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October NAC

Cadet Women's Foil

Sunday, October 29, 2023 at 8:00 AM

Orange County Convention Center - Orlando, FL, 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 ZHANG Yunjia - - - - 1% 14% 85%
2 JING Emily - - - - - 9% 90%
3 LIU Jaelyn A. - - - - 3% 26% 71%
3 YANG Iris - - - 4% 19% 42% 34%
5 SENIC Adeline - - - - 5% 29% 66%
6 HAYES Nadia - - - 1% 11% 39% 49%
7 CHUSID Mikayla - - - - 2% 19% 80%
8 LUO Ziyue - - 2% 14% 37% 37% 10%
9 GOOR Viviene E. - - 1% 7% 26% 43% 24%
10 SHEN Emilia - 1% 7% 24% 38% 26% 5%
11 EYER Hailey M. - - - - 4% 29% 67%
12 MI Aileen - 2% 14% 34% 36% 13%
13 SEO IRENE Y. - - 3% 19% 44% 34%
14 TAN Kaitlyn N. - - - - 3% 26% 71%
15 CHANG Elizabeth - 2% 15% 34% 34% 14% 2%
16 SHMAY Anastasia 1% 14% 34% 34% 15% 2%
17 FEDELI Caterina S. - - - - 3% 26% 70%
18 SONG Yuqiao Aprille - - 5% 21% 43% 30%
19 CHO Emily (Euran) - - 5% 24% 44% 24% 2%
20 CHO Rebecca H. - - - 2% 14% 40% 43%
21 CALISE Ella - - 1% 7% 31% 46% 15%
22 LIU Joy Zhaoyi - - - 1% 9% 39% 51%
23 SUN Ruoxi - - 1% 8% 27% 41% 23%
24 GU Maggie Runlin - 1% 8% 30% 42% 19%
25 LEE Lavender - - 1% 12% 42% 45%
26 LUO Sandra J. - 2% 14% 36% 37% 11%
27 SHEN Lydia - - - - 5% 34% 60%
28 ROY Layla - 1% 7% 30% 42% 18% 2%
29 CHOW Annabelle - 1% 6% 25% 41% 26% 2%
30 SHENG Chuxi - - 4% 22% 43% 29% 1%
31 WANDJI Anais - - 2% 13% 40% 39% 6%
32 FUNG Emma - - 1% 9% 33% 45% 12%
33 CHEN Allison V. - - - - 6% 35% 59%
34 DAVIS Bonnie Z. - - 1% 8% 32% 46% 13%
35 SHIM Grace J. - 1% 11% 31% 36% 18% 3%
36 GE Yu Ming - - 4% 16% 34% 34% 12%
37 YANG Audrey - 2% 11% 32% 39% 15% 1%
38 LI Sophia M. - 1% 5% 20% 39% 31% 4%
39 CHEN Renee - - 5% 26% 41% 23% 4%
40 SHA Yi Ling 3% 17% 36% 31% 11% 1%
41 WANG Jasmine - 10% 42% 37% 11% 1%
42 YU Jane 1% 19% 42% 29% 8% 1% -
43 PEVZNER Nicole - 1% 16% 39% 34% 9% 1%
44 KO Claire - 3% 20% 43% 28% 6% -
45 DOROSHKEVICH Taisiia - - 1% 11% 40% 41% 7%
46 DAVIES Ellie - 4% 20% 37% 29% 9% 1%
47 ZHOU Catherine - 1% 8% 30% 42% 19% 1%
48 PAULUS Sloane E. - 5% 23% 38% 26% 7% 1%
49 LIN Zhi Tong - 4% 18% 36% 32% 10%
50 SUN Chloe - 2% 20% 44% 29% 6%
51 FIELD Julianna 5% 28% 39% 22% 6% 1%
52 GEBALA Gabrielle Grace A. - - - 2% 14% 42% 43%
53 DAI Zizhuo (Zizi) - 2% 12% 34% 37% 14% 1%
54 COOPER Piper W. - 1% 6% 26% 41% 24% 3%
55 YAN Noelle - 2% 21% 49% 24% 4% -
56 KIM Rachel - - 1% 15% 46% 33% 4%
57 ZHANG Eunice - 5% 19% 36% 30% 9% 1%
58 ROZPEDOWSKI Claire 3% 17% 35% 32% 12% 2% -
59 YURKOVA Mariia - 4% 17% 34% 31% 13% 2%
60 WANG Alison - 5% 30% 42% 20% 3% -
61 SWANSON Alexa - 4% 21% 37% 28% 9% 1%
62 ZHANG Selena 3% 24% 41% 25% 6% -
63 HSU Kaylin - 2% 13% 33% 35% 15% 2%
64 LI Han (Helina) 8% 31% 38% 19% 4% - -
65 CHEN Chloe I. - - 1% 9% 28% 42% 20%
66 SAIFEE Lamya 1% 9% 30% 39% 19% 3%
67 WANG CAROL - 3% 17% 40% 32% 8% -
68 YANG Emma - 2% 14% 35% 36% 13% -
69 ZHANG Ivy 1% 10% 31% 38% 17% 3% -
70 FENG Grace - 2% 13% 31% 34% 16% 3%
71 PARK Lina - 5% 25% 39% 25% 6% -
72 LEVY Avery - 9% 33% 39% 16% 2% -
73 ZHENG Julie - 1% 7% 26% 41% 23% 3%
