#185 Minnesota-Duluth (7-10)

avg: 1177.59  •  sd: 69.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
63 Iowa Win 12-10 1924.81 Mar 2nd Midwest Throwdown 2024
373 Northwestern-B** Win 13-2 932.42 Ignored Mar 2nd Midwest Throwdown 2024
45 St Olaf** Loss 3-13 1211.34 Ignored Mar 2nd Midwest Throwdown 2024
193 Grinnell Win 7-6 1279.63 Mar 3rd Midwest Throwdown 2024
107 Iowa State Loss 9-10 1351.13 Mar 3rd Midwest Throwdown 2024
51 Missouri Loss 4-9 1164.88 Mar 3rd Midwest Throwdown 2024
134 Macalester Win 4-3 1497.12 Mar 3rd Midwest Throwdown 2024
237 Carthage Win 8-7 1114.65 Mar 30th Old Capitol Open 2024
299 Minnesota-C Win 12-7 1239.11 Mar 30th Old Capitol Open 2024
337 Wisconsin-Stevens Point** Win 9-3 1165.68 Ignored Mar 30th Old Capitol Open 2024
101 Colorado Mines Loss 5-13 912.08 Mar 31st Old Capitol Open 2024
49 Michigan State** Loss 4-10 1178.48 Ignored Mar 31st Old Capitol Open 2024
145 Southern Illinois-Edwardsville Loss 6-8 1031.26 Mar 31st Old Capitol Open 2024
12 Carleton College** Loss 1-15 1624.62 Ignored Apr 13th Northwoods D I Mens Conferences 2024
11 Minnesota** Loss 1-15 1639.55 Ignored Apr 13th Northwoods D I Mens Conferences 2024
107 Iowa State Loss 10-15 1022.53 Apr 27th North Central D I College Mens Regionals 2024
140 Marquette Loss 10-15 889.89 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)