#198 Illinois State (10-10)

avg: 745.95  •  sd: 61.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
150 Kansas Loss 5-13 361.07 Feb 25th Dust Bowl 2023
271 Texas A&M-B Win 13-7 965.13 Feb 25th Dust Bowl 2023
32 Oklahoma Christian Loss 6-10 1131.24 Feb 25th Dust Bowl 2023
278 Oklahoma Win 10-4 949.76 Feb 25th Dust Bowl 2023
102 Missouri S&T Loss 8-11 810.25 Feb 26th Dust Bowl 2023
147 Wichita State Loss 5-10 398.85 Feb 26th Dust Bowl 2023
187 North Texas Loss 6-9 378.2 Feb 26th Dust Bowl 2023
102 Missouri S&T Loss 3-12 575.86 Mar 4th Midwest Throwdown 2023
62 Northwestern** Loss 1-13 787.69 Ignored Mar 4th Midwest Throwdown 2023
291 Washington University-B Win 13-4 844.81 Mar 4th Midwest Throwdown 2023
142 Carleton College-CHOP Win 8-7 1118.41 Mar 5th Midwest Throwdown 2023
95 Chicago Loss 6-12 635.39 Mar 5th Midwest Throwdown 2023
299 Northwestern-B Win 11-7 650.94 Mar 5th Midwest Throwdown 2023
160 Carthage Win 13-12 1049.94 Mar 25th Old Capitol Open
164 Michigan Tech Loss 10-12 678.18 Mar 25th Old Capitol Open
309 Minnesota-C Win 7-5 454.74 Mar 25th Old Capitol Open
210 Toledo Loss 7-10 296.49 Mar 25th Old Capitol Open
280 Ball State Win 9-6 757.67 Mar 26th Old Capitol Open
297 Wisconsin-Stevens Point Win 13-3 790.72 Mar 26th Old Capitol Open
210 Toledo Win 10-4 1286.15 Mar 26th Old Capitol Open
**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)