#132 Iowa (12-10)

avg: 826.85  •  sd: 92.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
147 Wisconsin-La Crosse Win 6-5 849.89 Mar 1st Midwest Throwdown 2025
216 Washington University-B Win 12-2 864.3 Mar 1st Midwest Throwdown 2025
142 Saint Louis Win 7-5 1066.55 Mar 1st Midwest Throwdown 2025
129 Winona State Win 8-6 1146.23 Mar 1st Midwest Throwdown 2025
44 Washington University Loss 6-11 1016.41 Mar 2nd Midwest Throwdown 2025
92 Iowa State Win 9-8 1237.02 Mar 2nd Midwest Throwdown 2025
173 Grinnell Win 9-3 1193.47 Mar 29th Old Capitol Open 2025
99 Wisconsin-Eau Claire Loss 4-11 486.22 Mar 29th Old Capitol Open 2025
142 Saint Louis Loss 4-10 138.41 Mar 29th Old Capitol Open 2025
147 Wisconsin-La Crosse Loss 1-7 124.89 Mar 30th Old Capitol Open 2025
217 Wisconsin-Milwaukee Win 7-5 589.28 Mar 30th Old Capitol Open 2025
129 Winona State Loss 3-9 245.74 Mar 30th Old Capitol Open 2025
2 Carleton College** Loss 2-15 1963.52 Ignored Apr 12th Western North Central D I Womens Conferences 2025
195 Minnesota-B Win 9-5 997.56 Apr 12th Western North Central D I Womens Conferences 2025
92 Iowa State Loss 6-7 987.02 Apr 12th Western North Central D I Womens Conferences 2025
195 Minnesota-B Win 9-5 997.56 Apr 12th Western North Central D I Womens Conferences 2025
147 Wisconsin-La Crosse Win 9-4 1324.89 Apr 26th North Central D I College Womens Regionals 2025
24 Minnesota** Loss 3-15 1221.36 Ignored Apr 26th North Central D I College Womens Regionals 2025
217 Wisconsin-Milwaukee Win 13-2 861.14 Apr 26th North Central D I College Womens Regionals 2025
99 Wisconsin-Eau Claire Win 7-6 1211.22 Apr 26th North Central D I College Womens Regionals 2025
92 Iowa State Loss 11-13 883.18 Apr 27th North Central D I College Womens Regionals 2025
147 Wisconsin-La Crosse Loss 6-9 306.33 Apr 27th North Central D I College Womens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)