Philadelphia, PA - Philadelphia, PA, 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 | PEHLIVANI Zara | - | 1% | 5% | 20% | 36% | 30% | 9% |
2 | MILEWSKI Nicole | - | 1% | 5% | 19% | 35% | 30% | 10% |
3 | DOROSHKEVICH Victoriia | - | - | - | 2% | 12% | 39% | 48% |
3 | SMOTRITSKY Mia | - | 1% | 7% | 24% | 37% | 25% | 6% |
5 | REID Anousheh | - | - | 1% | 8% | 25% | 40% | 26% |
6 | MINOR Lindsey | 1% | 9% | 24% | 33% | 24% | 9% | 1% |
7 | GAO Judy | 1% | 6% | 19% | 32% | 28% | 13% | 2% |
8 | LEE Yedda | - | 4% | 16% | 30% | 31% | 16% | 3% |
9 | YANG Alisa | - | 3% | 14% | 29% | 32% | 18% | 4% |
10 | LONADIER Keira | 1% | 7% | 21% | 32% | 27% | 11% | 2% |
10 | FENG ge | 2% | 13% | 28% | 32% | 19% | 6% | 1% |
12 | FURMAN Maria | - | 4% | 16% | 30% | 30% | 16% | 3% |
13 | GLASSNER Sophia Rose S. | - | 2% | 8% | 23% | 34% | 26% | 8% |
14 | BOK Michelle | - | 2% | 9% | 24% | 34% | 24% | 7% |
15 | LIN Elaine | 2% | 13% | 29% | 32% | 19% | 5% | 1% |
16 | MUELLER Emma M. | 1% | 5% | 18% | 32% | 30% | 13% | 2% |
17 | YANG Chloe | - | 2% | 10% | 26% | 34% | 22% | 6% |
18 | KORFONTA Jolie | - | 1% | 6% | 20% | 34% | 30% | 10% |
19 | DAMRATOSKI Anna Z. | - | 1% | 8% | 24% | 35% | 25% | 7% |
20 | TAMRAGOURI Sucheta | - | 2% | 11% | 27% | 34% | 21% | 5% |
21 | LAN Alice S. | - | - | 1% | 5% | 21% | 42% | 31% |
22 | AHUJA Arianna | - | 2% | 12% | 29% | 33% | 19% | 4% |
23 | EBRAHIM Ameera H. | - | 2% | 11% | 26% | 33% | 22% | 6% |
24 | DESAI Meera P. | - | - | 3% | 17% | 43% | 38% | |
25 | HU Chelsea | 1% | 9% | 24% | 32% | 23% | 9% | 1% |
26 | DONDISCH Sophia | - | 3% | 13% | 29% | 33% | 19% | 4% |
27 | MILLER Veronica | 1% | 8% | 22% | 33% | 25% | 10% | 1% |
28 | LEE Kaitlyn M. | 3% | 16% | 32% | 31% | 15% | 3% | - |
29 | SZEWC Alexandra | - | 2% | 9% | 23% | 34% | 25% | 7% |
30 | YAO Yilin | 4% | 20% | 37% | 29% | 9% | 1% | |
31 | CHERNYSHOVA Victoria | 1% | 8% | 24% | 34% | 25% | 8% | 1% |
32 | PECK Maia A. | - | 3% | 13% | 28% | 32% | 19% | 4% |
33 | MAO Amy | - | - | 1% | 6% | 22% | 41% | 30% |
34 | CAPELLUA Mariasole | - | 1% | 5% | 18% | 35% | 31% | 10% |
35 | MALLAVARPU Aarthi C. | - | - | 2% | 10% | 29% | 39% | 20% |
36 | LI Alisha | 2% | 12% | 28% | 32% | 19% | 6% | 1% |
37 | PARKS Eliana | 22% | 38% | 27% | 10% | 2% | < 1% | - |
38 | PRIHODKO Nina | 2% | 11% | 27% | 33% | 21% | 6% | 1% |
39 | NAROTZKY Emma | - | 1% | 6% | 20% | 35% | 29% | 9% |
40 | YU Nicole J. | - | 4% | 17% | 32% | 30% | 14% | 2% |
41 | BENZAN India | 2% | 15% | 33% | 32% | 14% | 3% | - |
42 | KIM Jayna | 1% | 5% | 18% | 31% | 29% | 14% | 3% |
43 | KENT Elizabeth J. | - | 3% | 13% | 29% | 33% | 18% | 4% |
