#89 Carleton College-GoP (11-9)

avg: 1278.33  •  sd: 85.34  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
1 North Carolina** Loss 2-13 1730.39 Ignored Jan 25th Carolina Kickoff 2020
81 North Carolina-Charlotte Loss 8-9 1193.03 Jan 25th Carolina Kickoff 2020
92 Duke Loss 8-12 821.28 Jan 25th Carolina Kickoff 2020
155 North Carolina-Asheville Win 11-7 1464.05 Jan 26th Carolina Kickoff 2020
153 Florida State Win 10-5 1577.6 Jan 26th Carolina Kickoff 2020
66 Georgetown Loss 9-10 1267.84 Jan 26th Carolina Kickoff 2020
126 Chico State Win 10-3 1722.43 Feb 8th Stanford Open 2020
302 Santa Clara-B** Win 13-0 916.06 Ignored Feb 8th Stanford Open 2020
61 Washington University Win 11-5 2024.4 Feb 8th Stanford Open 2020
284 San Jose State** Win 13-0 1031.9 Ignored Feb 8th Stanford Open 2020
75 Nevada-Reno Loss 5-6 1235.69 Feb 9th Stanford Open 2020
88 Claremont Loss 4-5 1158.92 Feb 9th Stanford Open 2020
54 California-Davis Loss 3-8 878.06 Feb 9th Stanford Open 2020
145 Nebraska Win 9-8 1142.59 Mar 7th Midwest Throwdown 2020
198 Wisconsin-B-B Win 12-6 1415.24 Mar 7th Midwest Throwdown 2020
353 UW-Eau Claire-B** Win 13-0 466.57 Ignored Mar 7th Midwest Throwdown 2020
108 Chicago Win 8-5 1636.07 Mar 7th Midwest Throwdown 2020
112 Minnesota-Duluth Win 10-9 1294.57 Mar 8th Midwest Throwdown 2020
84 Missouri S&T Loss 4-9 707.9 Mar 8th Midwest Throwdown 2020
133 Missouri Loss 8-9 967.67 Mar 8th Midwest Throwdown 2020
**Blowout Eligible

FAQ

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
  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
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)