Phoenix Convention Center - Phoenix, AZ, USA
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 | ZHANG XUANYI | - | - | - | - | 1% | 17% | 82% |
2 | CHIARELLI Valentina | - | - | - | - | 3% | 25% | 72% |
3 | KURAEVA Vasilisa | - | - | - | 4% | 19% | 42% | 35% |
3 | GAY Sasha | 1% | 5% | 17% | 31% | 30% | 14% | 3% |
5 | VINOGOROVA Sofiia | - | - | - | 1% | 9% | 36% | 54% |
6 | YOUNG Charlotte G. | - | - | - | 1% | 8% | 34% | 56% |
7 | GONZALEZ Veronika | - | 1% | 5% | 20% | 35% | 30% | 9% |
8 | HSU leah | - | - | 2% | 12% | 30% | 37% | 18% |
9 | AWAD Royce | - | - | 3% | 15% | 33% | 35% | 14% |
10 | KANG Ellie | - | - | - | 3% | 17% | 42% | 37% |
11 | DAMBAL Sasha | - | - | 2% | 18% | 45% | 35% | |
12 | CONG Anne | - | - | 1% | 7% | 25% | 41% | 26% |
13 | LEOU Korina | - | - | 2% | 10% | 28% | 40% | 21% |
14 | GUGALA Hanna | - | 1% | 4% | 16% | 32% | 33% | 14% |
15 | LEI Zitong (Meya) | - | - | 4% | 21% | 43% | 32% | |
16 | SENGUPTA Jia | - | 2% | 14% | 40% | 35% | 9% | |
17 | CHAN Jolene | - | - | 1% | 7% | 24% | 40% | 27% |
17 | PANTALEON-MAZOLA Amari | - | - | 1% | 7% | 25% | 42% | 25% |
19 | WANG Callie | - | - | 1% | 5% | 22% | 42% | 30% |
20 | BUSH Bethany | - | - | 4% | 22% | 46% | 27% | |
21 | WANG JiaQi | - | 3% | 14% | 35% | 36% | 12% | |
22 | VINOKUR Anita | - | 1% | 5% | 18% | 36% | 32% | 9% |
23 | NGUYEN Summer | - | 1% | 9% | 25% | 35% | 24% | 6% |
24 | LIN Elaine | - | - | 4% | 18% | 37% | 33% | 9% |
25 | BAERENWALD Welles | - | - | 5% | 20% | 37% | 30% | 8% |
26 | DAVIDOVA Kira | - | - | 2% | 11% | 30% | 39% | 18% |
27 | FAN Alexandria | - | 2% | 12% | 31% | 36% | 17% | 3% |
28 | HUGHES-WILLIAMS Adelayde | - | - | 5% | 24% | 45% | 26% | |
29 | LIU Hannah | - | 1% | 9% | 34% | 41% | 15% | |
30 | NIU Jessica | 1% | 7% | 22% | 34% | 27% | 9% | 1% |
30 | MAK Kaitlin | - | 10% | 30% | 35% | 20% | 5% | - |
32 | BORGUETA Madison | - | 5% | 20% | 35% | 29% | 10% | 1% |
33 | HU Anna | - | 1% | 5% | 21% | 42% | 32% | |
34 | ZHAI AMY | - | - | 2% | 11% | 30% | 38% | 19% |
35 | LIU kai yin aria | 1% | 5% | 19% | 34% | 28% | 11% | 1% |
36 | TONG Laurie | - | 2% | 12% | 28% | 34% | 19% | 4% |
37 | GAIKWAD Ashmiee | 1% | 8% | 26% | 37% | 23% | 5% | |
38 | CHOI Charlotte | 2% | 15% | 32% | 32% | 15% | 3% | - |
39 | BERRIOS Catalina | - | 1% | 9% | 26% | 36% | 22% | 5% |
40 | SUNG Isabella | - | 3% | 13% | 30% | 33% | 17% | 3% |
41 | XIAÖ Cindy | 9% | 28% | 35% | 21% | 6% | 1% | - |
42 | WANG Jiayi | - | 1% | 6% | 20% | 36% | 29% | 8% |
43 | KWON Ava | - | - | 3% | 14% | 34% | 36% | 13% |
44 | MEYERSON Michelle | - | 4% | 17% | 32% | 30% | 13% | 2% |
44 | KAUL Tara | 1% | 9% | 26% | 34% | 22% | 7% | 1% |
