National Championships and July Challenge (Summer Nationals)

Div I-A Women's Épée

Sunday, June 29, 2025 at 8:00 AM

Baird Center - Milwaukee, WI, USA

Probability density of pool victories

Reset

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 YANG Alisa - - 1% 8% 31% 48% 12%
2 PECK Maia A. - 1% 5% 19% 37% 31% 8%
3 WANG Zoe - - 2% 13% 38% 39% 8%
3 EDWARDS Auprell - 1% 8% 30% 45% 16%
5 BUSH emma - 1% 6% 19% 34% 30% 10%
6 NEMETH Katherine - 1% 6% 21% 35% 28% 9%
7 MARTINEZ Christina 1% 9% 27% 36% 22% 5%
8 BEI Karen - - - - 5% 33% 61%
9 YU Nicole J. - - - 4% 20% 45% 31%
10 SPRINGER Sierra - - 1% 7% 25% 42% 26%
11 MCGLADE Jasmine - - 5% 25% 45% 25%
12 ZHU Serene M. 1% 11% 29% 35% 19% 4%
13 ZANGA Kaitlyn - 1% 7% 27% 42% 21% 2%
14 FURMAN Maria 1% 6% 22% 37% 27% 7%
15 FENG Ge (Catherine) - 1% 5% 21% 42% 32%
16 CHOY LeeAnn - - 1% 10% 41% 49%
17 DALEY Keira - 4% 18% 35% 31% 10% 1%
18 YOU Emily - 2% 12% 32% 38% 17%
19 DESAI Meera P. - - 4% 22% 46% 28%
20 KUNJAN Lena R. - - - 3% 18% 42% 37%
21 LEE Claire - 3% 15% 32% 33% 15% 2%
22 GARINA Ania (Ganusia) - - - - 6% 33% 61%
23 LOBANOVA Varvara 8% 28% 36% 21% 5% -
24 SMUK Alexandra S. - - 3% 14% 33% 36% 14%
25 SEREGIN Katya - - 3% 14% 31% 36% 16%
26 PADHYE Tanishka - - - 5% 22% 45% 28%
27 SHELIN Chelsea 1% 10% 31% 37% 18% 3% -
28 SMUK Daria A. - - 3% 14% 32% 35% 15%
29 ILYAS Ayah 1% 8% 25% 37% 24% 5%
30 WADE-CURRIE Ava S. - - - 2% 13% 40% 45%
31 CHUNG Penelope 1% 9% 24% 34% 24% 8% 1%
32 KUKVA Taisiya 1% 6% 20% 33% 27% 11% 2%
33 BOURDEAU Emily B. - - 4% 18% 37% 32% 9%
34 LI Fei - - 2% 13% 33% 37% 15%
35 MARTYNOVA Diana - 3% 15% 33% 33% 14% 2%
36 CHI Chelsea - 1% 6% 20% 34% 29% 10%
37 HANSEN Kira - - 2% 11% 29% 38% 19%
38 WANG Sophie Y. - 4% 15% 31% 32% 15% 2%
39 HAFEEZ Hania - - 4% 20% 42% 34%
40 WITTER Catherine A. - - 1% 5% 19% 41% 34%
41 LI Caroline 5% 22% 35% 26% 10% 2% -
42 HEPLER Sarah - 1% 8% 25% 37% 23% 5%
43 WANG Trinity - 3% 14% 30% 32% 17% 3%
44 MALLAVARPU saanvi - 2% 11% 30% 36% 18% 3%
44 BURICEA Ada 3% 18% 34% 30% 12% 2% -
46 SYKES Elynor - 1% 7% 23% 37% 26% 5%
47 SCHMITT Harper 1% 8% 24% 33% 24% 9% 1%
48 PRIHODKO Nina - 3% 14% 32% 36% 15%
49 HAFEEZ Hiba 2% 15% 33% 33% 15% 2%
50 SCHOR Elisabeth 7% 27% 36% 22% 6% 1%
51 LEE Yeriel 10% 31% 35% 18% 4% - -
52 WONG Caitlin - 4% 16% 32% 32% 14% 2%
53 XIONG Angelica - - 2% 11% 31% 39% 17%
54 BROWN Amanda - 4% 17% 32% 30% 13% 2%
55 NOVOJILOV Anastasia - 4% 19% 35% 29% 11% 1%
56 POLANICHKA Nicole - - 1% 7% 25% 44% 23%
57 ZHAO Alina - 3% 14% 30% 33% 17% 4%
58 CAVNAR Peyton - 3% 13% 29% 33% 18% 4%
59 GANSER Nicole 4% 19% 35% 29% 11% 1%
60 COLELLA Lauren 18% 40% 30% 10% 2% -
61 LIANG Jingjing 6% 29% 41% 20% 4% -
62 CAMPBELL Anahit M. - 2% 11% 27% 34% 21% 5%
62 KOVALCHUK Erika S. 7% 25% 35% 24% 8% 1% -
64 NORCONK Claire R. - - 1% 7% 26% 42% 24%
65 DAVIS Jennifer 1% 8% 24% 35% 24% 7% 1%
66 KROPP Anne (Charlie) 9% 31% 36% 19% 4% -
67 KALGAONKAR Arohi 1% 7% 24% 36% 25% 7% 1%
67 BALAKRISHNAN Trisha - 3% 12% 28% 34% 19% 4%
