January NAC

Div I Women's Épée

Saturday, January 8, 2022 at 12:00 PM

San Jose, CA, 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 HOLMES Katharine (Kat) W. - - - - 2% 19% 80%
2 LEONARD Ariane - - 4% 16% 35% 34% 11%
3 WASHINGTON Isis - - - 4% 20% 42% 33%
3 KETKAR Ketki - - 1% 7% 24% 41% 27%
5 LIU Charlene (Kai) - - - 1% 9% 36% 53%
6 LIN Jessica Y. - - - - 4% 28% 67%
7 GUZZI VINCENTI Margherita A. - - 1% 6% 22% 42% 29%
8 BASSA Francesca A. - - - 3% 18% 44% 34%
9 ISERT Sarah 1% 7% 23% 35% 26% 8% 1%
10 KOMAR Sofia - - 3% 15% 36% 38% 8%
11 O'DONNELL Amanda A. - 1% 4% 16% 32% 33% 14%
12 VAN BRUMMEN Anna C. - - - 1% 7% 36% 57%
13 NING Emma 1% 8% 23% 34% 25% 9% 1%
14 KHAMIS Yasmine A. - - 3% 15% 34% 36% 12%
15 LIN Katie Y. - 2% 13% 31% 35% 16% 2%
16 WANG Elizabeth - 3% 14% 29% 32% 18% 4%
17 MEHROTRA Anya 3% 15% 31% 31% 16% 4% < 1%
18 GRADY Miriam A. - - - 4% 17% 40% 39%
19 HUSISIAN Hadley N. - - - 1% 8% 35% 55%
20 JOYCE Michaela - - - 2% 15% 41% 41%
21 GAVRIELKO Nicole - - 1% 7% 33% 46% 12%
22 CHIN Isabella - 1% 4% 18% 37% 32% 8%
22 XIAO Ruien 1% 9% 30% 39% 19% 3% -
24 FALLON Kyle R. 1% 5% 19% 32% 29% 13% 2%
25 WADE-CURRIE Ava S. - 1% 9% 26% 38% 23% 3%
26 GU Sarah - 2% 10% 26% 34% 22% 6%
27 PARK Faith K. - - 1% 11% 34% 40% 13%
28 HU Grace - 2% 10% 25% 34% 23% 6%
29 KUZNETSOV Victoria - 4% 17% 33% 31% 13% 2%
30 MACHULSKY Leehi - 1% 6% 23% 42% 28%
31 NGUYEN Tallulah 6% 24% 36% 25% 8% 1% -
32 WATRALL Christina - 2% 10% 25% 34% 23% 6%
33 TOBY Natalia R. - - 2% 10% 28% 40% 20%
34 RAUSCH Ariana (Ari) M. - 1% 6% 22% 37% 28% 5%
35 WANG Karen - 1% 5% 17% 34% 33% 11%
36 DROVETSKY Alexandra M. - - 4% 17% 34% 32% 12%
37 ZIGALO Elizabeth 2% 15% 33% 32% 15% 3% -
38 CHU Audrey 1% 5% 20% 33% 28% 11% 2%
39 CEBULA Anne - - 2% 12% 30% 38% 17%
40 NIXON Catherine (Kasia) D. - - - 1% 7% 34% 58%
41 CHORNIY Kateryna - - 1% 8% 24% 40% 26%
42 MCLANE Lauren - 2% 12% 30% 35% 17% 3%
43 PIRKOWSKI Amanda L. - - 3% 15% 32% 35% 15%
44 XUAN Nicole J. 1% 7% 25% 37% 24% 6% -
45 BEI Karen - 1% 6% 23% 40% 27% 2%
46 MAO Amy 2% 10% 26% 33% 22% 7% 1%
47 NI Emma 1% 6% 21% 34% 28% 10% 1%
48 OXENREIDER Tierna A. - 1% 9% 27% 41% 22%
49 PATURU Meghana - 4% 18% 36% 32% 10%
50 GAJJALA Sharika R. 1% 8% 26% 37% 22% 5%
51 LIU Christina A. 2% 13% 32% 34% 16% 3%
52 LEE Michelle J. - 4% 18% 36% 32% 9%
53 WEBER Nora 1% 11% 29% 35% 19% 4% -
54 LUO Ashley 1% 9% 25% 34% 23% 7% 1%
55 MILLETTE Marie Frederique - 1% 8% 25% 37% 25% 4%
56 TEMIRYAEV Anna M. 5% 22% 35% 27% 10% 2% -
56 RUEDA Rylie 2% 14% 32% 34% 16% 3% -
58 SEBASTIAN Felicity A. 1% 6% 21% 34% 27% 11% 2%
58 LEE Sumin - 1% 8% 24% 35% 25% 7%
60 DOUGLAS Elizabeth (Liz) L. 3% 16% 33% 32% 14% 2% -
61 LU Junyao 1% 6% 22% 37% 27% 7% -
62 DESAI Meera P. 4% 20% 33% 28% 12% 2% -
63 HAMILTON Pauline S. - 3% 16% 33% 32% 14% 2%
64 SZEWC Alexandra 10% 30% 34% 19% 6% 1% -
65 BEZUGLAYA Varvara 20% 40% 29% 10% 1% -
66 WU Amelia - 3% 12% 27% 33% 20% 5%
67 LEUNG Natalie 1% 8% 24% 34% 24% 8% 1%
68 GAO Judy 13% 33% 32% 17% 5% 1% -
69 WANG Nora 2% 15% 36% 34% 12% 2% -
