#95 Chicago (10-8)

avg: 1214.71  •  sd: 45.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Penn State Loss 9-14 1051.84 Feb 11th Queen City Tune Up1
15 North Carolina State Loss 5-15 1205.28 Feb 11th Queen City Tune Up1
25 North Carolina-Wilmington Loss 7-12 1173.62 Feb 11th Queen City Tune Up1
35 Washington University Loss 11-15 1228.13 Feb 11th Queen City Tune Up1
122 Carnegie Mellon Win 11-9 1331.48 Feb 12th Queen City Tune Up1
61 Harvard Loss 11-12 1263.46 Feb 12th Queen City Tune Up1
279 Carleton College-Karls** Win 13-5 943.1 Ignored Mar 4th Midwest Throwdown 2023
88 St. Olaf Loss 9-11 1005.27 Mar 4th Midwest Throwdown 2023
125 Marquette Loss 10-11 938.86 Mar 4th Midwest Throwdown 2023
263 Illinois-B** Win 10-2 1056.04 Ignored Mar 5th Midwest Throwdown 2023
264 Wisconsin-Platteville** Win 13-3 1049.78 Ignored Mar 5th Midwest Throwdown 2023
198 Illinois State Win 12-6 1325.26 Mar 5th Midwest Throwdown 2023
205 Alabama-Birmingham Win 11-6 1260.04 Mar 11th Tally Classic XVII
113 Clemson Win 10-6 1616.55 Mar 11th Tally Classic XVII
61 Harvard Win 9-8 1513.46 Mar 11th Tally Classic XVII
253 Georgia Southern Win 11-8 857.62 Mar 11th Tally Classic XVII
113 Clemson Win 10-9 1245.39 Mar 12th Tally Classic XVII
70 Notre Dame Loss 12-13 1230.69 Mar 12th Tally Classic XVII
**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)