The future of US Fencing is at stake!

For transparency, fairness, and athlete support, VOTE NOW for:
(1) Maria Panyi, (2) Andrey Geva, (3) Igor Chirashnya, and (4) Sue Moheb.

Take Action

November NAC

Junior Women's Foil

Saturday, November 10, 2018 at 2:00 PM

Kansas City, MO - Kansas City, MO, USA

Probability density of pool victories

Reset

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 TIEU May L. - - 1% 6% 30% 55% 8%
2 DEVORE Delphine P. - - - 2% 15% 43% 40%
3 BINDER Sylvie A. - - - - 5% 28% 67%
3 DESCHNER Stefani K. - 1% 5% 20% 35% 30% 9%
5 SCRUGGS Lauren S. - - - 2% 31% 66%
6 MARINELLI Ever - - - 3% 16% 41% 40%
7 NOVOSELTSEVA Anna V. - - 1% 8% 27% 41% 23%
8 TAN Helen - - - 4% 21% 43% 32%
9 RHODES Zander - - - - 1% 13% 86%
10 GUO Jessica Zi Jia - - - - 5% 28% 67%
11 FERRARI Christina M. - - 2% 17% 56% 26%
12 ZHANG Rachel - 2% 9% 26% 36% 22% 5%
13 NISHIMURA Madeleine A. - - 1% 8% 34% 47% 11%
14 AHN Gabriella 1% 7% 23% 34% 25% 9% 1%
15 PARK Alexandra D. - 1% 6% 23% 36% 27% 7%
16 LUKINS Tianji - 1% 7% 23% 36% 27% 8%
17 PUSTILNIK Nicole - - - - 1% 16% 82%
18 HAO Grace - - 3% 16% 37% 36% 8%
19 CHUSID Renata (Renata Chusid) M. - - - 1% 10% 44% 45%
19 KOO Haley B. - - 1% 6% 25% 43% 25%
21 YANKOVSKIY Anastasia - - - - 7% 36% 57%
22 WEINTRAUB Maia M. - - - 2% 13% 42% 44%
23 FANG Sabrina - - 4% 18% 36% 32% 10%
24 CHO Sabrina N. - - - 5% 28% 54% 13%
25 TAN Elisha - - - 4% 27% 50% 18%
26 CAULFIELD Jane H. - - 2% 14% 38% 39% 7%
27 LUONG Paige K. - - 1% 5% 21% 43% 30%
28 ELIZONDO Isabelle M. - 5% 21% 34% 27% 11% 2%
29 HUNG Juliana K. - - - 1% 11% 38% 50%
30 HORSLEY Asherah - - 2% 13% 33% 37% 14%
31 HAO Alicia - - - 2% 15% 41% 42%
32 BREKER Anika - 1% 9% 29% 39% 21% 1%
33 WANG Karina (Karina Xue Wang) X. - - - 1% 6% 32% 61%
34 ABAYEVA Sasha - 1% 10% 30% 39% 18% 2%
35 TANG Louise - 3% 15% 33% 34% 14% 1%
35 SHALANSKY Julia B. - 1% 8% 28% 40% 21% 3%
37 BIASCO Anna - 1% 6% 24% 40% 24% 5%
38 LIAO Madeline M. - 2% 12% 32% 36% 16% 3%
38 KASI Anisha - - 1% 6% 25% 46% 22%
38 CHOI Lenna K. - - 2% 13% 32% 37% 16%
41 ZHOU Emme - - 3% 12% 30% 37% 18%
41 LEE Annora Y. - - 4% 18% 37% 31% 9%
41 PARK Rowan M. 1% 7% 25% 38% 24% 6% -
44 YAROSHENKO Karina - - - 2% 17% 47% 33%
45 KOKES Gabrielle - 4% 17% 34% 33% 12%
46 THOMPSON Julia T. - 2% 15% 39% 34% 9%
47 BHANOT Gayatri - - 2% 17% 61% 21%
48 MAK Tinney - - - 6% 39% 55%
49 TALAVERA Daena - - - 6% 32% 47% 14%
50 LEE Morgan A. - - - 3% 16% 41% 40%
51 LEE Yejine - 1% 7% 25% 37% 24% 6%
52 KNIGHT Skylar - - - 1% 8% 34% 57%
