Santa Clara Convention Center - Santa Clara, CA, 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 | NEELAM Navya | - | - | - | - | 3% | 26% | 72% |
2 | LIN Ariel | - | - | - | 1% | 8% | 37% | 54% |
3 | WANG Jessie | - | - | - | - | 3% | 26% | 71% |
3 | XU Yvette, Zixuan | - | - | - | - | 3% | 27% | 70% |
5 | LEE Emily | - | - | 1% | 11% | 38% | 39% | 11% |
6 | DYMAR Anna | - | - | 2% | 13% | 42% | 44% | |
7 | HE Anna | - | - | 2% | 11% | 28% | 38% | 21% |
8 | YUNG Bethany | - | 1% | 6% | 23% | 39% | 27% | 5% |
9 | ABUELFUTUH Sama | - | - | - | 1% | 10% | 37% | 52% |
10 | WANG Victoria | - | - | - | 3% | 14% | 39% | 44% |
11 | WU Jessica | - | - | - | 1% | 14% | 43% | 42% |
11 | LIN Laura | - | - | 1% | 10% | 33% | 43% | 12% |
13 | ARULKUMAR Lashia | - | 2% | 12% | 27% | 34% | 20% | 5% |
14 | FISCHBEIN Quinley | - | 1% | 10% | 30% | 40% | 19% | |
15 | CHIOU-WILLIAMS Matea | - | - | 1% | 10% | 35% | 45% | 8% |
16 | MOHAN Riya | - | - | 2% | 11% | 29% | 39% | 19% |
17 | YIP Allison | 5% | 24% | 37% | 25% | 8% | 1% | - |
18 | GIERAT-KATZ Izabella | - | 1% | 4% | 15% | 32% | 34% | 14% |
19 | RASHID Summer | 4% | 27% | 40% | 22% | 5% | 1% | - |
20 | FU Shannon | - | 2% | 11% | 31% | 39% | 17% | |
21 | KARTHIK Diya | - | 1% | 11% | 34% | 40% | 12% | |
22 | MERRIMAN Rie | - | - | 5% | 21% | 40% | 30% | 4% |
23 | HANKINS Morgan | - | - | - | 1% | 10% | 37% | 52% |
24 | CHO Jacey | 7% | 30% | 40% | 20% | 4% | - | - |
25 | SUN Karolyn | - | 1% | 8% | 25% | 39% | 24% | 4% |
26 | RICHARDSON Gianna | - | 2% | 10% | 28% | 36% | 20% | 3% |
27 | MIYOSHI Kylie | - | 4% | 16% | 30% | 31% | 16% | 3% |
28 | VIJAY Vaishnavi | - | 1% | 8% | 24% | 36% | 25% | 6% |
29 | DENEALE Lucia | 2% | 13% | 32% | 34% | 16% | 3% | |
30 | KUTSY Olga | - | - | 1% | 11% | 38% | 39% | 11% |
31 | LEE Adelynn | - | 3% | 15% | 34% | 35% | 12% | 1% |
32 | LI Carlie | 2% | 13% | 28% | 32% | 19% | 6% | 1% |
33 | NGUYEN Renee | - | - | 2% | 13% | 34% | 38% | 13% |
34 | BUDMAN Ava | 1% | 9% | 26% | 35% | 23% | 6% | |
35 | LI Anna | 1% | 6% | 21% | 35% | 28% | 9% | |
36 | ZHAO Emma | 1% | 6% | 19% | 33% | 29% | 12% | 2% |
37 | DWIGGINS Reese | - | 2% | 11% | 28% | 36% | 20% | 3% |
38 | LI Yunxuan (Joy) | - | 1% | 8% | 23% | 35% | 26% | 7% |
39 | MENG Fina | 2% | 11% | 27% | 33% | 21% | 6% | 1% |
40 | CHI Zoe | - | - | 5% | 21% | 41% | 29% | 3% |
41 | KANDALA Aanya | - | 3% | 16% | 35% | 34% | 12% | 1% |
42 | KIM Vivian | - | 4% | 18% | 37% | 31% | 9% | 1% |
43 | DU Chloe | - | 2% | 12% | 32% | 39% | 14% | 1% |
44 | YU Chloe | 2% | 12% | 29% | 34% | 19% | 4% | |
45 | WANG Olivia | 5% | 22% | 34% | 26% | 10% | 2% | - |
46 | XU Jessica | - | - | - | 3% | 18% | 43% | 36% |
47 | SUN Milly | 15% | 35% | 32% | 15% | 3% | - | - |
48 | ROBLES Claudia | - | 2% | 10% | 27% | 35% | 21% | 5% |
49 | HAN Sydney | 8% | 30% | 37% | 20% | 5% | 1% | - |
50 | BI Ziyi | 3% | 17% | 31% | 30% | 15% | 4% | - |
51 | MAENG Gloria | 3% | 16% | 32% | 30% | 15% | 4% | - |
