#355 Northwestern-B (6-13)

avg: 458.9  •  sd: 59.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
418 South Florida-B Win 10-7 490.1 Feb 8th Florida Warm Up 2019
300 High Point Loss 7-9 397.8 Feb 8th Florida Warm Up 2019
207 North Florida Loss 0-13 365.51 Feb 8th Florida Warm Up 2019
377 Stetson Win 13-3 953.38 Feb 9th Florida Warm Up 2019
294 Florida Gulf Coast Loss 7-10 307.87 Feb 9th Florida Warm Up 2019
246 Florida-B Loss 5-11 275.42 Feb 9th Florida Warm Up 2019
377 Stetson Win 15-4 953.38 Feb 10th Florida Warm Up 2019
415 Florida Tech-B Win 15-6 728.75 Feb 10th Florida Warm Up 2019
143 Minnesota-Duluth** Loss 1-9 599.07 Ignored Mar 9th D III Midwestern Invite 2019
- Wisconsin-Stevens Point Loss 4-6 184.62 Mar 9th D III Midwestern Invite 2019
240 Wisconsin-Eau Claire Loss 4-6 524.23 Mar 9th D III Midwestern Invite 2019
167 Minnesota State-Mankato Loss 3-6 542.59 Mar 10th D III Midwestern Invite 2019
362 Wisconsin-Oshkosh Win 7-5 744.51 Mar 10th D III Midwestern Invite 2019
391 John Carroll Win 13-8 768.57 Mar 23rd CWRUL Memorial 2019
210 Rochester Loss 5-10 379.4 Mar 23rd CWRUL Memorial 2019
158 Lehigh** Loss 3-13 529.08 Ignored Mar 23rd CWRUL Memorial 2019
286 Toledo Loss 4-12 122.93 Mar 23rd CWRUL Memorial 2019
331 Kenyon Loss 10-12 321.85 Mar 24th CWRUL Memorial 2019
347 Wright State Loss 9-15 -24.32 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)