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(1) Maria Panyi, (2) Andrey Geva, (3) Igor Chirashnya, and (4) Sue Moheb.

Division 1/Parafencing National Championships + April NAC

Div I Women's Épée

Friday, April 26, 2024 at 8:00 AM

Salt Palace Convention Center - Salt Lake City, UT, 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 HUSISIAN Hadley N. - - - - 3% 24% 73%
2 JOYCE Michaela - - - 3% 17% 43% 36%
3 GUZZI VINCENTI Margherita A. - - - 1% 8% 35% 56%
3 LEE Sumin - 1% 5% 21% 38% 30% 6%
5 MACHULSKY Leehi - - - 3% 17% 42% 38%
6 HOLMES Katharine (Kat) W. - - - - 1% 16% 83%
7 SEBASTIAN Felicity A. - 5% 22% 39% 27% 6%
8 CHIN Isabella - - 4% 22% 50% 24%
9 NIXON Catherine (Kasia) D. - - - 1% 8% 35% 56%
10 GU Sarah - - - 4% 18% 43% 35%
11 LIU Charlene (Kai) - - - - 4% 27% 68%
12 FALLON Kyle R. - 1% 6% 25% 43% 25%
13 BASSA Francesca A. - - - - 6% 32% 62%
14 LIN Jessica Y. - - - 3% 17% 44% 36%
15 GRADY Miriam A. - - - 3% 16% 43% 38%
16 ZUHARS Renee A. - 1% 5% 19% 38% 32% 6%
17 SPRINGER Sierra - 1% 10% 29% 37% 20% 3%
18 NIXON Caroline (Karolina) L. - - 1% 9% 30% 43% 17%
19 WATRALL Christina - - 2% 12% 34% 39% 13%
20 WASHINGTON Isis - - - - 5% 29% 66%
21 KORFONTA Jolie - 1% 6% 22% 38% 27% 6%
22 TOLSMA Chloe (CJ) - 1% 6% 22% 39% 28% 4%
23 OXENREIDER Tierna A. - - 1% 11% 44% 44%
24 SHIV Avni 3% 20% 42% 29% 6% -
25 ZIGALO Elizabeth - - 2% 10% 30% 39% 19%
26 MILEWSKI Nicole - 3% 15% 33% 34% 14% 1%
27 WANG Karen - - 2% 12% 32% 38% 15%
28 GAJJALA Sharika R. - 1% 6% 29% 49% 15%
29 CHISHOLM Phoebe C. 6% 24% 36% 25% 8% 1% -
30 CARRIER Meredith 8% 33% 37% 18% 4% - -
31 BUSH emma 5% 22% 35% 27% 10% 1% -
32 HAMILTON Pauline S. - 5% 20% 37% 29% 8%
33 BEI Karen - - 2% 12% 33% 39% 13%
34 PADHYE Tanishka 1% 7% 25% 37% 24% 5%
35 HAFEEZ Hania 1% 8% 27% 36% 22% 6% -
36 LEE REGINA - - 3% 14% 34% 35% 13%
37 NEMETH Katherine 3% 18% 35% 30% 11% 2% -
38 SHEFFIELD Lake Mawu - - 1% 5% 22% 43% 29%
39 KETKAR Ketki - - 1% 8% 39% 52%
40 GANDHI Sedna S. - - 2% 12% 33% 39% 14%
41 TOBY Natalia R. - - - 1% 11% 40% 48%
42 CAFASSO Natalya 1% 12% 33% 35% 16% 3% -
43 SWENSON Nikita G. - 4% 17% 34% 31% 12% 2%
44 RAKHOVSKI Alexandra - 3% 14% 33% 34% 15% 2%
45 RATZLAFF Jocelyn T. - - 2% 11% 32% 42% 13%
46 PEHLIVANI Zara - 2% 11% 29% 36% 19% 4%
47 WITTER Catherine A. 2% 13% 38% 37% 10% 1%
48 CHU Audrey - 1% 7% 25% 41% 26%
49 YAO Melinda 2% 14% 36% 37% 11% 1%
50 KIM Zoe L. - 3% 15% 34% 33% 13% 2%
50 LI Fei 3% 19% 36% 30% 11% 2% -
52 CHEN Lefu - 3% 14% 32% 34% 15% 2%
53 LEE Michelle J. - - 4% 17% 39% 36% 4%
54 LEE Scarlett 2% 13% 31% 33% 17% 4% -
55 WANG Jessie 17% 37% 31% 12% 2% - -
56 PROFIS Liora 9% 29% 36% 20% 5% 1% -
57 BARG Daniella 4% 19% 35% 29% 11% 2% -
58 LEE Natasha 1% 6% 21% 35% 28% 8% 1%
59 SONG Angela 1% 11% 31% 36% 18% 4% -
60 PIRKOWSKI Amanda L. - - < 1% 5% 35% 60%
61 GEBALA Natalie Brooke A. - 2% 14% 36% 34% 12% 1%
62 NEELAM Navya 19% 39% 30% 11% 2% - -
63 SMUK Alexandra S. 2% 12% 30% 35% 18% 3% -
