#179 Missouri S&T (11-10)

avg: 904.1  •  sd: 66.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
254 Oklahoma State Win 10-8 862.19 Feb 17th Dust Bowl 2024
209 Oklahoma Win 9-7 1063.34 Feb 17th Dust Bowl 2024
253 Nebraska Win 13-7 1163.21 Feb 17th Dust Bowl 2024
218 Texas-Dallas Loss 10-11 632.85 Feb 18th Dust Bowl 2024
161 Truman State Loss 5-15 392.69 Feb 18th Dust Bowl 2024
209 Oklahoma Win 15-11 1165.17 Feb 18th Dust Bowl 2024
125 Davidson Loss 8-13 650.41 Mar 2nd FCS D III Tune Up 2024
80 Lewis & Clark Loss 11-13 1110.85 Mar 2nd FCS D III Tune Up 2024
150 Navy Loss 9-13 615.73 Mar 2nd FCS D III Tune Up 2024
122 Oberlin Loss 7-13 595.02 Mar 2nd FCS D III Tune Up 2024
226 Embry-Riddle Loss 10-13 401.86 Mar 3rd FCS D III Tune Up 2024
199 Messiah Win 13-11 1061.08 Mar 3rd FCS D III Tune Up 2024
163 Xavier Loss 7-13 415.94 Mar 3rd FCS D III Tune Up 2024
351 Kansas State** Win 13-2 623.87 Ignored Mar 23rd Free State Classic
335 Wichita State** Win 13-3 721.02 Ignored Mar 23rd Free State Classic
180 Wisconsin-La Crosse Win 11-7 1365.05 Mar 23rd Free State Classic
254 Oklahoma State Win 12-7 1120.04 Mar 23rd Free State Classic
188 John Brown Loss 8-9 737.39 Mar 24th Free State Classic
253 Nebraska Win 12-6 1184.99 Mar 24th Free State Classic
161 Truman State Win 9-8 1117.69 Mar 24th Free State Classic
49 St Olaf Loss 12-14 1282.21 Mar 24th Free State Classic
**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)