#106 Illinois State (5-16)

avg: 1327.34  •  sd: 56.5  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
322 Mississippi** Win 11-2 1187.21 Ignored Jan 26th T Town Throwdown
36 Alabama Loss 8-13 1226.98 Jan 26th T Town Throwdown
48 Kennesaw State Win 11-9 1895.69 Jan 26th T Town Throwdown
24 Auburn Loss 5-12 1196.78 Jan 26th T Town Throwdown
72 Alabama-Huntsville Loss 12-14 1263.03 Jan 27th T Town Throwdown
37 Illinois Loss 12-15 1419.9 Jan 27th T Town Throwdown
160 Vanderbilt Win 15-6 1724.38 Jan 27th T Town Throwdown
25 South Carolina Loss 10-12 1548.57 Feb 8th Florida Warm Up 2019
127 Boston College Win 13-12 1399.72 Feb 8th Florida Warm Up 2019
80 Oklahoma Loss 9-10 1326.97 Feb 8th Florida Warm Up 2019
98 Kansas Loss 10-11 1238.18 Feb 9th Florida Warm Up 2019
15 Central Florida Loss 7-11 1523.42 Feb 9th Florida Warm Up 2019
29 Texas-Dallas Loss 8-9 1646.91 Feb 9th Florida Warm Up 2019
13 Wisconsin Loss 7-15 1400.97 Feb 9th Florida Warm Up 2019
83 Rutgers Loss 9-13 1014.4 Feb 10th Florida Warm Up 2019
55 Florida State Win 12-11 1736.67 Feb 10th Florida Warm Up 2019
46 Iowa State Loss 5-11 1059.23 Mar 30th Huck Finn XXIII
68 Cincinnati Loss 8-10 1252.71 Mar 30th Huck Finn XXIII
46 Iowa State Loss 3-7 1059.23 Mar 31st Huck Finn XXIII
152 Arkansas Loss 6-8 852.71 Mar 31st Huck Finn XXIII
111 Washington University Loss 6-7 1188.46 Mar 31st Huck Finn XXIII
**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)