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. |
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1st | Emory | 1,499.4571 | 1,471.9882 | 1,485.6592 | 58.78 | 71.67 | 0.284 |
2nd | Washington-St. Louis | 1,471.2765 | 1,452.1416 | 1,461.6777 | 59.84 | 66.33 | 0.257 |
3rd | Chicago | 1,462.2372 | 1,434.4974 | 1,448.3009 | 56.71 | 68.39 | 0.248 |
4th | Carnegie Mellon | 1,472.6191 | 1,422.9404 | 1,447.5666 | 58.89 | 66.96 | 0.262 |
5th | CWRU | 1,395.6361 | 1,374.8115 | 1,385.1846 | 54.38 | 63.64 | 0.147 |
6th | Rochester (NY) | 1,308.1474 | 1,288.0593 | 1,298.0645 | 52.52 | 57.48 | 0.099 |
7th | Brandeis | 1,276.0055 | 1,263.3513 | 1,269.6626 | 50.43 | 56.07 | 0.062 |
8th | NYU | 1,252.5282 | 1,233.4942 | 1,242.9748 | 46.82 | 55.72 | 0.029 |
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 | 0.3232 | 44.31 | 11.98 | 41.13 | 43.12 | 3.33 | 10.65 |
2nd | Chicago | 0.3182 | 41.55 | 9.73 | 38.70 | 48.55 | 3.32 | 9.16 |
3rd | Washington-St. Louis | 0.3155 | 44.00 | 12.45 | 40.15 | 42.56 | 3.39 | 10.35 |
4th | Carnegie Mellon | 0.3149 | 40.08 | 8.60 | 37.45 | 50.10 | 1.92 | 9.89 |
5th | CWRU | 0.2702 | 39.04 | 12.02 | 36.29 | 46.98 | 4.00 | 9.16 |
6th | Rochester (NY) | 0.2160 | 35.96 | 14.36 | 33.34 | 52.03 | 3.40 | 9.66 |
7th | Brandeis | 0.1714 | 32.09 | 14.95 | 30.10 | 57.32 | 3.80 | 10.47 |
8th | NYU | 0.1682 | 33.99 | 17.17 | 31.75 | 52.02 | 3.84 | 8.14 |
Rnk. | Team | O_Hit% | O_Kill% | O_HE% | O_AST% | DIG% | BLK% | O_ACE% |
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1st | Emory | 0.0388 | 22.26 | 18.39 | 20.89 | 64.14 | 6.07 | 3.66 |
2nd | Carnegie Mellon | 0.0525 | 22.74 | 17.49 | 20.89 | 63.33 | 5.53 | 5.60 |
3rd | Washington-St. Louis | 0.0587 | 24.98 | 19.11 | 23.25 | 60.90 | 6.48 | 7.40 |
4th | Chicago | 0.0699 | 23.77 | 16.79 | 22.27 | 62.45 | 4.44 | 6.28 |
5th | Brandeis | 0.1096 | 27.30 | 16.33 | 25.89 | 61.02 | 4.35 | 8.06 |
6th | Rochester (NY) | 0.1165 | 29.87 | 18.21 | 27.33 | 58.64 | 5.58 | 8.22 |
7th | CWRU | 0.1229 | 30.12 | 17.83 | 28.82 | 55.46 | 4.72 | 6.40 |
8th | NYU | 0.1395 | 31.08 | 17.13 | 28.88 | 54.71 | 5.78 | 6.89 |
Description | Average | Remove First and Last | Remove Top and Bottom 2 | Remove Top and Bottom 3 | Composite |
---|---|---|---|---|---|
Scores | 1,379.8864 | 1,385.0762 | 1,394.7792 | 1,416.3756 | 1,394.0293 |
Difference | 5.1898 | 14.8928 | 36.4893 | 18.8573 |
Offense | Defense | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Team | Sets | S | SP | SA | SE | SP% | S/SA | S/SE | OS | SPA | SAA | SEA | SO% | OS/SAA | OS/SEA |
Emory | 131 | 3,058 | 1,513 | 244 | 266 | 49.48 | 12.5 | 11.5 | 2,554 | 974 | 126 | 227 | 61.86 | 20.3 | 11.3 |
Carnegie Mellon | 128 | 2,972 | 1,499 | 228 | 205 | 50.44 | 13.0 | 14.5 | 2,575 | 1,076 | 176 | 198 | 58.21 | 14.6 | 13.0 |
Chicago | 112 | 2,089 | 999 | 170 | 179 | 47.82 | 12.3 | 11.7 | 1,867 | 766 | 177 | 188 | 58.97 | 10.5 | 9.9 |
Washington-St. Louis | 130 | 2,522 | 1,246 | 225 | 197 | 49.41 | 11.2 | 12.8 | 2,319 | 1,013 | 237 | 243 | 56.32 | 9.8 | 9.5 |
