#305 Boise State (7-10)

avg: 642.33  •  sd: 69.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
200 Montana Loss 9-11 735.03 Mar 2nd Big Sky Brawl 2019
76 Utah** Loss 4-15 873.73 Ignored Mar 2nd Big Sky Brawl 2019
133 Utah State Loss 9-13 826.71 Mar 2nd Big Sky Brawl 2019
191 Montana State Win 9-8 1149.96 Mar 3rd Big Sky Brawl 2019
202 Northern Arizona Win 10-9 1098.07 Mar 3rd Big Sky Brawl 2019
238 Denver Loss 1-15 297.8 Mar 3rd Big Sky Brawl 2019
312 Portland State Win 12-11 738.97 Mar 9th Palouse Open 2019
326 Western Washington University-B Loss 7-11 114.83 Mar 9th Palouse Open 2019
168 Whitworth Loss 7-12 566.72 Mar 9th Palouse Open 2019
312 Portland State Win 15-13 828.15 Mar 10th Palouse Open 2019
311 Central Washington Win 10-9 750.02 Mar 10th Palouse Open 2019
361 Miami Loss 10-11 297.51 Mar 23rd Trouble in Vegas 2019
254 Cal Poly-Pomona Loss 6-10 344.03 Mar 23rd Trouble in Vegas 2019
425 Cal State-Fullerton Win 13-8 516.79 Mar 23rd Trouble in Vegas 2019
265 Cal State-Long Beach Loss 11-13 571.86 Mar 23rd Trouble in Vegas 2019
361 Miami Win 13-6 1022.51 Mar 24th Trouble in Vegas 2019
261 Cal Poly-SLO-B Loss 5-7 493 Mar 24th Trouble in Vegas 2019
**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)