#92 Missouri S&T (14-7)

avg: 1429.82  •  sd: 62.49  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
189 Luther Win 11-7 1461.94 Feb 25th Dust Bowl 2023
116 John Brown Loss 9-10 1180.98 Feb 25th Dust Bowl 2023
326 Kansas State** Win 13-0 884.51 Ignored Feb 25th Dust Bowl 2023
217 Texas-Dallas Win 9-6 1291.52 Feb 25th Dust Bowl 2023
207 Illinois State Win 11-8 1276.47 Feb 26th Dust Bowl 2023
28 Oklahoma Christian Loss 6-11 1296.8 Feb 26th Dust Bowl 2023
161 Rice Win 8-7 1236.8 Feb 26th Dust Bowl 2023
93 Iowa Win 10-9 1551.29 Feb 26th Dust Bowl 2023
207 Illinois State Win 12-3 1510.86 Mar 4th Midwest Throwdown 2023
325 Washington University-B** Win 13-2 886.48 Ignored Mar 4th Midwest Throwdown 2023
54 Northwestern Loss 9-11 1366.98 Mar 4th Midwest Throwdown 2023
118 Marquette Win 10-8 1563.44 Mar 5th Midwest Throwdown 2023
40 Colorado College Loss 8-13 1239.19 Mar 5th Midwest Throwdown 2023
68 Wisconsin-Milwaukee Win 9-8 1674.93 Mar 5th Midwest Throwdown 2023
115 Michigan State Win 6-4 1676.26 Apr 1st Huck Finn1
98 Kentucky Loss 3-5 998.24 Apr 1st Huck Finn1
112 Illinois Loss 7-8 1190.98 Apr 1st Huck Finn1
64 St. Olaf Win 9-8 1693 Apr 1st Huck Finn1
104 Florida State Win 11-8 1710.62 Apr 2nd Huck Finn1
65 Indiana Loss 8-13 1069.68 Apr 2nd Huck Finn1
108 Vanderbilt Win 13-5 1927.62 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)