October NAC

Cadet Women's Épée

Sunday, October 9, 2022 at 8:00 AM

Minneapolis, MN, 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 XIAO Ruien - - - 1% 11% 40% 48%
2 GU Sarah - - - - 2% 20% 78%
3 JAKEL Sophia N. - - - - 4% 29% 67%
3 GAJJALA Sharika R. - - - - 6% 33% 60%
5 MACHULSKY Leehi - - - - 1% 15% 84%
6 KHAMIS Yasmine A. - - - - - 10% 89%
7 FALLON Kyle R. - - - 1% 11% 41% 47%
8 LEE REGINA - - 4% 19% 37% 31% 9%
9 WITTER Catherine A. - - 2% 10% 32% 43% 13%
10 MEHROTRA Anya - - - 1% 7% 34% 58%
11 YIN Julia - - 1% 5% 22% 42% 30%
12 DOROSHKEVICH Victoriia - - 1% 11% 38% 50%
13 SEBASTIAN Felicity A. - - - 3% 15% 41% 41%
14 SWENSON Nikita G. - 1% 5% 21% 39% 29% 5%
15 KORFONTA Jolie - - - 1% 10% 38% 51%
16 SHU Youshan - 3% 13% 33% 36% 14% 1%
17 LEE Natasha - - 1% 7% 29% 44% 19%
18 PADHYE Tanishka - - 1% 8% 25% 41% 25%
19 SMOTRITSKY Mia - - 1% 8% 28% 42% 21%
20 KIM Zoe L. - - - 3% 19% 45% 32%
21 PEHLIVANI Zara - - 1% 8% 28% 42% 22%
22 BEZUGLAYA Varvara - - 1% 7% 28% 44% 21%
23 YU Nicole J. - - 1% 9% 29% 41% 20%
24 ENRILE Erica 1% 11% 30% 35% 18% 4% -
25 KHARCHYNA Polina - - 3% 17% 43% 37%
26 LEE Scarlett - 2% 13% 33% 35% 16% 2%
27 REID Anousheh - - 2% 9% 27% 40% 22%
28 LAN Alice S. - 2% 13% 33% 37% 15%
29 SUN Renee R. - 6% 25% 40% 24% 5%
30 GORNOVSKY Abigail - - 3% 16% 38% 36% 6%
31 DOUGLAS Marketa F. 4% 20% 37% 28% 9% 1% -
32 MUELLER Emma M. - 1% 7% 26% 39% 24% 4%
33 ZIGALO Elizabeth - - - 2% 14% 40% 44%
34 SPRINGER Sierra - - 4% 16% 36% 35% 9%
35 NGUYEN Kira - - - 2% 14% 40% 44%
36 LI Zhenni (Jenny) - 5% 21% 36% 27% 9% 1%
37 CALDERA Lexi I. - 3% 19% 41% 30% 6%
38 LUO Ashley - - - 3% 16% 41% 40%
39 HAFEEZ Hania - 1% 7% 23% 35% 26% 7%
40 FENG ge - - 2% 15% 38% 36% 9%
41 WATTANAKIT Anda - 2% 12% 32% 36% 16% 2%
42 SHIV Avni - - 3% 18% 41% 32% 6%
43 NELSON-LOVE Lily B. - - 3% 14% 34% 36% 13%
44 CAFASSO Natalya - 4% 19% 36% 31% 9% 1%
45 ZHANG Victoria R. - - - 4% 20% 45% 32%
46 LEE Yedda - 1% 7% 25% 40% 25% 2%
47 DESAI Meera P. - - 5% 24% 43% 27%
48 REMEZA Alissa - 1% 10% 31% 40% 18%
49 HONG Elaine - 2% 14% 35% 37% 12%
50 RUNIONS Emersyn - 3% 17% 36% 33% 11%
51 LEE yat ching - 1% 5% 20% 37% 30% 7%
52 WONG Alexandra R. - 4% 17% 32% 30% 13% 2%
52 XUAN Nicole J. - - - 1% 9% 38% 52%
54 SONG Angela - 4% 16% 32% 31% 14% 2%
54 PHUKAN Indra 1% 9% 28% 36% 20% 5% -
56 GUJJA Misha - 1% 5% 20% 41% 32% 2%
57 SHARMA Sanvi - 3% 16% 33% 32% 14% 2%
58 XIONG Angelica 2% 15% 33% 33% 15% 3% -
59 LEE Olivia - 2% 12% 31% 36% 17% 3%
60 YAO Melinda - 1% 10% 31% 40% 18%
61 LIU Nicole - 2% 11% 29% 36% 19% 3%
62 WANG Ziqi (yoyo) - 2% 14% 38% 33% 11% 1%
63 HESS Heidi J. - 3% 15% 33% 33% 14% 1%
64 LIN Ashley 2% 16% 38% 32% 11% 1%
65 NGUYEN Tallulah - - 1% 5% 21% 42% 32%
