#96 Chicago (5-15)

avg: 1251  •  sd: 64.51  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
52 Yale Loss 12-15 1293.56 Feb 24th Commonwealth Cup Weekend 2 2024
21 Ohio State Loss 7-15 1482.16 Feb 24th Commonwealth Cup Weekend 2 2024
25 Pittsburgh Win 12-11 2087.92 Feb 24th Commonwealth Cup Weekend 2 2024
38 South Carolina Loss 6-7 1638.12 Feb 25th Commonwealth Cup Weekend 2 2024
90 Carnegie Mellon Loss 3-10 690.85 Feb 25th Commonwealth Cup Weekend 2 2024
16 Georgia** Loss 5-12 1554.75 Ignored Feb 25th Commonwealth Cup Weekend 2 2024
31 Brown** Loss 2-13 1274.19 Ignored Mar 16th Womens Centex 2024
108 Middlebury Win 10-9 1271.44 Mar 16th Womens Centex 2024
19 Colorado State** Loss 3-13 1513.2 Ignored Mar 16th Womens Centex 2024
27 Utah** Loss 5-13 1321.93 Ignored Mar 17th Womens Centex 2024
36 Texas-Dallas Loss 6-11 1246.8 Mar 17th Womens Centex 2024
46 Texas Loss 3-13 1076.99 Mar 17th Womens Centex 2024
21 Ohio State** Loss 2-15 1482.16 Ignored Mar 17th Womens Centex 2024
178 Minnesota-Duluth Win 7-3 1071.64 Mar 30th Old Capitol Open 2024
79 Kansas Loss 3-5 937.03 Mar 30th Old Capitol Open 2024
34 Minnesota Loss 2-9 1218.79 Mar 30th Old Capitol Open 2024
84 Iowa State Loss 5-7 995.66 Mar 30th Old Capitol Open 2024
145 Grinnell Win 9-5 1373.14 Mar 31st Old Capitol Open 2024
77 Michigan Tech Loss 2-6 770.43 Mar 31st Old Capitol Open 2024
140 Wisconsin-Milwaukee Win 11-4 1485.46 Mar 31st Old Capitol Open 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)