VADASZ Ibla P. | - | 3% | 15% | 35% | 35% | 13% | |
| 21 | BEVACQUA Aria F. | - | 5% | 24% | 39% | 26% | 6% | |
| 23 | LU Elaine | - | 1% | 7% | 23% | 37% | 26% | 7% |
| 24 | CHEN Ashley | 1% | 8% | 26% | 36% | 22% | 6% | - |
| 25 | PROCACCINI Ashten V. | - | - | - | 2% | 18% | 50% | 30% |
| 26 | KIM Marley I. | - | - | 2% | 12% | 33% | 38% | 15% |
| 27 | LEE Hannah | - | 1% | 6% | 25% | 41% | 23% | 4% |
| 28 | GHAYALOD reya | - | 1% | 9% | 30% | 41% | 19% | |
| 29 | RIZKALA Joanna | - | - | 2% | 17% | 47% | 34% | |
| 30 | WANG Jianning | 4% | 25% | 39% | 25% | 7% | 1% | |
| 31 | NG Sarah W. | 1% | 11% | 37% | 36% | 14% | 2% | |
| 32 | LIAO Siwen | - | 3% | 14% | 30% | 33% | 17% | 3% |
| 33 | OGANEZOVA Valerie | 1% | 13% | 35% | 36% | 13% | 1% | - |
| 34 | KRASTEV Minna | - | - | - | 4% | 23% | 45% | 27% |
| 35 | SINHA Anika | - | 4% | 17% | 36% | 33% | 11% | |
| 36 | CHIANG Emily | 1% | 10% | 30% | 36% | 19% | 4% | |
| 37 | CHEN Xinyan | - | - | 3% | 15% | 34% | 35% | 12% |
| 38 | PASHIN Anna | - | 2% | 9% | 27% | 37% | 22% | 3% |
| 39 | JAVERI Amaya | 13% | 34% | 33% | 16% | 4% | - | - |
| 40 | GRINBERG Aliya | 4% | 19% | 34% | 29% | 12% | 2% | - |
| 41 | ALCEBAR Kayla | - | 3% | 14% | 34% | 36% | 14% | |
| 42 | NYSTROM Sofia C. | - | 4% | 21% | 38% | 29% | 8% | |
| 43 | NATH Trisha | - | 3% | 20% | 40% | 29% | 7% | |
| 44 | REN Xinling | 4% | 36% | 40% | 17% | 3% | - | |
| 45 | GRAFF Sophie | 1% | 9% | 28% | 37% | 21% | 4% | |
| 46 | LIN Angela | - | 3% | 16% | 36% | 34% | 11% | |
| 47 | LEE Sophia | - | 3% | 15% | 37% | 35% | 9% | 1% |
| 48 | TSUI Natalie | - | 2% | 10% | 27% | 37% | 21% | 3% |
| 49 | WU Helen | 1% | 6% | 22% | 36% | 27% | 9% | 1% |
| 49 | XIAO julie | 1% | 7% | 27% | 40% | 21% | 4% | - |
| 51 | SHI Cathleen | - | 3% | 14% | 30% | 33% | 17% | 3% |
| 52 | MCKEE Ainsley | 3% | 16% | 32% | 30% | 14% | 3% | - |
| 53 | HE Lizbeth | - | 2% | 12% | 31% | 36% | 17% | 2% |
| 54 | MONTORIO Lily M. | 10% | 39% | 35% | 13% | 2% | - | - |
| 55 | FESTA Carina | - | 2% | 16% | 37% | 32% | 12% | 1% |
| 56 | YAN Ava | 16% | 38% | 32% | 12% | 2% | - | - |
| 57 | OBRADOVIC Ana | 3% | 17% | 35% | 31% | 12% | 2% | |
| 58 | MCCREARY Camryn A. | - | 2% | 14% | 35% | 36% | 13% | |
| 59 | DUCKETT Madison | - | 2% | 12% | 32% | 38% | 17% | |
| 60 | SIMONIAN Olivia A. | 2% | 14% | 31% | 32% | 16% | 4% | - |
