Salt Lake City, UT, 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 | LIU Jaelyn A. | - | - | 2% | 11% | 38% | 49% | |
2 | SENIC Adeline | - | - | - | - | 6% | 33% | 61% |
3 | CHEN Jia P. | - | - | - | - | 2% | 23% | 75% |
3 | TAN Kaitlyn N. | - | - | - | 2% | 16% | 44% | 38% |
5 | CHEN Allison V. | - | - | - | 2% | 12% | 40% | 46% |
6 | ZHANG Alina C. | - | - | 2% | 15% | 45% | 38% | |
7 | CHUSID Mikayla | - | - | - | - | 5% | 31% | 63% |
8 | YANG Iris | - | 1% | 6% | 26% | 44% | 23% | |
9 | GEBALA Gabrielle Grace A. | - | - | - | 6% | 35% | 59% | |
10 | PENG Amber L. | - | - | 2% | 11% | 30% | 39% | 17% |
11 | LI Rachel Y. | - | - | 3% | 18% | 43% | 36% | |
12 | RANDOLPH Piper | - | 1% | 6% | 25% | 44% | 25% | |
13 | LIN Zhi tong | - | 3% | 16% | 36% | 34% | 11% | 1% |
14 | NAMGALAURI Mariam | - | 2% | 16% | 38% | 34% | 9% | |
15 | GU Maggie Runlin | - | 1% | 8% | 29% | 40% | 20% | 2% |
16 | SONG Yuqiao Aprille | - | - | 2% | 12% | 37% | 40% | 9% |
17 | FEDELI Caterina S. | - | - | - | 2% | 12% | 40% | 46% |
18 | MANIKTALA Prisha | - | 2% | 12% | 31% | 35% | 17% | 2% |
19 | HAYES Nadia | - | - | 1% | 7% | 28% | 45% | 19% |
20 | CHO Rebecca H. | - | - | - | 1% | 11% | 39% | 49% |
21 | SOOD Ishani S. | - | - | 1% | 7% | 38% | 54% | |
22 | SEO IRENE Y. | - | 1% | 6% | 25% | 44% | 25% | |
23 | LIU Joy Zhaoyi | - | - | 1% | 11% | 39% | 49% | |
24 | CHEN Chloe I. | - | - | 5% | 21% | 39% | 29% | 6% |
25 | MI Aileen | - | - | 4% | 19% | 39% | 31% | 7% |
26 | LEE Lavender | - | - | 3% | 16% | 41% | 40% | |
27 | NISSINOFF Alexandra | 3% | 17% | 35% | 32% | 12% | 1% | |
28 | ZHENG Julie | - | 1% | 7% | 26% | 41% | 23% | 3% |
29 | SHEN Lydia | - | - | - | 1% | 11% | 37% | 51% |
30 | NIKOLIC Alexandra | - | - | 5% | 21% | 39% | 29% | 6% |
31 | CHEN Renee | - | 1% | 8% | 28% | 42% | 20% | 2% |
32 | ZHOU Catherine | 1% | 10% | 33% | 36% | 16% | 3% | - |
33 | PEVZNER Victoria | - | - | - | - | 6% | 32% | 62% |
34 | EYER Hailey M. | - | - | - | - | 5% | 34% | 60% |
35 | FUNG Vera | - | 1% | 8% | 26% | 39% | 23% | 4% |
36 | FUNG Emma | - | - | 5% | 23% | 42% | 27% | 3% |
37 | LEE Ji Ahn | 1% | 10% | 30% | 37% | 19% | 3% | |
38 | BATRA Chaahat | - | - | 4% | 30% | 51% | 15% | |
39 | LIPKOVITZ Rivka | - | 1% | 11% | 32% | 37% | 16% | 2% |
39 | LEUNG Mei Hang (Danise) | - | 3% | 14% | 31% | 34% | 16% | 2% |
41 | OH Ceana | 1% | 16% | 39% | 32% | 10% | 1% | - |
42 | YANG Audrey | 1% | 15% | 36% | 33% | 13% | 2% | - |
43 | SUN Ruoxi | - | - | 2% | 11% | 29% | 38% | 19% |
44 | WANG Jasmine | 5% | 21% | 34% | 27% | 10% | 2% | - |
44 | LAI Sophia | 4% | 20% | 36% | 28% | 10% | 1% | - |
