Gaylord National Resort and Convention Center - National Harbor, MD, 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 | - | - | - | - | 4% | 29% | 67% |
2 | XU Serena | - | - | - | - | 4% | 27% | 69% |
3 | QI Julieanne | - | - | - | 1% | 6% | 31% | 62% |
3 | LI Caroline | - | - | 1% | 7% | 26% | 43% | 24% |
5 | HE Anna | - | - | - | 5% | 27% | 51% | 16% |
6 | GOH Cayla | - | - | 2% | 12% | 30% | 38% | 18% |
7 | JIANG Serena | - | - | - | - | 5% | 33% | 62% |
8 | DHAIYA Tanya | - | 1% | 5% | 19% | 36% | 30% | 9% |
9 | ADYANTHAYA Anika | - | - | - | 2% | 13% | 41% | 44% |
10 | ELTERMAN Kate | - | - | 1% | 9% | 26% | 40% | 23% |
11 | TALANDZEVICIUS Sophia | - | - | 3% | 14% | 32% | 35% | 15% |
12 | SUICO Kyubi Emmanuelle | - | - | 1% | 6% | 24% | 42% | 28% |
13 | HALE Avery | - | 1% | 8% | 26% | 40% | 23% | 2% |
14 | NING Miranda | - | 1% | 10% | 29% | 39% | 19% | 2% |
15 | WANG Ailly | 2% | 17% | 37% | 31% | 11% | 2% | - |
16 | BI Michelle | - | 9% | 40% | 38% | 12% | 1% | |
17 | LI Azalea | - | - | - | 1% | 8% | 34% | 57% |
17 | OLELE Ifechi | - | - | 3% | 16% | 37% | 34% | 9% |
19 | LEE Kate | 1% | 8% | 24% | 34% | 24% | 8% | 1% |
20 | LIN Ariel | - | - | - | - | 7% | 36% | 57% |
21 | GAN Shelby | - | 1% | 8% | 29% | 39% | 21% | 4% |
22 | TISMENSKY Abigail | - | - | - | 4% | 20% | 45% | 32% |
23 | LI Alice | - | - | 1% | 7% | 26% | 42% | 24% |
24 | LEE Eden | - | 4% | 19% | 36% | 30% | 10% | 1% |
25 | WONG Angelina | - | 1% | 8% | 26% | 38% | 22% | 4% |
26 | XU Jessica | - | - | 4% | 20% | 44% | 32% | |
27 | ANEZIOKORO Zahra | - | 2% | 14% | 36% | 37% | 12% | |
28 | YAO Chloe | - | 2% | 17% | 40% | 32% | 8% | |
29 | CHEN Alina | - | 1% | 7% | 22% | 35% | 27% | 8% |
30 | KROPP Anne | - | 1% | 12% | 36% | 38% | 13% | |
31 | SHEIKH PAREE | 3% | 16% | 33% | 31% | 14% | 3% | - |
32 | BURROWS Beatrice | - | 2% | 12% | 33% | 36% | 16% | 2% |
33 | BEATIE Isabella M. | - | - | 4% | 20% | 43% | 33% | |
34 | STERN Savannah | - | - | - | 4% | 21% | 45% | 29% |
35 | BURT Emmalyne Grace | - | 2% | 15% | 34% | 33% | 14% | 2% |
36 | YANG Hannah | - | 2% | 12% | 32% | 36% | 15% | 2% |
37 | CHEN Laila | - | 5% | 23% | 39% | 26% | 7% | 1% |
38 | BOROTKO Katerina | - | - | 1% | 9% | 34% | 45% | 11% |
39 | ZHENG Erin | - | 1% | 7% | 26% | 39% | 24% | 3% |
40 | KAKANI Aditi | - | 1% | 7% | 24% | 38% | 24% | 5% |
41 | HALE Reagan | - | 3% | 18% | 38% | 31% | 9% | 1% |
42 | SHAYAKHMETOVA Suzanna | - | 2% | 17% | 42% | 33% | 6% | |
43 | JU Jennifer | 2% | 27% | 44% | 22% | 4% | - | |
44 | WANG Cecilia | - | - | 1% | 10% | 32% | 40% | 16% |
45 | TISMENSKY Avital | - | 1% | 8% | 26% | 37% | 23% | 5% |