46 | YERENKOVA Ameliia | 1% | 9% | 26% | 35% | 22% | 7% | 1% |
47 | LONG Jessie | 1% | 9% | 24% | 32% | 24% | 9% | 1% |
48 | NANDA Maanika | 1% | 6% | 21% | 35% | 27% | 9% | 1% |
49 | REN Katherine | 1% | 7% | 22% | 34% | 26% | 9% | 1% |
50 | KU Alathea-Joy | 1% | 9% | 28% | 36% | 21% | 4% | |
51 | DHAR Rana | 1% | 9% | 31% | 40% | 18% | 3% | |
52 | JIANG Evelyn | - | 3% | 17% | 34% | 32% | 13% | 1% |
53 | CASTELO Soleil | - | 2% | 12% | 29% | 34% | 19% | 4% |
54 | GONG Joy | - | - | 3% | 15% | 33% | 35% | 14% |
55 | NEMORIN Rei | - | 3% | 12% | 29% | 34% | 19% | 4% |
56 | BROWN Aria | 1% | 10% | 26% | 34% | 22% | 6% | - |
57 | PARK Haylie | 1% | 10% | 30% | 36% | 19% | 4% | - |
58 | MISHEV Lila | - | 3% | 14% | 33% | 34% | 15% | 2% |
59 | WONG Charlene | 7% | 34% | 40% | 17% | 3% | - | |
60 | IANNUZZI Lucy | 1% | 8% | 27% | 36% | 22% | 6% | 1% |
61 | KIM Alexia | 1% | 8% | 26% | 35% | 23% | 7% | 1% |
62 | ILAGAN Ava | - | 4% | 15% | 31% | 32% | 15% | 3% |
63 | FLEEGER Sophia | 18% | 38% | 30% | 11% | 2% | - | - |
64 | SCHMIDT Yehna | 5% | 20% | 33% | 27% | 12% | 3% | - |
65 | BLAKE Anna | - | - | 1% | 7% | 26% | 42% | 24% |
66 | ZHANG Ashley | 1% | 5% | 18% | 33% | 31% | 12% | 1% |
67 | SEBASTIAN Ava | 3% | 15% | 31% | 32% | 16% | 3% | - |
68 | NADKARNI Marisa | 1% | 5% | 19% | 33% | 30% | 11% | 1% |
69 | MACKAY Katherine | 1% | 9% | 27% | 35% | 22% | 6% | - |
70 | YOON Sela | 2% | 13% | 30% | 33% | 18% | 4% | - |
71 | JOHNSON Heaven | - | 10% | 30% | 35% | 20% | 5% | - |
72 | KWON Hannah | 2% | 15% | 35% | 33% | 13% | 2% | |
73 | LEE Irene | - | 2% | 12% | 29% | 34% | 19% | 4% |
74 | HWANG Sophie | 1% | 9% | 27% | 35% | 21% | 6% | 1% |
75 | FUNG Iris | - | 1% | 10% | 29% | 37% | 20% | 4% |
76 | YUEN Nicole | 2% | 17% | 34% | 31% | 13% | 2% | - |
77 | HU Heidi | 2% | 12% | 27% | 32% | 20% | 6% | 1% |
78 | MARAGH Farrah E. | 1% | 7% | 24% | 35% | 24% | 8% | 1% |
78 | WU Daisy | - | 6% | 25% | 37% | 24% | 7% | 1% |
80 | WANG Tina | - | 1% | 8% | 24% | 37% | 24% | 5% |
81 | CHENG Zijuan "Grace" | 7% | 26% | 36% | 23% | 7% | 1% | - |
82 | FOSS Persephone | - | 1% | 9% | 27% | 37% | 22% | 4% |
83 | STANLEY Garance | 2% | 14% | 31% | 33% | 17% | 4% | - |
84 | TA-ZHOU Sophia | 3% | 24% | 45% | 24% | 5% | - | |
85 | HAGN Luna | 29% | 43% | 22% | 5% | 1% | - | |
86 | WONG Cerise | 2% | 13% | 30% | 33% | 18% | 5% | - |
87 | LIANG Claire | 2% | 13% | 30% | 32% | 17% | 4% | - |
87 | HU Ashley | 2% | 16% | 34% | 31% | 13% | 3% | - |
89 | LAFFY Lily | 7% | 27% | 37% | 22% | 6% | 1% | - |
90 | YOUNG Sienna | 2% | 15% | 33% | 32% | 15% | 4% | - |
91 | CAI Joanna | - | 4% | 19% | 36% | 29% | 10% | 1% |
