October NAC

Div I Women's Foil

Sunday, October 20, 2019 at 8:00 AM

Kansas City, MO - Kansas City, MO, 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 KIEFER Lee O. - 2% 15% 37% 36% 9%
2 PUSTILNIK Nicole - - - 1% 8% 34% 57%
3 DUBROVICH Jacqueline - - - - 4% 26% 69%
3 BINDER Sylvie A. - - - 1% 9% 38% 52%
5 BLOW Iman (Mani) A. - - - - 2% 21% 77%
6 RHODES Zander - - 1% 4% 19% 41% 34%
7 ZHANG Rachel - 1% 4% 15% 32% 34% 14%
8 MASSIALAS Sabrina C. - - - 1% 9% 35% 55%
9 ROSS Nicole - - - - 6% 30% 64%
10 SCRUGGS Lauren S. - - - - 3% 24% 73%
11 MINARIK Natalie M. - 3% 16% 35% 35% 11%
12 TIEU May L. - - - 5% 22% 44% 28%
13 PARTRIDGE Morgan K. - - 1% 7% 24% 41% 27%
14 TUCKER ALARCON Ariadna C. - 1% 9% 27% 38% 22% 3%
15 CHUSID Renata (Renata Chusid) M. - 1% 6% 24% 39% 25% 4%
16 MARINELLI Ever - - - 4% 20% 43% 32%
17 KOO Haley B. - 1% 5% 20% 36% 29% 8%
18 FANG Sabrina - - 4% 17% 36% 32% 10%
19 ANDREYENKA Hanna - 2% 12% 32% 36% 16% 2%
20 LIAO Madeline M. 1% 5% 18% 33% 30% 12% 1%
21 MASSICK Laine - 10% 30% 36% 19% 4% -
22 LUONG Paige K. - 1% 7% 25% 39% 24% 5%
22 LESLIE Ryanne T. - 1% 9% 24% 35% 24% 6%
24 HAO Alicia - - 3% 14% 34% 36% 13%
25 LEE Allison (Allie) - 4% 17% 34% 31% 12% 2%
26 ZEISS Madison E. - - 1% 7% 25% 42% 25%
27 STAMOS Maria - - 1% 7% 27% 44% 22%
28 LAM Justina - - 1% 7% 24% 41% 26%
29 TANG Louise 2% 19% 39% 30% 9% 1%
30 CONWAY Josephina (JoJo) J. 1% 9% 26% 36% 23% 5%
31 LAMPSON-STIXRUD Dolly C. 3% 18% 34% 29% 12% 2% -
32 FLANAGAN Catherine H. - 2% 11% 29% 38% 19% 1%
33 ELIZONDO Isabelle M. - - 1% 11% 41% 46%
34 AHN Gabriella - 2% 9% 24% 35% 24% 6%
35 WEINTRAUB Maia M. - - - 1% 7% 35% 58%
36 HE Elizabeth W. - - 3% 14% 31% 36% 16%
37 ABAYEVA Sasha 2% 15% 32% 32% 15% 3% -
38 NOTT Adrienne (Adi) M. - - - 4% 20% 42% 34%
39 NOVOSELTSEVA Anna V. - - 3% 14% 32% 37% 14%
40 AHN Isabella 2% 13% 29% 33% 18% 4% -
41 FREEDMAN Miranda W. - 2% 12% 30% 36% 18% 1%
42 ZHENG Ivy 1% 9% 28% 37% 21% 4%
43 HOSONO Rei W. - 1% 10% 29% 37% 19% 3%
44 SERBAN Samantha M. 1% 6% 22% 37% 26% 8% 1%
45 KOO Rachel A. - 4% 17% 32% 32% 14% 1%
46 BREKER Anika - 3% 13% 29% 33% 18% 4%
47 CHENG Evelyn - 1% 11% 33% 38% 15% 1%
48 DESCHNER Stefani K. - - - 2% 12% 40% 47%
49 FERRARI Christina M. - - 2% 13% 37% 39% 9%
50 CHON Samantha 1% 6% 24% 38% 24% 6% -
50 LI Phoebe J. 3% 15% 31% 31% 16% 4% -
52 ZHENG Vivian - 2% 12% 29% 35% 18% 3%
53 MARTOS Sara A. - - 2% 12% 35% 40% 11%
54 CHEN Alyssa J. - 1% 6% 23% 42% 29%
55 YAMAGUCHI Kate M. 7% 29% 37% 21% 5% 1%
56 DEVORE Delphine P. - - - < 1% 4% 26% 70%
57 HUNG Juliana K. - 1% 5% 20% 37% 30% 8%
58 APELIAN Katherine 1% 9% 25% 33% 23% 7% 1%
59 BOLDOR Maria - - - 1% 8% 37% 55%
60 PETROVA Kristina - 2% 11% 29% 36% 19% 3%
