January NAC

Div I Women's Épée

Monday, January 6, 2025 at 8:00 AM

Kansas City Convention Center - Kansas City, MO, USA

Probability density of pool victories

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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 NIXON Catherine (Kasia) D. - - - - 5% 95%
2 PIRKOWSKI Amanda L. - - - 1% 8% 35% 55%
3 XUAN Nicole J. - - - 5% 27% 58% 10%
3 RAKHOVSKI Alexandra - 1% 8% 28% 41% 22%
5 LEE Sumin - - - 2% 12% 40% 46%
6 TOBY Natalia R. - - - - 2% 19% 80%
7 GUZZI VINCENTI Margherita A. - - - - 3% 22% 75%
8 BECKMAN Ana - - 3% 14% 32% 35% 15%
9 SWENSON Nikita G. - 4% 17% 34% 31% 12% 1%
10 HUSISIAN Hadley N. - - - - 1% 20% 79%
11 XIONG Angelica - 2% 15% 35% 34% 12% 1%
12 LEE Scarlett - 1% 9% 27% 40% 23%
13 MARCHANT Sandra M. - 6% 24% 38% 26% 6%
14 YANG Alisa 1% 10% 30% 38% 18% 2%
15 CAFASSO Natalya - 6% 25% 38% 25% 6%
16 CHIN Isabella - - 2% 11% 38% 49%
17 SPRINGER Sierra - - 2% 9% 28% 40% 21%
18 LEE Olivia - 3% 16% 36% 34% 10%
19 SMUK Alexandra S. - 2% 14% 33% 34% 15% 2%
20 SHIV Avni - 1% 5% 20% 36% 30% 9%
21 WANG Zoe 1% 9% 27% 37% 22% 4%
22 LI Olivia 1% 7% 25% 37% 24% 6%
23 SMOTRITSKY Mia - 2% 10% 28% 37% 21% 3%
24 FALLON Kyle R. - - - 2% 13% 42% 44%
25 SMUK Daria A. - 1% 8% 25% 36% 24% 6%
26 STEWART Tatijana I. - - 1% 7% 33% 59%
27 YAO Melinda - - 3% 15% 42% 40%
28 WAT Chok Weng - - 4% 22% 51% 23%
29 GUO Luxi - 5% 22% 38% 28% 7%
30 SU Evelyn 4% 21% 37% 28% 8% 1%
31 KOZLOWSKI Maya M. - 3% 16% 33% 33% 14% 1%
32 PROFIS Liora - 2% 14% 33% 34% 15% 2%
33 NIXON Caroline (Karolina) L. - - - 1% 9% 36% 53%
34 WITTER Catherine A. - 3% 16% 34% 32% 13% 2%
35 YILMAZ Pinar - 4% 16% 34% 33% 12%
36 SEREGIN Katya 2% 14% 31% 34% 16% 3%
37 XU Serena - 3% 15% 35% 35% 12%
38 RUNIONS Emersyn - 2% 10% 27% 35% 22% 5%
39 JIN Zhengtian 6% 24% 37% 25% 8% 1% -
40 SHEFFIELD Lake Mawu - - 1% 9% 28% 40% 21%
41 TOLSMA Chloe (CJ) - 1% 11% 32% 39% 17%
42 AI Amy - 5% 21% 36% 28% 9% 1%
43 YANG Hanting(Catherine) 2% 16% 34% 32% 14% 2% -
44 ILYAS Ayah 1% 10% 25% 33% 23% 7% 1%
45 SUICO Kyubi Emmanuelle 15% 37% 33% 13% 3% - -
46 GORNOVSKY Abigail 3% 18% 36% 31% 11% 1%
47 MONOVA Lilyana 7% 28% 40% 22% 3% -
48 XU Jessica 19% 41% 30% 9% 1% -
49 LI Judy 1% 5% 20% 35% 30% 9%
50 MCGLADE Jasmine 6% 27% 38% 23% 6% -
51 AZMEH nour - 5% 20% 36% 29% 9% 1%
52 CARRIER Meredith - 4% 16% 33% 31% 13% 2%
53 NEMETH Katherine - - 4% 15% 32% 35% 14%
53 SONG Angela 1% 10% 28% 35% 21% 5% -
55 STERR Isabella M. 1% 9% 26% 36% 22% 5% -
56 WU Michelle 9% 30% 36% 20% 5% 1% -
57 CHI Chelsea - 4% 18% 35% 31% 11% 1%
58 WELLS Tesla M. - 3% 16% 33% 33% 13% 2%
59 YOU Emily - 1% 8% 27% 41% 23%
60 LEE Camilla 18% 44% 29% 8% 1% -
61 ZHENG Evelyn 1% 7% 25% 40% 25% 3%
62 XIAO Nancy 12% 37% 34% 14% 2% -
63 SUN Hanya 5% 23% 38% 27% 7% -
64 SOBUS Yanka - 4% 21% 38% 28% 8% 1%
65 ZIGALO Elizabeth - - 1% 9% 41% 49%
66 NGUYEN Audrey - 2% 14% 34% 36% 14%
67 KUMAR Eva 9% 31% 37% 19% 4% -
68 NGUYEN Tallulah - - 3% 14% 35% 38% 11%
68 CAMPBELL Eva 1% 9% 26% 34% 23% 7% 1%
