March NAC

Y-14 Women's Saber

Sunday, March 3, 2019 at 10:00 AM

Cleveland, OH - Cleveland, OH, 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 SKARBONKIEWICZ Magda - - - - 2% 21% 77%
2 TZOU Alexandra - - 3% 14% 34% 36% 14%
3 SULLIVAN Siobhan R. - - 1% 8% 27% 41% 23%
3 FREEDMAN Janna N. - - - 5% 22% 43% 30%
5 WILLIAMS Chloe C. - - 1% 4% 18% 41% 36%
6 POSSICK Lola P. - - - - 5% 30% 65%
7 WU Erica L. - 1% 11% 33% 39% 15%
8 MOZHAEVA MARIA - 1% 7% 27% 42% 23%
9 FOUR-GARCIA Madison - - 1% 5% 22% 43% 29%
10 OISHI Megumi - - - 3% 16% 41% 40%
11 LI Victoria J. - 1% 6% 22% 39% 28% 4%
12 GREENBAUM Ella K. - - 3% 15% 33% 35% 14%
13 YONG Erika E. - - - 1% 8% 35% 56%
14 ATLURI Sara V. - - 1% 10% 35% 47% 7%
15 SZETO Chloe - - 2% 13% 33% 38% 14%
16 NAZLYMOV Tatiana F. - 1% 6% 28% 51% 15%
17 ANDRES Katherine A. - 2% 11% 29% 35% 20% 4%
18 CARVALHO Isabela A. - - - 4% 32% 63%
19 BENOIT Adelaide L. - 3% 24% 41% 26% 6%
20 OLSEN Natalie J. - 1% 5% 18% 34% 31% 11%
21 HULSEBURG Kaitlyn - - - 4% 19% 41% 35%
22 SHOMAN Miriam - 1% 8% 25% 36% 24% 6%
23 BLUM Leah I. - 1% 5% 21% 37% 29% 8%
24 SHOMAN Jenna - - 1% 8% 27% 41% 23%
25 ALCEBAR Kayla - - 6% 25% 38% 24% 6%
26 VESTEL Mira B. - 1% 8% 26% 36% 23% 5%
27 BALAKUMARAN Maya - 4% 17% 32% 31% 13% 2%
28 GRAFF Sophie - 4% 17% 32% 30% 13% 2%
29 BOIS Adele - 1% 11% 34% 39% 15%
30 PAK Kaitlyn - - 3% 20% 45% 32%
31 CHIOLDI Mina - 3% 15% 32% 33% 15% 2%
32 YANG Ashley M. - - 4% 21% 43% 27% 5%
33 CHEEMA Sophia - - 1% 9% 28% 41% 20%
34 DELSOIN Chelsea C. - - 3% 15% 32% 35% 15%
35 STONE Hava S. - 1% 5% 19% 37% 30% 9%
36 TONG Kunling - - 1% 6% 23% 42% 29%
37 CHANG Josephine S. - - 1% 9% 32% 42% 16%
38 WEI Vivian W. - 1% 6% 24% 39% 25% 4%
39 ROMAGNOLI Isabella - 3% 15% 32% 33% 15% 2%
39 MAREK SOFIA - 4% 18% 33% 30% 13% 2%
41 SCALAMONI-GOLDSTEIN Charlotte S. - 1% 9% 27% 36% 21% 4%
42 KALRA Himani V. - 2% 11% 29% 35% 19% 4%
43 CHIN Sophia J. - 1% 6% 23% 37% 27% 6%
44 ENGELMAN Madeline A. - 1% 5% 21% 37% 29% 8%
45 WIGGERS Susan Q. - - 2% 12% 33% 38% 15%
46 RIZKALA Joanna - 1% 12% 38% 37% 11%
47 MARSEE Samantha - 2% 24% 40% 26% 8% 1%
48 SHEARER Natalie E. - 5% 20% 34% 28% 11% 2%
49 PAUL Lila - - 7% 27% 38% 23% 5%
50 KRASTEV Minna 1% 7% 25% 36% 24% 7% 1%
51 BEVACQUA Aria F. - 1% 8% 27% 39% 22% 3%
52 PRIEUR Lauren 1% 6% 20% 34% 28% 10% 1%
53 MIKA Veronica - 3% 16% 33% 32% 13% 2%
54 ANDRES Charmaine G. 2% 21% 46% 25% 5% -
55 ERIKSON Kira R. - 2% 15% 37% 35% 11%
56 BAKER Audrey C. - 6% 23% 36% 26% 8% 1%
57 MCMAHON Byronie 26% 41% 24% 7% 1% - -
58 HERRERA Fernanda - 2% 12% 29% 34% 19% 4%
59 HWANG Gabriela M. 5% 25% 38% 24% 7% 1% -
60 SOURIMTO Valeria - 1% 7% 23% 36% 26% 7%
61 OBRADOVIC Ana 1% 16% 38% 32% 11% 1%
62 CALLAHAN Chase J. - 1% 9% 25% 35% 23% 6%
63 NG Sarah W. 13% 34% 34% 16% 4% - -
64 SCHIMINOVICH Sophia I. 3% 15% 32% 32% 15% 3% -
65 NEWELL Alexia C. - 1% 6% 19% 34% 30% 10%
66 LIN Angela 1% 7% 24% 34% 24% 8% 1%
67 SADOVA Olga 1% 8% 23% 33% 25% 9% 1%
68 LUKASHENKO Angelina - 5% 25% 40% 25% 5%
69 ARNIPALLI Hamsika 1% 9% 26% 36% 22% 6% -
