#250 Illinois State (6-13)

avg: 612.58  •  sd: 59.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
110 Berry Loss 7-13 650.02 Jan 25th T Town Throwdown XX
268 Harding Win 13-6 1135.96 Jan 25th T Town Throwdown XX
373 Tennessee-Chattanooga -B** Win 13-1 509.38 Ignored Jan 25th T Town Throwdown XX
160 LSU Loss 5-13 392.08 Jan 25th T Town Throwdown XX
104 Alabama** Loss 4-13 638.96 Ignored Jan 26th T Town Throwdown XX
172 Alabama-Birmingham Loss 5-11 350.21 Jan 26th T Town Throwdown XX
135 Mississippi State Loss 5-15 511.28 Jan 26th T Town Throwdown XX
72 Southern Illinois-Edwardsville Loss 5-9 867.55 Mar 1st Midwest Throwdown 2025
385 Wisconsin-Eau Claire-B** Win 10-3 265.55 Ignored Mar 1st Midwest Throwdown 2025
107 Iowa** Loss 4-13 622.36 Ignored Mar 1st Midwest Throwdown 2025
86 Marquette** Loss 5-13 712.53 Mar 2nd Midwest Throwdown 2025
265 St John's (Minnesota) Loss 10-12 314.87 Mar 2nd Midwest Throwdown 2025
309 Washington University-B Win 12-8 759.14 Mar 2nd Midwest Throwdown 2025
256 Illinois-B Win 12-7 1101.04 Mar 2nd Midwest Throwdown 2025
117 Colorado Mines Loss 9-13 770.69 Mar 29th Old Capitol Open 2025
178 Minnesota-B Loss 7-13 363.2 Mar 29th Old Capitol Open 2025
161 Wisconsin-Eau Claire Loss 7-13 426.64 Mar 29th Old Capitol Open 2025
203 Nebraska Loss 6-13 186.47 Mar 30th Old Capitol Open 2025
281 Wisconsin-Platteville Win 12-9 796.23 Mar 30th Old Capitol Open 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)