#57 Carnegie Mellon (8-10)

avg: 1587.38  •  sd: 56.44  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Alabama Loss 6-10 1226.98 Feb 9th Queen City Tune Up 2019 Men
108 North Carolina-Charlotte Win 12-6 1904.38 Feb 9th Queen City Tune Up 2019 Men
52 Notre Dame Win 10-9 1751.67 Feb 9th Queen City Tune Up 2019 Men
11 North Carolina State Loss 8-12 1586.41 Feb 9th Queen City Tune Up 2019 Men
40 Dartmouth Loss 9-13 1267.9 Feb 10th Queen City Tune Up 2019 Men
79 Tulane Win 12-11 1581.42 Feb 10th Queen City Tune Up 2019 Men
52 Notre Dame Loss 10-14 1227.97 Feb 10th Queen City Tune Up 2019 Men
81 Georgia Tech Win 12-8 1888.47 Mar 9th Classic City Invite 2019
22 Georgia Loss 9-13 1415.93 Mar 9th Classic City Invite 2019
26 North Carolina-Wilmington Loss 4-13 1180.98 Mar 9th Classic City Invite 2019
28 Northeastern Loss 9-10 1650.83 Mar 9th Classic City Invite 2019
55 Florida State Loss 8-9 1486.67 Mar 10th Classic City Invite 2019
61 Tennessee Win 9-8 1679.19 Mar 10th Classic City Invite 2019
92 John Brown Win 11-1 1977.68 Mar 30th Huck Finn XXIII
152 Arkansas Win 11-2 1753.2 Mar 30th Huck Finn XXIII
98 Kansas Win 9-5 1892.24 Mar 31st Huck Finn XXIII
37 Illinois Loss 5-6 1595.39 Mar 31st Huck Finn XXIII
31 Texas A&M Loss 6-9 1329.85 Mar 31st Huck Finn XXIII
**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)