64 POTAPENKO Margarita D. 1% 12% 32% 35% 17% 4% -
65 JAKEL Sophia N. - - 5% 25% 50% 19%
66 CHI Chelsea 4% 23% 40% 26% 5% -
67 ALEXANDROV Katherine S. 1% 8% 24% 36% 25% 7% -
68 YILMAZ Pinar 12% 37% 36% 13% 2% - -
69 MEHROTRA Anya - 2% 11% 28% 37% 20% 2%
70 SMUK Daria A. 4% 20% 35% 29% 10% 1% -
71 DAMRATOSKI Anna Z. 1% 8% 24% 35% 24% 7% -
72 YOU Emily 5% 25% 40% 24% 6% -
73 KIM Jayna 5% 25% 37% 24% 7% 1% -
74 KOZLOWSKI Maya M. 3% 18% 34% 30% 12% 2% -
75 REID Anousheh 1% 11% 28% 35% 20% 4% -
76 KETKAR Mallika - 3% 15% 33% 33% 15% 2%
77 LIN Elaine 1% 7% 26% 37% 22% 5% -
78 ZHANG Victoria R. - 3% 13% 30% 35% 17% 2%
78 LEE Olivia 7% 27% 36% 23% 7% 1% -
80 CALDERA Lexi I. 3% 16% 33% 31% 14% 2% -
81 PHUKAN Indra 6% 30% 38% 20% 5% 1% -
82 KOWALSKY Rachel A. - 1% 7% 27% 40% 21% 4%
83 GARIKIPATI Thansi 34% 42% 19% 4% - - -
84 WANG Zoe 5% 26% 40% 24% 5% -
85 XIONG Angelica 10% 32% 36% 18% 4% -
86 WATTANAKIT Anda 7% 27% 37% 22% 6% 1%
87 FURMAN Maria 12% 36% 37% 14% 2% -
88 BEAVER Ava 18% 39% 30% 11% 2% -
89 CAMPBELL Eva 19% 44% 29% 7% 1% -
90 LIN Ariel 19% 40% 29% 10% 2% - -
91 CANNING Charlotte 13% 35% 34% 14% 3% - -
92 MUELLER Emma M. 1% 7% 25% 36% 24% 6% -
93 YANG Alisa 1% 9% 28% 36% 21% 4% -
93 SMOTRITSKY Mia 1% 6% 23% 36% 26% 7% 1%
95 SEREGIN Katya 14% 39% 35% 11% 1% - -
96 MAO Amy - 1% 7% 24% 38% 25% 5%
97 WANG Ziqi (yoyo) 3% 18% 36% 30% 12% 2% -
98 MUN Brianna K. 4% 21% 37% 28% 9% 1% -
98 YAO Yilin 4% 22% 37% 27% 9% 1% -
98 LIANG Jingjing 3% 19% 36% 29% 11% 2% -
101 PECK Maia A. 4% 18% 33% 30% 13% 2% -
102 RUNIONS Emersyn 1% 9% 28% 36% 21% 5% -
103 GUO Luxi 10% 32% 36% 18% 4% - -
104 BASRALIAN Azniv 7% 28% 37% 21% 6% 1% -
105 PRESMAN Aerin 20% 42% 29% 8% 1% -
106 EDWARDS Auprell 10% 34% 37% 16% 2% -
107 MARTINEZ Christina 34% 45% 19% 3% - -
108 CHA Eugenie 16% 40% 33% 10% 1% -
109 FERREIRA DE MELO Adriana 24% 43% 26% 6% 1% -
110 LURYE Sarah - 1% 10% 32% 41% 17%
111 HAFEEZ Hiba 28% 43% 24% 5% - -
112 ZANGA Kaitlyn 18% 41% 32% 9% 1% -
113 ELSTON Sophia 6% 24% 36% 25% 8% 1% -
114 BALAKRISHNAN Trisha 14% 37% 33% 13% 2% - -
115 WANG Elizabeth - 3% 16% 35% 33% 12% 1%
115 NGUYEN Tallulah - 3% 15% 32% 34% 15% 1%
117 GUJJA Misha 15% 35% 32% 15% 3% - -
118 FENG Ge 2% 11% 28% 34% 20% 5% -
118 CHANG Chloe 47% 39% 12% 2% - - -
120 SHARMA Sanvi 35% 43% 18% 3% - - -
121 CHERNIS Zoe C. 14% 36% 33% 13% 3% - -
121 PINNAMANENI Drithi 15% 41% 32% 10% 1% - -
123 CAPELLUA Mariasole - 5% 19% 35% 30% 10% 1%
124 WEBER Nora - 6% 24% 36% 25% 7% 1%
125 SHELIN Chelsea 14% 38% 33% 12% 2% - -
126 IYER Ishana 31% 42% 21% 5% 1% - -
127 AZMEH nour 9% 29% 36% 20% 5% 1% -
128 RANDLEMAN Teresa 10% 30% 35% 19% 5% 1% -
129 MENDOZA zoie 46% 40% 12% 2% - - -
130 LIU Christina A. 1% 6% 24% 37% 25% 7% 1%
131 LONADIER Keira 9% 29% 36% 20% 5% 1% -
132 STERR Isabella M. 30% 43% 21% 5% - - -
133 JAKEL Alysa C. 20% 39% 29% 10% 2% - -
134 HUANG Lanlan 27% 44% 24% 5% - - -
135 FREEMAN Kit 51% 37% 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.