#105 Iowa (15-3)

avg: 1337.36  •  sd: 77.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
124 Wisconsin-Milwaukee Loss 10-11 1153.72 Mar 2nd Midwest Throwdown 2019
128 Saint Louis Loss 8-11 906.97 Mar 2nd Midwest Throwdown 2019
86 Marquette Win 12-11 1551.08 Mar 2nd Midwest Throwdown 2019
314 Wisconsin-C Win 10-9 734.54 Mar 2nd Midwest Throwdown 2019
226 Miami (Ohio) Win 13-9 1335.01 Mar 16th Shamrock Showdown 2019
269 Ball State Win 13-5 1385.46 Mar 16th Shamrock Showdown 2019
196 Middle Tennessee State Win 12-7 1523.65 Mar 16th Shamrock Showdown 2019
386 Southern Indiana** Win 13-3 887.49 Ignored Mar 16th Shamrock Showdown 2019
386 Southern Indiana** Win 15-4 887.49 Ignored Mar 17th Shamrock Showdown 2019
196 Middle Tennessee State Win 15-6 1603.14 Mar 17th Shamrock Showdown 2019
226 Miami (Ohio) Win 15-3 1516.45 Mar 17th Shamrock Showdown 2019
352 Nebraska-Omaha** Win 13-5 1075.83 Ignored Mar 30th Old Capitol Open 2019
390 Creighton** Win 13-2 874.58 Ignored Mar 30th Old Capitol Open 2019
249 Wisconsin- La Crosse Win 13-8 1353.17 Mar 30th Old Capitol Open 2019
423 Cornell College** Win 15-4 649.86 Ignored Mar 30th Old Capitol Open 2019
53 Indiana Loss 12-13 1501.62 Mar 31st Old Capitol Open 2019
313 Drake** Win 15-6 1213.8 Ignored Mar 31st Old Capitol Open 2019
156 Minnesota-B Win 15-13 1350.45 Mar 31st Old Capitol Open 2019
**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)