#129 Winona State (14-11)

avg: 845.74  •  sd: 56.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
132 Iowa Loss 6-8 526.35 Mar 1st Midwest Throwdown 2025
92 Iowa State Loss 4-6 746.41 Mar 1st Midwest Throwdown 2025
200 Truman State Win 6-3 983.64 Mar 1st Midwest Throwdown 2025
246 Wisconsin-B** Win 7-1 627.96 Ignored Mar 1st Midwest Throwdown 2025
200 Truman State Win 10-2 1036.94 Mar 2nd Midwest Throwdown 2025
142 Saint Louis Win 8-7 863.41 Mar 2nd Midwest Throwdown 2025
99 Wisconsin-Eau Claire Loss 2-11 486.22 Mar 2nd Midwest Throwdown 2025
121 Loyola-Chicago Win 6-4 1295.32 Mar 22nd Meltdown 2025
28 Missouri** Loss 2-10 1134.08 Ignored Mar 22nd Meltdown 2025
209 Wheaton (Illinois) Win 8-3 895.13 Mar 22nd Meltdown 2025
194 St Olaf-B Win 10-1 1071.63 Mar 22nd Meltdown 2025
121 Loyola-Chicago Loss 2-8 329.71 Mar 23rd Meltdown 2025
194 St Olaf-B Win 6-1 1071.63 Mar 23rd Meltdown 2025
62 Chicago Loss 3-8 743.7 Mar 29th Old Capitol Open 2025
118 Northwestern Loss 7-8 814.61 Mar 29th Old Capitol Open 2025
147 Wisconsin-La Crosse Win 8-6 1025.39 Mar 29th Old Capitol Open 2025
173 Grinnell Win 8-6 893.96 Mar 30th Old Capitol Open 2025
132 Iowa Win 9-3 1426.85 Mar 30th Old Capitol Open 2025
215 Minnesota-Duluth Win 11-6 815.47 Mar 30th Old Capitol Open 2025
228 Carleton College-C** Win 13-3 780.92 Ignored Apr 12th North Central D III Womens Conferences 2025
102 Macalester Loss 6-7 930.12 Apr 12th North Central D III Womens Conferences 2025
55 St Olaf Loss 4-7 945.73 Apr 12th North Central D III Womens Conferences 2025
259 St Thomas** Win 12-0 229.26 Ignored Apr 12th North Central D III Womens Conferences 2025
102 Macalester Loss 6-11 508.42 Apr 13th North Central D III Womens Conferences 2025
88 Michigan Tech Loss 7-14 552.54 Apr 13th North Central D III Womens Conferences 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)