#48 Missouri (12-6)

avg: 1514.77  •  sd: 78.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
12 Alabama-Huntsville Loss 7-12 1473.16 Feb 10th Queen City Tune Up 2024
72 Georgetown Win 13-8 1861.86 Feb 10th Queen City Tune Up 2024
29 South Carolina Loss 9-14 1210.04 Feb 10th Queen City Tune Up 2024
92 Tennessee Loss 9-15 751.62 Feb 10th Queen City Tune Up 2024
66 Virginia Win 15-9 1909.65 Feb 11th Queen City Tune Up 2024
61 William & Mary Win 9-6 1850.57 Feb 11th Queen City Tune Up 2024
78 Carleton College-CHOP Win 10-7 1738.61 Mar 2nd Midwest Throwdown 2024
283 Knox** Win 13-3 1030.89 Ignored Mar 2nd Midwest Throwdown 2024
255 St John's (Minnesota)** Win 13-3 1184.96 Ignored Mar 2nd Midwest Throwdown 2024
161 Truman State Win 10-6 1488.85 Mar 2nd Midwest Throwdown 2024
170 Minnesota-Duluth Win 9-4 1550.77 Mar 3rd Midwest Throwdown 2024
83 Northwestern Win 9-8 1460.48 Mar 3rd Midwest Throwdown 2024
49 St Olaf Loss 8-9 1378.16 Mar 3rd Midwest Throwdown 2024
53 Colorado State Win 12-10 1708.68 Mar 16th College Mens Centex Tier 1
139 LSU Win 11-5 1684.6 Mar 16th College Mens Centex Tier 1
44 Tulane Win 13-10 1869.82 Mar 16th College Mens Centex Tier 1
47 Oklahoma Christian Loss 5-8 1066.57 Mar 16th College Mens Centex Tier 1
41 Florida Loss 7-10 1181.36 Mar 17th College Mens Centex Tier 1
**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)