#104 Iowa (12-8)

avg: 966.86  •  sd: 56.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
157 Knox Win 14-3 1151.18 Mar 4th Midwest Throwdown 2023
180 Wisconsin-La Crosse** Win 13-1 934.74 Ignored Mar 4th Midwest Throwdown 2023
66 Kansas Loss 5-12 668.16 Mar 4th Midwest Throwdown 2023
91 Colorado College Win 10-8 1317.73 Mar 5th Midwest Throwdown 2023
52 Arkansas Loss 4-10 810.94 Mar 5th Midwest Throwdown 2023
72 Iowa State Loss 7-9 951.15 Mar 5th Midwest Throwdown 2023
171 Illinois Win 13-4 1008.98 Mar 18th Womens Centex1
190 Colorado-B Win 13-8 725.34 Mar 18th Womens Centex1
109 Texas State Win 11-7 1413.68 Mar 18th Womens Centex1
179 LSU** Win 13-4 947.6 Ignored Mar 18th Womens Centex1
91 Colorado College Loss 8-15 490.25 Mar 19th Womens Centex1
112 Rice Win 10-9 1032.62 Mar 19th Womens Centex1
70 Northwestern Loss 11-13 1009.93 Mar 19th Womens Centex1
124 Saint Louis Win 5-4 944.4 Mar 25th Old Capitol Open
211 Minnesota-B** Win 10-2 474.44 Ignored Mar 25th Old Capitol Open
138 Michigan State Loss 4-5 581.15 Mar 25th Old Capitol Open
68 Winona State Loss 4-6 901.31 Mar 25th Old Capitol Open
27 Minnesota** Loss 2-9 1085.25 Ignored Mar 26th Old Capitol Open
105 Michigan Tech Win 7-6 1084.82 Mar 26th Old Capitol Open
142 Macalester Win 11-5 1273.45 Mar 26th Old Capitol Open
**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)