#35 Missouri (17-4)

avg: 1786.83  •  sd: 97.79  •  top 16/20: 5.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
264 Oklahoma State** Win 12-4 1281.23 Ignored Feb 25th Dust Bowl 2023
252 Texas-B** Win 13-5 1330 Ignored Feb 25th Dust Bowl 2023
161 Rice Win 12-6 1691.11 Feb 25th Dust Bowl 2023
93 Iowa Win 10-8 1688.95 Feb 25th Dust Bowl 2023
161 Rice** Win 8-3 1711.8 Ignored Feb 26th Dust Bowl 2023
28 Oklahoma Christian Loss 7-8 1718.49 Feb 26th Dust Bowl 2023
116 John Brown Win 8-5 1759.58 Feb 26th Dust Bowl 2023
189 Luther Win 13-6 1595.04 Mar 4th Midwest Throwdown 2023
365 Grinnell-B** Win 13-0 109.44 Ignored Mar 4th Midwest Throwdown 2023
103 Truman State Win 9-6 1771.93 Mar 4th Midwest Throwdown 2023
148 Minnesota-Duluth Win 11-7 1627.91 Mar 5th Midwest Throwdown 2023
75 Grinnell Loss 7-8 1361.77 Mar 5th Midwest Throwdown 2023
103 Truman State Win 8-7 1478.36 Mar 5th Midwest Throwdown 2023
85 Alabama Win 9-5 1976.05 Apr 1st Huck Finn1
48 Iowa State Win 7-4 2142.5 Apr 1st Huck Finn1
38 Purdue Loss 5-6 1648.1 Apr 1st Huck Finn1
65 Indiana Win 8-3 2165.84 Apr 1st Huck Finn1
59 Cincinnati Win 14-5 2178.71 Apr 2nd Huck Finn1
94 Saint Louis Win 14-9 1898.67 Apr 2nd Huck Finn1
22 Washington University Loss 8-11 1539.71 Apr 2nd Huck Finn1
48 Iowa State Win 9-7 1925.68 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)