#206 Winona State (14-9)

avg: 1032.17  •  sd: 61.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
340 Rose-Hulman Win 15-8 1094.25 Mar 15th Grand Rapids Invite 2025
393 Wisconsin-C** Win 15-3 724.17 Ignored Mar 15th Grand Rapids Invite 2025
402 Wisconsin-Milwaukee-B** Win 15-0 602.25 Ignored Mar 15th Grand Rapids Invite 2025
268 Michigan State-B Win 15-14 924.33 Mar 16th Grand Rapids Invite 2025
268 Michigan State-B Loss 13-14 674.33 Mar 16th Grand Rapids Invite 2025
303 Luther Win 15-9 1181.44 Mar 16th Grand Rapids Invite 2025
171 Northern Iowa Win 11-10 1304.26 Mar 22nd Meltdown 2025
340 Rose-Hulman Win 11-6 1076.14 Mar 22nd Meltdown 2025
256 St Thomas Win 10-9 973.88 Mar 22nd Meltdown 2025
210 Wisconsin-Whitewater Win 9-5 1549.91 Mar 22nd Meltdown 2025
132 Kenyon Loss 7-10 926.1 Mar 23rd Meltdown 2025
72 St Olaf Loss 5-11 989.88 Mar 23rd Meltdown 2025
110 Iowa Loss 5-13 805.74 Mar 29th Old Capitol Open 2025
194 Minnesota-B Loss 3-13 470.89 Mar 29th Old Capitol Open 2025
136 Wisconsin-Eau Claire Loss 9-12 960.93 Mar 29th Old Capitol Open 2025
177 Minnesota-Duluth Win 9-7 1434.69 Mar 30th Old Capitol Open 2025
276 St John's (Minnesota) Win 13-2 1372.54 Mar 30th Old Capitol Open 2025
271 Wisconsin-Platteville Win 13-8 1291.78 Mar 30th Old Capitol Open 2025
363 Bethel Win 15-7 978.16 Apr 12th Northwoods D III Mens Conferences 2025
60 Carleton College-CHOP** Loss 6-15 1046.37 Ignored Apr 12th Northwoods D III Mens Conferences 2025
276 St John's (Minnesota) Win 15-7 1372.54 Apr 12th Northwoods D III Mens Conferences 2025
256 St Thomas Loss 10-14 450.18 Apr 13th Northwoods D III Mens Conferences 2025
151 Macalester Loss 12-15 960.83 Apr 13th Northwoods D III Mens 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)