CWRU | 112 | 2,400 | 1,154 | 178 | 145 | 48.08 | 13.5 | 16.6 | 2,240 | 986 | 174 | 202 | 55.98 | 12.9 | 11.1 |
Brandeis | 110 | 2,182 | 1,097 | 256 | 212 | 50.28 | 8.5 | 10.3 | 2,073 | 982 | 200 | 191 | 52.63 | 10.4 | 10.9 |
Rochester (NY) | 114 | 1,889 | 940 | 223 | 205 | 49.76 | 8.5 | 9.2 | 1,811 | 858 | 238 | 214 | 52.62 | 7.6 | 8.5 |
NYU | 120 | 2,265 | 970 | 169 | 200 | 42.83 | 13.4 | 11.3 | 2,551 | 1,277 | 225 | 218 | 49.94 | 11.3 | 11.7 |
Conference Average | 120 | 2,422 | 1,177 | 212 | 201 | 48.51 | 11.6 | 12.2 | 2,249 | 992 | 194 | 210 | 55.82 | 12.2 | 10.7 |
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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 |
73.98 |
2017-11-03 | Atlanta, GA |
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GAME |
72.08 |
2017-11-03 | Atlanta, Ga. |
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GAME |
71.93 |
2017-10-15 | Chicago, Ill. |
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GAME |
70.20 |
2017-10-14 | Chicago, Ill. |
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GAME |
68.45 |
2017-09-30 | Cleveland, Ohio |
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GAME |
66.87 |
2017-10-15 | Chicago, Ill. |
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GAME |
66.80 |
2017-10-01 | Cleveland, Ohio |
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GAME |
66.69 |
2017-11-04 | Atlanta, GA |
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GAME |
65.94 |
2017-10-01 | Cleveland, Ohio |
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GAME |
65.33 |
2017-09-30 | Cleveland, Ohio |
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Name | Team | Role |
---|---|---|
Sara Carr | Emory | A |
Eliza Donohue | Carnegie Mellon | A |
Julianne Malek | Washington-St. Louis | A |
Lauren Mueller | Carnegie Mellon | A |
Brianna Lemon | CWRU | D |
Kayla Yew | Carnegie Mellon | D |
Emma Griffith | Chicago | S |
Name | Team | Role |
---|---|---|
Heather Holton | Carnegie Mellon | A |
Sydney Leimbach | Emory | A |
Sarah Muisenga | Chicago | A |
Haley Sims | CWRU | A |
Elyse Thompson | Emory | D |
Faith Ellis | CWRU | S |
Maia So-Holloway | Carnegie Mellon | S |
Rank | Name | Team |
---|---|---|
1 | Lauren Mueller | Carnegie Mellon |
2 | Sara Carr | Emory |
3 | Kayla Yew | Carnegie Mellon |
4 | Emma Griffith | Chicago |
5 | Eliza Donohue | Carnegie Mellon |
Rank | Name | Team |
---|---|---|
1 | Lauren Mueller | Carnegie Mellon |
2 | Sara Carr | Emory |
3 | Eliza Donohue | Carnegie Mellon |
4 | Sarah Muisenga | Chicago |
5 | Julianne Malek | Washington-St. Louis |
Rank | Name | Team |
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1 | Emma Griffith | Chicago |
2 | Maia So-Holloway | Carnegie Mellon |
3 | Faith Ellis | CWRU |
4 | Kirby Knapp | Washington-St. Louis |
5 | Sarah Porter | Emory |
Rank | Name | Team |
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1 | Kayla Yew | Carnegie Mellon |
2 | Elyse Thompson | Emory |
3 | Yvette Cho | Brandeis |
4 | Zoe Baxter | Washington-St. Louis |
5 | Anne Stifter | Chicago |
Rk. | Name | Team | WPA |