66 BARG Daniella - 2% 11% 30% 36% 19% 3%
67 ELSTON Sophia - 2% 11% 28% 35% 20% 4%
68 SENYUVA Su - 1% 9% 34% 38% 15% 2%
69 KRUMHOLZ Nicole - 3% 19% 37% 30% 10% 1%
69 ANDERSON Claire 1% 6% 20% 34% 28% 10% 1%
71 PRESMAN Aerin 1% 13% 36% 36% 12% 1% -
72 MALLAVARPU saanvi - 6% 21% 36% 28% 8% 1%
73 LIN Elaine - 5% 23% 38% 27% 7%
74 RAKHOVSKI Alexandra - 1% 11% 32% 40% 16%
75 SUN Zeyu 8% 29% 37% 20% 5% - -
76 AI Amy - - 4% 21% 42% 29% 4%
77 PECK Maia A. - - 5% 25% 41% 24% 4%
78 BECKMAN Ana - 1% 11% 31% 36% 18% 3%
79 LI Fei 1% 5% 21% 36% 29% 8% -
80 JAKEL Alysa C. 1% 6% 22% 34% 26% 10% 1%
80 KUMAR Anusha 2% 12% 31% 35% 16% 3% -
82 HSIU Elizabeth 1% 9% 26% 34% 22% 7% 1%
83 CHA Eugenie - 6% 22% 35% 27% 9% 1%
84 LU Samantha R. - - 4% 19% 37% 31% 9%
85 SUN Hanya 1% 10% 29% 35% 19% 5% -
86 REKEDA Anna 18% 39% 30% 10% 2% - -
87 TOLSMA Chloe (CJ) - - 2% 12% 31% 38% 16%
88 ZHEREBCHEVSKA Veronika - 1% 10% 30% 37% 18% 3%
89 CHISHOLM Phoebe C. - 1% 9% 28% 39% 19% 3%
90 ALEXANDROV Katherine S. - - 5% 23% 43% 28%
91 KOZLOWSKI Maya M. - 6% 26% 40% 24% 5%
92 KIM Jayna 1% 9% 29% 38% 20% 3%
93 RICHARDSON Meredith 5% 32% 40% 19% 4% -
94 CANNING Charlotte 1% 8% 27% 39% 22% 3%
95 PINNAMANENI Drithi 1% 16% 37% 32% 12% 1%
96 AZMEH nour - 7% 28% 39% 21% 4%
97 CHENG Katherine 8% 35% 38% 16% 2% - -
98 DAHER Yasmine 4% 20% 35% 28% 11% 2% -
99 MEYER Rachel 26% 46% 23% 5% - - -
100 YOU Emily - - 2% 15% 43% 37% 3%
101 HUANG Lanlan - 5% 19% 34% 30% 11% 1%
102 RANDLEMAN Teresa - 1% 6% 21% 36% 28% 8%
103 SKOURLETOS Angelina - 4% 19% 36% 29% 10% 1%
104 LI Suri 1% 10% 32% 35% 18% 4% -
105 CARRIER Meredith 2% 15% 34% 32% 13% 2% -
106 CHEUNG Cheryl - 5% 18% 32% 29% 13% 2%
107 KUMAR Eva - 3% 14% 32% 33% 15% 2%
108 QIAN Irene - 11% 34% 35% 16% 3% -
109 YAO KATHARINE - 2% 14% 35% 34% 14% 2%
110 MUN Brianna K. 1% 10% 35% 37% 15% 2%
111 YANG Alisa - 3% 19% 39% 31% 8%
112 CHERNIS Zoe C. - 6% 24% 38% 26% 6%
113 QI Jarynne Valerie 1% 13% 34% 35% 14% 2%
114 WANG Angelina 2% 15% 34% 32% 14% 3% -
115 CHIRASHNYA Noya - 2% 13% 32% 35% 16% 2%
116 MONOVA Lilyana 9% 32% 37% 18% 4% - -
117 MENDOZA zoie 7% 26% 37% 24% 7% 1% -
118 SOBUS Yanka 3% 17% 34% 31% 13% 2% -
119 WONG Caitlin 20% 42% 28% 9% 1% - -
120 SCHMITT Harper 5% 27% 40% 22% 5% - -
121 POTAPENKO Margarita D. - - 1% 9% 29% 41% 20%
122 ZHU Serene M. - 3% 14% 33% 35% 15% 2%
123 YOU Isabel B. 1% 13% 33% 34% 15% 3% -
124 KOVALCHUK Erika S. 4% 22% 37% 27% 8% 1% -
125 BHATT Anisha 3% 22% 47% 23% 4% - -
126 VICKERMAN Aspen 35% 41% 19% 4% - - -
127 BALAKRISHNAN Trisha 48% 39% 11% 1% - -
128 NGUYEN Ashley L. 27% 47% 21% 4% - -
129 BARG Margaret 3% 17% 34% 31% 13% 2% -
130 NIX Reagan 2% 16% 33% 31% 15% 3% -
131 WALLER DEL VALLE Andrea 3% 18% 37% 31% 11% 1% -
