Nerdly Nebraska.
2023-2024 HuskerGeek Ratings Leaders
Sport | School | Rating |
---|---|---|
ViPR D1 Volleyball | Wisconsin | 1,711.3731 |
Sport | School | Rating |
---|---|---|
ViPR D1 Volleyball | Wisconsin | 1,711.3731 |
Rnk. | Team | Résumé | Recent | ViPR | Adj SP% | Adj SO% | Adj. Hit Mar. |
---|---|---|---|---|---|---|---|
1st | Emory | 1,562.9240 | 1,586.7238 | 1,574.7789 | 61.52 | 72.71 | 0.328 |
2nd | Washington-St. Louis | 1,494.1040 | 1,525.8438 | 1,509.8905 | 58.63 | 68.48 | 0.284 |
3rd | Carnegie Mellon | 1,468.6329 | 1,488.3711 | 1,478.4691 | 57.86 | 67.56 | 0.223 |
4th | Chicago | 1,441.5510 | 1,483.1909 | 1,462.2227 | 56.54 | 65.28 | 0.205 |
5th | CWRU | 1,406.2712 | 1,441.5422 | 1,423.7975 | 53.04 | 63.76 | 0.159 |
6th | Rochester (NY) | 1,377.2623 | 1,405.7237 | 1,391.4203 | 53.09 | 61.43 | 0.121 |
7th | NYU | 1,309.1160 | 1,334.1507 | 1,321.5741 | 49.89 | 57.38 | 0.071 |
8th | Brandeis | 1,254.1581 | 1,268.2511 | 1,261.1849 | 47.62 | 53.90 | -0.006 |
ViPR Adjusted Offenses and Defenses are adjusted to expected values against an average team in the same division.
Rnk. | Team | Hit% | Kill% | HE% | AST% | O_DIG% | O_BLK% | ACE% |
---|---|---|---|---|---|---|---|---|
1st | Emory | 0.3488 | 45.85 | 10.97 | 42.11 | 43.20 | 2.85 | 9.37 |
2nd | Washington-St. Louis | 0.3330 | 44.71 | 11.41 | 40.77 | 44.49 | 3.23 | 9.15 |
3rd | Chicago | 0.2830 | 38.82 | 10.52 | 35.84 | 49.75 | 3.10 | 9.47 |
4th | Carnegie Mellon | 0.2735 | 39.43 | 12.09 | 36.50 | 49.77 | 3.04 | 9.13 |
5th | CWRU | 0.2582 | 39.02 | 13.20 | 36.11 | 45.13 | 4.52 | 9.92 |
6th | Rochester (NY) | 0.2033 | 37.01 | 16.68 | 34.80 | 48.73 | 4.20 | 10.90 |
7th | NYU | 0.1903 | 34.09 | 15.06 | 31.33 | 52.15 | 3.11 | 9.24 |
8th | Brandeis | 0.1448 | 31.43 | 16.95 | 29.79 | 54.92 | 3.70 | 9.93 |
Rnk. | Team | O_Hit% | O_Kill% | O_HE% | O_AST% | DIG% | BLK% | O_ACE% |
---|---|---|---|---|---|---|---|---|
1st | Emory | 0.0206 | 21.59 | 19.54 | 20.53 | 63.38 | 6.36 | 4.70 |
2nd | Washington-St. Louis | 0.0490 | 23.09 | 18.18 | 21.20 | 62.01 | 6.71 | 6.18 |
3rd | Carnegie Mellon | 0.0506 | 23.69 | 18.64 | 22.24 | 63.14 | 5.41 | 5.68 |
4th | Chicago | 0.0776 | 23.63 | 15.86 | 22.37 | 63.51 | 4.22 | 4.91 |
5th | Rochester (NY) | 0.0821 | 25.78 | 17.57 | 23.62 | 61.46 | 5.59 | 5.86 |
6th | CWRU | 0.0992 | 27.39 | 17.48 | 25.02 | 57.32 | 5.44 | 6.75 |
7th | NYU | 0.1196 | 29.03 | 17.07 | 26.84 | 55.96 | 5.16 | 6.79 |
8th | Brandeis | 0.1512 | 31.40 | 16.28 | 29.86 | 57.55 | 4.14 | 8.56 |
Description | Average | Remove First and Last | Remove Top and Bottom 2 | Remove Top and Bottom 3 | Composite |
---|---|---|---|---|---|
Scores | 1,427.9173 | 1,431.2290 | 1,438.9774 | 1,443.0101 | 1,435.2834 |
Difference | 3.3118 | 11.0601 | 15.0929 | 9.8216 |
Offense | Defense | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Team | Sets | S | SP | SA | SE | SP% | S/SA | S/SE | OS | SPA | SAA | SEA | SO% | OS/SAA | OS/SEA |
Emory | 136 | 2,994 | 1,585 | 229 | 225 | 52.94 | 13.1 | 13.3 | 2,278 | 832 | 147 | 231 | 63.48 | 15.5 | 9.9 |
Carnegie Mellon | 122 | 2,060 | 1,001 | 182 | 133 | 48.59 | 11.3 | 15.5 | 1,867 | 776 | 181 | 230 | 58.44 | 10.3 | 8.1 |
Washington-St. Louis | 141 | 2,882 | 1,376 | 197 | 257 | 47.75 | 14.6 | 11.2 | 2,615 | 1,080 | 220 | 245 | 58.70 | 11.9 | 10.7 |
Rochester (NY) | 120 | 2,334 | 1,140 | 280 | 243 | 48.84 | 8.3 | 9.6 | 2,135 | 932 | 180 | 214 | 56.35 | 11.9 | 10.0 |
Chicago | 108 | 2,343 | 1,116 | 170 | 188 | 47.63 | 13.8 | 12.5 | 2,176 | 944 | 158 | 166 | 56.62 | 13.8 | 13.1 |
NYU | 122 | 2,555 | 1,248 | 233 | 213 | 48.85 | 11.0 | 12.0 | 2,441 | 1,136 | 196 | 225 | 53.46 | 12.5 | 10.8 |
CWRU | 119 | 1,912 | 873 | 202 | 173 | 45.66 | 9.5 | 11.1 | 1,847 | 809 | 176 | 205 | 56.20 | 10.5 | 9.0 |
Brandeis | 99 | 1,684 | 736 | 182 | 175 | 43.71 | 9.3 | 9.6 | 1,920 | 991 | 217 | 231 | 48.39 | 8.8 | 8.3 |
Conference Average | 121 | 2,346 | 1,134 | 209 | 201 | 47.99 | 11.4 | 11.8 | 2,160 | 938 | 184 | 218 | 56.45 | 11.9 | 10.0 |