92 | KIM Saeren | 4% | 22% | 37% | 26% | 9% | 1% | - |
93 | DOLEV Rony | - | 5% | 19% | 34% | 30% | 11% | 1% |
93 | VATS Ishita | - | 3% | 15% | 32% | 32% | 15% | 3% |
95 | YE Madeleine | 3% | 16% | 33% | 31% | 14% | 3% | - |
95 | XU Rachel | 6% | 30% | 37% | 20% | 5% | 1% | - |
97 | POWERS Langley | 20% | 39% | 29% | 10% | 2% | - | - |
98 | GONG suri | 2% | 13% | 30% | 32% | 17% | 5% | - |
99 | POWERS Waverly | 20% | 39% | 29% | 10% | 2% | - | - |
100 | BAIK Sarah | 6% | 25% | 37% | 25% | 7% | 1% | |
101 | LEE Kaitlin | 13% | 46% | 33% | 8% | 1% | - | |
102 | WANG Emily | 15% | 39% | 33% | 11% | 2% | - | |
103 | HWANG Charlotte | 3% | 22% | 41% | 27% | 6% | - | |
104 | LONG Chloe | 6% | 24% | 37% | 25% | 7% | 1% | |
105 | OSMINKINA-JONES Kai | 24% | 41% | 26% | 8% | 1% | - | |
106 | KIM Satie | 2% | 15% | 33% | 32% | 15% | 3% | - |
107 | KONZEN Iris | 4% | 21% | 36% | 27% | 10% | 2% | - |
108 | CAI Veronica | - | 4% | 14% | 29% | 32% | 17% | 4% |
109 | MULLER Inara | 2% | 12% | 29% | 34% | 18% | 4% | - |
109 | DESAUTELS Alexandra | 16% | 36% | 31% | 14% | 3% | - | - |
111 | DANG Madeleine | 7% | 29% | 36% | 21% | 6% | 1% | - |
112 | ZHU Avril | - | 8% | 27% | 36% | 22% | 6% | - |
113 | PRAKASH Aanika | 45% | 39% | 13% | 2% | - | - | - |
114 | ELLINGWOOD Sophia | 29% | 47% | 20% | 3% | - | - | - |
115 | CHOWDHERY Myra | 9% | 30% | 35% | 19% | 5% | 1% | - |
116 | XU Elaine | 14% | 33% | 32% | 16% | 4% | 1% | - |
117 | LEIGH Adalene | 11% | 36% | 35% | 15% | 3% | - | - |
118 | MOON Claire | 3% | 16% | 32% | 31% | 14% | 3% | - |
119 | HILD Anya | 3% | 15% | 33% | 32% | 14% | 3% | - |
120 | WILLIAMSON Morgan | 10% | 31% | 35% | 19% | 5% | 1% | - |
120 | NEGROIU Mara | 36% | 41% | 18% | 4% | - | - | - |
122 | WEST Mia | 8% | 27% | 35% | 22% | 7% | 1% | - |
123 | DEMRY Kylee | 29% | 41% | 23% | 6% | 1% | - | - |
124 | HUANG Pierra | 34% | 47% | 17% | 3% | - | - | - |
125 | SEAL Ayda | 45% | 39% | 13% | 2% | - | - | - |
126 | ROBBINS Adele | 42% | 41% | 14% | 2% | - | - | - |
127 | ZHANG Olivia | 3% | 16% | 32% | 31% | 15% | 3% | - |
128 | MEADE Kaia G. | 48% | 38% | 12% | 2% | - | - | - |
129 | SONG Emily | 56% | 36% | 8% | 1% | - | - | |
130 | KIM Grace | 2% | 13% | 30% | 33% | 18% | 4% | - |
131 | LIN Ariel | 1% | 11% | 32% | 37% | 17% | 3% | |
132 | CHACKO Anne | 57% | 36% | 7% | - | - | - | |
133 | CONVERSO-PARSONS Maia | 53% | 36% | 10% | 1% | - | - | - |
133 | GALLAGHER Isabella | 84% | 15% | 1% | - | - | - | - |
135 | BEATEY Piper | 35% | 41% | 19% | 5% | 1% | - | - |
136 | ELIASIK Josephine | 6% | 24% | 36% | 25% | 8% | 1% | - |
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.