#37 Illinois (14-11)

avg: 1720.39  •  sd: 59.51  •  top 16/20: 0.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
72 Alabama-Huntsville Win 13-7 2041.52 Jan 26th T Town Throwdown
132 Kentucky Win 11-5 1851.16 Jan 26th T Town Throwdown
24 Auburn Loss 4-11 1196.78 Jan 26th T Town Throwdown
27 LSU Loss 9-13 1359.17 Jan 26th T Town Throwdown
48 Kennesaw State Win 9-3 2246.49 Jan 27th T Town Throwdown
24 Auburn Loss 9-10 1671.78 Jan 27th T Town Throwdown
106 Illinois State Win 15-12 1627.83 Jan 27th T Town Throwdown
16 Southern California Loss 1-10 1376.15 Feb 16th Presidents Day Invite 2019
42 British Columbia Loss 7-8 1548.61 Feb 16th Presidents Day Invite 2019
271 San Diego State** Win 12-3 1380.82 Ignored Feb 17th Presidents Day Invite 2019
90 Santa Clara Win 9-6 1805.42 Feb 17th Presidents Day Invite 2019
45 California-Santa Barbara Win 12-6 2242.56 Feb 18th Presidents Day Invite 2019
76 Utah Loss 9-10 1348.73 Feb 18th Presidents Day Invite 2019
13 Wisconsin Loss 7-13 1443.44 Mar 16th Centex 2019 Men
12 Texas Loss 12-13 1884.9 Mar 16th Centex 2019 Men
31 Texas A&M Loss 9-13 1329.85 Mar 16th Centex 2019 Men
40 Dartmouth Loss 13-15 1472.29 Mar 16th Centex 2019 Men
29 Texas-Dallas Win 13-12 1896.91 Mar 17th Centex 2019 Men
31 Texas A&M Win 12-10 1986.53 Mar 17th Centex 2019 Men
86 Marquette Win 11-6 1972.77 Mar 30th Huck Finn XXIII
111 Washington University Win 11-2 1913.46 Mar 30th Huck Finn XXIII
18 Michigan Loss 5-11 1308.77 Mar 31st Huck Finn XXIII
68 Cincinnati Win 10-6 2011.53 Mar 31st Huck Finn XXIII
57 Carnegie Mellon Win 6-5 1712.38 Mar 31st Huck Finn XXIII
23 Texas Tech Win 8-7 1956.13 Mar 31st Huck Finn XXIII
**Blowout Eligible

FAQ

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
  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
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)