70 FOELLMER Kristin - - 1% 10% 32% 42% 15%
71 RICHARDSON Meredith 2% 12% 28% 32% 20% 6% 1%
72 GAO Judy 1% 9% 27% 35% 21% 5% -
73 MARTYNOVA Diana 1% 7% 25% 37% 24% 5%
74 LAI Amanda 2% 14% 34% 34% 14% 2%
75 LIANG Jingjing 14% 35% 33% 15% 3% -
76 PRIHODKO Nina 1% 10% 28% 35% 21% 5%
77 LI Fei 7% 28% 38% 22% 5% -
78 CHISHOLM Phoebe C. - 5% 22% 37% 28% 7%
79 GOH Cayla 8% 29% 37% 20% 5% -
80 WANG Ziqi (yoyo) 1% 11% 31% 36% 17% 3%
80 KALGAONKAR Arohi 7% 27% 36% 23% 7% 1%
82 WANG Jessie 5% 23% 38% 27% 7% -
83 ABUELFUTUH Sama 3% 15% 31% 31% 16% 4% -
84 BUSH emma - 3% 15% 33% 35% 14% 1%
85 WATTANAKIT Anda - 4% 20% 39% 31% 6% -
86 BALAKRISHNAN Trisha 3% 19% 39% 29% 9% 1% -
87 JOYAL Anne-Sophie - 4% 17% 34% 32% 12% 1%
88 KIM Zoe L. - 1% 10% 28% 36% 20% 4%
89 PHUKAN Indra 6% 25% 36% 24% 8% 1% -
89 IYER Ishana 5% 31% 39% 20% 5% 1% -
91 KAUR Manroop 6% 29% 39% 21% 5% - -
92 WANG Angelina 7% 34% 39% 17% 3% - -
93 PRESMAN Aerin - 2% 14% 34% 36% 14%
94 DEPOMMIER-GONZALEZ Isabelle 6% 26% 38% 24% 6% -
95 KANASKAR Ila 15% 37% 33% 13% 2% -
96 CAMAMA Tessa 1% 12% 32% 35% 17% 3%
97 LEE Claire 2% 14% 34% 36% 14% -
98 ZHAN Clare 44% 40% 13% 2% - -
99 SWENSON Alexandra 40% 43% 15% 2% - -
99 QI Jarynne Valerie 1% 7% 23% 36% 26% 7%
101 KUNJAN Lena R. - - 4% 18% 36% 32% 10%
102 MONTOYA Kimberlee C. - - 2% 10% 29% 40% 20%
103 WEBER Nora - 1% 7% 22% 36% 27% 7%
103 DAHER Yasmine 1% 15% 35% 32% 14% 3% -
105 LIN Ariel 4% 22% 39% 27% 7% 1% -
106 XU Celina 32% 42% 20% 5% - - -
107 SYKES Elynor 1% 12% 31% 35% 17% 4% -
108 MENDOZA zoie 8% 27% 34% 22% 7% 1% -
109 LAWSON Marie A. - 2% 10% 28% 38% 21% 2%
109 CHEN Stephanie 6% 25% 37% 24% 7% 1% -
111 SCHOR Elisabeth 13% 35% 34% 15% 3% - -
112 BO GENESIS 48% 38% 12% 2% - - -
113 YOUNG VIVIAN 2% 15% 34% 33% 14% 2%
114 CHIRASHNYA Mika 30% 43% 22% 5% - -
115 KROPP Anne 17% 38% 32% 11% 2% -
116 AIRES Julia 16% 38% 32% 12% 2% -
117 WALTER Anna 8% 31% 37% 19% 4% -
118 GUAN Isabella 22% 40% 28% 9% 1% -
119 ELTERMAN Kate 15% 38% 34% 12% 1% -
120 DESANTIS-IBANEZ Elena 18% 39% 31% 10% 2% -
121 WANG Victoria 5% 24% 40% 26% 5% -
122 HANSEN Kira - 3% 16% 36% 34% 11%
123 MCCOY Lauren 4% 20% 35% 28% 11% 1%
124 MARTINEZ Christina 9% 30% 36% 20% 5% - -
125 NELSON Grace E. 29% 42% 23% 6% 1% - -
126 HABEK Sophia 2% 14% 35% 35% 13% 1% -
127 PAN Angela 21% 40% 28% 9% 1% - -
128 KUDRYAVTSEVA Margarita 6% 26% 37% 23% 7% 1% -
129 BEAVER Ava 3% 16% 33% 31% 14% 3% -
130 SMOTRITSKY Liat 10% 32% 35% 17% 4% - -
131 FREEMAN Kit 40% 43% 15% 2% - - -
132 KENT Laurel 38% 42% 17% 3% - - -
133 QI Julieanne 9% 30% 36% 20% 5% -
134 DALEY Keira 3% 16% 33% 32% 14% 2%
135 FENG Iris 62% 32% 6% - - - -
136 HUANG Lanlan 23% 41% 27% 8% 1% -
137 ZHUANG Lauren 8% 26% 34% 22% 8% 1% -
138 WANG Ziqiao (Claire) 17% 44% 29% 8% 1% - -
139 WONG Caitlin 39% 42% 16% 3% - -
140 ZHU Riley 14% 36% 34% 14% 3% -
141 SUN Karolyn 54% 37% 9% 1% - - -
142 MURPHY Katherine 33% 42% 20% 5% 1% - -
143 HOFMAN Haejung 3% 19% 37% 29% 10% 1% -

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.