#52 Washington University (14-5)

avg: 1738.3  •  sd: 75.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
183 Harding** Win 11-3 1327.98 Ignored Feb 22nd Dust Bowl 2025
45 Missouri Loss 7-10 1438.22 Feb 22nd Dust Bowl 2025
180 Missouri State Win 9-5 1276.31 Feb 22nd Dust Bowl 2025
45 Missouri Loss 9-11 1578.68 Feb 23rd Dust Bowl 2025
187 St Olaf-B** Win 12-5 1268.07 Ignored Feb 23rd Dust Bowl 2025
66 St Olaf Win 6-4 1989.39 Feb 23rd Dust Bowl 2025
45 Missouri Loss 6-9 1409.32 Mar 1st Midwest Throwdown 2025
123 Northwestern Win 11-4 1742.51 Mar 1st Midwest Throwdown 2025
181 Truman State** Win 13-5 1339.96 Ignored Mar 1st Midwest Throwdown 2025
66 St Olaf Win 10-3 2223.78 Mar 1st Midwest Throwdown 2025
152 Iowa Win 11-6 1511.65 Mar 2nd Midwest Throwdown 2025
45 Missouri Win 10-9 1952.88 Mar 2nd Midwest Throwdown 2025
66 St Olaf Win 12-6 2203.1 Mar 2nd Midwest Throwdown 2025
39 California Win 9-8 1992.09 Mar 22nd Womens Centex 2025
195 Texas A&M** Win 13-0 1194.04 Ignored Mar 22nd Womens Centex 2025
136 Trinity** Win 13-5 1635.87 Ignored Mar 22nd Womens Centex 2025
39 California Loss 11-12 1742.09 Mar 23rd Womens Centex 2025
93 Rice Win 14-9 1819.49 Mar 23rd Womens Centex 2025
46 Texas-Dallas Loss 11-15 1441.11 Mar 23rd Womens Centex 2025
**Blowout Eligible

FAQ

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)