#107 Iowa (15-5)

avg: 1222.36  •  sd: 82.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
250 Illinois State** Win 13-4 1212.58 Ignored Mar 1st Midwest Throwdown 2025
385 Wisconsin-Eau Claire-B** Win 13-2 265.55 Ignored Mar 1st Midwest Throwdown 2025
72 Southern Illinois-Edwardsville Loss 6-9 978.05 Mar 1st Midwest Throwdown 2025
178 Minnesota-B Win 10-8 1183.4 Mar 2nd Midwest Throwdown 2025
82 St Olaf Win 10-9 1446 Mar 2nd Midwest Throwdown 2025
86 Marquette Win 10-9 1437.53 Mar 2nd Midwest Throwdown 2025
100 Missouri Loss 6-12 686.04 Mar 2nd Midwest Throwdown 2025
100 Missouri Loss 10-11 1140.35 Mar 15th Mens Centex 2025
103 Texas A&M Win 9-7 1520 Mar 15th Mens Centex 2025
74 Oklahoma Christian Loss 4-11 778.09 Mar 15th Mens Centex 2025
77 Iowa State Loss 7-13 792.5 Mar 15th Mens Centex 2025
268 Harding Win 15-12 836.45 Mar 16th Mens Centex 2025
103 Texas A&M Win 15-13 1454.84 Mar 16th Mens Centex 2025
135 Mississippi State Win 15-6 1711.28 Mar 16th Mens Centex 2025
220 Winona State Win 13-5 1314.32 Mar 29th Old Capitol Open 2025
161 Wisconsin-Eau Claire Win 13-9 1402.74 Mar 29th Old Capitol Open 2025
312 St Thomas Win 13-8 802.01 Mar 29th Old Capitol Open 2025
201 Northern Iowa Win 13-9 1229.69 Mar 30th Old Capitol Open 2025
123 Wisconsin-Milwaukee Win 11-8 1512.6 Mar 30th Old Capitol Open 2025
156 Wisconsin-La Crosse Win 13-6 1618.24 Mar 30th Old Capitol Open 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)