#91 Indiana (14-13)

avg: 1270.81  •  sd: 55.33  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
92 Tennessee Win 14-13 1392.11 Jan 27th Carolina Kickoff 2024
29 South Carolina Loss 6-15 1083.91 Jan 27th Carolina Kickoff 2024
28 North Carolina-Wilmington Loss 9-12 1389.14 Jan 27th Carolina Kickoff 2024
125 Davidson Win 14-6 1746.57 Jan 28th Carolina Kickoff 2024
72 Georgetown Win 11-10 1490.7 Jan 28th Carolina Kickoff 2024
126 Lehigh Win 11-10 1270.39 Jan 28th Carolina Kickoff 2024
66 Virginia Win 11-10 1519.17 Feb 10th Queen City Tune Up 2024
1 North Carolina** Loss 4-15 1688.56 Ignored Feb 10th Queen City Tune Up 2024
25 McGill Loss 9-15 1258.77 Feb 10th Queen City Tune Up 2024
28 North Carolina-Wilmington Loss 10-14 1335.8 Feb 10th Queen City Tune Up 2024
72 Georgetown Loss 12-14 1144.74 Feb 11th Queen City Tune Up 2024
106 Notre Dame Win 13-8 1706.48 Feb 11th Queen City Tune Up 2024
41 Florida Loss 9-12 1225.66 Feb 24th Mardi Gras XXXVI college
139 LSU Win 11-8 1450.21 Feb 24th Mardi Gras XXXVI college
230 Texas State Win 12-7 1235.03 Feb 24th Mardi Gras XXXVI college
261 Texas Tech** Win 13-3 1155.11 Ignored Feb 24th Mardi Gras XXXVI college
82 Central Florida Loss 2-9 737.27 Feb 25th Mardi Gras XXXVI college
97 Florida State Win 13-11 1476.61 Feb 25th Mardi Gras XXXVI college
37 Texas A&M Win 10-9 1715.94 Feb 25th Mardi Gras XXXVI college
87 Tennessee-Chattanooga Loss 4-13 709.98 Feb 25th Mardi Gras XXXVI college
53 Colorado State Win 10-9 1595.56 Mar 30th Huck Finn 2024
105 Mississippi State Loss 6-8 910.29 Mar 30th Huck Finn 2024
209 Oklahoma Win 11-8 1149.61 Mar 30th Huck Finn 2024
121 Iowa State Win 11-8 1520.67 Mar 30th Huck Finn 2024
50 Alabama Loss 3-13 901.57 Mar 31st Huck Finn 2024
67 Chicago Loss 7-9 1107.68 Mar 31st Huck Finn 2024
49 St Olaf Loss 7-12 982.65 Mar 31st Huck Finn 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)