#90 Chicago (13-12)

avg: 1433.78  •  sd: 47.73  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Penn State Loss 9-14 1303.75 Feb 11th Queen City Tune Up1
20 North Carolina State Loss 5-15 1345.4 Feb 11th Queen City Tune Up1
25 North Carolina-Wilmington Loss 7-12 1363.75 Feb 11th Queen City Tune Up1
22 Washington University Loss 11-15 1524.16 Feb 11th Queen City Tune Up1
134 Carnegie Mellon Win 11-9 1485.52 Feb 12th Queen City Tune Up1
62 Harvard Loss 11-12 1443.9 Feb 12th Queen City Tune Up1
318 Carleton College-Karls-C** Win 13-5 925.43 Ignored Mar 4th Midwest Throwdown 2023
64 St. Olaf Loss 9-11 1318.79 Mar 4th Midwest Throwdown 2023
118 Marquette Loss 10-11 1175.78 Mar 4th Midwest Throwdown 2023
313 Illinois-B** Win 10-2 954.3 Ignored Mar 5th Midwest Throwdown 2023
279 Wisconsin-Platteville** Win 13-3 1185.54 Ignored Mar 5th Midwest Throwdown 2023
207 Illinois State Win 12-6 1490.17 Mar 5th Midwest Throwdown 2023
209 Alabama-Birmingham Win 11-6 1446.77 Mar 11th Tally Classic XVII
110 Clemson Win 10-6 1818.36 Mar 11th Tally Classic XVII
62 Harvard Win 9-8 1693.9 Mar 11th Tally Classic XVII
268 Georgia Southern Win 11-8 1017.02 Mar 11th Tally Classic XVII
110 Clemson Win 10-9 1447.2 Mar 12th Tally Classic XVII
49 Notre Dame Loss 12-13 1518.26 Mar 12th Tally Classic XVII
104 Florida State Loss 4-5 1220.01 Apr 1st Huck Finn1
68 Wisconsin-Milwaukee Loss 6-7 1424.93 Apr 1st Huck Finn1
75 Grinnell Loss 4-5 1361.77 Apr 1st Huck Finn1
116 John Brown Win 10-5 1879.87 Apr 1st Huck Finn1
112 Illinois Win 10-8 1578.65 Apr 2nd Huck Finn1
108 Vanderbilt Win 11-10 1452.62 Apr 2nd Huck Finn1
65 Indiana Loss 6-9 1147.28 Apr 2nd Huck Finn1
**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)