#30 Auburn (11-8)

avg: 1709.26  •  sd: 63.47  •  top 16/20: 5.4%

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# Opponent Result Game Rating Status Date Event
64 North Carolina-Charlotte Win 11-8 1828.11 Feb 3rd Queen City Tune Up 2018 College Open
16 North Carolina-Wilmington Loss 9-10 1759.51 Feb 3rd Queen City Tune Up 2018 College Open
151 George Mason Win 11-5 1716.84 Feb 3rd Queen City Tune Up 2018 College Open
91 Penn State Win 11-4 1973.9 Feb 3rd Queen City Tune Up 2018 College Open
133 Case Western Reserve Win 11-5 1775.77 Feb 3rd Queen City Tune Up 2018 College Open
41 Northeastern Win 8-4 2168.17 Feb 4th Queen City Tune Up 2018 College Open
168 South Florida Win 13-7 1621.52 Feb 16th Warm Up A Florida Affair 2018
93 Cincinnati Win 12-6 1942.73 Feb 16th Warm Up A Florida Affair 2018
4 Minnesota Loss 8-13 1573.76 Feb 16th Warm Up A Florida Affair 2018
21 Texas A&M Loss 7-13 1264.53 Feb 16th Warm Up A Florida Affair 2018
52 Harvard Loss 12-13 1411.01 Feb 17th Warm Up A Florida Affair 2018
18 Brigham Young Loss 8-13 1357.23 Feb 17th Warm Up A Florida Affair 2018
39 Northwestern Loss 10-15 1175.09 Feb 17th Warm Up A Florida Affair 2018
37 Central Florida Win 13-10 1962.9 Feb 18th Warm Up A Florida Affair 2018
29 Texas Loss 9-11 1461.89 Feb 18th Warm Up A Florida Affair 2018
20 Cal Poly-SLO Win 13-11 2071.96 Mar 31st Easterns 2018
10 Virginia Tech Win 14-13 2048.3 Mar 31st Easterns 2018
93 Cincinnati Win 15-12 1663.91 Mar 31st Easterns 2018
6 Brown Loss 11-15 1665.55 Mar 31st Easterns 2018
**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)