#28 Carnegie Mellon (10-8)

avg: 1718.65  •  sd: 51.46  •  top 16/20: 3.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 5-11 1745.34 Feb 3rd Queen City Tune Up 2018 College Open
12 North Carolina State Loss 5-11 1318.86 Feb 3rd Queen City Tune Up 2018 College Open
22 Tufts Loss 6-10 1254.02 Feb 3rd Queen City Tune Up 2018 College Open
62 Vermont Win 8-7 1590.83 Feb 3rd Queen City Tune Up 2018 College Open
9 Georgia Loss 7-11 1482.39 Feb 3rd Queen City Tune Up 2018 College Open
16 North Carolina-Wilmington Loss 12-14 1663.55 Mar 10th Tally Classic XIII
97 Alabama Win 13-6 1947.93 Mar 10th Tally Classic XIII
140 Florida Tech Win 13-9 1586.04 Mar 10th Tally Classic XIII
8 Massachusetts Loss 10-11 1838.77 Mar 10th Tally Classic XIII
37 Central Florida Win 11-10 1759.75 Mar 10th Tally Classic XIII
46 South Carolina Win 11-8 1944.97 Mar 11th Tally Classic XIII
23 Georgia Tech Win 13-12 1868.96 Mar 11th Tally Classic XIII
61 James Madison Win 13-6 2072.52 Mar 24th Atlantic Coast Open 2018
48 Dartmouth Loss 9-10 1440.43 Mar 24th Atlantic Coast Open 2018
139 Luther Win 13-6 1768.04 Mar 24th Atlantic Coast Open 2018
78 Georgetown Win 10-5 1988.97 Mar 24th Atlantic Coast Open 2018
113 Lehigh Win 12-7 1804.59 Mar 25th Atlantic Coast Open 2018
22 Tufts Loss 8-10 1487.51 Mar 25th Atlantic Coast Open 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)