46 | CHANG Elizabeth | - | 4% | 20% | 38% | 30% | 7% | - |
47 | MU Allison | 1% | 9% | 30% | 38% | 19% | 3% | - |
48 | KOSTELNY Alexis | - | - | 5% | 22% | 40% | 27% | 5% |
49 | ORVANANOS Anice | - | 1% | 6% | 24% | 39% | 26% | 5% |
50 | SHIM Grace J. | - | 3% | 18% | 38% | 30% | 9% | 1% |
51 | DOROSHKEVICH Taisiia | - | - | 6% | 29% | 48% | 16% | |
52 | SEIGEL Norah | - | 2% | 17% | 49% | 28% | 4% | |
53 | SHA Yi Ling | 4% | 20% | 37% | 29% | 9% | 1% | |
54 | DAVIES Ellie | 4% | 23% | 39% | 26% | 7% | 1% | |
55 | JANG Kimberley | - | - | - | 1% | 11% | 39% | 49% |
56 | WONG Sophia M. | - | 2% | 12% | 33% | 36% | 14% | 2% |
57 | DAVIS Bonnie Z. | - | - | 4% | 19% | 40% | 31% | 5% |
58 | WANDJI Anais | - | - | 1% | 6% | 29% | 49% | 15% |
59 | SUN Emily | 1% | 9% | 28% | 36% | 22% | 5% | - |
60 | SEAL Cameron I. | 1% | 16% | 37% | 32% | 12% | 2% | - |
61 | MEI Sarah | - | 2% | 23% | 46% | 25% | 4% | |
62 | ROY Layla | - | 4% | 23% | 42% | 26% | 5% | |
63 | GOOR Viviene E. | - | - | 2% | 12% | 35% | 42% | 9% |
64 | HSU Kaylin | 1% | 16% | 35% | 32% | 14% | 2% | - |
65 | BIODROWICZ Julia | - | 9% | 45% | 36% | 9% | 1% | |
66 | CALISE Ella | - | 3% | 17% | 39% | 33% | 7% | |
67 | BRYZGALOVA Svitlana | - | - | 4% | 25% | 48% | 23% | |
68 | SIMONOV Dasha | 5% | 24% | 38% | 25% | 7% | 1% | |
69 | YAN Noelle | 3% | 36% | 43% | 16% | 2% | - | |
70 | WHELAN Amelia | 1% | 9% | 32% | 38% | 18% | 3% | |
71 | WANG Zoie Z. | - | 6% | 26% | 39% | 23% | 5% | - |
72 | CHOI Kailyn | 2% | 20% | 39% | 29% | 9% | 1% | - |
73 | MARKOVSKY Nina | - | 1% | 11% | 32% | 37% | 17% | 2% |
74 | VAUGHAN Norah | - | 4% | 22% | 40% | 27% | 7% | - |
75 | LAMBERT Mahala | - | 1% | 5% | 22% | 39% | 28% | 5% |
76 | ZHANG Selena | 4% | 19% | 37% | 29% | 10% | 1% | - |
77 | SUN Ning Lu (Sophia) | 1% | 15% | 36% | 33% | 13% | 2% | - |
78 | CHARALEL Jessica | 5% | 25% | 38% | 24% | 7% | 1% | |
79 | HAYES Alyssa R. | 2% | 14% | 37% | 34% | 12% | 1% | |
80 | LEE Allison | - | 9% | 34% | 42% | 14% | 1% | |
81 | LUO Sandra J. | - | 1% | 7% | 26% | 43% | 23% | |
82 | KIM Rachel | 1% | 8% | 29% | 40% | 20% | 3% | |
83 | CHOW Annabelle | 1% | 11% | 32% | 37% | 17% | 2% | |
83 | WANG Alison | 7% | 29% | 38% | 21% | 5% | - | |
85 | WELBORN Calissa | 2% | 16% | 36% | 33% | 11% | 1% | |
86 | SHEN Emilia | 3% | 19% | 38% | 30% | 9% | 1% | |
87 | HOBSON Ava | 2% | 22% | 52% | 21% | 2% | - | |
87 | TAYLOR-CASAMAYOR Marisol | 40% | 42% | 16% | 3% | - | - | |
89 | GU Emily | 1% | 7% | 25% | 38% | 25% | 5% | |
90 | KULKARNI Sohah A. | 1% | 20% | 44% | 29% | 6% | - | |
91 | KO Claire | 24% | 41% | 26% | 7% | 1% | - | |
92 | TRACZ Calleigh D. | 2% | 15% | 36% | 33% | 12% | 1% | - |
