Capitol Clash SYC/RCC & Y8

Y-14 Women's Foil

Sunday, January 18, 2026 at 1:30 PM

Gaylord National Resort and Convention Center - National Harbor, MD, 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 LIU Qianchen E. - - - - 3% 27% 70%
2 JIANG Chloe - - - - 3% 26% 70%
3 KRAHE Annika - - - 1% 8% 37% 54%
3 WATSON Evelyn - - - 1% 8% 34% 57%
5 CAO Amelie - - - 1% 10% 43% 47%
6 WANG Amabel - - - - - 10% 89%
7 LI Joy - - - - 4% 25% 71%
8 GE Deanna 1% 7% 24% 37% 25% 6%
9 SIROTA Francis - - - 1% 11% 47% 41%
10 LUO lucy - - 2% 11% 30% 38% 18%
10 ZHU Ella - - - 3% 16% 42% 39%
12 LI Savannah - - - 4% 22% 50% 24%
13 HOROWITZ Shuli - - - 1% 10% 37% 52%
14 XING Melly - - - 4% 20% 44% 32%
15 GONG Hai - - - 1% 14% 51% 34%
16 MILLER Anna 6% 24% 37% 25% 8% 1%
17 IWERSEN Marte - 3% 14% 33% 36% 13%
18 ZHAO LIN EN OLIVIA - - - 3% 28% 69%
19 JOO Sara - - - - 4% 27% 69%
21 GENG Emma - 1% 7% 27% 42% 22% 1%
22 WEN Cynthia - 1% 7% 25% 38% 24% 5%
23 MA Sophia - 3% 20% 39% 29% 9% 1%
24 CHUNG Stella - - 4% 22% 44% 30%
25 PECK Madeleine - 1% 8% 26% 41% 24%
26 NWODO Naila - - 2% 11% 32% 39% 16%
27 HOLLIS Priscillia - 3% 17% 41% 32% 6% -
28 ZHU Audrey - 1% 8% 26% 38% 24% 4%
29 DONG Iris - 2% 14% 38% 38% 8%
30 ZHILKOV Anya - 1% 14% 36% 36% 12%
31 RIVERA Leahy - 1% 7% 24% 42% 27%
32 DZIWULSKI Elisabeth Claire 1% 10% 30% 35% 19% 4% -
33 ORBÉ-AUSTIN Maya - - - 1% 10% 40% 49%
34 DONG Katelyn 2% 20% 41% 29% 7% 1%
35 SHIN Elizabeth - - 1% 11% 39% 49%
36 ZHU Alivia - - 5% 25% 41% 25% 4%
37 TELEB Farida - - 2% 10% 29% 39% 20%
38 KASHUBA Mila - 3% 13% 31% 35% 17% 1%
39 ZHANG Priscilla - 5% 19% 33% 29% 12% 2%
40 JIANG Ziqing - - 3% 21% 44% 28% 3%
41 HAFEZ Sahar - - 2% 11% 33% 42% 13%
42 SMOLICH Emily - 1% 8% 25% 37% 24% 4%
43 SHAOOLIAN Maya - 1% 14% 36% 36% 13%
44 ELLISON Ingrid 6% 30% 41% 20% 3% -
45 KIM Yuna - - 2% 15% 41% 36% 5%
46 ENRIQUEZ Bianca Perla - 1% 9% 25% 35% 23% 6%
47 EPSTEIN Naomi - 2% 13% 37% 38% 10%
48 DING Jennifer 9% 30% 36% 20% 5% 1% -
49 MACKINTOSH Quinn - 2% 15% 36% 34% 12% 1%
50 DUVVA Sanika - - 3% 19% 40% 32% 6%
51 LEO Jenna - 2% 15% 38% 33% 11% 1%
52 KUANG Bella - 1% 7% 26% 42% 23% 1%
53 BALIN Julia - - 7% 26% 39% 23% 5%
54 YOUM Amelia 1% 7% 23% 35% 26% 8% -
55 XU Charlene - 6% 25% 37% 24% 7% 1%
56 HUANG Emma 3% 18% 35% 30% 12% 2%
57 PARK Hannah - 7% 28% 41% 21% 3%
58 WU Gloria - 3% 18% 41% 32% 6% -
59 FENG Claire - 1% 9% 27% 36% 22% 5%
60 KOESTERS Florentine - 4% 17% 34% 33% 11% 1%
60 JOSEPH Hannah - 3% 18% 40% 31% 8%
62 REILLY Carys 1% 8% 26% 37% 23% 4% -
63 BRADSHAW Tamira - 6% 29% 39% 20% 5% -
64 JAZWINSKI Ivy 10% 31% 36% 18% 4% -
65 BO Iris - - 1% 8% 30% 43% 17%
66 BYK Karalina - - 1% 7% 27% 42% 23%
67 SINGH Evangelina - 1% 6% 25% 40% 23% 4%
68 MALIK Manha - - 2% 15% 40% 37% 6%
69 MURPHY Genevieve - 2% 16% 38% 34% 10%
70 CHO Olivia - 2% 18% 39% 32% 9%
71 BELL Blake 17% 38% 32% 12% 2% -
72 KHETPAL Aalia - 1% 11% 34% 39% 14%
73 WANG Joann - - 5% 22% 43% 26% 3%
74 GOITIA Genevieve - 1% 10% 29% 36% 20% 4%
