#107 Iowa (8-5)

avg: 846.98  •  sd: 77.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
154 Knox Win 14-3 966.53 Mar 4th Midwest Throwdown 2023
- Wisconsin-La Crosse** Win 13-1 363.34 Ignored Mar 4th Midwest Throwdown 2023
67 Kansas Loss 5-12 550.74 Mar 4th Midwest Throwdown 2023
98 Colorado College Win 10-8 1176.22 Mar 5th Midwest Throwdown 2023
56 Arkansas Loss 4-10 723.24 Mar 5th Midwest Throwdown 2023
70 Iowa State Loss 7-9 842.22 Mar 5th Midwest Throwdown 2023
152 Illinois Win 13-4 993.16 Mar 18th Womens Centex1
176 Colorado-B Win 13-8 602.4 Mar 18th Womens Centex1
110 Texas State Win 11-7 1300.88 Mar 18th Womens Centex1
168 LSU** Win 13-4 829.32 Ignored Mar 18th Womens Centex1
98 Colorado College Loss 8-15 348.74 Mar 19th Womens Centex1
114 Rice Win 10-9 922.12 Mar 19th Womens Centex1
71 Northwestern Loss 11-13 888.21 Mar 19th Womens Centex1
**Blowout Eligible


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)