#57 Illinois (13-6)

avg: 1515.42  •  sd: 56.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
8 Brigham Young Loss 8-13 1574.4 Jan 25th Santa Barbara Invite 2025
9 California-Santa Cruz Loss 4-13 1421.18 Jan 25th Santa Barbara Invite 2025
29 Utah Valley Win 12-9 2097.62 Jan 25th Santa Barbara Invite 2025
14 California Loss 6-13 1369.23 Jan 26th Santa Barbara Invite 2025
23 Victoria Loss 5-11 1248.42 Jan 26th Santa Barbara Invite 2025
129 Arizona State Win 9-7 1403.97 Mar 15th Mens Centex 2025
76 Colorado College Win 11-4 1969.71 Mar 15th Mens Centex 2025
66 Dartmouth Win 8-6 1752.74 Mar 15th Mens Centex 2025
135 Mississippi State Win 11-10 1236.28 Mar 15th Mens Centex 2025
76 Colorado College Win 15-11 1750.88 Mar 16th Mens Centex 2025
17 Tufts Loss 11-14 1583.67 Mar 16th Mens Centex 2025
29 Utah Valley Loss 9-14 1278.39 Mar 16th Mens Centex 2025
147 Butler Win 13-5 1654.95 Mar 29th Corny Classic College 2025
146 Dayton Win 12-11 1180.78 Mar 29th Corny Classic College 2025
142 Grand Valley Win 10-8 1329.15 Mar 29th Corny Classic College 2025
59 Asbury Win 8-7 1619.24 Mar 30th Corny Classic College 2025
59 Asbury Win 7-6 1619.24 Mar 30th Corny Classic College 2025
142 Grand Valley Win 13-8 1562.64 Mar 30th Corny Classic College 2025
158 Vanderbilt Win 9-7 1282.15 Mar 30th Corny Classic College 2025
**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)