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1 | Lauren Mueller | Carnegie Mellon | 32.6417 |
2 | Sara Carr | Emory | 24.6697 |
3 | Kayla Yew | Carnegie Mellon | 24.2436 |
4 | Elyse Thompson | Emory | 20.1153 |
5 | Maia So-Holloway | Carnegie Mellon | 19.2339 |
6 | Emma Griffith | Chicago | 19.1147 |
7 | Brianna Lemon | CWRU | 18.6818 |
8 | Sarah Muisenga | Chicago | 17.6683 |
9 | Eliza Donohue | Carnegie Mellon | 17.5763 |
10 | Zoe Baxter | Washington-St. Louis | 17.0486 |
Rk. | Name | Team | OWPA |
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1 | Lauren Mueller | Carnegie Mellon | 18.5621 |
2 | Sara Carr | Emory | 14.9073 |
3 | Eliza Donohue | Carnegie Mellon | 13.3454 |
4 | Julianne Malek | Washington-St. Louis | 12.7035 |
5 | Morgan McKnight | Emory | 11.5549 |
6 | Maia So-Holloway | Carnegie Mellon | 11.1979 |
7 | Emma Griffith | Chicago | 11.0973 |
8 | Sarah Jurgens | Carnegie Mellon | 11.0317 |
9 | Faith Ellis | CWRU | 10.4253 |
10 | Heather Holton | Carnegie Mellon | 9.4398 |
Rk. | Name | Team | DWPA |
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1 | Kayla Yew | Carnegie Mellon | 22.5023 |
2 | Elyse Thompson | Emory | 18.5817 |
3 | Zoe Baxter | Washington-St. Louis | 15.1216 |
4 | Yvette Cho | Brandeis | 14.6270 |
5 | Lauren Mueller | Carnegie Mellon | 14.0795 |
6 | Anne Stifter | Chicago | 12.1492 |
7 | Brianna Lemon | CWRU | 10.4608 |
8 | Courtney Vidovich | Rochester (NY) | 9.9411 |
9 | Sara Carr | Emory | 9.7624 |
10 | Jacqueline Kupeli | NYU | 9.0070 |
Rk. | Name | Team | WPA/S |
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1 | Lauren Mueller | Carnegie Mellon | 0.2591 |
2 | Emma Griffith | Chicago | 0.2148 |
3 | Sara Carr | Emory | 0.2006 |
4 | Sarah Muisenga | Chicago | 0.1920 |
5 | Kayla Yew | Carnegie Mellon | 0.1894 |
6 | Brianna Lemon | CWRU | 0.1796 |
7 | Yvette Cho | Brandeis | 0.1721 |
8 | Elyse Thompson | Emory | 0.1609 |
9 | Julianne Malek | Washington-St. Louis | 0.1600 |
10 | Faith Ellis | CWRU | 0.1574 |
Rk. | Name | Team | OWPA/S |
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1 | Lauren Mueller | Carnegie Mellon | 0.1473 |
2 | Emma Griffith | Chicago | 0.1247 |
3 | Sara Carr | Emory | 0.1212 |
4 | Julianne Malek | Washington-St. Louis | 0.1210 |
5 | Eliza Donohue | Carnegie Mellon | 0.1141 |
6 | Sarah Muisenga | Chicago | 0.1025 |
7 | Audrey Scrafford | Chicago | 0.1022 |
8 | Anabella Pinton | Chicago | 0.1018 |
9 | Faith Ellis | CWRU | 0.0965 |
10 | Sydney Leimbach | Emory | 0.0952 |
Rk. | Name | Team | DWPA/S |
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1 | Kayla Yew | Carnegie Mellon | 0.1758 |
2 | Yvette Cho | Brandeis | 0.1508 |
3 | Elyse Thompson | Emory | 0.1487 |
4 | Anne Stifter | Chicago | 0.1413 |
5 | Courtney Vidovich | Rochester (NY) | 0.1400 |
6 | Zoe Baxter | Washington-St. Louis | 0.1350 |
7 | Jacqueline Kupeli | NYU | 0.1287 |
8 | Lauren Mueller | Carnegie Mellon | 0.1117 |
9 | Brianna Lemon | CWRU | 0.1006 |
10 | Emma Griffith | Chicago | 0.0901 |
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.