Rockland Community College, Eugene Levy Field House - 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 | LI Han (Helina) | - | - | - | - | 5% | 38% | 57% |
| 2 | KIM Claire | - | - | - | 2% | 13% | 40% | 46% |
| 3 | FENG Audrey | - | - | - | - | 3% | 27% | 70% |
| 3 | WANG Carol | - | - | - | 6% | 36% | 58% | |
| 5 | KAPRAN Anastasia | - | - | 1% | 8% | 28% | 44% | 20% |
| 6 | JIANG Chloe | - | - | - | 5% | 26% | 50% | 19% |
| 7 | CAO Amelie | - | 1% | 7% | 32% | 52% | 9% | |
| 8 | WANG Dina C. | - | - | 4% | 24% | 49% | 23% | |
| 9 | TSIMIKLIS Aphrodite | - | - | - | 4% | 19% | 44% | 32% |
| 10 | BING Charlotte | - | - | - | 3% | 18% | 45% | 35% |
| 12 | LIU Qianchen E. | - | - | - | 3% | 22% | 53% | 21% |
| 13 | AL-BATTAINEH Eva | - | - | 1% | 7% | 26% | 44% | 23% |
| 14 | WANG Amabel | - | - | 2% | 14% | 47% | 37% | |
| 15 | JOO Sara | - | - | 4% | 22% | 42% | 26% | 5% |
| 16 | ZHAO Olivia | - | - | 1% | 6% | 24% | 44% | 26% |
| 17 | WANG Joanna | - | - | - | 2% | 14% | 42% | 41% |
| 18 | FIELD Elizabeth | - | - | 4% | 23% | 48% | 25% | |
| 19 | JOO Natalie | - | - | - | 1% | 21% | 77% | |
| 20 | TANG Melody Fujiao | - | - | - | 2% | 29% | 70% | |
| 21 | MURPHY Genevieve | - | 1% | 7% | 27% | 41% | 21% | 2% |
| 22 | PEVZNER Nicole | - | - | - | 2% | 14% | 42% | 41% |
| 23 | REZA Fukaina | 1% | 6% | 24% | 38% | 25% | 6% | 1% |
| 24 | DIMATULAC Elise Ann | - | - | 1% | 12% | 44% | 42% | |
| 25 | REN Kayley | - | - | 2% | 15% | 46% | 37% | |
| 26 | ZHU Ella | 1% | 8% | 30% | 42% | 16% | 2% | |
| 27 | GE Deanna | 5% | 26% | 42% | 23% | 4% | - | |
| 28 | ZHANG Priscilla | 8% | 33% | 40% | 16% | 2% | - | |
| 29 | ORRINGER Lottie | - | - | 2% | 12% | 34% | 39% | 13% |
| 30 | ZHILKOV Anya | - | - | 7% | 37% | 42% | 13% | 1% |
| 31 | ZHANG Vivian | - | - | 1% | 15% | 63% | 20% | |
| 32 | KUANG Bella | 7% | 31% | 41% | 19% | 2% | - | |
| 33 | SHENG Katherine | - | - | - | 1% | 9% | 41% | 50% |
| 34 | KRAHE Annika | - | - | 1% | 10% | 33% | 41% | 14% |
| 35 | HARRIS Parker | - | - | 4% | 18% | 37% | 33% | 8% |
| 36 | SFINTESCU Emma | - | 3% | 32% | 54% | 10% | - | |
| 37 | CHAN Jolene | - | 6% | 25% | 39% | 24% | 6% | - |
| 37 | MANGLANI Maya | 1% | 7% | 29% | 41% | 20% | 2% | - |
| 39 | FRASER Morgan | - | 2% | 13% | 32% | 36% | 16% | 2% |
| 39 | PHAN Logan | 1% | 11% | 30% | 35% | 18% | 4% | - |
| 41 | HAO Danica | - | - | 1% | 7% | 30% | 44% | 18% |
| 42 | HUSSIAN Annabelle | - | 2% | 11% | 30% | 36% | 17% | 2% |
| 43 | WU Maggie Lei | - | 5% | 22% | 40% | 27% | 6% | - |
| 44 | MCFARLANE Asha | - | - | 2% | 16% | 49% | 32% | |
| 45 | DONG Iris | 2% | 14% | 37% | 36% | 10% | 1% | |
| 46 | ZHU Alivia | 2% | 15% | 38% | 35% | 9% | 1% | |
| 47 | ELLISON Ingrid | 10% | 36% | 38% | 14% | 2% | - | |
| 48 | ENRIQUEZ Bianca Perla | 3% | 21% | 41% | 27% | 7% | 1% | - |
| 49 | SON Ayoung | - | 7% | 27% | 38% | 22% | 5% | - |
| 50 | LIN Katherine | 1% | 13% | 34% | 35% | 15% | 2% | - |