44 | SCHAFF Marlene M. | - | 3% | 16% | 32% | 31% | 15% | 3% |
44 | YOU Emily | 10% | 29% | 34% | 20% | 6% | 1% | - |
46 | HICKS Grace | 6% | 22% | 34% | 26% | 10% | 2% | - |
47 | PRIMES Amanda M. | - | 3% | 12% | 26% | 33% | 21% | 5% |
48 | KIM Elizabeth Y. | - | - | 4% | 17% | 33% | 33% | 13% |
49 | YAO KATHARINE | - | 4% | 15% | 30% | 32% | 17% | 3% |
50 | GUMAGAY Erika L. | - | - | 5% | 21% | 43% | 30% | |
51 | BYBEE Lucy J. | 7% | 26% | 38% | 23% | 6% | 1% | |
52 | WEISS Olivia | - | 2% | 9% | 23% | 34% | 25% | 7% |
53 | PACHECO Naomi | 1% | 7% | 21% | 33% | 26% | 10% | 2% |
54 | SIBLEY Elisabeth J. | 1% | 6% | 21% | 33% | 27% | 11% | 2% |
55 | PENSULA Sophia E. | - | 2% | 12% | 30% | 35% | 17% | 2% |
56 | TAUBLER Michelle | 4% | 18% | 31% | 28% | 14% | 4% | - |
57 | HIRSCH Naomi B. | 2% | 14% | 29% | 31% | 18% | 5% | 1% |
57 | SAAL Anna | - | 3% | 15% | 33% | 32% | 15% | 3% |
59 | SMUK Daria A. | - | 1% | 9% | 29% | 43% | 19% | |
60 | MESCHIA Maggie | - | 4% | 16% | 31% | 30% | 15% | 3% |
61 | PAN Michelle | - | 3% | 13% | 28% | 32% | 19% | 4% |
62 | ANDERSON Claire | 7% | 24% | 34% | 24% | 9% | 2% | - |
63 | MORIN Jenna | 7% | 27% | 35% | 22% | 7% | 1% | - |
63 | ARAYE Nasro | 2% | 10% | 26% | 32% | 22% | 7% | 1% |
65 | BAJAJ Nikita K. | - | 1% | 5% | 19% | 35% | 31% | 9% |
66 | SCHULTZ Gillian | 1% | 6% | 20% | 32% | 28% | 12% | 2% |
66 | PEARSON Heila | 3% | 15% | 30% | 30% | 17% | 5% | 1% |
68 | ABRAMSON Mariela R. | 1% | 8% | 22% | 32% | 25% | 10% | 2% |
69 | WITTE Vera | - | 3% | 15% | 31% | 32% | 16% | 3% |
70 | RAI Ananya | 4% | 20% | 35% | 28% | 10% | 2% | - |
71 | GROSSL Karina | 4% | 26% | 40% | 24% | 6% | - | |
72 | KIM Erika S. | - | 7% | 28% | 40% | 21% | 3% | |
73 | TOFFELMIRE Amelia A. | - | 2% | 12% | 35% | 39% | 12% | |
74 | KANEVSKY Samantha | 11% | 34% | 36% | 16% | 3% | - | |
75 | TAYLOR Beth | - | 3% | 12% | 27% | 32% | 21% | 5% |
76 | BOLES Savvianna | 1% | 9% | 25% | 34% | 23% | 7% | 1% |
76 | NGUYEN Audrey | 5% | 22% | 35% | 26% | 10% | 2% | - |
78 | LI Meilin | 1% | 5% | 17% | 31% | 30% | 14% | 3% |
79 | JAMES Josephine | - | 2% | 13% | 31% | 34% | 17% | 3% |
80 | MCGEE Sophia | 7% | 24% | 34% | 24% | 9% | 2% | - |
81 | CHANG Ella | 4% | 18% | 31% | 29% | 14% | 4% | - |
82 | APPLEBEE Andralyn | 22% | 40% | 27% | 9% | 2% | - | - |
83 | KRUMHOLZ Nicole | 3% | 18% | 35% | 30% | 12% | 2% | - |
84 | WIESSLER-HUGHES Linda | 13% | 32% | 33% | 17% | 5% | 1% | - |
85 | MING Cynthia | - | 1% | 7% | 24% | 37% | 26% | 5% |
86 | SAINT-PHARD Shana | 4% | 26% | 41% | 24% | 5% | - | |
87 | GABERKORN Nadia | - | - | 2% | 11% | 30% | 38% | 19% |
88 | HOSANAGAR Inchara | 2% | 28% | 40% | 23% | 6% | 1% | - |
