#273 Colorado State-B (9-8)

avg: 775.73  •  sd: 60.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 New Mexico Loss 4-11 679.41 Jan 26th New Year Fest 2019
289 Brigham Young-B Win 11-2 1311.02 Jan 26th New Year Fest 2019
400 Arizona State-C Win 11-4 821.92 Jan 26th New Year Fest 2019
403 Texas-El Paso Win 11-5 790.26 Jan 26th New Year Fest 2019
222 Grand Canyon Win 10-7 1309.55 Jan 26th New Year Fest 2019
272 Arizona State-B Loss 9-13 357.91 Jan 27th New Year Fest 2019
238 Denver Loss 11-13 668.95 Jan 27th New Year Fest 2019
125 Colorado School of Mines Loss 4-13 678.32 Feb 23rd Denver Round Robin 2019
406 Colorado School of Mines - B Win 13-3 787.01 Feb 23rd Denver Round Robin 2019
238 Denver Win 12-11 1022.8 Feb 23rd Denver Round Robin 2019
170 Colorado-Denver Loss 6-13 483.91 Feb 23rd Denver Round Robin 2019
324 Stephen F. Austin Win 12-10 823.13 Mar 16th Centex 2019 Men
409 Texas-Dallas-B Win 10-6 659.27 Mar 16th Centex 2019 Men
205 Wisconsin-B Loss 9-10 845.59 Mar 16th Centex 2019 Men
175 North Texas Loss 10-15 613.49 Mar 16th Centex 2019 Men
296 LSU-B Loss 12-13 570.81 Mar 17th Centex 2019 Men
378 Houston Win 15-4 949.7 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)