SportsPlex at Metuchen - Metuchen, 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 | ELWOOD Sebastian F. | - | - | - | - | 1% | 16% | 83% |
| 2 | KLOTZ Isaiah | - | - | - | 1% | 9% | 36% | 54% |
| 3 | TANG Albert | - | - | - | 1% | 8% | 37% | 55% |
| 3 | SONG Noel | - | - | - | 3% | 16% | 42% | 40% |
| 5 | TRAUGOT Owen G. | - | - | - | - | 1% | 15% | 84% |
| 6 | WANG Winston | - | - | 1% | 12% | 41% | 46% | |
| 7 | SHAPIRO Leon | - | - | 1% | 10% | 35% | 48% | 6% |
| 8 | GULCHIN Mark (Yerma) | - | - | 5% | 24% | 41% | 25% | 5% |
| 9 | MILLER Jordan | - | - | - | 4% | 22% | 47% | 27% |
| 10 | AKYAMAC Bora | - | - | 5% | 25% | 44% | 25% | |
| 11 | LEE Brendan | - | - | 2% | 13% | 44% | 41% | |
| 12 | TANG August L. | - | - | - | 3% | 19% | 48% | 30% |
| 13 | LI Bradley | - | - | 2% | 13% | 36% | 38% | 12% |
| 14 | SIMONOV Timofey | - | - | - | 7% | 31% | 43% | 19% |
| 15 | GAO Payton | - | 1% | 10% | 33% | 40% | 15% | |
| 16 | LEE Jonah | - | - | - | 3% | 19% | 43% | 34% |
| 17 | GHEDINI Luca | - | - | - | 6% | 27% | 44% | 23% |
| 18 | YAO Bradley | - | - | 1% | 9% | 29% | 40% | 20% |
| 19 | YAP Nathan | - | - | 5% | 24% | 44% | 26% | |
| 20 | FENG Michael | 1% | 12% | 33% | 35% | 16% | 3% | |
| 21 | LIU Derek | - | - | - | 3% | 19% | 46% | 32% |
| 22 | SHAO Eric | - | - | 2% | 11% | 35% | 42% | 11% |
| 23 | NICOLL William | - | 1% | 13% | 41% | 34% | 10% | 1% |
| 24 | BAI Brian | - | 1% | 10% | 31% | 40% | 16% | 2% |
| 25 | BRAIZINHA Thomas | - | - | 4% | 18% | 40% | 35% | 3% |
| 26 | LEE Eugene | - | 1% | 7% | 24% | 38% | 25% | 5% |
| 27 | CHEN Kyle P. | - | 1% | 5% | 20% | 36% | 29% | 8% |
| 28 | JIMENEZ Naveen | - | 2% | 9% | 26% | 37% | 22% | 4% |
| 29 | HOLLIS Sean | 1% | 9% | 28% | 37% | 21% | 4% | - |
| 30 | MARTIN Darius | - | 1% | 9% | 30% | 40% | 18% | 2% |
| 31 | TANG Alexander L. | - | - | 1% | 15% | 39% | 35% | 10% |
| 32 | BLACK Zachary | - | 6% | 22% | 36% | 26% | 8% | 1% |
| 33 | CHENG Ethan | - | 1% | 15% | 40% | 36% | 8% | |
| 34 | WONG Jackson | - | 1% | 10% | 31% | 39% | 18% | |
| 35 | JURMAN Therin | 2% | 17% | 39% | 32% | 8% | 1% | |
| 36 | HUANG Eythan | - | - | - | 3% | 18% | 46% | 33% |
| 36 | TANG Alex | 7% | 28% | 37% | 21% | 6% | 1% | - |
| 38 | SICAT Justin | 1% | 11% | 31% | 36% | 18% | 3% | - |
| 39 | POLONI Giovanni | - | 3% | 23% | 42% | 27% | 5% | |
| 40 | ORLOV Dmitriy | - | - | 5% | 24% | 48% | 23% | |
| 41 | SHANNON Jack | 8% | 34% | 37% | 17% | 3% | - | |
| 42 | ZENG Rick | - | 3% | 17% | 39% | 32% | 9% | |
| 43 | HAN Alexander | 4% | 24% | 42% | 24% | 6% | - | |
| 44 | SRINIVASAN Vedant | - | 2% | 14% | 38% | 35% | 10% | |
| 45 | CHEN Hanson | - | - | 5% | 19% | 37% | 30% | 9% |
| 46 | XU Brian | 12% | 47% | 35% | 6% | - | - | - |
| 47 | WONG Jacob W. | 1% | 8% | 25% | 35% | 23% | 6% | - |
| 48 | MAO Lucas | - | 1% | 9% | 32% | 41% | 17% | |
| 49 | SURESH Rohan | 2% | 16% | 38% | 34% | 10% | 1% | |
| 50 | SION Andrew | 4% | 25% | 41% | 24% | 6% | - | |
