Kansas City Convention Center - Kansas City, MO, 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 | CALISE Ella | - | - | 3% | 17% | 42% | 37% | |
2 | GEBALA Gabrielle Grace A. | - | - | - | - | 6% | 32% | 62% |
3 | SONG Yuqiao Aprille | - | - | 1% | 5% | 22% | 42% | 30% |
3 | LIU Joy Zhaoyi | - | - | - | 1% | 12% | 42% | 44% |
5 | NISSINOFF Alexandra | - | - | 4% | 19% | 42% | 34% | |
6 | YANG Audrey | - | 4% | 19% | 36% | 30% | 9% | |
7 | CHEN Renee | - | 4% | 18% | 36% | 32% | 10% | |
8 | TANG Melody Fujiao | 2% | 13% | 33% | 34% | 15% | 2% | |
9 | CHO Rebecca H. | - | - | - | 1% | 7% | 33% | 59% |
10 | TAN Kaitlyn N. | - | - | - | - | 6% | 33% | 60% |
11 | ZHANG Soleil C. | - | 1% | 7% | 25% | 40% | 24% | 3% |
12 | LAI Sophia | - | - | 4% | 20% | 40% | 31% | 5% |
13 | YANG Iris | - | - | 1% | 7% | 25% | 42% | 26% |
14 | CHO Emily (Euran) | - | - | 2% | 12% | 31% | 37% | 17% |
15 | WANG CAROL | 1% | 9% | 28% | 37% | 21% | 4% | |
16 | GOOR Viviene E. | - | - | 3% | 16% | 42% | 39% | |
17 | YANG Emma | - | 2% | 12% | 36% | 40% | 10% | |
18 | ROZPEDOWSKI Claire | - | - | 6% | 27% | 43% | 21% | 3% |
19 | SHENG Chuxi | - | 3% | 17% | 38% | 34% | 9% | |
20 | LUO Ziyue | - | - | - | 6% | 35% | 59% | |
21 | MANIKTALA Prisha | 1% | 14% | 35% | 35% | 13% | 2% | |
22 | MU Allison | - | 1% | 9% | 28% | 37% | 21% | 4% |
23 | LIN Zhi Tong | - | - | 1% | 9% | 33% | 42% | 14% |
24 | JOO Natalie | - | 5% | 20% | 35% | 28% | 10% | 1% |
24 | LEUNG Mei Hang (Danise) | - | - | 3% | 15% | 38% | 37% | 8% |
26 | HSU Kaylin | - | 1% | 7% | 25% | 37% | 25% | 6% |
27 | WANG Joanna | - | 2% | 12% | 30% | 34% | 17% | 3% |
28 | ORVANANOS Anice | - | 2% | 12% | 31% | 38% | 17% | |
29 | BAE Yooju | 2% | 14% | 38% | 35% | 11% | 1% | |
30 | DENG Melissa | 2% | 13% | 33% | 34% | 15% | 2% | |
31 | DAI Zizhuo (Zizi) | 1% | 6% | 25% | 39% | 24% | 5% | |
32 | TSIMIKLIS Aphrodite | 7% | 28% | 37% | 21% | 6% | 1% | - |
33 | LEE Lavender | - | - | 1% | 8% | 37% | 54% | |
34 | FENG Audrey | 1% | 10% | 29% | 35% | 20% | 5% | 1% |
35 | MI Aileen | - | 3% | 18% | 36% | 32% | 10% | |
36 | CHEN Chloe I. | - | 1% | 7% | 27% | 42% | 22% | |
37 | SHEN Emilia | - | 1% | 7% | 28% | 43% | 21% | |
38 | CHOW Annabelle | - | 2% | 14% | 35% | 38% | 11% | |
39 | PEVZNER Nicole | - | 2% | 12% | 30% | 35% | 17% | 3% |
40 | LI Han (Helina) | 1% | 8% | 31% | 37% | 19% | 4% | - |
41 | PENG Charlotte | 10% | 32% | 36% | 18% | 4% | - | - |
42 | OH Ceana | - | 1% | 7% | 25% | 40% | 24% | 3% |
43 | ZHENG Julie | 5% | 22% | 36% | 27% | 9% | 1% | |
44 | HO Addison | 1% | 13% | 35% | 36% | 14% | 1% | |
45 | SEO IRENE Y. | - | 3% | 13% | 31% | 37% | 16% | |
46 | DONG Angela | 7% | 32% | 38% | 19% | 4% | - | |
47 | SUN Emily | 1% | 7% | 25% | 37% | 25% | 6% | |
48 | FENG Grace | - | 5% | 20% | 37% | 30% | 7% | |
49 | LIU Samantha | 13% | 34% | 33% | 15% | 3% | - | |
50 | WANG YiXi | - | - | 4% | 18% | 36% | 32% | 9% |
51 | GU Maggie Runlin | - | - | 3% | 15% | 33% | 35% | 14% |
