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 | Chicago | 1,595.4864 | 1,558.3250 | 1,576.7962 | 60.25 | 67.25 | 0.283 |
2nd | Emory | 1,583.3285 | 1,547.7439 | 1,565.4351 | 61.39 | 69.98 | 0.284 |
3rd | Carnegie Mellon | 1,537.0835 | 1,502.9180 | 1,519.9048 | 59.25 | 63.72 | 0.230 |
4th | WashU | 1,537.9533 | 1,501.0400 | 1,519.3845 | 55.81 | 65.09 | 0.227 |
5th | NYU | 1,476.9069 | 1,440.7151 | 1,458.6988 | 55.25 | 62.07 | 0.194 |
6th | CWRU | 1,466.8793 | 1,432.7378 | 1,449.7081 | 54.25 | 61.40 | 0.160 |
7th | Rochester (NY) | 1,356.7682 | 1,317.0440 | 1,336.7586 | 51.35 | 52.89 | 0.040 |
8th | Brandeis | 1,353.4546 | 1,318.3526 | 1,335.7883 | 56.04 | 49.94 | 0.035 |
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 | 0.3362 | 46.31 | 12.69 | 42.58 | 39.82 | 3.30 | 10.02 |
2nd | Chicago | 0.3098 | 41.46 | 10.48 | 37.85 | 46.39 | 2.92 | 11.13 |
3rd | WashU | 0.3095 | 43.27 | 12.32 | 39.71 | 43.76 | 3.02 | 8.78 |
4th | Carnegie Mellon | 0.2920 | 40.28 | 11.07 | 37.08 | 48.28 | 2.60 | 10.09 |
5th | NYU | 0.2673 | 38.55 | 11.83 | 35.30 | 50.83 | 2.27 | 7.89 |
6th | CWRU | 0.2504 | 36.38 | 11.34 | 33.59 | 53.05 | 2.73 | 8.86 |
7th | Brandeis | 0.1787 | 32.75 | 14.89 | 29.63 | 53.57 | 4.75 | 11.43 |
8th | Rochester (NY) | 0.1783 | 33.27 | 15.44 | 31.01 | 54.76 | 3.42 | 12.03 |
Rnk. | Team | O_Hit% | O_Kill% | O_HE% | O_AST% | DIG% | BLK% | O_ACE% |
---|---|---|---|---|---|---|---|---|
1st | Chicago | 0.0272 | 20.23 | 17.52 | 19.43 | 65.63 | 5.69 | 5.41 |
2nd | Emory | 0.0527 | 24.41 | 19.15 | 22.44 | 60.01 | 6.15 | 4.02 |
3rd | Carnegie Mellon | 0.0621 | 25.81 | 19.60 | 23.65 | 58.09 | 6.07 | 6.14 |
4th | NYU | 0.0735 | 26.92 | 19.57 | 25.28 | 55.90 | 7.22 | 8.36 |
5th | WashU | 0.0826 | 25.91 | 17.66 | 24.28 | 58.19 | 5.64 | 6.92 |
6th | CWRU | 0.0908 | 26.54 | 17.46 | 24.37 | 58.13 | 4.38 | 7.32 |
7th | Rochester (NY) | 0.1386 | 30.47 | 16.61 | 27.93 | 55.33 | 5.04 | 9.65 |
8th | Brandeis | 0.1432 | 30.78 | 16.45 | 27.93 | 53.64 | 5.57 | 11.24 |
Description | Average | Remove First and Last | Remove Top and Bottom 2 | Remove Top and Bottom 3 | Composite |
---|---|---|---|---|---|
Scores | 1,470.3093 | 1,474.9816 | 1,486.9240 | 1,489.0417 | 1,480.3142 |
Difference | 4.6723 | 16.6147 | 18.7324 | 13.3398 |
Offense | Defense | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Team | Sets | S | SP | SA | SE | SP% | S/SA | S/SE | OS | SPA | SAA | SEA | SO% | OS/SAA | OS/SEA |
Emory | 110 | 2,397 | 1,328 | 191 | 268 | 55.40 | 12.5 | 8.9 | 1,816 | 690 | 106 | 240 | 62.00 | 17.1 | 7.6 |
Carnegie Mellon | 99 | 2,389 | 1,363 | 190 | 171 | 57.05 | 12.6 | 14.0 | 1,850 | 806 | 128 | 198 | 56.43 | 14.5 | 9.3 |
NYU | 115 | 2,545 | 1,343 | 197 | 202 | 52.77 | 12.9 | 12.6 | 2,139 | 906 | 198 | 247 | 57.64 | 10.8 | 8.7 |
Chicago | 98 | 2,090 | 1,066 | 200 | 198 | 51.01 | 10.5 | 10.6 | 1,790 | 756 | 129 | 182 | 57.77 | 13.9 | 9.8 |
