#100 Missouri (12-9)

avg: 1265.35  •  sd: 73.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
178 Minnesota-B Win 12-7 1441.24 Mar 1st Midwest Throwdown 2025
265 St John's (Minnesota)** Win 13-3 1152.99 Ignored Mar 1st Midwest Throwdown 2025
337 Purdue-B** Win 13-1 785.29 Ignored Mar 1st Midwest Throwdown 2025
256 Illinois-B** Win 13-1 1180.53 Ignored Mar 2nd Midwest Throwdown 2025
107 Iowa Win 12-6 1801.67 Mar 2nd Midwest Throwdown 2025
156 Wisconsin-La Crosse Win 12-8 1459.4 Mar 2nd Midwest Throwdown 2025
72 Southern Illinois-Edwardsville Win 11-8 1762.22 Mar 2nd Midwest Throwdown 2025
107 Iowa Win 11-10 1347.36 Mar 15th Mens Centex 2025
74 Oklahoma Christian Loss 10-12 1139.97 Mar 15th Mens Centex 2025
103 Texas A&M Win 11-5 1840.66 Mar 15th Mens Centex 2025
29 Utah Valley Loss 2-13 1152.25 Mar 15th Mens Centex 2025
46 Middlebury Loss 3-13 997.79 Mar 16th Mens Centex 2025
136 North Texas Win 15-10 1564.07 Mar 16th Mens Centex 2025
62 Tulane Loss 13-14 1337.36 Mar 16th Mens Centex 2025
150 Kentucky Loss 9-10 927.26 Mar 29th Huck Finn 2025
103 Texas A&M Loss 10-12 1002.54 Mar 29th Huck Finn 2025
154 Macalester Loss 10-12 804.82 Mar 29th Huck Finn 2025
84 Ohio State Win 10-8 1582.34 Mar 29th Huck Finn 2025
90 Missouri S&T Loss 6-15 695.31 Mar 30th Huck Finn 2025
154 Macalester Loss 10-11 917.94 Mar 30th Huck Finn 2025
194 Saint Louis Win 8-2 1452 Mar 30th Huck Finn 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)