#274 St John's (Minnesota) (5-11)

avg: 788.87  •  sd: 66.51  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
195 Minnesota-B Loss 7-11 616.97 Mar 1st Midwest Throwdown 2025
96 Missouri** Loss 3-13 877.57 Ignored Mar 1st Midwest Throwdown 2025
351 Purdue-B Win 10-6 925.45 Mar 1st Midwest Throwdown 2025
238 Illinois State Win 12-10 1162.65 Mar 2nd Midwest Throwdown 2025
73 Iowa State Loss 10-13 1273.74 Mar 2nd Midwest Throwdown 2025
158 Grinnell Loss 9-13 815.43 Mar 29th Old Capitol Open 2025
176 Northern Iowa Loss 10-13 842.69 Mar 29th Old Capitol Open 2025
143 Wisconsin-Milwaukee Loss 7-13 731.05 Mar 29th Old Capitol Open 2025
187 Nebraska Loss 5-11 518.9 Mar 30th Old Capitol Open 2025
253 St Thomas Win 10-7 1252.66 Mar 30th Old Capitol Open 2025
204 Winona State Loss 2-13 445.92 Mar 30th Old Capitol Open 2025
357 Bethel Win 12-10 631.44 Apr 12th Northwoods D III Mens Conferences 2025
60 Carleton College-CHOP** Loss 3-15 1061.88 Ignored Apr 12th Northwoods D III Mens Conferences 2025
204 Winona State Loss 7-15 445.92 Apr 12th Northwoods D III Mens Conferences 2025
357 Bethel Win 15-6 993.31 Apr 13th Northwoods D III Mens Conferences 2025
288 Carleton College-C Loss 11-12 614.46 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)