70 MEYTIN Sophia E. 35% 48% 16% 2% < 1% - -
71 GORMAN Victoria M. - - 2% 10% 29% 40% 19%
72 CHEN Xinyan - 4% 20% 37% 29% 9% 1%
72 PANIGRAHI Emersen 16% 38% 32% 11% 2% - -
74 LIN Selena 48% 39% 11% 1% < 1% - -
75 LI Amanda C. - 1% 10% 35% 40% 13%
76 PLONKA Kaley V. 5% 26% 41% 23% 4% -
77 PATEL Riya - 3% 16% 36% 35% 10% -
78 KRYLOVA Valery 1% 7% 26% 36% 23% 7% 1%
78 KONG Carys H. - 1% 9% 26% 36% 23% 5%
80 NATHANSON Samantha E. 1% 9% 27% 35% 22% 6% 1%
81 SUBRAMANIAN Nitika - 3% 13% 29% 33% 18% 4%
82 MU Vicki Y. 6% 27% 38% 22% 6% 1% -
83 HILD Nisha 3% 19% 36% 29% 11% 2% -
84 LU Amy 5% 23% 38% 26% 7% 1% -
85 NEIBART Fiona 3% 17% 35% 31% 12% 2% -
86 SU Emma 3% 20% 38% 28% 9% 2% -
87 GRINBERG Aliya 3% 19% 40% 31% 7% -
88 YERRAMILLI Kavya 8% 33% 37% 18% 4% - -
89 JOHNSON Lauren - - 4% 20% 37% 30% 9%
90 DUCKETT Madison 15% 37% 32% 13% 2% - -
91 BAKER Amelia M. 16% 36% 31% 13% 3% - -
92 WANG Jianning 3% 18% 38% 30% 10% 1% -
93 HUNG Anna 4% 20% 34% 28% 11% 2% -
94 LU Elaine 1% 8% 27% 36% 22% 6% 1%
95 KER Grace 1% 15% 34% 32% 15% 3% -
96 KIM Marley I. - 2% 12% 29% 34% 19% 4%
96 YUAN Greta - 5% 20% 36% 29% 9% 1%
98 CHEN Grace 6% 26% 36% 23% 8% 1% -
99 CHAPMAN-LAYLAND Astrid M. - 3% 21% 44% 26% 6% -
100 LI Zhishan 6% 24% 36% 25% 8% 1% -
101 BURCH Keona Y. - 1% 7% 23% 36% 26% 7%
102 YANG Angelina 1% 8% 23% 34% 25% 8% 1%
103 TAO Hannah J. - 1% 13% 36% 37% 13%
104 LEE Sophia 3% 29% 41% 22% 5% -
105 SLOBODSKY Sasha L. 9% 45% 36% 9% 1% -
106 ADAMS Morrigan B. 21% 42% 29% 7% 1% -
107 LIU Shengyao 47% 44% 8% 1% - -
108 ELNATAN Mica A. 30% 52% 16% 2% - -
109 FANG Victoria W. - 1% 10% 28% 37% 21% 3%
110 PASHIN Anna 3% 20% 37% 28% 10% 2% -
111 SINHA Anika - 6% 24% 36% 25% 8% 1%
111 XI Shining 1% 6% 21% 34% 28% 10% 1%
111 NYSTROM Sofia C. 9% 28% 34% 21% 7% 1% -
114 SHIH Christina 9% 28% 35% 21% 6% 1% -
115 SIMONIAN Olivia A. 6% 30% 40% 20% 4% - -
116 GOMES Diana C. 6% 24% 35% 25% 9% 2% -
117 DUBOIS Lauren N. - 2% 13% 31% 35% 17% 3%
118 HUANG Sharon 15% 37% 32% 13% 3% - -
119 CHIANG Emily 2% 14% 34% 34% 13% 2% -
120 HOLMES Emma 28% 60% 11% 1% - - -
121 JOHNSON Dagny L. 17% 39% 30% 11% 2% - -
122 NGUYEN Ella 17% 42% 30% 9% 2% - -
123 HUANG Tina 69% 28% 3% - - -
124 JAVERI Amaya 3% 17% 34% 31% 13% 2% -
125 JEAN Olympe G. 19% 42% 30% 8% 1% -
126 PANIGRAHI Kingsley 43% 41% 14% 2% - - -
127 YUN Emma 16% 38% 32% 12% 2% - -
128 TREACY Aisling 33% 42% 20% 5% - - -
129 SHI Cathleen 3% 17% 34% 31% 13% 2% -
130 MUNGOVAN Cecilia C. 15% 42% 32% 10% 1% - -
131 GUTHIKONDA Sunanya 7% 27% 37% 23% 6% 1% -
132 CHAN Chloe 44% 40% 13% 2% - - -
133 WILMORE Claire 76% 22% 2% - - - -
134 YEN Natalie 15% 39% 34% 11% 2% - -
135 DONDERIS Hannah E. 7% 26% 35% 23% 8% 1% -
135 HURST Kennedy 2% 30% 40% 22% 5% 1% -
137 PI Sophia 37% 43% 17% 3% - - -
137 ABAZA mariam 4% 26% 41% 23% 5% - -
139 WU Helen 74% 23% 3% - - -
140 ZENG Xiaoyi 76% 21% 2% - - - -
141 LITVAK-HINENZON Michaela 62% 36% 2% - - - -
142 WANG Zidan 47% 40% 12% 2% - - -
143 LIN Sophia 38% 43% 16% 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.