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Game Link | EPIC | Game Date | Location | Teams | Sets | Set 1 | Set 2 | Set 3 | Set 4 | Set 5 |
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GAME |
74.69 |
2016-11-04 | St. Louis, Mo. |
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GAME |
72.16 |
2016-10-01 | Pittsburgh, Pa. |
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GAME |
71.22 |
2016-10-15 | Waltham, Mass. |
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GAME |
70.74 |
2016-11-04 | St. Louis, Mo. |
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GAME |
69.77 |
2016-10-02 | Pittsburgh, Pa. |
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GAME |
69.05 |
2016-10-15 | Waltham, Mass. |
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GAME |
68.86 |
2016-10-16 | Waltham, Mass. |
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GAME |
68.61 |
2016-11-05 | St. Louis, Mo. |
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GAME |
66.71 |
2016-10-16 | Waltham, Mass. |
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GAME |
66.05 |
2016-10-02 | Pittsburgh, Pa. |
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Name | Team | Role |
---|---|---|
Jessica Holler | Emory | A |
Sydney Leimbach | Emory | A |
Julianne Malek | Washington-St. Louis | A |
Sarah Muisenga | Chicago | A |
Alex Sheredy | Washington-St. Louis | A |
Mary Tuohy | Chicago | D |
Erin Risk | Chicago | S |
Name | Team | Role |
---|---|---|
Caroline Dupont | Washington-St. Louis | A |
Rayne Ellis | NYU | A |
Anabella Pinton | Chicago | A |
Haley Sims | CWRU | A |
Becky Breuer | Emory | D |
Sarah Maher | Emory | D |
Emily Newton | Carnegie Mellon | S |
Rank | Name | Team |
---|---|---|
1 | Erin Risk | Chicago |
2 | Alex Sheredy | Washington-St. Louis |
3 | Sarah Muisenga | Chicago |
4 | Jessica Holler | Emory |
5 | Mary Tuohy | Chicago |
Rank | Name | Team |
---|---|---|
1 | Alex Sheredy | Washington-St. Louis |
2 | Sarah Muisenga | Chicago |
3 | Jessica Holler | Emory |
4 | Anabella Pinton | Chicago |
5 | Caroline Dupont | Washington-St. Louis |
Rank | Name | Team |
---|---|---|
1 | Erin Risk | Chicago |
2 | Emily Newton | Carnegie Mellon |
3 | Chloe Stile | Washington-St. Louis |
4 | Aimee Kohler | Rochester (NY) |
5 | Sarah Porter | Emory |
Rank | Name | Team |
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1 | Mary Tuohy | Chicago |
2 | Becky Breuer | Emory |
3 | Molly Higgins | Carnegie Mellon |
4 | Zoe Baxter | Washington-St. Louis |
5 | Meghan Connor | Rochester (NY) |
Rk. | Name | Team | WPA |
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1 | Alex Sheredy | Washington-St. Louis | 25.8413 |
2 | Erin Risk | Chicago | 22.8139 |
3 | Sarah Muisenga | Chicago | 22.7068 |
4 | Caroline Dupont | Washington-St. Louis | 19.5828 |
5 | Chloe Stile | Washington-St. Louis | 19.1328 |
6 | Emily Newton | Carnegie Mellon | 19.1057 |
7 | Jessica Holler | Emory | 18.2185 |
8 | Sarah Maher | Emory | 18.0093 |
9 | Zoe Baxter | Washington-St. Louis | 17.3372 |
10 | Molly Higgins | Carnegie Mellon | 17.0398 |
Rk. | Name | Team | OWPA |
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1 | Chloe Stile | Washington-St. Louis | 14.3762 |
2 | Emily Newton | Carnegie Mellon | 13.5039 |
3 | Jessica Holler | Emory | 13.1716 |
4 | Erin Risk | Chicago | 13.1457 |
5 | Alex Sheredy | Washington-St. Louis | 13.0759 |
6 | Caroline Dupont | Washington-St. Louis | 12.2306 |
7 | Sarah Muisenga | Chicago | 11.7592 |
8 | Audrey Scrafford | Chicago | 10.8241 |
9 | Ifeoma Ufondu | Washington-St. Louis | 10.4713 |
10 | Shannon Carroll | CWRU | 10.1070 |
Rk. | Name | Team | DWPA |
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1 | Zoe Baxter | Washington-St. Louis | 16.8275 |
2 | Molly Higgins | Carnegie Mellon | 16.2648 |
3 | Mary Tuohy | Chicago | 15.9824 |
4 | Meghan Connor | Rochester (NY) | 13.9548 |
