#7 Carleton College (14-3)

avg: 2152.01  •  sd: 53.52  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
50 California-Santa Cruz** Win 14-2 1906.35 Ignored Jan 28th Santa Barbara Invitational 2023
68 Northwestern** Win 13-4 1715.06 Ignored Jan 28th Santa Barbara Invitational 2023
30 California** Win 15-6 2131.91 Ignored Jan 28th Santa Barbara Invitational 2023
8 Stanford Win 11-9 2356.43 Jan 29th Santa Barbara Invitational 2023
17 Victoria Win 13-4 2325.75 Jan 29th Santa Barbara Invitational 2023
18 California-San Diego Win 15-3 2297.49 Jan 29th Santa Barbara Invitational 2023
59 Ohio** Win 12-0 1776.86 Ignored Feb 11th Queen City Tune Up1
55 Appalachian State** Win 15-4 1831.85 Ignored Feb 11th Queen City Tune Up1
14 Virginia Win 13-7 2366.17 Feb 11th Queen City Tune Up1
38 Chicago** Win 15-3 2054.66 Ignored Feb 11th Queen City Tune Up1
5 Vermont Loss 8-9 2122.07 Feb 12th Queen City Tune Up1
4 Tufts Loss 8-10 2037.63 Feb 12th Queen City Tune Up1
9 Washington Win 13-12 2181.54 Mar 25th Northwest Challenge1
29 UCLA Win 13-8 2033.05 Mar 25th Northwest Challenge1
8 Stanford Win 13-11 2336.06 Mar 26th Northwest Challenge1
20 Western Washington Win 13-10 1982 Mar 26th Northwest Challenge1
3 Colorado Loss 8-13 1844.8 Mar 26th Northwest Challenge1
**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)