#92 Tennessee (3-8)

avg: 1083.19  •  sd: 90.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
74 Indiana Loss 13-14 1086.97 Jan 27th Carolina Kickoff 2024
34 South Carolina Loss 8-15 995.94 Jan 27th Carolina Kickoff 2024
25 North Carolina-Wilmington Loss 6-15 1067.14 Jan 27th Carolina Kickoff 2024
69 Carleton College-CHOP Loss 11-15 860.4 Jan 28th Carolina Kickoff 2024
199 North Carolina-B Win 15-12 645.22 Jan 28th Carolina Kickoff 2024
76 Georgetown Loss 11-15 824.43 Feb 10th Queen City Tune Up 2024
34 South Carolina Loss 8-15 995.94 Feb 10th Queen City Tune Up 2024
12 Alabama-Huntsville Loss 8-15 1285.87 Feb 10th Queen City Tune Up 2024
56 Missouri Win 15-9 1869.85 Feb 10th Queen City Tune Up 2024
23 McGill Loss 7-15 1109.57 Feb 11th Queen City Tune Up 2024
114 Harvard Win 8-7 1091.79 Feb 11th Queen City Tune Up 2024
**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)