#201 Northern Iowa (11-7)

avg: 811.12  •  sd: 55.53  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
323 Kansas State Win 13-8 765.7 Feb 22nd Dust Bowl 2025
239 Texas-Dallas Win 12-10 884.89 Feb 22nd Dust Bowl 2025
74 Oklahoma Christian Loss 9-13 959.52 Feb 22nd Dust Bowl 2025
309 Washington University-B Win 13-7 875.51 Feb 22nd Dust Bowl 2025
155 Grinnell Loss 8-9 894.89 Feb 23rd Dust Bowl 2025
194 Saint Louis Loss 4-8 287.19 Feb 23rd Dust Bowl 2025
355 Rose-Hulman Win 11-5 653.18 Mar 22nd Meltdown 2025
220 Winona State Loss 10-11 589.32 Mar 22nd Meltdown 2025
312 St Thomas Win 11-8 671.46 Mar 22nd Meltdown 2025
284 Wisconsin-Whitewater Win 13-5 1035.85 Mar 22nd Meltdown 2025
314 Wisconsin-Stevens Point Win 10-5 866.89 Mar 23rd Meltdown 2025
284 Wisconsin-Whitewater Win 11-4 1035.85 Mar 23rd Meltdown 2025
216 Minnesota-Duluth Loss 11-13 512.94 Mar 29th Old Capitol Open 2025
156 Wisconsin-La Crosse Win 12-11 1143.24 Mar 29th Old Capitol Open 2025
265 St John's (Minnesota) Win 13-10 881.13 Mar 29th Old Capitol Open 2025
155 Grinnell Loss 3-11 419.89 Mar 30th Old Capitol Open 2025
107 Iowa Loss 9-13 803.79 Mar 30th Old Capitol Open 2025
178 Minnesota-B Win 11-9 1169.94 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)