#207 Illinois State (12-15)

avg: 910.86  •  sd: 55.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
146 Kansas Loss 5-13 568.89 Feb 25th Dust Bowl 2023
271 Texas A&M-B Win 13-7 1190.22 Feb 25th Dust Bowl 2023
28 Oklahoma Christian Loss 6-10 1347.33 Feb 25th Dust Bowl 2023
254 Oklahoma Win 10-4 1317.48 Feb 25th Dust Bowl 2023
92 Missouri S&T Loss 8-11 1064.21 Feb 26th Dust Bowl 2023
156 Wichita State Loss 5-10 553.56 Feb 26th Dust Bowl 2023
193 North Texas Loss 6-9 556.28 Feb 26th Dust Bowl 2023
92 Missouri S&T Loss 3-12 829.82 Mar 4th Midwest Throwdown 2023
54 Northwestern** Loss 1-13 1016.19 Ignored Mar 4th Midwest Throwdown 2023
325 Washington University-B** Win 13-4 886.48 Ignored Mar 4th Midwest Throwdown 2023
142 Carleton College-CHOP Win 8-7 1308.5 Mar 5th Midwest Throwdown 2023
90 Chicago Loss 6-12 854.47 Mar 5th Midwest Throwdown 2023
334 Northwestern-B Win 11-7 704.18 Mar 5th Midwest Throwdown 2023
145 Carthage Win 13-12 1297.13 Mar 25th Old Capitol Open
121 Michigan Tech Loss 10-12 1055.18 Mar 25th Old Capitol Open
321 Minnesota-C Win 7-5 632.16 Mar 25th Old Capitol Open
255 Toledo Loss 7-10 325.94 Mar 25th Old Capitol Open
280 Ball State Win 9-6 997.04 Mar 26th Old Capitol Open
309 Wisconsin-Stevens Point Win 13-3 988.87 Mar 26th Old Capitol Open
255 Toledo Win 10-4 1315.6 Mar 26th Old Capitol Open
151 Arizona State Loss 4-12 546.59 Apr 1st Huck Finn1
351 Central Michigan** Win 13-0 628.27 Ignored Apr 1st Huck Finn1
118 Marquette Loss 6-7 1175.78 Apr 1st Huck Finn1
131 Georgia State Loss 2-7 642.58 Apr 1st Huck Finn1
151 Arizona State Loss 8-11 780.98 Apr 2nd Huck Finn1
115 Michigan State Loss 5-12 710.66 Apr 2nd Huck Finn1
274 DePaul Win 12-7 1133.2 Apr 2nd Huck Finn1
**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)