#188 Luther (12-7)

avg: 796.21  •  sd: 68.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
111 John Brown Loss 6-13 528.33 Feb 25th Dust Bowl 2023
102 Missouri S&T Loss 7-11 708.97 Feb 25th Dust Bowl 2023
307 Kansas State** Win 13-3 741.42 Ignored Feb 25th Dust Bowl 2023
213 Texas-Dallas Loss 9-11 423.4 Feb 25th Dust Bowl 2023
219 Baylor Win 9-6 1052.88 Feb 26th Dust Bowl 2023
278 Oklahoma Win 10-9 474.76 Feb 26th Dust Bowl 2023
243 Texas-B Win 8-5 983.5 Feb 26th Dust Bowl 2023
136 Truman State Loss 7-12 499.18 Mar 4th Midwest Throwdown 2023
60 Missouri Loss 6-13 794.56 Mar 4th Midwest Throwdown 2023
328 Grinnell-B** Win 13-1 498.15 Ignored Mar 4th Midwest Throwdown 2023
339 Wisconsin-Oshkosh** Win 13-2 176.13 Ignored Mar 5th Midwest Throwdown 2023
248 Northern Iowa Win 11-4 1108.6 Mar 5th Midwest Throwdown 2023
250 DePaul Win 12-4 1099.38 Mar 5th Midwest Throwdown 2023
272 Georgia Tech-B Win 13-5 1001.52 Mar 18th College Southerns XXI
238 Georgia College Win 13-7 1104.96 Mar 18th College Southerns XXI
- Florida Gulf Coast University Win 13-8 794.13 Mar 18th College Southerns XXI
142 Carleton College-CHOP Loss 8-15 428.6 Mar 19th College Southerns XXI
206 Georgia-B Win 12-7 1230.7 Mar 19th College Southerns XXI
130 East Carolina Loss 9-15 527.29 Mar 19th College Southerns XXI
**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)