46 | LEE Kaitlyn | - | 1% | 9% | 33% | 41% | 15% | 1% |
47 | VOSKOV Olivia | - | - | 1% | 6% | 25% | 46% | 23% |
48 | EKSHTUT Leah | - | - | 2% | 13% | 36% | 41% | 8% |
49 | YAN Ximei (Alicia) | - | 2% | 14% | 35% | 37% | 11% | 1% |
50 | ZHANG Jane | - | 2% | 14% | 33% | 34% | 14% | 2% |
51 | JAYAWEERA Vonadie | - | - | 2% | 16% | 46% | 36% | |
52 | VENKATESAN Harshitha | - | 1% | 6% | 22% | 37% | 27% | 7% |
53 | ZHANG Yuchen | 1% | 10% | 33% | 37% | 17% | 3% | - |
54 | WRIGHT Madison | 1% | 10% | 27% | 35% | 21% | 6% | 1% |
54 | CHI Zoe | - | - | 2% | 13% | 32% | 38% | 15% |
56 | LEE Gloria Y. | - | - | - | 3% | 16% | 42% | 38% |
57 | CHEN Stephanie | 1% | 10% | 29% | 36% | 19% | 4% | - |
58 | WONG Sydney | - | - | 3% | 14% | 35% | 37% | 10% |
59 | WANG Olivia | 9% | 31% | 36% | 19% | 5% | 1% | - |
60 | SONG Charlotte | 3% | 31% | 43% | 19% | 3% | - | |
61 | LAW Mila | 17% | 40% | 31% | 10% | 1% | - | |
62 | CHOI Arianna | - | 4% | 17% | 34% | 31% | 12% | 2% |
63 | LI Xier | - | 7% | 25% | 38% | 23% | 6% | - |
64 | YU Livia | 2% | 10% | 27% | 34% | 21% | 5% | - |
65 | WANG Selina | - | 3% | 15% | 35% | 35% | 11% | 1% |
66 | ZHONG Evelyn | - | 2% | 12% | 29% | 35% | 19% | 2% |
67 | DESANTIS-IBANEZ Elena | - | - | 1% | 6% | 25% | 45% | 24% |
67 | WEI Sherry | - | - | 2% | 11% | 34% | 41% | 12% |
69 | PETROFF Eva | - | - | 3% | 15% | 36% | 35% | 10% |
70 | MAEDJE Abigail | 23% | 41% | 27% | 8% | 1% | - | - |
71 | GOLIYAD Lisa | - | 1% | 8% | 25% | 37% | 24% | 6% |
72 | FAN Joy | - | 3% | 17% | 36% | 32% | 11% | 1% |
73 | SUN Milly | 2% | 16% | 36% | 31% | 12% | 2% | - |
74 | HAN Emma | 4% | 19% | 36% | 30% | 10% | 1% | - |
75 | RICHARDS Axelle | 2% | 13% | 33% | 33% | 15% | 3% | - |
76 | KOUAME Candice | 1% | 9% | 31% | 37% | 18% | 4% | - |
77 | KANG Yenna | - | 3% | 15% | 31% | 32% | 16% | 3% |
78 | RAJPUT Mahek | 6% | 27% | 36% | 22% | 7% | 1% | - |
79 | BOBE Arianna | - | 2% | 14% | 40% | 34% | 9% | 1% |
80 | BUNCH Halle | 4% | 21% | 38% | 28% | 8% | 1% | - |
81 | MEGGERS Arya | 13% | 33% | 33% | 16% | 4% | - | - |
82 | CHANG Hannah | 10% | 37% | 36% | 14% | 3% | - | - |
83 | KWON Claire | 5% | 27% | 40% | 22% | 5% | - | - |
84 | NAPOLI Eleanor | 4% | 25% | 45% | 22% | 4% | - | - |
85 | XIAO Katelyn | 3% | 17% | 36% | 31% | 12% | 2% | - |
86 | JIANG Chenxi | 7% | 30% | 39% | 20% | 4% | - | |
87 | JEYOON Lauren | - | 5% | 21% | 40% | 28% | 6% | |
88 | WU Madisen | 5% | 56% | 32% | 6% | 1% | - | |
89 | BERGAN Bailey | - | 5% | 23% | 38% | 27% | 7% | 1% |
90 | FENG Esther | 3% | 19% | 36% | 30% | 11% | 1% | - |
91 | SUN Karolyn | - | 1% | 8% | 30% | 38% | 19% | 3% |
92 | DESAI Ela | 1% | 7% | 25% | 37% | 23% | 6% | - |
