#218 RIT (5-7)

avg: 754.25  •  sd: 74.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Brandeis Loss 9-13 703.29 Feb 22nd Oak Creek Challenge 2020
209 Pittsburgh-B Loss 9-13 393.6 Feb 22nd Oak Creek Challenge 2020
79 Liberty Loss 8-11 955.61 Feb 22nd Oak Creek Challenge 2020
375 Regent University** Win 15-1 600 Ignored Feb 23rd Oak Creek Challenge 2020
151 George Washington Loss 13-14 881.59 Feb 23rd Oak Creek Challenge 2020
211 Maine Win 14-13 929.9 Mar 7th Mash Up 2020
96 Dayton Loss 5-13 633.66 Mar 7th Mash Up 2020
247 Towson Loss 10-15 201.51 Mar 7th Mash Up 2020
239 Rhode Island Win 11-8 1058.64 Mar 8th Mash Up 2020
304 North Carolina State-B Win 13-7 867.2 Mar 8th Mash Up 2020
147 Wesleyan Loss 9-13 589.65 Mar 8th Mash Up 2020
247 Towson Win 13-9 1073.68 Mar 8th Mash Up 2020
**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)