#122 Minnesota-B (16-7)

avg: 1400.63  •  sd: 89.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP Win 12-11 1729.23 Feb 10th Ugly Dome 2024
134 Macalester Loss 10-12 1133.99 Feb 10th Ugly Dome 2024
45 St Olaf Loss 10-12 1573.22 Feb 10th Ugly Dome 2024
94 Wisconsin-Eau Claire Win 13-5 2134.71 Feb 10th Ugly Dome 2024
299 Minnesota-C** Win 13-4 1318.6 Ignored Feb 12th Ugly Dome 2024
278 St Thomas Win 11-8 1206.73 Mar 21st Minneapolis Makeup
391 Bethel** Win 11-3 782.71 Ignored Mar 23rd Minneapolis Makeup
134 Macalester Loss 6-7 1247.12 Mar 23rd Minneapolis Makeup
299 Minnesota-C** Win 11-1 1318.6 Ignored Mar 28th Minneapolis Makeup
101 Colorado Mines Loss 8-11 1146.47 Mar 30th Old Capitol Open 2024
414 Wisconsin-Milwaukee-B** Win 13-0 135.23 Ignored Mar 30th Old Capitol Open 2024
373 Northwestern-B** Win 13-3 932.42 Ignored Mar 30th Old Capitol Open 2024
145 Southern Illinois-Edwardsville Loss 10-13 1003.61 Mar 30th Old Capitol Open 2024
223 Eastern Michigan Win 13-4 1639.98 Mar 31st Old Capitol Open 2024
200 Northern Iowa Win 12-2 1721.31 Mar 31st Old Capitol Open 2024
184 Wisconsin-La Crosse Win 8-7 1311.43 Mar 31st Old Capitol Open 2024
368 Iowa State-B** Win 15-3 1000.66 Ignored Apr 13th North Central Dev Mens Conferences 2024
400 Iowa-B** Win 15-2 642.04 Ignored Apr 13th North Central Dev Mens Conferences 2024
299 Minnesota-C** Win 15-2 1318.6 Ignored Apr 14th North Central Dev Mens Conferences 2024
257 Wisconsin-B Win 15-6 1517.85 Apr 14th North Central Dev Mens Conferences 2024
107 Iowa State Loss 14-15 1351.13 Apr 27th North Central D I College Mens Regionals 2024
299 Minnesota-C** Win 15-2 1318.6 Ignored Apr 27th North Central D I College Mens Regionals 2024
140 Marquette Loss 14-15 1218.49 Apr 27th North Central D I College Mens Regionals 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)