#68 Northwestern (9-11)

avg: 1115.06  •  sd: 72.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
50 California-Santa Cruz Loss 6-11 759.65 Jan 28th Santa Barbara Invitational 2023
7 Carleton College** Loss 4-13 1552.01 Ignored Jan 28th Santa Barbara Invitational 2023
30 California Loss 5-7 1203.77 Jan 28th Santa Barbara Invitational 2023
50 California-Santa Cruz Loss 10-11 1181.35 Jan 29th Santa Barbara Invitational 2023
54 Cal Poly-SLO Loss 6-7 1127.19 Jan 29th Santa Barbara Invitational 2023
74 Lewis & Clark Loss 5-7 721.1 Jan 29th Santa Barbara Invitational 2023
43 Wisconsin Win 7-6 1499.38 Jan 29th Santa Barbara Invitational 2023
211 St. Olaf-B** Win 11-0 307.67 Ignored Mar 4th Midwest Throwdown 2023
159 Illinois** Win 11-1 971.58 Ignored Mar 4th Midwest Throwdown 2023
205 Purdue-B** Win 11-0 479.48 Ignored Mar 4th Midwest Throwdown 2023
185 Wisconsin-Milwaukee** Win 11-0 746.87 Ignored Mar 4th Midwest Throwdown 2023
121 Saint Louis Win 11-3 1298.72 Mar 5th Midwest Throwdown 2023
122 Purdue Win 11-4 1290.74 Mar 5th Midwest Throwdown 2023
47 Washington University Loss 6-7 1230.16 Mar 5th Midwest Throwdown 2023
78 Central Florida Loss 7-9 733.2 Mar 18th Womens Centex1
56 Georgia Tech Loss 9-10 1101.21 Mar 18th Womens Centex1
47 Washington University Loss 10-13 1027.01 Mar 18th Womens Centex1
102 Iowa Win 13-11 1088.35 Mar 19th Womens Centex1
22 Texas-Dallas Loss 7-10 1234.18 Mar 19th Womens Centex1
120 Texas A&M Win 15-9 1226.02 Mar 19th Womens Centex1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)