5 | Alex Sheredy | Washington-St. Louis | 12.7655 |
6 | Grace Napolitiano | NYU | 12.2396 |
7 | Sarah Muisenga | Chicago | 10.9475 |
8 | Becky Breuer | Emory | 10.8716 |
9 | Yvette Cho | Brandeis | 10.6494 |
10 | Sarah Maher | Emory | 9.8064 |
Rk. | Name | Team | WPA/S |
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1 | Erin Risk | Chicago | 0.2194 |
2 | Sarah Muisenga | Chicago | 0.2183 |
3 | Emily Newton | Carnegie Mellon | 0.2171 |
4 | Alex Sheredy | Washington-St. Louis | 0.2035 |
5 | Molly Higgins | Carnegie Mellon | 0.1893 |
6 | Shannon Carroll | CWRU | 0.1876 |
7 | Mary Tuohy | Chicago | 0.1870 |
8 | Lauren Mueller | Carnegie Mellon | 0.1709 |
9 | Anabella Pinton | Chicago | 0.1700 |
10 | Aimee Kohler | Rochester (NY) | 0.1675 |
Rk. | Name | Team | OWPA/S |
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1 | Emily Newton | Carnegie Mellon | 0.1535 |
2 | Erin Risk | Chicago | 0.1264 |
3 | Chloe Stile | Washington-St. Louis | 0.1188 |
4 | Audrey Scrafford | Chicago | 0.1152 |
5 | Shannon Carroll | CWRU | 0.1136 |
6 | Sarah Muisenga | Chicago | 0.1131 |
7 | Sarah Porter | Emory | 0.1091 |
8 | Jessica Holler | Emory | 0.1089 |
9 | Alex Sheredy | Washington-St. Louis | 0.1030 |
10 | Caroline Dupont | Washington-St. Louis | 0.0971 |
Rk. | Name | Team | DWPA/S |
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1 | Molly Higgins | Carnegie Mellon | 0.1807 |
2 | Mary Tuohy | Chicago | 0.1776 |
3 | Zoe Baxter | Washington-St. Louis | 0.1391 |
4 | Meghan Connor | Rochester (NY) | 0.1355 |
5 | Yvette Cho | Brandeis | 0.1253 |
6 | Grace Napolitiano | NYU | 0.1155 |
7 | Becky Breuer | Emory | 0.1087 |
8 | Sarah Muisenga | Chicago | 0.1053 |
9 | Karina Bondelid | CWRU | 0.1052 |
10 | Alex Sheredy | Washington-St. Louis | 0.1005 |
Conference Strength – The Conference Strength table has two parts. The first row is a list of averages of the scores for a selection of teams in the conference ranging from all of them under the heading “Average” to an average of teams in the conference if we remove the top and bottom three teams. This is designed to check if a conference is propped up by its elite teams of held down by its weakest teams. The Composite score on the far right is an average of those scores. It is a weighted score where the middle teams have a higher value than the edge teams. The second row containing difference is simply a measure of how different removing the edge teams makes the conference from its initial average. If the numbers are positive, then removing the edge teams increases the conferences rating. If a value grows from the value before it, then the team removed at the bottom of the ratings was rated farther outside of the mean than the team removed at the top of the ratings. It was weighing the average down so to speak. The Composite difference at the far right is simply an average of the differences.
The Best Conference Games – A short list of the best games played between two members of the conference which is calculated using the EPIC score of each game. EPIC score is essentially very simple amounting to adding the teams combined ViPR Rating and the total Win Probability Added scored by each team.
All-Conference Teams – All-conference teams are calculated using Win Probability Added per Set Played and the quality of the team that the player plays on. Team quality is included because better teams tend to have better players and more of them. This often means that players on better teams have fewer opportunities than standouts on lesser teams.
Awards Lists – Each awards list uses the same formula that is used to calculate All-Conference Teams, and decides based on the focus of the list. Player of the Year has no limitation on how the player score is added up. While Attacker of the Year must have a higher attack score than any other metric. Similarly Setter and Defensive Player must acquire most of their score through those metrics.