#155 Grinnell (8-4)

avg: 1019.89  •  sd: 65.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
268 Harding Win 9-5 1065.02 Feb 22nd Dust Bowl 2025
267 Texas Tech Win 11-2 1142.88 Feb 22nd Dust Bowl 2025
169 Kansas Win 10-8 1223.13 Feb 22nd Dust Bowl 2025
201 Northern Iowa Win 9-8 936.12 Feb 23rd Dust Bowl 2025
90 Missouri S&T Loss 4-9 695.31 Feb 23rd Dust Bowl 2025
136 North Texas Loss 8-9 985.47 Feb 23rd Dust Bowl 2025
265 St John's (Minnesota) Win 13-9 971.56 Mar 29th Old Capitol Open 2025
123 Wisconsin-Milwaukee Loss 8-13 650.83 Mar 29th Old Capitol Open 2025
216 Minnesota-Duluth Win 13-7 1299.31 Mar 29th Old Capitol Open 2025
117 Colorado Mines Loss 8-12 748.1 Mar 30th Old Capitol Open 2025
161 Wisconsin-Eau Claire Win 7-6 1109.17 Mar 30th Old Capitol Open 2025
201 Northern Iowa Win 11-3 1411.12 Mar 30th Old Capitol Open 2025
**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)