52 | WANG Chantal | - | 4% | 15% | 30% | 31% | 16% | 3% |
52 | FENG Esther | 5% | 23% | 38% | 26% | 8% | 1% | - |
54 | BURKS Madison | 2% | 13% | 30% | 32% | 18% | 4% | - |
55 | ZHAO Yanning | - | 3% | 14% | 31% | 33% | 16% | 3% |
56 | CHEN Julia Z. | 1% | 7% | 23% | 35% | 26% | 7% | |
57 | LU Chloe | 13% | 33% | 33% | 16% | 4% | - | |
58 | SIMHADRI Sanjana | 1% | 7% | 26% | 39% | 22% | 5% | - |
59 | ZHAO Ellie | 4% | 20% | 36% | 28% | 10% | 1% | - |
60 | RADOV Una | - | 2% | 11% | 30% | 37% | 18% | 3% |
61 | ZU Jacqueline | 4% | 19% | 36% | 30% | 10% | 1% | - |
62 | KELLER Mia | 20% | 42% | 28% | 8% | 1% | - | - |
63 | CHEN Stephanie | 3% | 18% | 38% | 30% | 10% | 1% | - |
64 | YAN Ximei (Alicia) | 2% | 13% | 30% | 33% | 17% | 4% | - |
65 | FLYNN Kensington | 9% | 29% | 36% | 20% | 5% | 1% | - |
66 | LI Wan-Tao | 1% | 12% | 36% | 36% | 13% | 1% | |
67 | BUCA Nora | 1% | 5% | 19% | 33% | 29% | 12% | 2% |
68 | XIONG Alice | 2% | 17% | 36% | 31% | 12% | 2% | - |
69 | BRINDAVAN Vyahriti | 11% | 34% | 36% | 16% | 3% | - | - |
70 | LIN Isabel | - | 1% | 6% | 24% | 42% | 25% | 3% |
71 | YOUSSEF Margaret | 5% | 23% | 36% | 26% | 9% | 1% | - |
72 | LI Mia | 1% | 8% | 29% | 41% | 18% | 3% | - |
73 | LE Luana | 1% | 11% | 35% | 37% | 15% | 2% | |
74 | SUNG Audrey | 3% | 18% | 35% | 31% | 12% | 2% | |
75 | SCANLAN Alina Nev | 1% | 9% | 28% | 37% | 20% | 5% | - |
76 | LEE Grace | 9% | 30% | 37% | 20% | 4% | - | - |
77 | XU Aasta | 17% | 36% | 30% | 13% | 3% | - | - |
78 | LI Katherine | 1% | 11% | 32% | 37% | 16% | 3% | - |
79 | WANG Nicole | 2% | 11% | 27% | 33% | 20% | 6% | 1% |
79 | HUANG Jui-An | 11% | 37% | 39% | 12% | 1% | - | - |
81 | LI Chloe | 8% | 28% | 35% | 22% | 6% | 1% | - |
82 | DEKERMANJI kate | 31% | 43% | 21% | 4% | - | - | - |
83 | LI Allison | 10% | 31% | 35% | 19% | 5% | 1% | - |
84 | AGARWAL Keya | 27% | 42% | 24% | 6% | 1% | - | - |
85 | YOUN Kylie | 26% | 42% | 25% | 7% | 1% | - | |
86 | ARNOLD Evangeline | 10% | 33% | 36% | 17% | 4% | - | |
87 | MUKKU Emily | 6% | 38% | 39% | 15% | 2% | - | |
88 | RAPTIS Layla | 9% | 32% | 37% | 18% | 4% | - | - |
89 | TRUONG Audrey | 14% | 39% | 34% | 11% | 1% | - | - |
90 | WANG Yvonne | 11% | 36% | 36% | 15% | 2% | - | - |
91 | TRUONG Chloe | 27% | 44% | 24% | 6% | 1% | - | - |
92 | REN Ivanka | 16% | 35% | 31% | 14% | 3% | - | - |
92 | PARKE Jaime | 29% | 45% | 22% | 4% | - | - | - |
94 | WANG Ailly | 15% | 36% | 33% | 13% | 3% | - | - |
95 | MADANNAVAR Trisha | 9% | 30% | 36% | 19% | 5% | - | - |
96 | TISHKOVA-ROBERTS Daria | 12% | 33% | 34% | 17% | 4% | - | |
97 | PARK Rachel | 27% | 41% | 24% | 7% | 1% | - | - |
98 | UEMURA Lyllia | 14% | 36% | 34% | 14% | 2% | - | - |
99 | OAKES Delaney | 65% | 30% | 5% | - | - | - | |
100 | LUCENA Yaretzi | 3% | 15% | 30% | 31% | 17% | 4% | - |
101 | YOON Claire | 55% | 35% | 8% | 1% | - | - | - |
102 | KO Hannah | 5% | 21% | 34% | 27% | 11% | 2% | - |
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.