#264 Northwestern-B (2-8)

avg: -3.52  •  sd: 70.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
207 Wisconsin-Eau Claire Loss 3-5 80.27 Mar 9th D III Midwestern Invite 2019
160 DePaul** Loss 2-11 214.05 Ignored Mar 9th D III Midwestern Invite 2019
208 Wisconsin-Oshkosh Loss 3-4 369.94 Mar 9th D III Midwestern Invite 2019
172 Northern Iowa Loss 2-5 124.75 Mar 10th D III Midwestern Invite 2019
114 Minnesota-Duluth** Loss 3-11 452.38 Ignored Mar 10th D III Midwestern Invite 2019
185 Kenyon** Loss 2-12 12.2 Mar 23rd CWRUL Memorial 2019
285 Eastern Michigan** Win 13-1 -3.52 Ignored Mar 23rd CWRUL Memorial 2019
262 Michigan-B Loss 6-7 -90.4 Mar 23rd CWRUL Memorial 2019
228 Xavier Loss 4-13 -266.51 Mar 24th CWRUL Memorial 2019
285 Eastern Michigan Win 9-4 -3.52 Mar 24th CWRUL Memorial 2019
**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)