WashU | 116 | 2,506 | 1,222 | 170 | 229 | 48.76 | 14.7 | 10.9 | 2,220 | 936 | 183 | 281 | 57.84 | 12.1 | 7.9 |
CWRU | 113 | 2,233 | 1,131 | 152 | 111 | 50.65 | 14.7 | 20.1 | 2,417 | 1,310 | 203 | 221 | 45.80 | 11.9 | 10.9 |
Rochester (NY) | 109 | 1,701 | 810 | 218 | 222 | 47.62 | 7.8 | 7.7 | 1,898 | 1,005 | 233 | 196 | 47.05 | 8.1 | 9.7 |
Brandeis | 92 | 1,824 | 905 | 184 | 132 | 49.62 | 9.9 | 13.8 | 2,082 | 1,186 | 238 | 154 | 43.04 | 8.7 | 13.5 |
Conference Average | 107 | 2,211 | 1,146 | 188 | 192 | 51.61 | 12.0 | 12.3 | 2,027 | 949 | 177 | 215 | 53.45 | 12.1 | 9.7 |
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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 |
73.74 |
2019-10-20 | Cleveland, Ohio |
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GAME |
70.40 |
2019-10-06 | Atlanta, GA |
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GAME |
70.14 |
2019-10-06 | Atlanta, Ga. |
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GAME |
68.96 |
2019-10-20 | Cleveland, Ohio |
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GAME |
68.14 |
2019-09-22 | Boston, MA |
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GAME |
68.05 |
2019-09-22 | St. Louis, MO |
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GAME |
67.10 |
2019-10-20 | Cleveland, Ohio |
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GAME |
67.04 |
2019-10-20 | Cleveland, Ohio |
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GAME |
66.85 |
2019-09-21 | Waltham, MA |
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GAME |
66.52 |
2019-10-06 | Atlanta, GA |
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Name | Team | Role |
---|---|---|
Vivian Beaudoin | Carnegie Mellon | A |
Katie Kaminski | CWRU | A |
Leila King | WashU | A |
Madison Pearson | Chicago | A |
Anne Stifter | Chicago | D |
Katherine Wilson | Chicago | D |
Emma Griffith | Chicago | S |
Name | Team | Role |
---|---|---|
Michaela Bach | WashU | A |
Caitlin Lorenz | WashU | A |
Leah Saunders | Emory | A |
Finn Wilkins | Emory | A |
Mikayla Hardy | Chicago | D |
Kayla Yew | Carnegie Mellon | D |
Kirby Knapp | WashU | S |
Rank | Name | Team |
---|---|---|
1 | Emma Griffith | Chicago |
2 | Anne Stifter | Chicago |
3 | Mikayla Hardy | Chicago |
4 | Fredericka Paulson | Chicago |
5 | Madison Pearson | Chicago |
Rank | Name | Team |
---|---|---|
1 | Madison Pearson | Chicago |
2 | Katie Kaminski | CWRU |
3 | Michaela Bach | WashU |
4 | Leah Saunders | Emory |
5 | Leila King | WashU |
Rank | Name | Team |
---|---|---|
1 | Emma Griffith | Chicago |
2 | Kirby Knapp | WashU |
3 | Cassie Srb | Emory |
4 | Marissa Borgert | Brandeis |
5 | Maia So-Holloway | Carnegie Mellon |
Rank | Name | Team |
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1 | Katherine Wilson | Chicago |
2 | Kayla Yew | Carnegie Mellon |
3 | Kaitlyn Oh | Brandeis |
4 | Zoe Baxter | WashU |
