#190 Northern Iowa (9-8)

avg: 975.78  •  sd: 71.8  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
233 Missouri Loss 7-13 254.87 Mar 3rd Midwest Throwdown 2018
342 Washington University-B Win 14-8 932.4 Mar 3rd Midwest Throwdown 2018
420 Wisconsin-C** Win 15-3 427.08 Ignored Mar 3rd Midwest Throwdown 2018
47 Iowa State Loss 8-15 1003.44 Mar 4th Midwest Throwdown 2018
96 Missouri State Loss 8-13 854.34 Mar 4th Midwest Throwdown 2018
170 Kansas State Win 15-14 1184.64 Mar 4th Midwest Throwdown 2018
144 Dayton Loss 8-13 657.37 Mar 24th CWRUL Memorial 2018
378 Michigan State-B** Win 13-3 854.04 Ignored Mar 24th CWRUL Memorial 2018
229 Toledo Win 13-10 1160.52 Mar 24th CWRUL Memorial 2018
105 Wisconsin-Milwaukee Loss 7-15 717.43 Mar 24th CWRUL Memorial 2018
178 Shippensburg Win 10-8 1282.52 Mar 25th CWRUL Memorial 2018
245 West Virginia Win 12-6 1360.1 Mar 25th CWRUL Memorial 2018
133 Case Western Reserve Loss 9-11 926.56 Mar 25th CWRUL Memorial 2018
123 Nebraska Loss 9-11 1001.23 Mar 31st Illinois Invite 2018
246 Winona State Win 10-9 904.81 Mar 31st Illinois Invite 2018
201 Wisconsin-Eau Claire Win 9-6 1351.2 Mar 31st Illinois Invite 2018
118 Wisconsin-Whitewater Loss 7-9 985.49 Mar 31st Illinois Invite 2018
**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)