#299 Minnesota-C (5-16)

avg: 718.6  •  sd: 78.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
78 Carleton College-CHOP** Loss 3-13 1004.23 Ignored Feb 10th Ugly Dome 2024
45 St Olaf** Loss 3-13 1211.34 Ignored Feb 10th Ugly Dome 2024
94 Wisconsin-Eau Claire** Loss 2-13 934.71 Ignored Feb 10th Ugly Dome 2024
134 Macalester Loss 7-10 982.45 Feb 10th Ugly Dome 2024
122 Minnesota-B** Loss 4-13 800.63 Ignored Feb 12th Ugly Dome 2024
122 Minnesota-B** Loss 1-11 800.63 Ignored Mar 28th Minneapolis Makeup
63 Iowa** Loss 3-13 1086.69 Ignored Mar 30th Old Capitol Open 2024
278 St Thomas Win 12-4 1441.12 Mar 30th Old Capitol Open 2024
185 Minnesota-Duluth Loss 7-12 657.08 Mar 30th Old Capitol Open 2024
223 Eastern Michigan Loss 6-12 460.67 Mar 31st Old Capitol Open 2024
263 Illinois State Loss 6-11 351.5 Mar 31st Old Capitol Open 2024
200 Northern Iowa Loss 6-8 820.82 Mar 31st Old Capitol Open 2024
400 Iowa-B** Win 15-5 642.04 Ignored Apr 13th North Central Dev Mens Conferences 2024
414 Wisconsin-Milwaukee-B** Win 15-4 135.23 Ignored Apr 13th North Central Dev Mens Conferences 2024
257 Wisconsin-B Loss 8-11 552.24 Apr 13th North Central Dev Mens Conferences 2024
368 Iowa State-B Win 15-10 854.27 Apr 14th North Central Dev Mens Conferences 2024
122 Minnesota-B** Loss 2-15 800.63 Ignored Apr 14th North Central Dev Mens Conferences 2024
11 Minnesota** Loss 3-15 1639.55 Ignored Apr 27th North Central D I College Mens Regionals 2024
122 Minnesota-B** Loss 2-15 800.63 Ignored Apr 27th North Central D I College Mens Regionals 2024
257 Wisconsin-B Loss 8-14 381.81 Apr 27th North Central D I College Mens Regionals 2024
368 Iowa State-B Win 15-10 854.27 Apr 28th 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)