#211 Utah State (4-14)

avg: 907.84  •  sd: 54.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Southern California Loss 7-13 1076.36 Feb 17th Presidents Day Invitational Tournament 2018
32 California** Loss 3-15 1095.8 Ignored Feb 17th Presidents Day Invitational Tournament 2018
24 Western Washington** Loss 6-15 1142.07 Ignored Feb 17th Presidents Day Invitational Tournament 2018
148 San Diego State Loss 10-11 1022.07 Feb 18th Presidents Day Invitational Tournament 2018
53 UCLA** Loss 5-15 934.42 Ignored Feb 18th Presidents Day Invitational Tournament 2018
143 California-San Diego Loss 7-12 640.4 Feb 19th Presidents Day Invitational Tournament 2018
247 Boise State Win 9-8 903.11 Mar 3rd Big Sky Brawl 2018
321 Idaho Win 13-3 1102.01 Mar 3rd Big Sky Brawl 2018
127 Montana Loss 7-13 649.52 Mar 3rd Big Sky Brawl 2018
67 Utah Loss 11-12 1332.97 Mar 3rd Big Sky Brawl 2018
146 Nevada-Reno Loss 8-9 1024.3 Mar 4th Big Sky Brawl 2018
206 Washington State Win 8-7 1048.57 Mar 4th Big Sky Brawl 2018
272 Miami Loss 12-13 576.69 Mar 24th Trouble in Vegas 2018
128 Colorado School of Mines Loss 5-9 674.8 Mar 24th Trouble in Vegas 2018
156 Colorado-Denver Loss 3-13 506.91 Mar 24th Trouble in Vegas 2018
53 UCLA Loss 8-13 1038.26 Mar 24th Trouble in Vegas 2018
237 New Mexico Win 9-6 1217.37 Mar 25th Trouble in Vegas 2018
131 Chico State Loss 8-10 925.98 Mar 25th Trouble in Vegas 2018
**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)