#137 Illinois (12-6)

avg: 936.64  •  sd: 60.69  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
181 Arkansas Win 10-2 1229.19 Mar 2nd Midwest Throwdown 2019
72 Texas-Dallas Loss 5-10 752.5 Mar 2nd Midwest Throwdown 2019
207 Wisconsin-Eau Claire Win 9-3 1098.84 Mar 2nd Midwest Throwdown 2019
75 Purdue Loss 3-9 688.08 Mar 2nd Midwest Throwdown 2019
265 Notre Dame-B** Win 13-0 595.6 Ignored Mar 16th Tally Classic XIV
114 Minnesota-Duluth Win 13-11 1281.22 Mar 16th Tally Classic XIV
204 Georgia Southern Win 9-7 785.94 Mar 16th Tally Classic XIV
220 Florida Tech Win 13-2 992.07 Mar 16th Tally Classic XIV
41 Harvard** Loss 5-14 967.65 Ignored Mar 17th Tally Classic XIV
93 Kennesaw State Loss 7-11 721.15 Mar 17th Tally Classic XIV
115 South Florida Loss 7-14 467.73 Mar 17th Tally Classic XIV
218 Loyola-Chicago Win 9-3 1023.24 Mar 30th Illinois Invite 8
148 Marquette Loss 6-9 462.37 Mar 30th Illinois Invite 8
156 Wisconsin-Milwaukee Win 7-4 1341.18 Mar 30th Illinois Invite 8
222 Valparaiso Win 6-1 984.15 Mar 31st Illinois Invite 8
- Washington University-B Win 11-2 970.42 Mar 31st Illinois Invite 8
208 Wisconsin-Oshkosh Win 12-4 1094.94 Mar 31st Illinois Invite 8
148 Marquette Win 12-10 1119.06 Mar 31st Illinois Invite 8
**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)