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.

Junior Olympic National Championships

Cadet Women's Saber

Monday, February 18, 2019 at 8:00 AM

Denver, CO - Denver, CO, 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 AVAKIAN Mikaela - - - - 1% 16% 83%
2 DUNGEY Amelia S. - 2% 11% 29% 36% 19% 3%
3 SECK Chejsa-Kaili F. - - 2% 12% 31% 38% 17%
3 MILLER Sky - - - - 3% 25% 71%
5 LI Anna M. - - 1% 8% 25% 41% 25%
6 MOYA Keona L. - - - - 6% 36% 57%
7 KOVACS Sophia - - 2% 16% 38% 35% 8%
8 LIANG Megan - - 3% 13% 30% 36% 18%
9 DI PERNA Chiara I. - - - 1% 10% 41% 48%
10 SKARBONKIEWICZ Magda - - - 2% 12% 38% 48%
11 KONG Vera - - 1% 10% 29% 40% 21%
12 STRZALKOWSKI Aleksandra (Ola) M. - - - 2% 13% 41% 44%
13 WHANG Rebecca - - 1% 6% 22% 42% 29%
14 KIM Zoe - - 1% 5% 22% 43% 29%
15 HOOGENDOORN Sterre - - 4% 17% 35% 32% 11%
16 DELSOIN Chelsea C. - 3% 16% 35% 32% 12% 1%
17 FAHRI Monir J. - - 1% 12% 36% 38% 13%
18 TONG Kunling - - 1% 8% 25% 41% 25%
19 WILLIAMS Chloe C. - - 1% 8% 31% 46% 14%
19 LEE Alexandra B. - - - 2% 13% 40% 45%
21 GREENBAUM Atara R. - - 1% 8% 25% 40% 26%
22 HARRISON Imogen N. - - - 4% 19% 42% 35%
22 LU Vivian Y. - 1% 6% 23% 37% 27% 7%
24 CAO Stephanie X. - - 2% 13% 33% 37% 15%
24 TZOU Alexandra - 1% 5% 18% 35% 31% 10%
26 HUNTER Nina B. - - 1% 8% 26% 40% 24%
26 POSSICK Lola P. - - - 1% 10% 37% 51%
28 WIGGERS Susan Q. - 1% 8% 27% 39% 22% 4%
29 YONG Erika E. - 2% 11% 27% 34% 21% 5%
30 YURT Leyla - 6% 28% 41% 21% 4% -
31 WU Erica L. - 2% 10% 26% 35% 22% 5%
32 GUTHIKONDA Nithya - - 1% 9% 29% 40% 20%
33 FREEDMAN Janna N. - - 1% 10% 32% 43% 14%
34 FOUR-GARCIA Madison - 1% 8% 25% 36% 24% 6%
35 GOUHIN Chloe - - - 1% 8% 37% 54%
36 HE Charlotte - 3% 13% 29% 34% 19% 4%
37 YANG Kaitlyn H. - - 3% 21% 41% 29% 5%
38 WEINBERG Alexandra L. - 1% 7% 24% 37% 26% 6%
39 LACSON Sarah - 1% 5% 20% 36% 29% 9%
40 CASHMAN Natalie - 1% 11% 34% 38% 15% 1%
41 REDDY Shreya - 1% 8% 26% 36% 23% 5%
42 KOBOZEVA Tamara V. 1% 8% 26% 36% 23% 6% 1%
43 SULLIVAN Siobhan R. - - 1% 8% 30% 45% 16%
44 BEALE Zoe M. - 10% 35% 36% 15% 2% -
45 YUN Maya - 9% 29% 36% 21% 5% 1%
46 CHIN Erika J. - 1% 7% 21% 34% 28% 8%
47 SUNGA Arabella Krystienne M. - 4% 19% 36% 30% 10% -
47 SATHYANATH Kailing - 1% 10% 31% 39% 18% 1%
49 RIZKALA Joanna - 1% 8% 23% 35% 26% 7%
50 CARVALHO Isabela A. - - 2% 12% 35% 38% 13%
51 BENOIT Adelaide L. 1% 22% 44% 26% 7% 1% -
52 GREENBAUM Ella K. - 1% 7% 24% 36% 25% 6%
53 YAP Madeline - 4% 20% 36% 28% 10% 1%
54 ATLURI Sara V. - 5% 21% 36% 28% 9% 1%
55 LIM Isabel K. - 3% 13% 29% 33% 18% 4%
56 OISHI Megumi - - - 5% 24% 44% 27%
57 KIM Catherine - - 3% 18% 37% 32% 10%
58 ENDO Miyuki N. 50% 38% 11% 1% - - -
