Suffern, NY - 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 | TOLBA Abdelrahman | - | - | - | - | 5% | 30% | 65% |
2 | SHA Yi Peng | - | - | - | 3% | 17% | 44% | 36% |
3 | ORVANANOS Jorge | - | - | - | 3% | 18% | 43% | 36% |
3 | DOUGLAS Oscar M. | - | - | 4% | 21% | 44% | 30% | |
5 | GUO Sean | - | - | 2% | 11% | 36% | 38% | 13% |
6 | ACHILOV Sayid | - | 2% | 13% | 33% | 35% | 15% | 2% |
7 | AHN Jun | - | - | 1% | 11% | 41% | 47% | |
8 | SYOMICHEV Gleb A. | - | 4% | 30% | 43% | 21% | 3% | |
9 | MARX Jackson L. | - | - | - | 1% | 8% | 36% | 55% |
10 | RUSADZE Nickolas | - | - | - | 1% | 9% | 37% | 52% |
11 | KALIPERSAD Neil A. | - | 1% | 5% | 21% | 38% | 30% | 6% |
12 | PITERBARG Maxim | 1% | 10% | 29% | 36% | 19% | 4% | - |
13 | GUO Jacob | 1% | 7% | 24% | 36% | 26% | 7% | |
14 | LIN James G. | - | 1% | 18% | 42% | 31% | 7% | |
15 | GARCIA Sebastian R. | - | - | 2% | 14% | 42% | 42% | |
16 | JIANG Owen | - | 1% | 8% | 28% | 43% | 21% | |
17 | ALIMI Yacine A. | - | 2% | 12% | 31% | 37% | 17% | |
18 | LIGOS Alex M. | 2% | 16% | 38% | 34% | 10% | 1% | |
19 | LEE Jacob J | - | - | 2% | 11% | 33% | 40% | 15% |
20 | SONG Bryan | - | 1% | 8% | 27% | 38% | 22% | 3% |
21 | GUO Cheng Jin Morris | 4% | 19% | 37% | 29% | 9% | 1% | - |
22 | PARK Joseph | 1% | 11% | 31% | 35% | 18% | 4% | - |
23 | ZHEN Ethan | - | 1% | 16% | 41% | 34% | 7% | |
24 | WANG Jackson | - | 2% | 11% | 31% | 37% | 17% | 3% |
25 | AUGUSTINE Aaron A. | - | 1% | 6% | 23% | 39% | 27% | 4% |
25 | YOON DYLAN | 3% | 24% | 39% | 25% | 7% | 1% | - |
27 | GUO Justin | - | 3% | 15% | 34% | 34% | 13% | 1% |
28 | LI Yao (Liam) | - | - | 1% | 10% | 33% | 40% | 15% |
29 | XU Andy P. | - | - | 1% | 6% | 27% | 43% | 23% |
30 | TRAUGOT Owen G. | - | 1% | 7% | 28% | 47% | 18% | |
31 | ZHOU Leon | 5% | 24% | 38% | 26% | 7% | 1% | |
32 | SHAO Eric | 1% | 11% | 30% | 35% | 18% | 4% | - |
33 | SIMA Congyu Josh | 1% | 7% | 25% | 40% | 23% | 5% | - |
34 | LEE Jonah | 4% | 20% | 36% | 29% | 10% | 1% | |
35 | WANG Julang | 7% | 27% | 39% | 22% | 5% | - | - |
36 | CHENG Ethan | - | 2% | 11% | 30% | 37% | 17% | 3% |
37 | LIU Derek | - | - | 4% | 19% | 39% | 31% | 6% |
38 | LIU Ryan | 9% | 35% | 38% | 16% | 3% | - | - |
39 | GOH William C. | 1% | 8% | 26% | 36% | 23% | 6% | - |
40 | TORRES Treston | 2% | 13% | 31% | 34% | 17% | 3% | |
41 | UNGERER Roger M. | 6% | 27% | 40% | 22% | 4% | - | |
42 | VENKATESH Aditya | 28% | 43% | 23% | 5% | - | - | |
43 | SVERDLOV Seth | 11% | 33% | 35% | 17% | 3% | - | |
44 | RNO Kyler | 2% | 15% | 32% | 32% | 15% | 3% | - |
45 | LOGAN Kai | 11% | 34% | 36% | 15% | 3% | - | - |
46 | MELE-KEAN Forrest | 2% | 14% | 31% | 33% | 17% | 3% | - |
47 | TRUBETSKI David | 2% | 16% | 37% | 32% | 12% | 2% | - |
48 | KLOTZ Isaiah | 1% | 11% | 32% | 36% | 17% | 3% | - |
49 | JIA Jing Yu | 6% | 28% | 41% | 21% | 4% | - | - |
50 | ARCE Andrew W. | 13% | 37% | 34% | 14% | 3% | - | - |
51 | RADOSLAVOV Ivan-Asen | 12% | 37% | 36% | 14% | 2% | - | |
52 | LI Ayren | 8% | 29% | 37% | 21% | 5% | - | |
53 | LEWIS Akhil | 12% | 34% | 35% | 16% | 3% | - | |
54 | PLASTARAS Trey | 21% | 39% | 28% | 10% | 2% | - | |
55 | DALBERG Anders | 20% | 61% | 17% | 2% | - | - | |
56 | LI Jinghua E. | 1% | 9% | 30% | 37% | 19% | 4% | - |
57 | FRANK Amir | 36% | 43% | 17% | 3% | - | - | - |
58 | LEWIS Nikhil I. | 25% | 43% | 26% | 6% | 1% | - | - |
59 | JIE WanYue | 21% | 43% | 28% | 7% | 1% | - | - |
60 | WU Benjamin | 6% | 24% | 37% | 24% | 7% | 1% | - |
61 | LEE Eugene | 61% | 32% | 6% | 1% | - | - | - |
62 | LUNA Nathan | 4% | 18% | 32% | 29% | 14% | 3% | - |
63 | BABKINE-OSTERRATH Alexandre | 4% | 19% | 34% | 29% | 12% | 2% | |
64 | REZA Farazi | 20% | 41% | 30% | 9% | 1% | - | - |
65 | TING Clement | 70% | 29% | 1% | - | - | - | |
66 | LU Jason | 31% | 43% | 21% | 5% | 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.