| 51 | SHENG Dalton | - | 3% | 17% | 36% | 33% | 10% | |
| 52 | LIU Ryan | - | 6% | 33% | 41% | 17% | 2% | |
| 53 | SENANI Arjun | 2% | 12% | 31% | 35% | 17% | 3% | - |
| 54 | DIAZ Gabriel | 4% | 32% | 41% | 19% | 4% | - | - |
| 55 | ONIK Ari N. | 1% | 15% | 37% | 33% | 11% | 1% | - |
| 56 | MARTIRE Luca | 8% | 28% | 37% | 21% | 5% | - | - |
| 57 | XIE Jicheng | 1% | 11% | 34% | 36% | 15% | 2% | - |
| 58 | BHAN Amar | 7% | 26% | 37% | 23% | 6% | 1% | - |
| 59 | MOREA Anderson | 31% | 45% | 21% | 4% | - | - | |
| 60 | SORSAIA John | 11% | 40% | 36% | 12% | 2% | - | |
| 61 | CHANDRAMOHAN Aran | 10% | 31% | 35% | 19% | 5% | 1% | - |
| 62 | KENNEDY christo | - | 1% | 9% | 38% | 37% | 13% | 2% |
| 63 | MUNDY Ezra | 1% | 7% | 26% | 38% | 24% | 5% | - |
| 64 | LI Ayren | - | < 1% | 4% | 18% | 38% | 32% | 8% |
| 65 | TJON Calum | - | 3% | 30% | 41% | 20% | 4% | - |
| 66 | BASKIN Lukáš | 1% | 6% | 21% | 35% | 28% | 10% | 1% |
| 67 | LI Allen | - | 1% | 18% | 40% | 31% | 9% | 1% |
| 68 | MATTOS Luis Felipe | 2% | 12% | 30% | 34% | 18% | 4% | - |
| 69 | BOURGUIGNAT James | 9% | 31% | 36% | 18% | 4% | - | - |
| 70 | SUN Henry | 1% | 13% | 39% | 36% | 10% | 1% | - |
| 71 | POLEPALLI Vinil | 19% | 46% | 28% | 6% | 1% | - | |
| 72 | PECK Quinn | 1% | 7% | 25% | 38% | 24% | 5% | - |
| 73 | TAM Kyle | 1% | 16% | 38% | 33% | 11% | 1% | - |
| 74 | LI Tristan | 2% | 16% | 35% | 31% | 13% | 2% | - |
| 75 | COUAILLIER Leo | 1% | 8% | 30% | 41% | 17% | 3% | - |
| 76 | POLEBOYINA Amrit | 3% | 29% | 45% | 21% | 2% | - | - |
| 77 | MATSUUCHI Lance | 19% | 41% | 29% | 9% | 1% | - | - |
| 78 | FOGELSON Hugh | 1% | 10% | 33% | 38% | 16% | 2% | - |
| 79 | DA SILVA Jamie | 5% | 35% | 47% | 12% | 1% | - | - |
| 80 | MILLER Dillon | - | 6% | 22% | 37% | 27% | 7% | |
| 81 | MIRCHANDANI Aditya | 25% | 53% | 19% | 2% | - | - | |
| 82 | ZAPPALA Nikolai | 6% | 32% | 40% | 19% | 4% | - | - |
| 83 | WEISS Zachary | 60% | 33% | 7% | 1% | - | - | - |
| 84 | KRZYWON Dylan | 36% | 43% | 18% | 3% | - | - | - |
| 85 | GOODMAN Elliott | 24% | 41% | 26% | 8% | 1% | - | - |
| 85 | EGE Nathan | 45% | 49% | 6% | - | - | - | - |
| 87 | ROSE Jack | 2% | 12% | 31% | 34% | 17% | 4% | - |
| 88 | WILSON Eric | 26% | 43% | 24% | 6% | 1% | - | - |
| 89 | BARBANEL Joseph | 13% | 38% | 33% | 13% | 2% | - | - |
| 90 | PERLMAN Taiyo | 20% | 44% | 29% | 7% | 1% | - | |
| 91 | VO Aidan | 38% | 44% | 16% | 2% | - | - | |
| 91 | BONTHA Aadit | 46% | 41% | 12% | 1% | - | - | |
| 93 | MCLENDON Dario | 23% | 41% | 27% | 8% | 1% | - | - |
| 94 | DHANOA Kian | 48% | 39% | 11% | 1% | - | - | |
| 95 | FRIEDMAN Marcus | 43% | 51% | 6% | - | - | - | - |
| 96 | OEHLER Mac | 61% | 34% | 5% | - | - | - | - |
| 97 | MENDEZ Ren | 53% | 40% | 6% | - | - | - | |
| 98 | DULGER Kerem | 17% | 38% | 31% | 12% | 2% | - | - |
| 98 | PERKINS Nathaniel | 60% | 34% | 5% | - | - | - | - |
| 98 | BUNDUS Luca | 70% | 27% | 3% | - | - | - | - |
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.