53 PROESTAKIS Katina A. - - - 5% 22% 45% 28%
54 STAMOS Maria - - - 4% 20% 44% 31%
56 NOVOSELTSEVA Elizabeth (Liza) M. - - 1% 5% 22% 44% 28%
57 LAM Justina - - - 1% 14% 54% 31%
58 QIAN Crystal - 2% 11% 34% 36% 15% 2%
59 KIM Rachael - 3% 13% 29% 34% 18% 4%
60 KALOPER Sofia 7% 27% 37% 22% 6% 1% -
61 UYANIK Nerine 3% 21% 38% 28% 9% 1% -
62 LI Grace Q. - 3% 16% 36% 34% 10% 1%
63 HE Xiangxin 16% 43% 33% 8% 1% -
64 ALFONSO Czarina M. 5% 25% 42% 25% 3% -
65 DU Katie J. - - 1% 12% 49% 33% 6%
66 JING Alexandra - - 4% 20% 40% 30% 7%
67 LEVI Lea - 2% 14% 32% 33% 16% 3%
68 HARFELD Skylar - 1% 7% 22% 35% 27% 8%
69 ANDREYENKA Hanna 1% 18% 50% 26% 5% < 1% -
71 MARTOS Sara A. - - - 1% 12% 42% 44%
72 RASO Sofia G. - 3% 18% 37% 32% 10% -
73 FLANAGAN Catherine H. - 1% 7% 28% 43% 21%
74 ZHENG Vivian - - 1% 10% 32% 40% 16%
75 FREEDMAN Miranda W. - - 2% 14% 35% 36% 12%
76 IBEN Claire L. - 5% 26% 39% 23% 5% -
77 THOMAS Aaria S. 8% 33% 40% 17% 3% - -
78 CAO Ying 1% 9% 25% 33% 23% 8% 1%
79 MAROTTA Veronica L. 1% 12% 36% 37% 12% 1% -
80 LIU Cynthia 1% 10% 34% 38% 15% 2% -
81 BARNETTE Zoe - - 4% 16% 35% 34% 11%
82 LO Ashley - - 2% 11% 34% 41% 13%
83 ZHENG Ivy - - 4% 36% 42% 16% 2%
84 HE Elizabeth W. - 3% 14% 29% 32% 17% 3%
85 NECHANICKY Mackensie J. - 4% 23% 38% 26% 7% 1%
86 CAO Arianna L. - 1% 10% 28% 38% 21% 2%
87 HOLLE Aviella S. 3% 15% 32% 32% 15% 3% -
88 KONG Olivia 1% 9% 28% 37% 20% 4% -
88 BOODELL Ella 1% 14% 35% 34% 13% 2% -
90 HUNT Tarleton Q. 1% 8% 26% 37% 23% 5%
91 ZUZULO Isabella E. - 3% 16% 34% 34% 13%
92 MCDONALD Alexandra 14% 41% 35% 8% 1% -
93 YAMAGUCHI Kate M. 1% 7% 24% 35% 24% 8% 1%
94 MANDOUR Sophia M. - 1% 11% 36% 39% 12% 1%
94 APELIAN Katherine - 1% 6% 24% 40% 25% 4%
96 CHENG Evelyn - 4% 21% 38% 29% 8% -
96 CHEN Kelly 1% 15% 34% 32% 14% 3% -
98 KOO Rachel A. - - - < 1% 5% 31% 63%
99 CONWAY Josephina (JoJo) J. - 1% 9% 29% 38% 20% 3%
99 LESLIE Ryanne T. - - 4% 16% 33% 34% 14%
101 GOMES Rafaella T. - 1% 11% 36% 41% 10% 1%
101 SOOD Ishani S. 1% 8% 24% 34% 24% 8% 1%
103 CROMPTON Celia N. 1% 6% 22% 37% 27% 7% -
104 GAYDOS Sofia C. - 1% 7% 32% 44% 16% 1%
105 ZAROFF Roxanne - 1% 6% 22% 36% 27% 7%
106 JING Emily 3% 15% 31% 31% 16% 4% -
107 VONA Elena M. 15% 40% 33% 10% 1% - -
108 LEE Alina - 4% 18% 36% 32% 11% 1%
108 LAU Chloe M. 1% 13% 34% 36% 14% 2% -
108 LUO ZIWEN 3% 18% 36% 30% 11% 2% -
111 SHEN Jasmine N. - 1% 6% 24% 41% 26% 3%
112 SCHATZ Kristina J. 1% 12% 36% 35% 14% 2% -
113 MASSICK Laine 1% 9% 27% 36% 22% 5% -
114 BALOT Corinne - - 2% 23% 44% 26% 5%
