#141 Iowa (10-8)

avg: 906.31  •  sd: 92.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
37 Washington University** Loss 1-13 1070.56 Ignored Mar 2nd Midwest Throwdown 2019
89 Iowa State Loss 3-8 623.1 Mar 2nd Midwest Throwdown 2019
75 Purdue Loss 1-13 688.08 Mar 2nd Midwest Throwdown 2019
207 Wisconsin-Eau Claire Win 10-1 1098.84 Mar 2nd Midwest Throwdown 2019
191 Texas Christian Win 9-3 1178.39 Mar 23rd Womens College Centex 2019
109 Texas State Loss 4-9 464.56 Mar 23rd Womens College Centex 2019
287 Houston** Win 11-3 -340.65 Ignored Mar 23rd Womens College Centex 2019
132 Boston University Loss 4-11 379.62 Mar 23rd Womens College Centex 2019
199 Miami Win 13-8 1049.78 Mar 24th Womens College Centex 2019
168 Rice Win 8-7 891.54 Mar 24th Womens College Centex 2019
173 Baylor Win 10-8 984.98 Mar 24th Womens College Centex 2019
69 Notre Dame Loss 8-9 1203.85 Mar 30th Old Capitol Open 2019
175 Kansas Loss 8-9 592.25 Mar 30th Old Capitol Open 2019
162 Nebraska Loss 8-9 671.75 Mar 30th Old Capitol Open 2019
140 Cincinnati Win 8-7 1037.84 Mar 30th Old Capitol Open 2019
219 Cornell College Win 13-1 1005.19 Mar 31st Old Capitol Open 2019
193 Drake Win 13-4 1173.3 Mar 31st Old Capitol Open 2019
177 Wisconsin-La Crosse Win 15-2 1269 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)