52 | SHIM Grace J. | - | 2% | 10% | 29% | 38% | 19% | 2% |
53 | YAO Ada | - | 1% | 9% | 28% | 37% | 21% | 4% |
54 | SUN Chloe | - | 4% | 19% | 37% | 31% | 9% | |
55 | FIELD Julianna | 2% | 17% | 38% | 32% | 10% | 1% | |
56 | BRYZGALOVA Svitlana | 1% | 6% | 24% | 38% | 26% | 6% | |
57 | ZHANG Eunice | - | 8% | 29% | 39% | 20% | 3% | |
57 | KIM Rachel | - | 5% | 19% | 35% | 31% | 10% | |
59 | LI Sophia M. | 2% | 12% | 30% | 34% | 18% | 4% | |
60 | DOROSHKEVICH Taisiia | - | 3% | 18% | 39% | 33% | 7% | |
61 | FIELD Elizabeth | 4% | 21% | 37% | 28% | 9% | 1% | - |
62 | BIODROWICZ Julia | - | 2% | 11% | 31% | 38% | 17% | 2% |
63 | DUAN Sophie | 5% | 23% | 37% | 26% | 8% | 1% | - |
64 | POEI Lauren | 13% | 38% | 35% | 13% | 2% | - | |
65 | HU Felice | 1% | 21% | 40% | 29% | 8% | 1% | |
66 | LIU Ariana | 24% | 42% | 26% | 7% | 1% | - | - |
67 | PARK Lina | - | 1% | 12% | 38% | 36% | 12% | 1% |
68 | HAFEZ Tahiyah | 4% | 20% | 37% | 29% | 9% | 1% | - |
69 | BROWN Lola | 10% | 34% | 36% | 17% | 3% | - | |
70 | SHMAY Anastasia | 1% | 18% | 39% | 31% | 10% | 1% | |
71 | LI Joy | 46% | 41% | 12% | 1% | - | - | |
71 | KIM Claire | 28% | 43% | 23% | 5% | 1% | - | |
73 | ZHUANG Christina | 9% | 29% | 35% | 20% | 5% | 1% | |
74 | WYNN Kylie | 6% | 31% | 39% | 20% | 4% | - | |
75 | CASTANEDA Keira | - | 5% | 21% | 38% | 28% | 8% | 1% |
76 | LENK Sophie | 3% | 16% | 35% | 32% | 13% | 2% | - |
77 | LI Eleanor | - | 7% | 24% | 36% | 25% | 8% | 1% |
78 | ORBÉ-AUSTIN Maya | 15% | 41% | 32% | 10% | 1% | - | - |
79 | DESAI Esha | 27% | 42% | 24% | 6% | 1% | - | - |
80 | XIE Su | 4% | 24% | 38% | 25% | 8% | 1% | - |
81 | SWANSON Alexa | 7% | 28% | 39% | 21% | 5% | - | |
82 | SAIFEE Lamya | 2% | 15% | 36% | 34% | 12% | 1% | |
83 | KIM Sydney | 19% | 41% | 30% | 10% | 1% | - | |
84 | WANG SIQI | 1% | 13% | 36% | 37% | 12% | 1% | |
85 | ZHANG Ivy | 22% | 44% | 26% | 7% | 1% | - | |
86 | DONG Iris | 50% | 38% | 10% | 1% | - | - | |
87 | ZELDIN Nadia | 29% | 44% | 21% | 5% | - | - | |
88 | LIU Ashley | 62% | 32% | 6% | 1% | - | - | - |
89 | YU Jane | 3% | 16% | 35% | 32% | 12% | 2% | - |
90 | SHMUKLER Maria | 4% | 22% | 38% | 26% | 8% | 1% | - |
91 | PARK Zena | 8% | 29% | 37% | 20% | 5% | - | - |
92 | TAO Ann | 14% | 40% | 35% | 9% | 1% | - | - |
93 | HOROWITZ Shuli | 24% | 45% | 26% | 5% | - | - | - |
94 | DAI Iris Yuyang | 30% | 44% | 21% | 4% | - | - | - |
95 | WANG DINA C. | 19% | 49% | 26% | 6% | 1% | - | - |
96 | ORBE-AUSTIN Nia | 21% | 44% | 29% | 6% | - | - | - |
97 | WANG Sophia | 25% | 43% | 25% | 7% | 1% | - | |
98 | SHELTON Olivia | 48% | 39% | 11% | 1% | - | - | |
99 | FRASER Morgan | 50% | 40% | 9% | 1% | - | - | - |
100 | MCFARLANE Asha | 13% | 41% | 34% | 11% | 1% | - | |
101 | BEAVER Ava | 31% | 42% | 21% | 5% | 1% | - | |
102 | TOMASI Samantha | 84% | 15% | 1% | - | - | - | |
103 | ZHENG Zoe | 34% | 42% | 19% | 4% | - | - | - |
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.