132 WALLER DEL VALLE Alexandra 25% 43% 25% 7% 1% - -
133 READ Lyla 18% 38% 31% 12% 2% - -
134 KORKIN Alice - - 5% 24% 45% 25% 2%
135 DENG Annie 1% 8% 27% 38% 22% 5% -
136 LEE Claire 6% 31% 40% 19% 4% -
137 ZHAN Nancy 3% 21% 40% 28% 8% 1%
138 LIU Baihan 7% 31% 38% 19% 4% -
139 MYRAH Vivienne 5% 25% 38% 24% 7% 1% -
140 SMUK Alexandra S. - 2% 11% 31% 38% 17% 1%
141 WANG Victoria 3% 21% 41% 26% 7% 1% -
142 GRANERT Ysabel 3% 30% 40% 21% 5% 1% -
143 LEE Camilla 1% 7% 27% 40% 21% 4% -
144 HOAGLAND Sally 19% 52% 25% 3% - - -
144 GUAN Isabella 3% 18% 35% 30% 12% 2% -
146 TANG RUIRUI 13% 39% 34% 13% 2% - -
147 MOLLINIER Angel 11% 36% 35% 15% 3% - -
147 SHEN Yongen 7% 26% 37% 23% 6% 1% -
149 VICKERMAN Sofia 15% 38% 33% 12% 2% - -
150 MIINEA Elena 2% 16% 33% 31% 14% 3% -
151 MARTIN Adriana 10% 40% 37% 12% 1% - -
152 XU Celina 9% 36% 38% 14% 2% - -
153 BO GENESIS 80% 19% 2% - - -
154 MARTYNOVA Diana 1% 22% 40% 28% 8% 1%
155 VICKERMAN Lilly 24% 46% 24% 6% 1% -
156 ZHAO Alina 32% 45% 19% 4% - -
157 IYER Ishana 36% 46% 16% 2% - -
158 GEVA Eliana 4% 20% 37% 28% 10% 1% -
159 DEPOMMIER Isabelle 6% 29% 38% 21% 5% 1% -
160 PRIHODKO Nina - 2% 14% 33% 34% 15% 2%
161 LIN Ariel 8% 29% 37% 21% 5% 1% -
162 SU Evelyn - < 1% 7% 30% 41% 19% 3%
163 FENG Iris 3% 20% 40% 29% 8% 1% -
164 PAN Angela 38% 41% 17% 3% - - -
164 FONG Ellis 9% 35% 37% 15% 2% - -
166 DAVIS Elisabeth 55% 37% 7% 1% - - -
167 NGUYEN Ella 34% 42% 19% 4% - - -
168 POON Desiree 14% 37% 33% 13% 2% - -
169 WONG HIN SANG Annabelle 35% 43% 18% 3% - - -
170 ESTRADA Ariana 32% 42% 20% 4% - - -
171 JAMES Ashley 52% 40% 8% 1% - - -
172 WANG Jessie 4% 19% 34% 28% 12% 2% -
173 LIANG Jingjing 38% 41% 17% 3% - - -
174 WALLER DEL VALLE Alanis 42% 40% 15% 3% - - -
174 FREEMAN Kate 5% 29% 43% 19% 3% - -
174 CHUNG Penelope 26% 40% 25% 7% 1% - -
177 BLANCO Ariia 3% 39% 40% 15% 3% - -
178 CHIRASHNYA Mika 9% 29% 37% 20% 5% - -
179 FAN Elizabeth 2% 13% 32% 34% 16% 3% -
180 NGUYEN Jolie T. 2% 12% 32% 35% 16% 3% -
181 BEAVER Ava < 1% 2% 12% 30% 35% 18% 3%
181 VARAN Kateryna 35% 43% 18% 3% - - -
183 DONG Ava 3% 19% 36% 29% 11% 2% -
184 TRAN Helena 2% 14% 32% 33% 15% 2% -
184 LI Azalea 2% 16% 33% 31% 14% 3% -
186 NELSON Grace E. 9% 30% 37% 19% 4% - -
187 LY Hannah 63% 31% 5% - - -
188 MISHIMA Audrey 7% 29% 37% 21% 5% 1% -
189 MEYER Rebecca 58% 34% 7% 1% - -
190 YOUSSEF Caroline 50% 39% 10% 1% - - -
191 PANG Ashley 27% 45% 23% 5% - - -
192 DAVIS Violet 76% 22% 2% - - - -
192 PYE Emily 53% 38% 8% 1% - - -
194 ZOU You yang 85% 14% 1% - - - -
195 TURIANO Nascharene 13% 34% 34% 16% 3% - -
195 WALTER Anna 34% 42% 19% 4% - - -
197 CHANG chaehee 42% 42% 14% 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.