#240 Georgetown-B (7-5)

avg: 232.56  •  sd: 72.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
273 American-B Win 11-2 401.49 Feb 16th Cherry Blossom Classic 2019
226 Brown-B Loss 1-9 -243.03 Feb 16th Cherry Blossom Classic 2019
281 Towson-B** Win 9-1 188.3 Ignored Feb 16th Cherry Blossom Classic 2019
184 Northeastern-B Loss 4-8 55.56 Feb 17th Cherry Blossom Classic 2019
164 Pittsburgh-B Loss 1-13 179.09 Feb 17th Cherry Blossom Classic 2019
276 Indiana (Pennsylvania) Win 9-4 301.94 Mar 30th Country Roads Classic
198 Shippensburg Loss 7-10 168.72 Mar 30th Country Roads Classic
279 Carnegie Mellon-B Win 8-5 100.68 Mar 30th Country Roads Classic
244 Allegheny Win 11-3 785.96 Mar 31st Country Roads Classic
244 Allegheny Win 7-5 514.1 Mar 31st Country Roads Classic
273 American-B Win 9-7 80.83 Mar 31st Country Roads Classic
198 Shippensburg Loss 5-9 29.33 Mar 31st Country Roads Classic
**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)