59 XU Ellen - 3% 15% 32% 33% 15% 3%
60 STONE Hava S. - 1% 9% 29% 37% 20% 4%
61 CHEEMA Sophia - 4% 17% 33% 30% 13% 2%
62 FEARNS Zara A. 1% 8% 37% 37% 14% 2% -
63 CHERNOMORSKY Maria - 2% 10% 26% 35% 22% 5%
64 FERRARI-BRIDGERS Marinella O. 1% 8% 26% 36% 23% 6% -
65 PAK Kaitlyn - - 1% 8% 28% 43% 20%
66 WILLIAMS Jadeyn E. - - - 1% 10% 38% 51%
67 SCHMITT Alana P. 6% 25% 35% 24% 8% 2% -
68 LIU Rachel - 1% 8% 25% 36% 24% 6%
69 SZETO Chloe - 1% 7% 24% 36% 26% 7%
70 BUHAY Rachel T. - - 8% 32% 39% 18% 2%
71 KALRA Siya L. - 2% 13% 30% 33% 18% 4%
72 CHANG Josephine S. - 2% 12% 29% 35% 19% 4%
73 KOBERSTEIN Maggie - 5% 20% 35% 28% 10% 1%
74 KALINICHENKO Alexandra (Sasha) 1% 10% 33% 36% 17% 4% -
75 HOOGENDOORN Levi 2% 13% 31% 33% 17% 4% -
76 ZINNI Kaylyn M. 1% 12% 34% 35% 15% 2% -
77 TANG Catherine H. - 2% 14% 36% 36% 11% 1%
77 GULATI Ria - 3% 15% 31% 32% 16% 3%
79 KALRA Himani V. - - 5% 20% 36% 30% 9%
80 BENTOLILA Yedida 10% 30% 35% 20% 5% 1% -
81 KATZ Anat - 2% 11% 29% 37% 19% 3%
82 OLSEN Natalie J. - 2% 22% 41% 27% 7% -
83 CHIN Sophia J. - 2% 14% 32% 34% 15% 2%
84 SHAY-TANNAS Zoe 2% 15% 34% 33% 13% 2% -
85 SKAGGS Natalie M. 1% 9% 27% 35% 21% 6% 1%
86 BERMAN Stella - 1% 8% 23% 35% 26% 7%
87 ENGELMAN Madeline A. - 1% 9% 27% 37% 22% 3%
88 BHATTACHARJEE Rhea - 6% 23% 37% 26% 7% 1%
89 TANG Annie L. - - 7% 28% 41% 21% 3%
90 YANG Ashley M. - 1% 10% 28% 37% 21% 3%
91 TAO Hannah J. - 3% 17% 35% 31% 12% 2%
92 NAZLYMOV Tatiana F. - 3% 15% 34% 32% 14% 2%
93 CHANG Emily - 1% 17% 39% 32% 10% 1%
94 ZIELINSKI Isabella G. - 4% 18% 36% 31% 9% 1%
95 MOZHAEVA MARIA - 2% 11% 30% 36% 18% 3%
96 KIM Emily 1% 13% 32% 34% 17% 4% -
97 NEWELL Alexia C. - 6% 26% 41% 22% 5% -
98 TUCKER Iman R. 1% 7% 22% 34% 26% 9% 1%
99 CHEN Erica - 7% 27% 37% 23% 6% 1%
100 ANDRES Katherine A. - 3% 24% 40% 26% 7% 1%
101 RHIE Lena 9% 28% 34% 21% 7% 1% -
101 BROWN Emma - 4% 18% 36% 31% 10% -
103 BALMASEDA Sabrina F. 3% 22% 39% 26% 8% 1% -
103 MATAIEV Natalie S. 1% 13% 33% 34% 16% 3% -
103 PATEL Riya 1% 21% 39% 28% 10% 2% -
103 BAE EMMELINE 7% 26% 36% 22% 7% 1% -
107 TIMOFEYEV Nicole - - 3% 17% 36% 34% 10%
107 FAY Zoe A. 5% 28% 38% 22% 6% 1% -
109 KOZAK Sonja A. - 1% 5% 20% 35% 30% 9%
109 BLUM Leah I. - - 4% 20% 37% 30% 9%
109 GEYER Carolina M. 1% 10% 28% 35% 21% 6% 1%
112 HOVERMAN Hannah A. 1% 30% 42% 21% 5% - -
113 ROH Rachel E. - - 2% 11% 30% 39% 19%
114 PRIESTLEY Catherine (Cate) C. - 1% 9% 30% 40% 19% 2%
115 CANNON Sophia E. 1% 7% 23% 34% 25% 8% 1%
116 ZUO Xiaofan 1% 17% 38% 30% 11% 2% -
117 LI Victoria J. - 1% 12% 36% 36% 13% 1%
118 VESTEL Mira B. - 2% 18% 40% 32% 9% 1%
119 MARSEE Samantha 1% 9% 25% 34% 23% 8% 1%
