#263 Illinois State (7-10)

avg: 898.19  •  sd: 68.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
337 Wisconsin-Stevens Point Win 11-7 1032.58 Mar 23rd Free State Classic
243 Nebraska Win 10-9 1108.44 Mar 23rd Free State Classic
200 Northern Iowa Loss 6-13 521.31 Mar 23rd Free State Classic
313 Kansas-B Win 13-4 1267.26 Mar 23rd Free State Classic
184 Wisconsin-La Crosse Loss 4-13 586.43 Mar 24th Free State Classic
101 Colorado Mines Loss 6-13 912.08 Mar 30th Old Capitol Open 2024
304 Luther College Win 13-7 1252.5 Mar 30th Old Capitol Open 2024
145 Southern Illinois-Edwardsville Loss 5-12 731.75 Mar 30th Old Capitol Open 2024
237 Carthage Loss 4-13 389.65 Mar 31st Old Capitol Open 2024
184 Wisconsin-La Crosse Loss 6-8 885.94 Mar 31st Old Capitol Open 2024
299 Minnesota-C Win 11-6 1265.29 Mar 31st Old Capitol Open 2024
61 Chicago** Loss 4-15 1094.33 Ignored Apr 13th Illinois D I Mens Conferences 2024
340 DePaul Win 14-4 1155.29 Apr 13th Illinois D I Mens Conferences 2024
145 Southern Illinois-Edwardsville Loss 7-14 748.86 Apr 13th Illinois D I Mens Conferences 2024
106 Northwestern Loss 7-15 882.91 Apr 13th Illinois D I Mens Conferences 2024
340 DePaul Win 13-4 1155.29 Apr 14th Illinois D I Mens Conferences 2024
324 Illinois-Chicago Loss 9-10 497.98 Apr 14th Illinois D I Mens Conferences 2024
**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)