#367 Texas-B (4-6)

avg: 391.24  •  sd: 97.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
357 San Jose State Win 11-10 573.54 Feb 9th Stanford Open 2019
90 Santa Clara** Loss 1-13 786.86 Ignored Feb 9th Stanford Open 2019
184 California-B Loss 5-10 458.62 Feb 9th Stanford Open 2019
58 Whitman** Loss 5-13 979.65 Ignored Feb 9th Stanford Open 2019
378 Houston Loss 8-11 -15.91 Mar 16th Centex 2019 Men
277 Texas-San Antonio Loss 3-13 169.49 Mar 16th Centex 2019 Men
363 Texas State -B Win 11-10 540.34 Mar 16th Centex 2019 Men
324 Stephen F. Austin Loss 11-15 203.84 Mar 17th Centex 2019 Men
363 Texas State -B Win 15-14 540.34 Mar 17th Centex 2019 Men
409 Texas-Dallas-B Win 15-8 727.92 Mar 17th Centex 2019 Men
**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)