#76 Chicago (11-6)

avg: 1415.31  •  sd: 79.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
20 Cal Poly-SLO Loss 9-15 1327.64 Feb 17th Presidents Day Invitational Tournament 2018
65 California-Santa Barbara Loss 9-11 1213.17 Feb 17th Presidents Day Invitational Tournament 2018
19 Colorado Loss 2-15 1250.95 Feb 17th Presidents Day Invitational Tournament 2018
59 Santa Clara Win 14-7 2082.66 Feb 18th Presidents Day Invitational Tournament 2018
143 California-San Diego Win 11-10 1285.92 Feb 18th Presidents Day Invitational Tournament 2018
60 Cornell Loss 8-9 1348.23 Feb 19th Presidents Day Invitational Tournament 2018
65 California-Santa Barbara Loss 8-14 926.34 Feb 19th Presidents Day Invitational Tournament 2018
150 North Carolina-Asheville Win 10-9 1256.08 Mar 17th College Southerns 2018
340 Stetson** Win 13-3 1010.63 Ignored Mar 17th College Southerns 2018
273 Wake Forest** Win 13-2 1301.68 Ignored Mar 17th College Southerns 2018
125 Georgia College Win 12-11 1340.81 Mar 17th College Southerns 2018
224 Georgia Southern Win 13-3 1461.24 Mar 18th College Southerns 2018
69 Carleton College-GoP Win 12-6 2028.77 Mar 18th College Southerns 2018
125 Georgia College Win 7-4 1711.97 Mar 18th College Southerns 2018
89 John Brown Win 14-13 1507.31 Mar 31st Huck Finn 2018
95 Purdue Win 15-12 1653.43 Mar 31st Huck Finn 2018
75 Tennessee-Chattanooga Loss 8-15 850.86 Mar 31st Huck Finn 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)