115 CHEN Nicole Y. 6% 25% 38% 24% 6% 1% -
115 CASTANEDA Erika L. - 5% 20% 36% 29% 9% 1%
117 YU Seneca 18% 40% 31% 10% 1% - -
118 SADAN Jordan E. - 7% 31% 37% 19% 4% -
119 HOOSHI Erica S. - - 5% 27% 45% 22%
120 WALKER Mayah J. 8% 27% 36% 22% 7% 1%
121 TRAN Ava D. 3% 22% 44% 28% 3% -
122 MERGES Gretl C. 1% 12% 33% 35% 15% 2% -
123 TUCKER ALARCON Ariadna C. - 1% 8% 24% 37% 25% 5%
124 FANG Serena 4% 21% 41% 28% 5% - -
125 LAU Sydnee M. 1% 7% 26% 37% 23% 6% 1%
126 CHON Sydney 27% 44% 24% 5% - -
127 FERNANDES Thea 7% 27% 37% 22% 6% 1%
128 YEH Samantha - 1% 7% 25% 41% 22% 4%
129 LEE Allison (Allie) - - 2% 10% 30% 40% 18%
130 WU Catherine 2% 12% 30% 33% 18% 4% -
131 MCGILLION-MOORE Katie - - 2% 12% 33% 38% 16%
132 GRIFFIN Emma G. - - 4% 18% 37% 32% 9%
133 XU Marie-Anne J. 39% 41% 16% 3% < 1% - -
134 MOLHO Sofia 9% 28% 34% 21% 7% 1% -
135 ZHANG Sylvia 2% 14% 32% 32% 16% 4% -
136 ATLURI Srija 5% 22% 35% 26% 10% 2% -
137 LIN Annie X. 2% 13% 31% 34% 17% 4% -
138 OUTHRED Maya E. 1% 9% 27% 36% 22% 5% -
139 GALAVOTTI Claire Teresa 2% 35% 42% 18% 3% - -
140 PETROVA Kristina - 1% 6% 26% 42% 22% 3%
141 PAPADAKIS Lily 1% 7% 25% 40% 23% 5% -
142 ALTEN Ayaka - 6% 25% 37% 25% 7% -
143 AHN Isabella 4% 23% 37% 26% 9% 1% -
144 SU Alysa J. 3% 38% 51% 7% - - -
145 LIAO Lu Jia (Lucy) - 4% 19% 38% 31% 8% 1%
145 HO Brianna W. 1% 10% 28% 36% 20% 4% -
147 CHO Taylor S. - 9% 30% 37% 20% 4% -
148 SUH Kailey E. - 17% 38% 31% 11% 2% -
149 SHITAMOTO Audrey F. 15% 36% 32% 14% 3% - -
150 YE Eileen - 1% 8% 24% 37% 25% 4%
151 HECKMANN Emma 22% 43% 28% 6% - -
152 VEERKAMP Molly 15% 36% 32% 14% 3% -
153 GONG Christina S. - 13% 35% 34% 15% 3% -
154 LEVERMANN Lexa L. 7% 32% 38% 19% 4% - -
155 KOENIG Charlotte R. - 1% 10% 33% 37% 16% 3%
156 TONG Ophelia 2% 10% 27% 34% 21% 6% 1%
157 LI Phoebe J. - 5% 22% 39% 26% 6% -
158 MORAN Emma 17% 39% 31% 11% 2% - -
159 LIM Rachel 5% 29% 41% 21% 4% - -
160 CHENG Lydia A. 5% 21% 33% 27% 12% 3% -
161 KIM Lauren C 2% 11% 28% 34% 20% 5% -
162 UPTON Elizabeth 1% 8% 25% 34% 23% 8% 1%
162 REN Olivia Y. - 1% 7% 24% 39% 26% 3%
164 SHANG Andrea 8% 28% 36% 22% 6% 1% -
165 LEE Brianna J. - 3% 13% 31% 34% 16% 2%
166 KIM Elisabeth (Gracie) 2% 16% 36% 33% 12% 2% -
167 YUGOV Elizabeth (Liz Yugov) 1% 11% 30% 35% 18% 4% -
168 SABATINI Isabella Ravenne 9% 31% 37% 19% 4% - -
168 SHEN Sophia H. 1% 13% 33% 34% 16% 3% -
170 WU Renee 1% 10% 29% 36% 19% 4% -
171 VOHRA Anusha 40% 41% 16% 3% - - -
172 YHIP Mikaela M. - 3% 18% 38% 30% 10% 1%
173 KIM Alyssa 4% 24% 40% 25% 6% 1% -
174 SEAL Grace (Gracie) C. 2% 13% 36% 35% 12% 1% -