| 51 | DE CASTRO Kai | - | - | 4% | 19% | 41% | 31% | 4% |
| 52 | ZHU Audrey | 6% | 26% | 38% | 23% | 6% | 1% | - |
| 53 | LI Christina | - | 1% | 12% | 36% | 39% | 10% | 1% |
| 54 | IWERSEN Marte | 2% | 15% | 37% | 33% | 12% | 2% | - |
| 55 | SHIN Elizabeth | - | 2% | 14% | 34% | 35% | 13% | 2% |
| 55 | LI Junhan | 1% | 7% | 28% | 40% | 20% | 4% | - |
| 57 | SUN Erin | 9% | 35% | 39% | 15% | 2% | - | |
| 58 | POLING Katherine | 9% | 35% | 39% | 15% | 2% | - | |
| 59 | LI Joy | - | - | 5% | 27% | 51% | 17% | |
| 60 | CHO Olivia | 2% | 14% | 38% | 37% | 9% | - | |
| 61 | LI Savannah | - | 2% | 10% | 30% | 38% | 17% | 2% |
| 62 | LI Doreen | 35% | 42% | 19% | 4% | - | - | - |
| 63 | MCCLELLAN Florence | 12% | 64% | 22% | 2% | - | - | |
| 64 | HUANG Sophie | - | 2% | 12% | 32% | 36% | 16% | 2% |
| 65 | WEN Cynthia | 6% | 28% | 41% | 21% | 3% | - | |
| 66 | HUANG Gabrielle | 15% | 41% | 33% | 10% | 1% | - | - |
| 67 | HAFEZ Sahar | 3% | 21% | 42% | 28% | 7% | - | - |
| 68 | CAVANAGH Emma | 15% | 40% | 33% | 10% | 1% | - | |
| 69 | BO Iris | 2% | 13% | 33% | 34% | 15% | 3% | - |
| 70 | ZHENG Annalyn | - | - | 7% | 36% | 42% | 13% | 1% |
| 71 | STEWART Isla | 6% | 26% | 38% | 24% | 6% | 1% | - |
| 72 | LENZ Phoebe | 7% | 48% | 38% | 6% | - | - | - |
| 73 | HOLLIS Priscillia | 11% | 36% | 35% | 15% | 3% | - | - |
| 74 | LEO Jenna | - | 13% | 54% | 30% | 3% | - | |
| 75 | PRINZ-STRATEMAN Matilda | 34% | 44% | 19% | 3% | - | - | |
| 75 | XING Melly | 1% | 8% | 30% | 42% | 17% | 2% | |
| 77 | CHAN Kaitlyn | 32% | 44% | 20% | 3% | - | - | |
| 78 | LUO lucy | 1% | 10% | 30% | 36% | 19% | 4% | - |
| 79 | BENNETT Emi | 51% | 39% | 10% | 1% | - | - | - |
| 80 | ALBRECHT-SMITH Anne | 24% | 42% | 26% | 7% | 1% | - | - |
| 81 | PEVZNER Sophia | 5% | 26% | 41% | 23% | 5% | - | - |
| 82 | KHETPAL Aalia | 5% | 24% | 38% | 25% | 7% | 1% | - |
| 83 | GOMEZ Sofia | 2% | 31% | 51% | 14% | 1% | - | - |
| 84 | KIM Lael | 34% | 42% | 19% | 4% | - | - | - |
| 85 | ZHANG Caroline | 10% | 39% | 36% | 13% | 2% | - | - |
| 86 | PAEK Mila | 40% | 43% | 15% | 2% | - | - | - |
| 87 | DUNCAN Kate | 49% | 40% | 10% | 1% | - | - | - |
| 88 | BAULIN Zoya | 6% | 33% | 39% | 18% | 4% | - | - |
| 89 | MIN Claire | 77% | 22% | 2% | - | - | - | - |
| 90 | LIN Claire | 14% | 40% | 34% | 10% | 1% | - | |
| 91 | FEDER Acadia | 15% | 41% | 34% | 10% | 1% | - | |
| 91 | VACCARO Lillian | 36% | 43% | 18% | 3% | - | - | |
| 93 | BRADSHAW Tamira | 12% | 41% | 35% | 11% | 1% | - | - |
| 94 | MACKINTOSH Quinn | 16% | 43% | 31% | 8% | 1% | - | - |
| 94 | LIU Bella | 15% | 39% | 33% | 12% | 2% | - | - |
| 96 | RAFFAELE Nancy | 28% | 44% | 23% | 5% | - | - | |
| 96 | KIRBY Emelie | 41% | 42% | 15% | 2% | - | - | |
| 98 | WOLF Fiona | 34% | 44% | 18% | 3% | - | - | - |
| 99 | HUDSON Sophie | 62% | 32% | 6% | - | - | - | - |
| 100 | KUI Ava | 78% | 21% | 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.