61 KIM Rachael 1% 7% 23% 35% 26% 8% -
61 LIAO Lu Jia (Lucy) 5% 24% 37% 25% 8% 1% -
63 PARK Rowan M. 1% 7% 25% 37% 24% 6% -
64 STUTCHBURY Carolina J. 4% 19% 34% 28% 12% 2% -
65 TSAI Xiao-Qing E. - 1% 12% 39% 35% 12% 1%
66 HOOSHI Erica S. - 2% 11% 31% 39% 16% 1%
67 LAFFEY Jessie S. - - - 4% 21% 45% 30%
68 GONG Christina S. 1% 6% 20% 34% 28% 11% 1%
69 CHOI Lenna K. - 3% 13% 32% 36% 16% 1%
70 FILBY Sarah M. - - 2% 12% 33% 38% 15%
71 YANKOVSKIY Anastasia - 2% 10% 28% 36% 20% 4%
72 YAROSHENKO Karina - 1% 10% 33% 42% 14%
73 JING Alexandra 2% 15% 33% 33% 14% 2%
74 MOLHO Sofia 2% 12% 30% 34% 18% 4% -
75 LEE Alina 1% 7% 26% 40% 22% 4% -
75 TALAVERA Daena 1% 7% 24% 36% 25% 6% -
77 TAN Helen - - 1% 9% 28% 40% 21%
78 LEE Paulina 1% 9% 26% 35% 23% 6% -
79 NOVOSELTSEVA Elizabeth (Liza) M. - 4% 17% 33% 31% 13% 2%
80 CHO Sabrina N. - - 2% 10% 30% 39% 19%
81 TAN Elisha - - 3% 16% 37% 36% 8%
82 DAVIA Daniella V. 2% 13% 32% 34% 16% 3% -
83 LOCKE Savannah 4% 20% 34% 28% 11% 2% -
84 CHEN Kelly 8% 29% 36% 20% 5% 1% -
85 MERGES Gretl C. 4% 18% 33% 29% 13% 3% -
86 LEE Annora Y. - 1% 8% 27% 41% 23%
87 HO Brianna W. 6% 23% 35% 26% 9% 1%
88 HERNANDEZ Susanne 1% 7% 25% 36% 25% 6%
89 HE Fenghuan 1% 7% 23% 35% 26% 8% 1%
90 LIN Annie X. - 4% 16% 32% 32% 14% 2%
91 LUKINS Tianji - 2% 14% 34% 35% 12% 1%
92 WANG Karina (Karina Xue Wang) X. - 2% 9% 26% 36% 23% 5%
93 MILLER Naomi E. 14% 43% 32% 10% 1% - -
94 REN Olivia Y. 1% 8% 24% 35% 24% 7% 1%
95 SHALANSKY Julia B. - 4% 16% 33% 33% 13% 1%
96 GALAVOTTI Claire Teresa 6% 23% 35% 25% 9% 1% -
97 ZAROFF Roxanne - 4% 16% 32% 32% 15% 2%
98 KONG Olivia 7% 24% 34% 25% 9% 2% -
99 SHEN Sophia H. 1% 10% 29% 35% 20% 4% -
100 ANDREYENKA Yana 1% 9% 24% 33% 24% 8% 1%
101 COSTELLO Angeline S. 9% 32% 38% 18% 4% - -
102 YU Seneca 10% 32% 36% 17% 4% - -
103 CHO Cameron S. 15% 38% 32% 12% 2% - -
104 REDDY Dhruthi S. 12% 33% 33% 17% 4% 1% -
105 SEAL Julie T. 2% 14% 30% 32% 18% 5% -
105 LI Grace Q. - 2% 10% 28% 37% 20% 4%
107 HORSLEY Asherah - - 2% 12% 30% 38% 17%
108 HE Xiangxin 5% 24% 36% 25% 8% 1% -
109 SEAL Grace (Gracie) C. 7% 30% 39% 20% 4% - -
110 BANBURY Justine - 3% 15% 31% 32% 16% 3%
111 VONA Elena M. 11% 31% 34% 18% 5% 1% -
112 BANIN Alexandra G. 10% 32% 35% 18% 4% 1% -
112 BIASCO Anna - 6% 26% 39% 23% 5% -
114 CAO Arianna L. - 3% 15% 31% 32% 15% 2%
115 HUNT Tarleton Q. - 3% 15% 31% 34% 15% 2%
116 KOENIG Charlotte R. 3% 17% 36% 31% 11% 2% -
117 GOMES Rafaella T. 9% 29% 36% 20% 6% 1% -
118 CHEN Jessie S. 26% 42% 24% 7% 1% - -
119 LAU Sydnee M. 1% 8% 24% 36% 25% 7%
120 QIAN Crystal 2% 17% 39% 32% 9% 1%
121 MCGILLION-MOORE Katie 8% 28% 37% 22% 6% 1%
122 JO Mia C. 16% 36% 32% 13% 3% -