174 RENTON Samantha 44% 41% 13% 2% - - -
174 GUERRA Sofia E. 2% 12% 27% 33% 20% 6% -
177 XU Christine 2% 12% 31% 35% 17% 3% -
178 ROSBERGER Jessica 3% 20% 37% 29% 10% 1% -
179 DU Hannah 30% 42% 22% 6% 1% - -
179 KOROL Dana 4% 22% 39% 27% 8% 1% -
181 SONG ShuXuan (Liliya) 19% 41% 30% 8% 1% - -
182 MILLER Veronica 27% 42% 24% 6% 1% - -
183 CHON Samantha 1% 9% 35% 46% 9% -
184 WONG Julia M. 25% 45% 25% 4% - -
185 LOCKE Savannah 10% 35% 37% 15% 3% -
186 TALWALKAR Apoorva 15% 39% 34% 11% 1% -
187 FERRETTI Anna Rebecca - 2% 15% 35% 35% 12% 1%
188 KEESING Liana 8% 42% 36% 12% 2% - -
189 KIM Lauren Hyomin 1% 7% 20% 32% 27% 12% 2%
190 SHAW Kayla M. 6% 28% 37% 22% 6% 1% -
191 LIN Maggie 1% 11% 29% 35% 20% 4% -
192 KIM Hyunchae Y. 47% 39% 12% 2% - - -
193 YU Jaime L. 12% 41% 38% 8% 1% - -
194 MATZ Samantha J. 15% 36% 32% 14% 3% - -
195 LEE Angelina S. 3% 28% 40% 23% 6% 1% -
196 CHENG Fang 7% 31% 39% 19% 4% - -
196 DUAN Konnie 16% 39% 32% 11% 2% - -
198 WU Irene M. 6% 25% 38% 24% 6% 1% -
199 MCKEE Alexandra K. - 2% 16% 35% 32% 13% 2%
199 LEE Paulina 1% 8% 24% 35% 24% 8% 1%
201 HO Rachel E. 28% 46% 22% 4% - - -
202 CHO Gracie L. 25% 42% 25% 6% 1% - -
203 RAMAN Tanvi 2% 17% 43% 30% 7% - -
204 SHIH Diane 7% 49% 34% 9% 1% - -
205 D'ARCY Annie M. 39% 44% 15% 2% - - -
206 LIU Alice M. 2% 12% 30% 35% 18% 3% -
206 HO SEJIN 6% 33% 38% 19% 4% - -
208 LAI Annika 33% 47% 18% 2% - - -
209 CHEN Jessie S. 17% 38% 31% 12% 2% - -
210 TAN Clarisse 1% 17% 42% 31% 8% 1% -
211 KOROL Neta 6% 29% 38% 21% 5% 1% -
212 BOLES Sophia 10% 49% 36% 4% - - -
213 ADAMS KIM Natalie 8% 31% 38% 19% 4% - -
214 NARANG Maya 42% 40% 15% 3% - - -
215 LUNG Katerina 5% 22% 37% 26% 9% 1% -
216 YIN Helen 19% 47% 27% 6% 1% - -
217 WU Kyra 8% 27% 35% 22% 7% 1% -
218 HEISER Anna M. 24% 46% 25% 4% - - -
218 NEWHARD Zelia K. 14% 35% 33% 15% 3% - -
220 KLINE Melissa C. 23% 46% 26% 5% - -
221 PRIETO Sofia M. 73% 25% 2% - - - -
222 BAKER-ROSENBERG Raynor S. 33% 43% 19% 4% - - -
223 LIN Ashley 3% 19% 35% 29% 11% 2% -
224 LIN Joyce 76% 23% 2% - - - -
225 VENFORD Annetta S. 97% 3% - - - - -
225 CONVERSE Madilynn E. 26% 46% 24% 3% - - -
225 MCGOFF Sofia D. 65% 32% 3% - - - -
225 ZHEN Ellen 53% 38% 9% 1% - - -
225 WANG ROBIN 41% 48% 10% 1% - - -
230 SULTANA-HOLE Olivia B. 36% 42% 18% 3% - - -
231 XAGORARIS Madison 31% 44% 21% 4% - - -
231 SIMPSON Lydia Q. 80% 19% 2% - - - -
231 CHENG Gloria 57% 35% 8% 1% - - -
231 HICKS Bronwen 89% 10% - - - - -

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.