#67 Chicago (9-9)

avg: 1387.02  •  sd: 45.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
24 British Columbia Loss 6-12 1221.22 Jan 27th Santa Barbara Invite 2024
5 Cal Poly-SLO Loss 8-13 1678.55 Jan 27th Santa Barbara Invite 2024
79 Grand Canyon Win 11-9 1589.9 Jan 27th Santa Barbara Invite 2024
47 Oklahoma Christian Loss 7-11 1053.28 Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Loss 10-13 1141.5 Jan 28th Santa Barbara Invite 2024
115 Southern California Win 13-10 1512.93 Jan 28th Santa Barbara Invite 2024
98 Dartmouth Win 8-8 1245.64 Mar 16th College Mens Centex Tier 1
20 Northeastern Loss 7-13 1272.79 Mar 16th College Mens Centex Tier 1
40 Illinois Loss 9-10 1454.68 Mar 16th College Mens Centex Tier 1
41 Florida Loss 6-8 1270.53 Mar 16th College Mens Centex Tier 1
139 LSU Win 13-3 1684.6 Mar 17th College Mens Centex Tier 1
82 Central Florida Win 9-8 1462.27 Mar 30th Huck Finn 2024
204 Ohio Win 13-6 1409.59 Mar 30th Huck Finn 2024
118 Michigan Tech Win 11-10 1298.62 Mar 30th Huck Finn 2024
108 Wisconsin-Milwaukee Win 12-8 1640.85 Mar 30th Huck Finn 2024
19 Washington University Loss 5-11 1265.17 Mar 31st Huck Finn 2024
66 Virginia Loss 7-9 1114.83 Mar 31st Huck Finn 2024
91 Indiana Win 9-7 1550.14 Mar 31st Huck Finn 2024
**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)