69 KUDRYAVTSEVA Margarita 3% 21% 40% 27% 8% 1% -
69 GOLIYAD Lisa 9% 29% 35% 20% 6% 1% -
71 ENRILE Erica 1% 13% 35% 35% 14% 2% -
72 ZHEREBCHEVSKA Veronika - 4% 15% 31% 32% 16% 3%
73 BRAGG Leah 1% 7% 25% 36% 25% 6%
74 PATIL Amulya 1% 9% 30% 41% 17% 2%
75 XU Serena - 5% 24% 42% 25% 5%
76 KENT Laurel 21% 40% 28% 9% 1% -
77 CHUANG Ramona 23% 42% 26% 8% 1% -
78 ZOU You yang (Yoyo) 11% 35% 38% 14% 2% -
79 NORRIS Morgan 24% 43% 26% 6% 1% -
80 HAUSHEER Kuncen 2% 13% 34% 35% 14% 2% -
81 LIN Cynthia 31% 41% 22% 6% 1% - -
82 GUAN Isabella 4% 20% 34% 28% 12% 3% -
83 LAI Amanda 2% 14% 31% 31% 17% 4% -
84 BISONO Valentina 18% 40% 31% 10% 2% - -
85 WALTER Anna 5% 22% 35% 26% 10% 2% -
86 MONTOYA Kimberlee C. - - 1% 5% 23% 45% 26%
87 DESAI Ela 42% 40% 15% 3% - - -
88 QIAN Irene 5% 22% 37% 26% 8% 1% -
89 YU Eva 9% 31% 37% 19% 4% - -
90 SCHULTZ Nomi 5% 24% 38% 25% 7% 1% -
91 SHU Youshan 1% 11% 30% 35% 18% 3%
92 FANG Kayla 3% 20% 36% 29% 11% 1%
92 FLITMAN Gabrielle 25% 41% 25% 7% 1% -
94 CHEN Ava 40% 40% 16% 3% - -
95 BEAVER Hannah 8% 29% 36% 20% 5% 1%
96 MONTOYA Amy C. 1% 9% 27% 36% 22% 4%
97 SCHULTZ Michelle M. - 4% 19% 36% 30% 9%
98 PILSON Rebecca 3% 17% 32% 30% 14% 3% -
99 FREEMAN Kit 19% 40% 29% 10% 2% - -
99 MEMON Insha 61% 33% 6% - - - -
101 MYRAH Vivienne 1% 11% 29% 35% 19% 4% -
102 DUNSEATH Lauren M. - 4% 16% 31% 30% 15% 3%
102 BEATIE Isabella M. 8% 32% 38% 18% 4% - -
102 MURPHY Katherine 7% 28% 37% 21% 6% 1% -
105 TOTEMEIER Ann M. - - 5% 22% 38% 28% 7%
106 KAZMIEROWSKI Chrissa 24% 40% 26% 8% 1% - -
107 HOFMAN Haejung 1% 6% 21% 36% 27% 9% 1%
108 JORGENSEN Kjirsten 7% 27% 36% 23% 7% 1% -
109 XU Jessica 6% 33% 39% 17% 3% - -
110 WATTANAKIT Anda - 4% 20% 38% 30% 7% -
111 AIRES Julia 17% 39% 31% 11% 2% - -
112 HUANG Selina 26% 42% 25% 7% 1% - -
113 CHEN Alicia 8% 27% 35% 22% 7% 1% -
114 XU Celina 5% 25% 39% 25% 6% 1% -
115 BRIDGE Rebekah 2% 13% 31% 34% 17% 3%
116 SCHULTZ Gillian 1% 9% 27% 37% 22% 4%
117 ASHER Valerie - 2% 10% 29% 39% 20%
118 FOMINA Polina 18% 43% 31% 8% 1% -
119 LONADIER Keira 1% 6% 24% 37% 26% 6%
120 ZHUANG Lauren 2% 12% 31% 34% 17% 3%
121 LEWIS Rachel 2% 15% 31% 31% 16% 4% -
122 LANE Leah 4% 21% 37% 27% 9% 1% -
123 DASILVA Mia 9% 29% 34% 20% 6% 1% -
124 BROWN Riley 2% 12% 30% 35% 18% 4% -
125 SCHMID Carola K. 1% 6% 21% 34% 27% 10% 1%
125 CHENG Wanrong 18% 40% 30% 10% 2% - -
127 YOU Isabel B. 5% 29% 40% 21% 5% - -
128 GAN Shelby 20% 38% 29% 11% 2% - -
129 ZHAN Clare 19% 40% 29% 10% 1% - -
129 NGUYEN Celena 35% 43% 19% 4% - - -
131 DONGES Anna 2% 12% 29% 32% 19% 5% 1%
132 RAFFERTY Catherine 20% 39% 29% 10% 2% - -
133 DEPAUW Adeline 31% 45% 21% 4% - -
134 SLAVIN-VINIKOV Ana 4% 19% 33% 28% 13% 3% -
135 PETROFF Eva 14% 39% 35% 12% 1% -
136 LEVKOV Maita 59% 34% 7% 1% - - -
137 JAMES Ashley 15% 36% 32% 14% 3% - -
138 BARTOSZUK Camilla 13% 34% 34% 15% 3% -
139 BURKHARDT Krista L. 15% 37% 32% 12% 2% - -
139 GANSER Yuliya < 1% 3% 17% 36% 31% 12% 2%

Explanation

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.