#88 Tennessee-Chattanooga (8-12)

avg: 1419.19  •  sd: 56.91  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
87 Case Western Reserve Loss 11-13 1193.72 Feb 2nd Mid Atlantic Warmup 2019
91 Mary Washington Win 12-10 1620.63 Feb 2nd Mid Atlantic Warmup 2019
157 Drexel Win 11-9 1378.61 Feb 2nd Mid Atlantic Warmup 2019
32 William & Mary Loss 9-11 1497.48 Feb 2nd Mid Atlantic Warmup 2019
85 Richmond Win 11-9 1678.91 Feb 3rd Mid Atlantic Warmup 2019
113 Davidson Win 15-9 1817.38 Feb 3rd Mid Atlantic Warmup 2019
32 William & Mary Loss 11-15 1365.52 Feb 3rd Mid Atlantic Warmup 2019
33 Johns Hopkins Loss 7-13 1173.63 Feb 16th Easterns Qualifier 2019
101 Connecticut Loss 9-12 1010.87 Feb 16th Easterns Qualifier 2019
145 Dayton Win 12-4 1789.68 Feb 16th Easterns Qualifier 2019
44 Virginia Loss 11-12 1546.41 Feb 16th Easterns Qualifier 2019
120 James Madison Win 14-9 1756.67 Feb 17th Easterns Qualifier 2019
87 Case Western Reserve Win 11-8 1788.17 Feb 17th Easterns Qualifier 2019
64 Ohio Loss 13-15 1325.22 Feb 17th Easterns Qualifier 2019
143 Minnesota-Duluth Loss 11-12 1074.07 Mar 16th Tally Classic XIV
55 Florida State Loss 11-13 1382.83 Mar 16th Tally Classic XIV
68 Cincinnati Loss 14-15 1390.37 Mar 16th Tally Classic XIV
24 Auburn Loss 7-12 1276.27 Mar 16th Tally Classic XIV
119 Clemson Win 15-12 1584.04 Mar 17th Tally Classic XIV
103 Georgia State Loss 10-13 1020.24 Mar 17th Tally Classic XIV
**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)