#124 Carleton College-Eclipse (11-9)

avg: 1352.52  •  sd: 75.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 California-Santa Cruz Loss 8-10 1325.09 Feb 10th Stanford Open 2018
189 Sonoma State Win 12-6 1518.97 Feb 10th Stanford Open 2018
147 Humboldt State Win 10-7 1582.22 Feb 10th Stanford Open 2018
138 Santa Clara Loss 8-11 893.93 Feb 10th Stanford Open 2018
76 Pacific Lutheran Loss 3-13 1092.71 Feb 11th Stanford Open 2018
147 Humboldt State Win 11-10 1317.56 Feb 11th Stanford Open 2018
251 Tulsa** Win 11-1 1018.35 Ignored Feb 24th Dust Bowl 2018
161 Saint Louis Win 7-3 1727.7 Feb 24th Dust Bowl 2018
206 Missouri Win 7-3 1418.69 Feb 24th Dust Bowl 2018
114 Nebraska Loss 7-13 849.23 Feb 25th Dust Bowl 2018
156 Missouri S&T Win 7-2 1742.48 Feb 25th Dust Bowl 2018
92 John Brown Loss 8-12 1109.46 Feb 25th Dust Bowl 2018
108 Wisconsin-Eau Claire Loss 9-10 1322.12 Mar 17th College Southerns 2018
174 Florida-B Win 13-3 1637.55 Mar 17th College Southerns 2018
271 Charleston** Win 13-0 600 Ignored Mar 17th College Southerns 2018
62 Central Florida Loss 4-12 1198.88 Mar 17th College Southerns 2018
108 Wisconsin-Eau Claire Loss 3-8 847.12 Mar 18th College Southerns 2018
174 Florida-B Win 14-2 1637.55 Mar 18th College Southerns 2018
54 Florida State Loss 6-8 1555.56 Mar 18th College Southerns 2018
180 South Florida Win 13-2 1600.57 Mar 18th College Southerns 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)