75 SHUI Zola 5% 22% 36% 27% 9% 1% -
76 DINAR Julia 1% 7% 24% 36% 25% 7% 1%
77 BENNETT Emi - 6% 30% 42% 18% 3% -
78 ZHANG Selina - - 5% 22% 41% 29% 4%
79 HO Peyton 1% 5% 21% 37% 29% 8%
80 CHEN bridgette 1% 9% 27% 36% 22% 6% -
81 WANG Allyson 2% 12% 32% 35% 17% 3% -
82 CHAN Kaitlyn 1% 11% 29% 34% 19% 5% 1%
83 ZOLDAN Nolabelle - 1% 9% 27% 36% 22% 5%
84 CHOI Cara - 2% 15% 36% 34% 12% 1%
85 JU Victoria 4% 19% 34% 29% 12% 2% -
86 LIU Doris-Zihan Zhou 6% 26% 37% 23% 7% 1% -
87 KIRBY Emelie 10% 31% 35% 19% 5% - -
88 LI Doreen 1% 11% 29% 35% 19% 4% -
89 PAEK Mila 1% 10% 34% 37% 15% 3% -
89 KRISHNAN Maya 18% 41% 30% 10% 1% - -
91 FEDER Acadia - 1% 6% 22% 38% 27% 6%
92 LIU Sophia 3% 17% 37% 32% 10% 1% -
92 DONG Audrey - 5% 22% 39% 26% 6% -
94 ZHANG Selene T. 6% 28% 42% 21% 4% - -
95 DOUGLAS Addison - 3% 13% 30% 33% 18% 3%
96 HUANG Rosalyn 3% 20% 41% 28% 8% 1%
97 XU-FERGUSON Victoria 1% 10% 33% 41% 16% 1%
98 SUN Erin 5% 29% 41% 21% 3% -
99 KIM Lael 19% 38% 30% 11% 2% -
100 HUANG Gabrielle 9% 30% 37% 20% 5% -
101 KLIVANS Gwyneth 8% 39% 37% 13% 2% - -
102 SARAVANA Supriti 5% 23% 36% 26% 9% 1% -
103 LIU Bella 1% 10% 27% 35% 22% 6% -
104 HOU Xu 9% 29% 36% 20% 6% 1% -
105 WU Charlotte 2% 14% 31% 32% 17% 4% -
106 TANG Clementine 15% 46% 30% 8% 1% - -
107 PHAN Logan - 2% 12% 30% 35% 18% 2%
108 FAROOQI Eliora 8% 31% 38% 19% 4% - -
108 MCCLAIN Madison 7% 25% 37% 24% 7% 1% -
110 SOMAN Indraa 9% 31% 38% 19% 4% - -
111 SILVA Lia 1% 11% 38% 37% 12% 1% -
112 FAN Melody 7% 35% 39% 16% 3% -
113 CHEN Aimee 3% 22% 44% 26% 6% 1% -
114 GONZALEZ Lauren 14% 60% 23% 3% - -
115 HEMPHILL Mia 2% 16% 34% 32% 14% 2% -
116 SHARAIEVSKA Mariia 4% 39% 38% 15% 3% - -
118 FENG Christy 4% 22% 40% 26% 7% 1% -
119 IRIZARRY Nyla 2% 16% 38% 31% 11% 1% -
120 CHAN Hailey 1% 8% 30% 38% 19% 4% -
121 GONG Maggie 44% 40% 14% 2% - - -
121 LIU Doris 45% 41% 12% 1% - -
123 DAVIS Gabriella 63% 33% 4% - - - -
124 ZELDIN Nina 15% 45% 32% 8% 1% -
125 FERRON Sofia 6% 24% 36% 24% 8% 1% -
126 SHAOOLIAN Alexa 7% 26% 37% 23% 6% 1%
127 FANG Sophie 1% 17% 42% 32% 7% - -
128 PAEK Ellie 7% 27% 37% 23% 6% 1% -
129 LI Beatrice 6% 28% 39% 22% 4% - -
130 TEOH Kaylee 47% 43% 10% 1% - - -
131 JIN Emily 12% 45% 33% 8% 1% - -
132 KAGAN Natalie 24% 44% 26% 6% 1% - -
133 ZHU Alicia 25% 44% 25% 6% 1% - -
134 SAKATA Jamie 58% 34% 7% 1% - - -
135 CHEN Emma 16% 37% 32% 13% 2% - -
136 MA Laurie 22% 45% 27% 6% - - -
137 BROWN Katelyn 51% 38% 10% 1% - -
138 HUANG Annabelle 74% 24% 2% - - -
139 LEE Annabel 28% 45% 23% 4% - - -
139 GOEL Saanvi 50% 40% 10% 1% - - -
141 CHEN Rachel 82% 17% 1% - - - -
142 FURLONG Anna 22% 39% 28% 10% 2% - -
142 MA Cloris 30% 43% 22% 5% - - -
142 BENTLEY Amelia 26% 43% 25% 6% 1% - -
145 YAO Emma 5% 25% 37% 24% 8% 1% -
145 LEE Soli 16% 55% 24% 4% - - -
147 ZHOU Jennifer 61% 33% 6% - - -
148 JIANG Arwen 56% 35% 8% 1% - - -
149 CHIN Harper 56% 36% 7% - - - -
149 KRYSKO Zlata 24% 51% 21% 3% - - -

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.