#50 Case Western Reserve (14-12)

avg: 1640.01  •  sd: 61.97  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 Brigham Young Loss 7-15 1718.3 Jan 28th Santa Barbara Invitational 2023
44 Victoria Win 12-11 1821.72 Jan 28th Santa Barbara Invitational 2023
15 UCLA Loss 12-13 1903.29 Jan 28th Santa Barbara Invitational 2023
109 Southern California Win 14-10 1722.61 Jan 28th Santa Barbara Invitational 2023
53 Utah Loss 6-13 1019.99 Jan 29th Santa Barbara Invitational 2023
17 Washington Loss 10-13 1662 Jan 29th Santa Barbara Invitational 2023
54 Northwestern Win 12-8 2057.34 Jan 29th Santa Barbara Invitational 2023
62 Harvard Win 15-10 2022.51 Feb 11th Queen City Tune Up1
38 Purdue Win 15-13 1987.28 Feb 11th Queen City Tune Up1
24 North Carolina-Charlotte Win 13-11 2123.32 Feb 11th Queen City Tune Up1
1 North Carolina Loss 10-15 1939.05 Feb 11th Queen City Tune Up1
41 William & Mary Loss 8-9 1593.88 Feb 12th Queen City Tune Up1
25 North Carolina-Wilmington Loss 9-10 1759.26 Feb 12th Queen City Tune Up1
150 George Washington Win 13-12 1273.25 Feb 25th Easterns Qualifier 2023
51 Virginia Loss 8-13 1139.3 Feb 25th Easterns Qualifier 2023
71 Cornell Win 13-11 1732.44 Feb 25th Easterns Qualifier 2023
26 Georgia Tech Loss 9-13 1449.77 Feb 25th Easterns Qualifier 2023
37 McGill Win 15-14 1898.37 Feb 26th Easterns Qualifier 2023
49 Notre Dame Win 15-11 2024.42 Feb 26th Easterns Qualifier 2023
26 Georgia Tech Loss 7-15 1268.33 Feb 26th Easterns Qualifier 2023
134 Carnegie Mellon Win 12-5 1836.31 Mar 25th Carousel City Classic
153 Columbia Win 14-12 1361.58 Mar 25th Carousel City Classic
71 Cornell Loss 11-14 1190.26 Mar 25th Carousel City Classic
37 McGill Loss 7-13 1215.84 Mar 26th Carousel City Classic
62 Harvard Win 11-9 1818.11 Mar 26th Carousel City Classic
82 Binghamton Win 14-13 1586.54 Mar 26th Carousel City Classic
**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)