#312 Texas-Arlington (3-8)

avg: 251.93  •  sd: 65.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
242 Abilene Christian Loss 4-11 83.81 Feb 1st Big D in lil d 2020 Open
138 North Texas Loss 6-11 497.2 Feb 1st Big D in lil d 2020 Open
136 Oklahoma** Loss 2-13 463.06 Ignored Feb 1st Big D in lil d 2020 Open
199 Texas Christian Loss 6-10 338.92 Feb 1st Big D in lil d 2020 Open
199 Texas Christian Loss 8-9 710.08 Feb 2nd Big D in lil d 2020 Open
- Texas-Dallas-B Win 15-11 196.18 Feb 2nd Big D in lil d 2020 Open
249 Creighton Loss 10-15 189.17 Feb 22nd Dust Bowl 2020
226 Texas A&M-B Loss 5-15 140.96 Feb 22nd Dust Bowl 2020
344 Colorado School of Mines-B Win 11-9 226.05 Feb 23rd Dust Bowl 2020
324 Nebraska-Omaha Win 15-13 369.89 Feb 23rd Dust Bowl 2020
275 Texas State -B Loss 3-15 -122.47 Feb 23rd Dust Bowl 2020
**Blowout Eligible

FAQ

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
  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
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)