#227 St John's (Minnesota) (9-9)

avg: 1029.82  •  sd: 53.71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP Loss 1-13 1004.23 Mar 2nd Midwest Throwdown 2024
294 Knox Win 11-10 862.81 Mar 2nd Midwest Throwdown 2024
51 Missouri** Loss 3-13 1164.88 Ignored Mar 2nd Midwest Throwdown 2024
143 Truman State Loss 5-10 763.16 Mar 2nd Midwest Throwdown 2024
373 Northwestern-B Win 9-6 750.98 Mar 3rd Midwest Throwdown 2024
161 Saint Louis Loss 6-9 864.91 Mar 3rd Midwest Throwdown 2024
320 Washington University-B Win 9-6 1069.9 Mar 3rd Midwest Throwdown 2024
223 Eastern Michigan Loss 7-8 914.98 Mar 30th Old Capitol Open 2024
373 Northwestern-B** Win 13-0 932.42 Ignored Mar 30th Old Capitol Open 2024
134 Macalester Loss 6-13 772.12 Mar 30th Old Capitol Open 2024
304 Luther College Win 12-7 1215.48 Mar 30th Old Capitol Open 2024
278 St Thomas Win 13-6 1441.12 Mar 31st Old Capitol Open 2024
304 Luther College Win 13-4 1294.97 Mar 31st Old Capitol Open 2024
391 Bethel** Win 15-4 782.71 Ignored Apr 13th Northwoods D III Mens Conferences 2024
353 Carleton College-Karls-C Win 13-6 1094.35 Apr 13th Northwoods D III Mens Conferences 2024
45 St Olaf Loss 8-14 1275.31 Apr 13th Northwoods D III Mens Conferences 2024
134 Macalester Loss 9-10 1247.12 Apr 14th Northwoods D III Mens Conferences 2024
278 St Thomas Loss 11-13 612.28 Apr 14th Northwoods D III Mens Conferences 2024
**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)