#81 Iowa (15-3)

avg: 1337.61  •  sd: 90  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
49 St Olaf Loss 8-12 1062.01 Mar 2nd Midwest Throwdown 2024
170 Minnesota-Duluth Loss 10-12 712.65 Mar 2nd Midwest Throwdown 2024
370 Northwestern-B** Win 13-0 394.7 Ignored Mar 2nd Midwest Throwdown 2024
317 Washington University-B** Win 11-2 870.22 Ignored Mar 3rd Midwest Throwdown 2024
259 Wisconsin-B Win 7-4 1056.58 Mar 3rd Midwest Throwdown 2024
161 Truman State Win 9-6 1411.25 Mar 3rd Midwest Throwdown 2024
176 Saint Louis Win 10-7 1315.91 Mar 3rd Midwest Throwdown 2024
215 Texas-B Win 13-6 1361.63 Mar 9th Centex Tier 2 2024
300 Texas A&M-B** Win 13-4 951.81 Ignored Mar 9th Centex Tier 2 2024
172 Texas-San Antonio Win 13-3 1540.29 Mar 9th Centex Tier 2 2024
136 Houston Win 15-5 1707.21 Mar 10th Centex Tier 2 2024
212 San Diego State Win 15-8 1335.73 Mar 10th Centex Tier 2 2024
248 Carthage** Win 13-1 1230.79 Ignored Mar 30th Old Capitol Open 2024
299 Minnesota-C** Win 13-3 957.54 Ignored Mar 30th Old Capitol Open 2024
337 St Thomas** Win 11-4 716.69 Ignored Mar 30th Old Capitol Open 2024
102 Davenport Win 9-6 1635.83 Mar 31st Old Capitol Open 2024
124 Macalester Win 9-7 1425.92 Mar 31st Old Capitol Open 2024
55 Michigan State Loss 8-9 1340.76 Mar 31st Old Capitol Open 2024
**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)