70 ZHANG Victoria R. 7% 26% 36% 23% 7% 1% -
71 PARKER Allegra H. 1% 11% 30% 35% 19% 4% -
72 LIN Waiyuk 11% 33% 35% 17% 4% -
73 SWEET Ryleigh E. 34% 41% 19% 5% 1% - -
73 ZUHARS Renee A. 2% 12% 30% 34% 18% 4% -
75 RATH Lauren N. 24% 40% 26% 8% 1% - -
76 CAPELLUA Mariasole 7% 25% 35% 24% 8% 1% -
77 BOUDREAU Justine 2% 12% 29% 33% 19% 5% -
78 ZHENG Evelyn 1% 10% 25% 32% 22% 8% 1%
79 CHEN Zhengnan(Janet) - 5% 17% 32% 30% 14% 2%
80 KIM Elizabeth Y. 2% 12% 32% 34% 16% 3% -
81 VERA Kalista R. 4% 21% 38% 28% 8% 1% -
82 DOROSHKEVICH Victoriia 1% 6% 20% 33% 27% 11% 2%
83 LAWSON Marie A. 1% 7% 20% 32% 27% 11% 2%
84 VAN MARION Kirsten C. 1% 8% 25% 35% 23% 7% 1%
85 WELLS Tessa M. 11% 33% 34% 17% 4% 1% -
86 MYERS Jeanelle Christina A. 17% 37% 31% 12% 2% - -
87 SUN Renee R. 3% 16% 33% 32% 14% 2% -
88 WHITTEMORE Lucy K. 2% 13% 29% 32% 18% 5% -
89 CHEN Lefu 2% 15% 33% 33% 15% 2%
90 TYLER Syd - 2% 13% 30% 33% 18% 4%
91 WEISS Talia L. 2% 14% 32% 32% 16% 4% -
91 DING Jiahe (Heidi) - - 3% 16% 40% 33% 8%
93 DONDISCH Sophia 5% 23% 36% 26% 9% 1% -
94 MONTOYA Kimberlee C. - 1% 8% 24% 35% 25% 6%
95 DAMRATOSKI Anna Z. 12% 32% 34% 18% 5% 1% -
96 KAIN Isabel J. 5% 25% 37% 24% 7% 1% -
97 DOUGLAS Julia F. 4% 20% 34% 28% 12% 2% -
98 PAN Michelle 17% 41% 31% 10% 1% - -
99 REID Anousheh 6% 23% 34% 26% 10% 2% -
100 SMOTRITSKY Mia 6% 26% 38% 23% 6% 1% -
101 HAWKINS Laura A. 37% 42% 17% 3% - - -
101 KIM Jayna 35% 41% 19% 4% - - -
103 KETKAR Mallika - 5% 18% 33% 30% 13% 2%
104 RUNIONS Emersyn 6% 24% 35% 24% 9% 2% -
105 GEBALA Natalie Brooke A. - 4% 16% 31% 30% 15% 3%
106 LONADIER Keira 8% 28% 35% 21% 6% 1% -
107 SEMIKIN Julia 2% 11% 28% 33% 20% 6% 1%
107 WONG Alexandra R. 15% 38% 33% 13% 2% - -
109 ZHANG Tina Tianyi - 2% 11% 32% 38% 16% 2%
110 REID Sobia 4% 21% 35% 28% 10% 2% -
110 MING Cynthia 26% 44% 24% 5% - - -
112 FELAND Alexandra 4% 22% 40% 27% 7% 1% -
113 YIN Julia 7% 27% 37% 23% 6% 1%
114 GUMAGAY Erika L. 11% 33% 36% 17% 3% -
115 LEE Natasha 23% 41% 27% 8% 1% -
116 MCCUTCHEN Lauren (Lulu) 3% 17% 31% 30% 15% 4% -
116 SLACKMAN Valerie 20% 39% 29% 10% 2% - -
118 KIM Zoe L. 5% 20% 34% 28% 11% 2% -
119 LEE Olive 6% 24% 36% 25% 9% 1% -
120 SMIK Leonie A. 13% 33% 33% 16% 4% 1% -
121 LAVERY Chloe K. 8% 31% 36% 19% 5% 1% -
122 POLANICHKA Nicole P. - 5% 19% 35% 30% 10% 1%
123 MYERS Helen Sophia A. 11% 30% 33% 19% 6% 1% -
124 SHEN Stephanie 9% 30% 36% 20% 5% 1% -
125 KALE Anika A. 9% 31% 35% 19% 5% 1% -
126 LAN Alice S. 3% 16% 33% 31% 14% 3% -
127 MILLER Veronica 17% 41% 31% 9% 1% - -
128 LEANG Priscilla Y. 2% 13% 29% 31% 18% 5% 1%
128 LI Charlotte 32% 42% 21% 5% 1% - -
130 RASMUSSEN Ashelee 9% 29% 35% 21% 6% 1% -
131 BARBARA Camille 31% 42% 22% 5% 1% - -
132 PEHLIVANI Zara 4% 18% 33% 30% 13% 2% -
133 LI Alisha 29% 44% 22% 5% - - -
134 UYANIK Nerine 2% 13% 31% 33% 17% 4% -
135 ASSADOURIAN Nouneh 26% 42% 24% 6% 1% - -
136 DARANOUVONG Logan 17% 37% 31% 12% 2% - -
137 JOBALIA Ria 1% 8% 25% 36% 24% 6% -
137 HUELLAS-BRUSKIEWICZ Marie-Rose R. 2% 11% 28% 34% 20% 6% 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.