#129 Carnegie Mellon (2-9)

avg: 1028.27  •  sd: 68.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
60 Appalachian State Loss 6-14 757.46 Feb 11th Queen City Tune Up1
70 Maryland Loss 8-12 880.89 Feb 11th Queen City Tune Up1
17 South Carolina Loss 8-15 1128.14 Feb 11th Queen City Tune Up1
13 Tufts** Loss 6-15 1187.74 Ignored Feb 11th Queen City Tune Up1
107 Chicago Loss 9-11 876.81 Feb 12th Queen City Tune Up1
116 Tennessee Win 10-7 1467.7 Feb 12th Queen City Tune Up1
43 Penn State Loss 5-11 851.82 Mar 4th Fish Bowl
25 North Carolina-Wilmington Loss 7-9 1346.11 Mar 4th Fish Bowl
85 Delaware Loss 9-10 1125.14 Mar 5th Fish Bowl
142 Connecticut Win 10-9 1103.09 Mar 5th Fish Bowl
58 James Madison Loss 5-12 761.63 Mar 5th Fish Bowl
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)