#94 Carnegie Mellon (14-5)

avg: 1184.72  •  sd: 81.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
145 American Win 9-7 1171.11 Feb 16th Cherry Blossom Classic 2019
258 Maryland-Baltimore County** Win 13-1 675.92 Ignored Feb 16th Cherry Blossom Classic 2019
242 West Virginia Win 10-8 479.08 Feb 16th Cherry Blossom Classic 2019
147 George Washington Win 10-8 1143.76 Feb 16th Cherry Blossom Classic 2019
145 American Loss 8-11 526.16 Feb 17th Cherry Blossom Classic 2019
166 Richmond Win 10-7 1160.14 Feb 17th Cherry Blossom Classic 2019
147 George Washington Win 13-5 1481.09 Feb 17th Cherry Blossom Classic 2019
111 Michigan State Win 9-3 1658.24 Mar 23rd CWRUL Memorial 2019
85 Dayton Loss 8-9 1118.31 Mar 23rd CWRUL Memorial 2019
75 Purdue Win 13-8 1784.24 Mar 23rd CWRUL Memorial 2019
81 Ohio Win 11-10 1393.3 Mar 24th CWRUL Memorial 2019
262 Michigan-B** Win 12-1 634.6 Ignored Mar 24th CWRUL Memorial 2019
58 Penn State Loss 9-12 1105.68 Mar 24th CWRUL Memorial 2019
91 Case Western Reserve Win 11-10 1327.67 Mar 24th CWRUL Memorial 2019
144 Tennessee Win 12-10 1134.18 Mar 30th I 85 Rodeo 2019
57 Cornell Loss 6-12 881.31 Mar 30th I 85 Rodeo 2019
18 South Carolina** Loss 3-13 1371.42 Ignored Mar 30th I 85 Rodeo 2019
97 Swarthmore Win 10-9 1277.85 Mar 31st I 85 Rodeo 2019
179 Davidson Win 15-10 1099.18 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)