#144 Tennessee (5-12)

avg: 896.06  •  sd: 83.52  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Vanderbilt** Loss 4-13 1073.28 Ignored Jan 26th Clutch Classic 2019
25 Clemson** Loss 2-13 1272.28 Ignored Jan 26th Clutch Classic 2019
204 Georgia Southern Win 12-1 1106.6 Jan 26th Clutch Classic 2019
102 LSU Win 8-6 1419.92 Jan 26th Clutch Classic 2019
18 South Carolina** Loss 3-12 1371.42 Ignored Jan 27th Clutch Classic 2019
93 Kennesaw State Loss 1-13 588.04 Jan 27th Clutch Classic 2019
98 Mississippi State Loss 7-10 738.49 Jan 27th Clutch Classic 2019
180 Georgia State Win 7-3 1230.73 Feb 16th Luminous 2019
18 South Carolina** Loss 2-11 1371.42 Ignored Feb 16th Luminous 2019
93 Kennesaw State Loss 2-10 588.04 Feb 16th Luminous 2019
261 Emory-B** Win 9-3 657.5 Ignored Feb 16th Luminous 2019
18 South Carolina** Loss 1-13 1371.42 Ignored Mar 30th I 85 Rodeo 2019
57 Cornell Loss 9-12 1115.25 Mar 30th I 85 Rodeo 2019
94 Carnegie Mellon Loss 10-12 946.6 Mar 30th I 85 Rodeo 2019
97 Swarthmore Loss 4-11 552.85 Mar 31st I 85 Rodeo 2019
164 Pittsburgh-B Win 14-9 1252.95 Mar 31st I 85 Rodeo 2019
105 Liberty Loss 6-14 487.19 Mar 31st I 85 Rodeo 2019
**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)