74 HOBSON Ava - 12% 34% 35% 16% 3% -
75 LAI Sophia - 1% 9% 35% 38% 15% 2%
76 DAVIS Logan - 1% 9% 30% 41% 17% 2%
77 FUNG Vera - 1% 10% 36% 42% 12%
78 LI Eleanor 1% 11% 31% 36% 17% 3%
79 HAFEZ Tahiyah 2% 20% 40% 29% 8% 1%
80 BIODROWICZ Julia - - 2% 14% 38% 39% 7%
81 MEI Sarah - 1% 6% 23% 38% 27% 6%
82 FIELD Elizabeth 2% 14% 34% 35% 14% 2% -
83 LIU Enjia sherry 2% 19% 38% 30% 10% 1% -
84 WANG Yudi 2% 19% 51% 24% 3% - -
85 SUN Emily - 2% 15% 36% 34% 12% 1%
86 OH Ceana - 6% 26% 38% 23% 6% 1%
87 CASTANEDA Keira - 1% 5% 23% 41% 27% 2%
88 HU Felice 4% 20% 36% 28% 10% 2% -
88 LEVESQUE Brielle 11% 36% 36% 14% 2% - -
90 MANIKTALA Prisha - - 4% 18% 38% 32% 7%
91 NISSINOFF Alexandra - - 5% 23% 44% 28%
92 YAO Ada - 4% 19% 40% 30% 7%
93 BEAVER Ava 7% 35% 39% 16% 3% -
94 LEE emily 3% 26% 41% 24% 5% -
95 ORVANANOS Anice - 2% 13% 34% 37% 13%
96 DONG Angela 1% 19% 41% 30% 8% 1% -
97 WANG SIQI - 4% 21% 37% 28% 9% 1%
98 TRACZ Calleigh D. 2% 20% 39% 28% 9% 1% -
99 HAN Crystal - 3% 17% 36% 31% 11% 1%
100 FENG Audrey 27% 41% 24% 7% 1% - -
101 AMORE Victoria - 2% 15% 34% 33% 14% 2%
102 TANG Melody Fujiao 4% 25% 46% 21% 4% - -
102 XIE Su 6% 27% 42% 21% 4% - -
104 SONG Erin 26% 44% 24% 5% - - -
105 RICHARD Clara 8% 31% 39% 19% 4% - -
106 LI Yuhe - 3% 18% 38% 31% 9% 1%
107 BORGES Valeryn - 2% 20% 41% 29% 6% -
108 HAN Ashley 3% 19% 35% 30% 11% 2% -
109 LAWRENCE Nia 25% 56% 17% 2% - - -
110 OJEDA Andrea 1% 7% 28% 39% 21% 4% -
111 RICHARD Dominique 4% 21% 38% 28% 8% 1% -
112 PENG Charlotte 2% 31% 43% 20% 4% - -
113 CHAKRAPANI Ila 46% 44% 9% 1% - - -
114 PAULUS Isabella 20% 41% 29% 9% 1% -
115 MATOS Lívia - 3% 18% 36% 32% 10%
116 LIU Jingyi (Eva) 10% 59% 27% 4% - -
117 SHMUKLER Maria 12% 35% 34% 15% 3% - -
118 WANG Celine S. 2% 28% 41% 23% 6% 1% -
118 PARK Zena 1% 20% 45% 27% 6% - -
120 WANG Sophia 5% 25% 40% 24% 6% - -
121 DESERANNO Seren 33% 44% 19% 4% - - -
122 IQBAL Mariam 25% 49% 23% 3% - - -
123 SONG Jenna 6% 44% 38% 10% 1% - -
124 ZHENG Zoe 3% 33% 42% 18% 3% - -
125 OWENS Elise 4% 38% 39% 16% 3% - -
125 ARMSTRONG Olivia 20% 44% 29% 7% 1% - -
125 LI Xiang (Shining) 54% 37% 8% 1% - - -
128 KIM Sydney 23% 47% 24% 5% - - -
129 CANO Sofia 77% 22% 2% - - - -
130 SHIN Jaelynn 76% 21% 2% - - - -
131 BROWN Lily 59% 34% 7% 1% - -
132 SHERLOCK Gabriella 40% 45% 13% 1% - - -
133 SHTEPA Rada 68% 27% 4% - - -
134 CHENG Isa 38% 45% 15% 2% - - -
135 CHATEL Margot 5% 21% 35% 27% 10% 2% -
136 BABER Eshaal 56% 36% 8% 1% - -
137 PICO DIB Olga Cristina 14% 46% 31% 9% 1% - -
138 DANIELYANTS Gabriela 29% 43% 22% 5% 1% - -
139 MARTIRE Alessandra 17% 41% 32% 10% 1% - -
140 TAN Isabella 29% 52% 17% 2% - - -
141 BAE Yooju 20% 41% 29% 9% 1% -
142 QUINTERO Camila 81% 18% 1% - - -
143 MULLER Van 90% 9% - - - - -
143 CHANG Janelle 56% 39% 5% - - - -
143 HWANG Chanel 32% 45% 20% 3% - - -
143 CHOI ERIN 84% 15% 1% - - - -
147 ORBE-AUSTIN Nia 88% 11% - - - - -
148 BEN YAHMED Farah 76% 22% 2% - - - -
149 WANG DINA C. 35% 43% 19% 4% - - -

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.