#106 RIT (8-5)

avg: 1315.75  •  sd: 93.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
103 Delaware Win 13-10 1652.33 Feb 24th Oak Creek Challenge 2018
173 Oberlin Win 13-10 1361.89 Feb 24th Oak Creek Challenge 2018
107 Rutgers Win 13-10 1642.51 Feb 24th Oak Creek Challenge 2018
145 Drexel Loss 13-15 935.13 Feb 25th Oak Creek Challenge 2018
80 Amherst Loss 11-15 1028.76 Feb 25th Oak Creek Challenge 2018
56 Temple Win 15-12 1810.1 Feb 25th Oak Creek Challenge 2018
180 Pittsburgh-B Win 9-6 1430.45 Mar 24th CWRUL Memorial 2018
94 Kentucky Loss 10-12 1124.54 Mar 24th CWRUL Memorial 2018
93 Cincinnati Loss 8-13 867.26 Mar 24th CWRUL Memorial 2018
144 Dayton Win 14-7 1736.41 Mar 25th CWRUL Memorial 2018
180 Pittsburgh-B Win 13-9 1430.45 Mar 25th CWRUL Memorial 2018
178 Shippensburg Win 15-11 1401.02 Mar 25th CWRUL Memorial 2018
105 Wisconsin-Milwaukee Loss 5-10 743.53 Mar 25th CWRUL Memorial 2018
**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)