#210 Cedarville (6-5)

avg: 484.08  •  sd: 95.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
249 Alabama-Birmingham Win 10-8 415.43 Feb 16th First Annual Jillz Jamboree
111 Michigan State Loss 8-9 933.24 Feb 16th First Annual Jillz Jamboree
151 Kentucky Win 9-8 987.08 Feb 16th First Annual Jillz Jamboree
- Union Win 10-2 710.01 Feb 16th First Annual Jillz Jamboree
143 Alabama Loss 6-13 297.51 Feb 17th First Annual Jillz Jamboree
146 Belmont Loss 11-13 659.76 Feb 17th First Annual Jillz Jamboree
280 Swarthmore-B** Win 12-1 202.07 Ignored Mar 30th I 85 Rodeo 2019
179 Davidson Loss 7-13 88.04 Mar 30th I 85 Rodeo 2019
192 William & Mary-B Loss 2-13 -22.99 Mar 30th I 85 Rodeo 2019
271 Virginia-B** Win 9-3 416.54 Ignored Mar 30th I 85 Rodeo 2019
245 Ohio State-B Win 10-6 681.99 Mar 31st I 85 Rodeo 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)