| 61 | ZHANG Sophie | 15% | 37% | 33% | 13% | 2% | - | - |
| 62 | LIGH Erenei J. | 1% | 11% | 31% | 36% | 18% | 3% | |
| 63 | CRUZ Sonia | 9% | 35% | 36% | 16% | 3% | - | - |
| 64 | XU Emily T. | - | 5% | 20% | 36% | 29% | 10% | 1% |
| 65 | YUAN Greta | - | 3% | 13% | 33% | 36% | 14% | 1% |
| 66 | GOSALIA Nidhi | 9% | 27% | 34% | 22% | 7% | 1% | - |
| 67 | LI Angela | 4% | 24% | 40% | 25% | 6% | 1% | - |
| 68 | ALTIRS Kate | 1% | 14% | 51% | 29% | 5% | - | |
| 69 | SHEN Jamie | 1% | 14% | 39% | 33% | 12% | 2% | - |
| 70 | HITOMI Nadya | 2% | 13% | 32% | 34% | 16% | 4% | - |
| 71 | SHPILSKY Maya | 2% | 11% | 29% | 34% | 19% | 4% | - |
| 72 | JEONG Katie | - | 5% | 20% | 35% | 28% | 10% | 1% |
| 72 | DAVIS Jordan | 4% | 18% | 35% | 30% | 11% | 1% | - |
| 74 | SO Catelyn | 1% | 11% | 30% | 36% | 18% | 3% | |
| 75 | WANG Audrey | 51% | 37% | 10% | 1% | - | - | |
| 76 | DENG Brooke | 8% | 32% | 37% | 18% | 4% | - | |
| 77 | YOUNG Audrey | 16% | 39% | 32% | 11% | 2% | - | |
| 78 | NAYAK Anika | 39% | 42% | 16% | 3% | - | - | |
| 79 | YEN Natalie | 57% | 34% | 8% | 1% | - | - | |
| 79 | ZAMAN Rania | 27% | 54% | 17% | 2% | - | - | |
| 81 | CHOU Amy R. | 3% | 21% | 40% | 28% | 8% | 1% | - |
| 82 | SHINCHUK Ellisha | 4% | 19% | 33% | 28% | 12% | 2% | - |
| 82 | DAMDINSUREN Sophie | 38% | 43% | 16% | 3% | - | - | - |
| 84 | XU ALINA | 13% | 40% | 35% | 11% | 1% | - | - |
| 85 | BERNSTEIN Aiden S. | 45% | 40% | 13% | 2% | - | - | - |
| 86 | JENKINS Scotland | 5% | 25% | 41% | 23% | 5% | - | - |
| 87 | CHEN Kevy | 3% | 21% | 37% | 28% | 9% | 1% | - |
| 88 | FEO ZAKHAROVA Isabella | 46% | 42% | 11% | 1% | - | - | - |
| 89 | KHAN Alissa | 25% | 42% | 25% | 7% | 1% | - | |
| 90 | LEUNG Ashlyn K. | 7% | 25% | 35% | 24% | 8% | 1% | - |
| 91 | ELNATAN Mica A. | 14% | 34% | 33% | 15% | 3% | - | - |
| 92 | ZENG Megan | 54% | 41% | 5% | - | - | - | |
| 93 | WANG Natalie | 13% | 38% | 33% | 13% | 3% | - | - |
| 94 | PRADO-TUCKER Isabel | 79% | 19% | 2% | - | - | - | |
| 95 | DUBE Naomi | 20% | 48% | 26% | 6% | 1% | - | - |
| 95 | LIU Sophie | 3% | 18% | 37% | 30% | 10% | 1% | - |
| 97 | SUN Michelle | 13% | 57% | 25% | 4% | - | - | |
| 98 | GORTI Ramya | 57% | 35% | 8% | 1% | - | - | - |
| 99 | CHOKSHI Soha | 14% | 44% | 31% | 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.