#47 Case Western Reserve (14-12)

avg: 1455.38  •  sd: 59.43  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
3 Brigham Young Loss 7-15 1517.51 Jan 28th Santa Barbara Invitational 2023
33 Victoria Win 12-11 1751.01 Jan 28th Santa Barbara Invitational 2023
14 UCLA Loss 12-13 1705.83 Jan 28th Santa Barbara Invitational 2023
110 Southern California Win 14-10 1530.26 Jan 28th Santa Barbara Invitational 2023
57 Utah Loss 6-13 806.45 Jan 29th Santa Barbara Invitational 2023
20 Washington Loss 10-13 1413.91 Jan 29th Santa Barbara Invitational 2023
62 Northwestern Win 12-8 1828.84 Jan 29th Santa Barbara Invitational 2023
61 Harvard Win 15-10 1842.06 Feb 11th Queen City Tune Up1
73 Purdue Win 15-13 1548.89 Feb 11th Queen City Tune Up1
24 North Carolina-Charlotte Win 13-11 1929.26 Feb 11th Queen City Tune Up1
2 North Carolina Loss 10-15 1684.78 Feb 11th Queen City Tune Up1
39 William & Mary Loss 8-9 1410.44 Feb 12th Queen City Tune Up1
25 North Carolina-Wilmington Loss 9-10 1569.13 Feb 12th Queen City Tune Up1
154 George Washington Win 13-12 1071.65 Feb 25th Easterns Qualifier 2023
50 Virginia Loss 8-13 947.44 Feb 25th Easterns Qualifier 2023
48 Cornell Win 13-11 1682.59 Feb 25th Easterns Qualifier 2023
28 Georgia Tech Loss 9-13 1254.22 Feb 25th Easterns Qualifier 2023
34 McGill Win 15-14 1747.7 Feb 26th Easterns Qualifier 2023
70 Notre Dame Win 15-11 1736.86 Feb 26th Easterns Qualifier 2023
28 Georgia Tech Loss 7-15 1072.79 Feb 26th Easterns Qualifier 2023
122 Carnegie Mellon Win 12-5 1682.27 Mar 25th Carousel City Classic
148 Columbia Win 14-12 1189.63 Mar 25th Carousel City Classic
48 Cornell Loss 11-14 1140.41 Mar 25th Carousel City Classic
34 McGill Loss 7-13 1065.17 Mar 26th Carousel City Classic
61 Harvard Win 11-9 1637.66 Mar 26th Carousel City Classic
74 Binghamton Win 14-13 1443.61 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)