93 | WANG Celine S. | 14% | 38% | 34% | 12% | 2% | - | - |
94 | LI Eleanor | 11% | 38% | 35% | 13% | 2% | - | - |
95 | DAI Zizhuo (Zizi) | 1% | 14% | 35% | 34% | 14% | 2% | - |
96 | SHUM Maya | 1% | 12% | 35% | 37% | 14% | 2% | - |
97 | CHATEL Margot | 6% | 24% | 35% | 24% | 9% | 2% | - |
98 | PAULUS Sloane E. | 7% | 25% | 34% | 24% | 8% | 1% | - |
99 | GANDLURI Sreehitha | 3% | 27% | 42% | 23% | 5% | - | - |
100 | BOLES Amanda X. | 3% | 20% | 39% | 29% | 9% | 1% | - |
101 | VO Bao-Vy | 2% | 14% | 31% | 33% | 17% | 4% | - |
102 | LUO Miranda | 3% | 31% | 41% | 20% | 4% | - | - |
103 | DANIELYANTS Gabriela | 17% | 46% | 29% | 7% | 1% | - | - |
104 | PAULUS Isabella | 36% | 42% | 18% | 4% | - | - | - |
105 | BEAVER Hannah | 1% | 10% | 30% | 35% | 19% | 5% | - |
106 | KAPOOR Saanvi | 59% | 35% | 6% | - | - | - | |
107 | YU Jane | 24% | 53% | 21% | 2% | - | - | |
108 | RICHARD Dominique | 15% | 42% | 32% | 10% | 1% | - | |
109 | WANG Chloe | 9% | 34% | 37% | 16% | 3% | - | |
110 | LI Yuhe | 6% | 27% | 38% | 23% | 6% | - | |
111 | CHENG Isa | 77% | 22% | 1% | - | - | - | |
112 | NIRGUDE Siddhi | 43% | 42% | 13% | 2% | - | - | |
113 | HAFEZ Tahiyah | 1% | 18% | 41% | 30% | 9% | 1% | - |
114 | DESAI Esha | 55% | 36% | 8% | 1% | - | - | - |
115 | WANG Yudi | 2% | 17% | 39% | 30% | 10% | 1% | - |
116 | CASTANEDA Keira | 1% | 8% | 27% | 38% | 21% | 5% | - |
117 | YURKOVA Mariia | 1% | 10% | 27% | 34% | 21% | 6% | 1% |
118 | GOEL Riyana | 45% | 42% | 12% | 1% | - | - | - |
119 | GAO Anne | 40% | 42% | 15% | 2% | - | - | - |
120 | LIN Ju-An Adrianne | 1% | 15% | 35% | 33% | 13% | 2% | - |
121 | PENG Charlotte | 13% | 41% | 34% | 11% | 1% | - | - |
122 | LEVY Avery | 8% | 31% | 39% | 18% | 4% | - | - |
123 | MARTIRE Alessandra | 30% | 44% | 22% | 4% | - | - | - |
124 | PHILLIPS Hattie | 6% | 32% | 39% | 18% | 4% | - | - |
125 | CHAKRAPANI Ila | 65% | 29% | 5% | - | - | - | - |
126 | LEE emily | 37% | 42% | 17% | 3% | - | - | |
127 | CHEW Alexis T. | < 1% | 5% | 21% | 38% | 29% | 5% | |
128 | PATTERSON Natalia | 36% | 42% | 18% | 4% | - | - | |
129 | HWANG Chanel | 69% | 27% | 4% | - | - | - | - |
130 | TAYLOR Gabrielle | 83% | 16% | 1% | - | - | - | |
131 | MCKAY Teresa | 77% | 21% | 2% | - | - | - | - |
132 | XIE Su | 13% | 61% | 23% | 3% | - | - | |
133 | BASSIK Eva | 14% | 49% | 29% | 7% | 1% | - | |
133 | SOLOMIE Sabina | 52% | 41% | 7% | - | - | - | |
135 | LAWRENCE Nia | 77% | 21% | 2% | - | - | - | - |
136 | FONG Carys | 17% | 53% | 25% | 5% | - | - | - |
136 | FONG Ellis | 15% | 40% | 33% | 11% | 1% | - | - |
138 | PADDOCK Hannah | 80% | 19% | 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.