89 | WHITE Zara | 1% | 11% | 33% | 35% | 17% | 4% | - |
90 | ZHANG mickey | - | 3% | 15% | 31% | 32% | 16% | 3% |
91 | SWEET Ryleigh E. | - | 4% | 18% | 34% | 30% | 12% | 2% |
92 | HABERMAN Hailey | 18% | 39% | 30% | 11% | 2% | - | - |
92 | MANDAP Svetlanna | 6% | 24% | 34% | 25% | 9% | 2% | - |
94 | LESPERANCE Jordan | 15% | 36% | 32% | 14% | 3% | - | - |
95 | SHICK Klaudia | 11% | 31% | 34% | 18% | 5% | 1% | - |
96 | BOWIE Charlotta | 4% | 19% | 33% | 29% | 13% | 3% | - |
97 | MCSHINE Katelyn H. | 3% | 20% | 35% | 28% | 11% | 2% | - |
98 | GUJJA Misha | 2% | 14% | 30% | 32% | 17% | 4% | - |
99 | GROENING Joanne | 2% | 11% | 27% | 33% | 21% | 6% | 1% |
100 | RIST Rebecca (Beck) J. | 4% | 20% | 35% | 28% | 11% | 2% | - |
101 | PALANSKI Cate | - | 1% | 7% | 25% | 37% | 25% | 6% |
102 | DOERR Zoe | 9% | 27% | 33% | 22% | 8% | 1% | - |
103 | TAYLOR-CASAMAYOR Maia | - | 2% | 12% | 29% | 34% | 19% | 4% |
104 | AUCHTERLONIE Seven | 21% | 38% | 28% | 11% | 2% | - | - |
105 | LORES Alicia | - | 5% | 18% | 32% | 29% | 13% | 2% |
106 | FLANIGAN KENDRA | 16% | 38% | 32% | 12% | 2% | - | |
107 | MILEWSKI Samantha | - | 3% | 12% | 27% | 33% | 20% | 5% |
108 | CASTANEDA Erika L. | - | 3% | 14% | 29% | 32% | 18% | 4% |
109 | ZENG Katrina | 2% | 18% | 36% | 30% | 12% | 2% | - |
110 | SEMENETS Mira | - | 4% | 16% | 31% | 31% | 15% | 3% |
110 | BALSKUS Sophia | - | 5% | 17% | 32% | 30% | 14% | 2% |
112 | CAREY Michele S. | 1% | 7% | 21% | 32% | 26% | 11% | 2% |
112 | DUARTE-GARCIA Zoya A. | 7% | 24% | 34% | 24% | 9% | 2% | - |
114 | SHEVCHENKO Viktoriia | - | 1% | 4% | 16% | 32% | 34% | 14% |
115 | RAINEY Zoe-Andrea | 11% | 30% | 33% | 19% | 6% | 1% | - |
116 | ROWLAND May | 11% | 32% | 35% | 18% | 5% | 1% | - |
117 | NORCONK Claire R. | - | < 1% | 3% | 14% | 31% | 35% | 16% |
118 | SOTELO Michelle | 10% | 33% | 35% | 17% | 5% | 1% | - |
119 | BOTNER Olivia | 1% | 7% | 22% | 34% | 26% | 10% | 1% |
119 | LEWIS Rachel | 13% | 32% | 32% | 17% | 5% | 1% | - |
119 | DONDISCH Andrea | 79% | 19% | 2% | - | - | - | - |
122 | UPHAM Karolyn | 2% | 14% | 32% | 32% | 15% | 3% | - |
123 | NOLLNER Jennifer R. | 19% | 37% | 29% | 12% | 2% | - | - |
124 | GARTNER Kacie | 21% | 38% | 28% | 10% | 2% | - | - |
125 | GELMAN Emily | 32% | 42% | 21% | 5% | 1% | - | - |
125 | NEELAM Neha | 31% | 41% | 21% | 6% | 1% | - | - |
127 | XU Katelyn | 26% | 43% | 24% | 6% | 1% | - | - |
128 | BARREIRO Katrina | 58% | 34% | 7% | 1% | - | - | |
129 | GLOVER Cynthia E. | 1% | 9% | 24% | 32% | 23% | 8% | 1% |
130 | EGENOLF Gabriella | 53% | 37% | 9% | 1% | - | - | - |
131 | LI Charlotte | 2% | 13% | 29% | 31% | 19% | 6% | 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.