#229 Northern Iowa (10-8)

avg: 715.79  •  sd: 66.4  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
314 Kansas-B Win 12-10 521.07 Feb 17th Dust Bowl 2024
47 Oklahoma Christian Loss 6-11 973.48 Feb 17th Dust Bowl 2024
218 Texas-Dallas Loss 6-9 339.29 Feb 17th Dust Bowl 2024
335 Wichita State Win 11-7 587.91 Feb 17th Dust Bowl 2024
335 Wichita State Win 13-9 539.59 Feb 18th Dust Bowl 2024
254 Oklahoma State Loss 8-10 336.86 Feb 18th Dust Bowl 2024
260 Illinois State Win 13-6 1158.62 Mar 23rd Free State Classic
314 Kansas-B Win 11-2 882.94 Mar 23rd Free State Classic
253 Nebraska Win 9-8 730.68 Mar 23rd Free State Classic
342 Wisconsin-Stevens Point** Win 11-2 690.84 Ignored Mar 23rd Free State Classic
253 Nebraska Win 13-6 1205.68 Mar 24th Free State Classic
254 Oklahoma State Loss 10-11 474.53 Mar 24th Free State Classic
102 Davenport Loss 9-10 1092.27 Mar 30th Old Capitol Open 2024
55 Michigan State** Loss 3-11 865.76 Ignored Mar 30th Old Capitol Open 2024
317 Washington University-B Win 13-2 870.22 Mar 30th Old Capitol Open 2024
195 Grinnell Loss 4-9 246.06 Mar 31st Old Capitol Open 2024
140 Minnesota-B Loss 2-12 478.04 Mar 31st Old Capitol Open 2024
299 Minnesota-C Win 8-6 658.04 Mar 31st Old Capitol Open 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)