120 WEBER Juliana I. - 1% 9% 25% 37% 23% 4%
121 DARINGA Arianna 2% 18% 36% 30% 12% 2% -
122 BENTOLILA Thalia 38% 44% 16% 2% - - -
123 HURST Kennedy 10% 35% 36% 16% 3% - -
124 D'ORAZIO Isabella - 9% 33% 38% 16% 2% -
125 BURCH Makana Y. - 8% 42% 36% 12% 2% -
126 OXENSTIERNA Carolina 2% 12% 29% 33% 19% 5% -
127 SCALAMONI-GOLDSTEIN Charlotte S. - 1% 10% 28% 36% 21% 4%
128 SCHIKORE Anna M. 8% 31% 36% 19% 5% 1% -
129 KONG Isabel - 5% 21% 36% 28% 9% 1%
130 CODY Alexandra C. - 6% 24% 37% 25% 8% 1%
131 CHAN Audrey - 1% 9% 28% 39% 21% 1%
132 CHEN Xinyan 8% 29% 37% 21% 5% 1% -
133 NOBREGA Carolina S. 3% 15% 31% 31% 16% 4% -
134 LARIMER Katherine E. - 4% 17% 33% 30% 13% 2%
135 FLOREZ Melissa - 5% 21% 35% 28% 9% 1%
135 CALVERT Sarah-Jane E. 3% 17% 33% 31% 14% 3% -
137 ABOUDAHER Janna A. 2% 15% 35% 32% 13% 2% -
138 HU Allison C. 5% 25% 38% 24% 7% 1% -
138 JULIEN Michelle 1% 7% 26% 37% 23% 6% 1%
140 SHIN Andrea Y. 1% 9% 26% 35% 22% 6% 1%
141 NOVICK Mia J. 9% 41% 35% 12% 2% - -
142 HAYES Grace Y. 6% 29% 41% 20% 4% - -
143 DEPEW Charlotte R. 18% 38% 31% 11% 2% - -
144 PETE Gillian C. 8% 37% 36% 15% 3% - -
145 ABDULLAHI Salma 5% 31% 41% 20% 4% - -
146 TODD Peregrine 7% 36% 39% 15% 2% - -
146 HAN Lauren 14% 34% 32% 15% 4% - -
148 TURNOF Kayla M. 3% 16% 32% 31% 15% 3% -
148 XI Shining 1% 12% 31% 34% 17% 4% -
148 SHI Cathleen 3% 21% 38% 28% 9% 1% -
151 MA Grace C. 13% 40% 35% 10% 1% - -
152 FREEDMAN Amelia F. 51% 41% 8% 1% - - -
153 PLONKA Kaley V. 16% 43% 30% 9% 1% - -
154 BATRA Simran 48% 39% 11% 2% - - -
155 ULIBARRI Nevaeh L. 54% 36% 9% 1% - - -
155 WHEELER Kira 13% 65% 20% 2% - - -
155 CHAN Chloe 15% 35% 32% 14% 3% - -
158 BOWDEN Ms Hope A. 37% 50% 11% 1% - - -
159 RANGANATHAN Ruchi 84% 15% 1% - - - -
160 FOX-GITOMER Francesca M. 15% 38% 32% 12% 2% - -
161 DHAR Aamina 5% 42% 39% 13% 2% - -
161 ROSENER Riley 26% 41% 25% 7% 1% - -
163 NEIBART Fiona 6% 33% 38% 18% 4% - -
164 CHU Catherine G. 42% 40% 15% 3% - - -
165 KRYLOVA Valery 2% 16% 37% 33% 12% 2% -
165 TSAI Anna A. 52% 37% 10% 1% - - -
165 CHO Adella 27% 56% 16% 2% - - -
168 ADAMS Morrigan B. 61% 33% 6% - - - -
168 KIM Sujin 13% 36% 34% 14% 3% - -
168 GRAFLUND Ashley L. 25% 41% 26% 8% 1% - -
168 CHAGARES Sarah M. 61% 33% 6% - - - -
168 BLUMSTEIN Alannah 47% 48% 5% - - - -
174 OSTROWSKI Annika 24% 42% 25% 7% 1% - -
175 FAUSTINO Emily 76% 23% - - - - -
175 BHOGAL Sukhleen 90% 9% - - - - -
177 BURTON Sequoia 27% 46% 23% 4% - - -
178 ROSENER Campbell 31% 42% 21% 5% 1% - -
178 DRAEKER Margaret 75% 22% 3% - - - -
180 ROCCA Mary J. 42% 41% 14% 2% - - -
180 BELLANTONI Eva 85% 14% 1% - - - -
182 CHARLES Caitlin 78% 21% 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.