#143 Minnesota-Duluth (7-4)

avg: 1199.07  •  sd: 52.23  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
355 Northwestern-B** Win 9-1 1058.9 Ignored Mar 9th D III Midwestern Invite 2019
236 Wisconsin-Platteville Win 8-7 1027 Mar 9th D III Midwestern Invite 2019
362 Wisconsin-Oshkosh Win 4-2 912.53 Mar 9th D III Midwestern Invite 2019
167 Minnesota State-Mankato Loss 7-8 964.29 Mar 10th D III Midwestern Invite 2019
240 Wisconsin-Eau Claire Win 8-5 1343.44 Mar 10th D III Midwestern Invite 2019
159 Mississippi State Win 13-12 1250.81 Mar 16th Tally Classic XIV
55 Florida State Loss 9-13 1193.11 Mar 16th Tally Classic XIV
88 Tennessee-Chattanooga Win 12-11 1544.19 Mar 16th Tally Classic XIV
24 Auburn Loss 3-13 1196.78 Mar 16th Tally Classic XIV
52 Notre Dame Loss 10-15 1173.07 Mar 17th Tally Classic XIV
165 Georgia Southern Win 14-13 1216.91 Mar 17th Tally Classic XIV
**Blowout Eligible

FAQ

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
  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
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)