#239 Columbia-B (5-6)

avg: 234.82  •  sd: 103.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
184 Northeastern-B Loss 3-8 20.36 Feb 16th Cherry Blossom Classic 2019
164 Pittsburgh-B Loss 5-8 325.48 Feb 16th Cherry Blossom Classic 2019
263 George Washington-B Win 13-1 622.34 Feb 16th Cherry Blossom Classic 2019
273 American-B Win 10-7 191.16 Feb 17th Cherry Blossom Classic 2019
281 Towson-B** Win 11-2 188.3 Ignored Feb 17th Cherry Blossom Classic 2019
263 George Washington-B Loss 1-8 -577.66 Feb 17th Cherry Blossom Classic 2019
159 SUNY-Albany Loss 6-10 328.28 Mar 30th Garden State 9
256 College of New Jersey Win 8-3 709.93 Mar 30th Garden State 9
- Dickinson Loss 4-10 -10.6 Mar 30th Garden State 9
248 Johns Hopkins University Loss 9-10 37.52 Mar 31st Garden State 9
266 Messiah Win 8-3 521.97 Mar 31st Garden State 9
**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)