5 | Elyse Thompson | Emory |
Rk. | Name | Team | WPA |
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1 | Emma Griffith | Chicago | 21.4135 |
2 | Anne Stifter | Chicago | 20.1119 |
3 | Kirby Knapp | WashU | 18.0900 |
4 | Brianna Lemon | CWRU | 17.2528 |
5 | Katie Kaminski | CWRU | 17.0288 |
6 | Michaela Bach | WashU | 16.8597 |
7 | Kayla Yew | Carnegie Mellon | 15.8116 |
8 | Fredericka Paulson | Chicago | 15.6541 |
9 | Cassie Srb | Emory | 14.7071 |
10 | Leah Saunders | Emory | 14.5408 |
Rk. | Name | Team | OWPA |
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1 | Emma Griffith | Chicago | 12.2191 |
2 | Cassie Srb | Emory | 11.2673 |
3 | Kirby Knapp | WashU | 11.0525 |
4 | Morgan McKnight | Emory | 9.5680 |
5 | Haley Holz | NYU | 9.3939 |
6 | Michaela Bach | WashU | 9.1353 |
7 | Katie Kaminski | CWRU | 8.8020 |
8 | Anne Stifter | Chicago | 8.7057 |
9 | Madison Pearson | Chicago | 8.5046 |
10 | Leila King | WashU | 7.9189 |
Rk. | Name | Team | DWPA |
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1 | Kayla Yew | Carnegie Mellon | 15.3700 |
2 | Katherine Wilson | Chicago | 12.9602 |
3 | Zoe Baxter | WashU | 12.7650 |
4 | Jacqueline Kupeli | NYU | 12.4525 |
5 | Jana Giaquinto | CWRU | 11.9720 |
6 | Anne Stifter | Chicago | 11.4063 |
7 | Brianna Lemon | CWRU | 10.8047 |
8 | Elyse Thompson | Emory | 9.9365 |
9 | Fredericka Paulson | Chicago | 9.9192 |
10 | Emma Griffith | Chicago | 9.1944 |
Rk. | Name | Team | WPA/S |
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1 | Emma Griffith | Chicago | 0.2580 |
2 | Anne Stifter | Chicago | 0.2453 |
3 | Fredericka Paulson | Chicago | 0.1886 |
4 | Mikayla Hardy | Chicago | 0.1841 |
5 | Kayla Yew | Carnegie Mellon | 0.1738 |
6 | Katherine Wilson | Chicago | 0.1718 |
7 | Kirby Knapp | WashU | 0.1707 |
8 | Brianna Lemon | CWRU | 0.1675 |
9 | Madison Pearson | Chicago | 0.1664 |
10 | Kaitlyn Oh | Brandeis | 0.1649 |
Rk. | Name | Team | OWPA/S |
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1 | Emma Griffith | Chicago | 0.1472 |
2 | Cassie Srb | Emory | 0.1186 |
3 | Madison Pearson | Chicago | 0.1077 |
4 | Anne Stifter | Chicago | 0.1062 |
5 | Morgan McKnight | Emory | 0.1051 |
6 | Kirby Knapp | WashU | 0.1043 |
7 | Haley Holz | NYU | 0.0939 |
8 | Marissa Borgert | Brandeis | 0.0920 |
9 | Tara Martin | Emory | 0.0868 |
10 | Michaela Bach | WashU | 0.0846 |
Rk. | Name | Team | DWPA/S |
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1 | Kayla Yew | Carnegie Mellon | 0.1689 |
2 | Katherine Wilson | Chicago | 0.1581 |
3 | Kaitlyn Oh | Brandeis | 0.1440 |
4 | Anne Stifter | Chicago | 0.1391 |
5 | Zoe Baxter | WashU | 0.1251 |
6 | Fredericka Paulson | Chicago | 0.1195 |
7 | Jana Giaquinto | CWRU | 0.1174 |
8 | Jacqueline Kupeli | NYU | 0.1164 |
9 | Elyse Thompson | Emory | 0.1155 |
10 | Emma Griffith | Chicago | 0.1108 |
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.