#67 Indiana (12-8)

avg: 1443.25  •  sd: 61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Clemson Win 13-7 1691.67 Feb 8th Bulldog Brawl
72 Southern Illinois-Edwardsville Win 13-11 1625.45 Feb 8th Bulldog Brawl
185 Union (Tennessee) Win 12-9 1224.29 Feb 8th Bulldog Brawl
183 Kennesaw State Win 15-10 1344.34 Feb 9th Bulldog Brawl
133 Lipscomb Win 15-8 1679.67 Feb 9th Bulldog Brawl
72 Southern Illinois-Edwardsville Win 15-7 1996.61 Feb 9th Bulldog Brawl
96 Appalachian State Win 13-10 1602.57 Feb 22nd Easterns Qualifier 2025
66 Dartmouth Win 12-11 1577.25 Feb 22nd Easterns Qualifier 2025
52 William & Mary Win 10-8 1814.15 Feb 22nd Easterns Qualifier 2025
49 North Carolina State Win 11-9 1814 Feb 22nd Easterns Qualifier 2025
44 Emory Loss 10-14 1209.49 Feb 23rd Easterns Qualifier 2025
64 James Madison Loss 12-13 1332.38 Feb 23rd Easterns Qualifier 2025
47 McGill Loss 7-15 991.44 Feb 23rd Easterns Qualifier 2025
35 Chicago Loss 6-15 1054.33 Mar 29th Huck Finn 2025
39 Cincinnati Loss 9-10 1505.04 Mar 29th Huck Finn 2025
11 Davenport Loss 7-14 1398.06 Mar 29th Huck Finn 2025
42 Stanford Loss 11-14 1304.03 Mar 29th Huck Finn 2025
44 Emory Loss 4-11 1008.19 Mar 30th Huck Finn 2025
77 Iowa State Win 13-11 1578.87 Mar 30th Huck Finn 2025
84 Ohio State Win 11-10 1444.67 Mar 30th Huck Finn 2025
**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)