#176 Rice (8-7)

avg: 808.37  •  sd: 51.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
65 Missouri Loss 6-12 757.3 Feb 25th Dust Bowl 2023
234 Texas-B Win 11-5 1061.31 Feb 25th Dust Bowl 2023
111 Iowa Loss 5-12 509.63 Feb 25th Dust Bowl 2023
249 Oklahoma State Win 10-7 789.05 Feb 25th Dust Bowl 2023
65 Missouri Loss 3-8 736.61 Feb 26th Dust Bowl 2023
112 Missouri S&T Loss 7-8 982.72 Feb 26th Dust Bowl 2023
210 Texas-Dallas Win 8-7 733.76 Feb 26th Dust Bowl 2023
162 Wichita State Win 8-7 1024.42 Feb 26th Dust Bowl 2023
260 Texas A&M-B Win 13-6 933.6 Mar 11th Centex Tier 2
190 Texas State Loss 10-12 487.45 Mar 11th Centex Tier 2
279 Houston** Win 13-3 752.79 Ignored Mar 11th Centex Tier 2
234 Texas-B Win 13-6 1061.31 Mar 12th Centex Tier 2
161 Colorado-B Loss 10-15 450.01 Mar 12th Centex Tier 2
111 Iowa Loss 12-15 809.14 Mar 12th Centex Tier 2
243 Texas-San Antonio Win 15-4 1017.66 Mar 12th Centex Tier 2
**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)