123 GRIFFIN Emma G. - 10% 31% 36% 19% 4% -
124 MAK Tinney - 1% 6% 21% 39% 29% 4%
125 PEVZNER Victoria 1% 6% 21% 35% 28% 10% 1%
126 RASO Sofia G. 6% 22% 34% 26% 11% 2% -
127 FERRETTI Anna Rebecca 5% 22% 35% 27% 10% 1% -
128 KOKES Gabrielle 1% 6% 21% 35% 28% 9% 1%
129 LEE Brianna J. 1% 6% 20% 34% 29% 11% 1%
130 LEE Yejine - 1% 6% 21% 36% 29% 8%
131 VEERKAMP Molly 30% 44% 21% 4% - - -
132 CROMPTON Celia N. 3% 16% 31% 31% 15% 4% -
132 MING YUE 36% 43% 18% 3% - - -
134 WU Catherine 6% 23% 35% 25% 9% 1% -
135 CHO Gracie L. 3% 15% 32% 32% 15% 3% -
136 CHEN Cynthia 6% 24% 36% 24% 8% 1% -
137 GUERRA Sofia E. 3% 18% 34% 30% 12% 2% -
138 GUO Kaitlyn S. 11% 35% 36% 15% 3% - -
139 MOON Elina C. 6% 23% 34% 25% 9% 2% -
139 CHEN Jia P. 1% 13% 35% 35% 14% 2% -
141 CEPERO Rosabel 4% 18% 33% 29% 13% 2% -
142 LUO ZIWEN 6% 24% 37% 25% 8% 1% -
143 KOROL Dana 22% 42% 27% 8% 1% - -
144 KIM Elisabeth (Gracie) 3% 16% 35% 33% 12% 1% -
145 KNIGHT Skylar 2% 13% 32% 34% 16% 3%
146 KONG Chin-Yi 10% 31% 36% 19% 4% -
147 CHEN Nicole Y. 4% 27% 41% 23% 5% -
148 WALKER Mayah J. 32% 42% 21% 5% 1% -
149 LEVI Lea 1% 10% 29% 36% 19% 4% -
150 BHANOT Gayatri 2% 13% 29% 32% 18% 5% 1%
151 BANIN Isabelle J. 1% 18% 37% 30% 12% 2% -
152 RENTON Samantha 16% 36% 31% 13% 3% - -
153 ALFONSO Czarina M. 1% 8% 27% 38% 22% 5% -
154 JEWETT Jamie 17% 36% 30% 13% 3% - -
155 ALTEN Ayaka 12% 33% 34% 16% 4% - -
156 ATLURI Srija 13% 34% 33% 16% 4% - -
157 DE LA CRUZ Alyssa 16% 36% 32% 13% 3% - -
158 PROCOPIO Lucia 14% 34% 32% 15% 4% - -
159 MASUI Konami W. 7% 25% 34% 23% 8% 1% -
160 LEE Ariana 34% 43% 19% 4% - - -
161 WONG Allison M. 14% 35% 33% 15% 3% - -
162 SADAN Jordan E. 5% 20% 34% 28% 11% 2% -
163 LIN Ashley 6% 28% 39% 21% 5% - -
164 XU Christine 8% 28% 36% 21% 6% 1% -
165 SABATINI Isabella Ravenne 3% 17% 32% 30% 15% 4% -
165 TURNER Stephanie E. 81% 17% 1% - - - -
165 BATRA Chaahat 29% 41% 23% 6% 1% - -
168 YE Eileen 13% 35% 34% 14% 3% - -
169 KOROL Neta 13% 38% 34% 12% 2% - -
170 CHENG Lydia A. 4% 19% 35% 28% 11% 2% -
171 YUGOV Elizabeth (Liz Yugov) 14% 36% 33% 14% 3% -
172 DEBACK Greta I. 16% 38% 32% 12% 2% - -
173 LEE Jessica Doyun 48% 40% 11% 1% - - -
174 PRIETO Sofia M. 23% 40% 27% 8% 1% - -
175 SHIH Diane 22% 41% 27% 9% 1% - -
176 LOW Sharon J. 5% 24% 38% 25% 8% 1% -
177 SIMONTOV Sofia M. 19% 39% 29% 11% 2% - -
177 NEWHARD Zelia K. 20% 42% 28% 8% 1% - -
179 FANG Serena 54% 36% 9% 1% - -
179 SHI Haoqing 4% 21% 37% 28% 9% 1%
181 CHON Sydney 26% 41% 25% 7% 1% - -
182 KIM Lauren 14% 40% 34% 10% 1% - -
182 LUU Shanon K. 28% 43% 23% 5% 1% - -
184 SCHATZ Kristina J. 19% 39% 30% 11% 2% - -
185 CHO Taylor S. 3% 20% 37% 28% 9% 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.