#142 Saint Louis (7-11)

avg: 738.41  •  sd: 91.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
132 Iowa Loss 5-7 498.7 Mar 1st Midwest Throwdown 2025
92 Iowa State Loss 4-11 512.02 Mar 1st Midwest Throwdown 2025
216 Washington University-B Win 13-1 864.3 Mar 1st Midwest Throwdown 2025
147 Wisconsin-La Crosse Win 10-9 849.89 Mar 1st Midwest Throwdown 2025
129 Winona State Loss 7-8 720.74 Mar 2nd Midwest Throwdown 2025
147 Wisconsin-La Crosse Win 7-6 849.89 Mar 2nd Midwest Throwdown 2025
132 Iowa Win 10-4 1426.85 Mar 29th Old Capitol Open 2025
102 Macalester Loss 8-9 930.12 Mar 29th Old Capitol Open 2025
215 Minnesota-Duluth Win 13-3 868.78 Mar 29th Old Capitol Open 2025
74 Purdue Loss 1-11 660.76 Mar 29th Old Capitol Open 2025
102 Macalester Loss 5-7 726.97 Mar 30th Old Capitol Open 2025
110 Michigan State Loss 5-10 432.05 Mar 30th Old Capitol Open 2025
118 Northwestern Win 7-4 1435.77 Mar 30th Old Capitol Open 2025
111 Kansas Loss 6-13 401.06 Apr 12th Ozarks D I Womens Conferences 2025
97 Arkansas Loss 5-11 493.15 Apr 13th Ozarks D I Womens Conferences 2025
216 Washington University-B Win 10-9 389.3 Apr 13th Ozarks D I Womens Conferences 2025
4 Colorado** Loss 1-15 1727.97 Ignored Apr 26th South Central D I College Womens Regionals 2025
28 Missouri** Loss 5-12 1134.08 Ignored Apr 27th South Central D I College Womens Regionals 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)