93 | XU Aasta | 6% | 26% | 38% | 23% | 6% | 1% | - |
94 | YU Chloe | 1% | 7% | 22% | 35% | 26% | 8% | 1% |
95 | IVANOV Angela-Sophie | 3% | 18% | 36% | 31% | 10% | 1% | - |
96 | MARKS Madeline | 5% | 24% | 37% | 25% | 8% | 1% | - |
97 | LI Nicole | - | 6% | 25% | 39% | 24% | 6% | - |
98 | LAMBA Sayona | 37% | 45% | 15% | 2% | - | - | - |
99 | KIM Abigail | 30% | 43% | 22% | 5% | - | - | - |
100 | ALLIEVI Simone | 4% | 26% | 39% | 24% | 7% | 1% | - |
101 | HOWARD Brielle | 13% | 43% | 32% | 10% | 1% | - | - |
102 | SHETH Anayaà | 1% | 9% | 30% | 36% | 19% | 5% | - |
103 | HAGERMAN Siona | 8% | 35% | 40% | 14% | 2% | - | - |
104 | LIN Alexis | 1% | 6% | 21% | 34% | 27% | 10% | 2% |
105 | SPEZAKIS Hypatia | 1% | 6% | 21% | 34% | 27% | 10% | 1% |
106 | LIN Isabel | - | 1% | 10% | 34% | 40% | 14% | |
107 | ZHANG Ashley | 9% | 37% | 38% | 14% | 2% | - | - |
108 | MOKRETSOV Leah | - | 5% | 19% | 33% | 29% | 12% | 2% |
109 | KUPPUSAMY Mahaa | - | 3% | 17% | 37% | 32% | 10% | 1% |
110 | KWON Allison | 45% | 42% | 12% | 1% | - | - | - |
111 | GORDON Hannah | 35% | 42% | 19% | 4% | - | - | - |
112 | KALI Aiana | 40% | 44% | 14% | 2% | - | - | - |
113 | KANG Yian | - | 7% | 28% | 40% | 20% | 4% | - |
114 | LAI Juliet | 22% | 41% | 28% | 8% | 1% | - | - |
115 | GAUVEY Amelia | 55% | 37% | 7% | 1% | - | - | - |
115 | SHIN Jamie | 14% | 37% | 34% | 13% | 2% | - | - |
117 | CHAKRAPANI Tara | 12% | 41% | 34% | 12% | 2% | - | - |
118 | IYENGAR Sthuthi | 61% | 32% | 6% | 1% | - | - | - |
119 | TISHKOVA-ROBERTS Daria | 10% | 30% | 35% | 20% | 6% | 1% | - |
120 | BUNCH Helena | 32% | 42% | 21% | 5% | - | - | - |
121 | MORET Teagan | 46% | 41% | 12% | 1% | - | - | - |
122 | D'ANGELO Olivia | 22% | 42% | 28% | 7% | 1% | - | |
123 | PRAVEEN Nehara | 10% | 32% | 36% | 18% | 4% | - | - |
123 | KELSEY Fabienne | 32% | 41% | 21% | 5% | 1% | - | - |
123 | NIKOLLA Vivienne | 25% | 41% | 26% | 8% | 1% | - | - |
126 | LEE Zoe | 20% | 40% | 30% | 9% | 1% | - | - |
126 | DROLET Julia | 1% | 13% | 36% | 35% | 13% | 2% | - |
128 | HUFF Elizabeth | 15% | 40% | 32% | 11% | 2% | - | - |
129 | CLERMONT Chloe | 1% | 6% | 24% | 38% | 25% | 6% | - |
130 | NISHANTH Krishvi | 21% | 49% | 25% | 4% | - | - | - |
131 | KRAVCHENKO Tatyana | 18% | 46% | 28% | 7% | 1% | - | - |
132 | ECCLESTONE Tara | 26% | 48% | 22% | 4% | - | - | - |
133 | MALIK zara | 47% | 40% | 11% | 1% | - | - | - |
134 | JONES Elizabeth | 13% | 35% | 34% | 15% | 4% | - | - |
134 | FONS Amandine | 49% | 39% | 11% | 1% | - | - | - |
136 | TUMULA Anima | 87% | 13% | 1% | - | - | - | |
137 | BROSNAN Mabel | 78% | 20% | 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.