#205 Gonzaga (7-10)

avg: 925.34  •  sd: 69.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
290 Portland State Win 15-11 999.29 Jan 20th Flat Tail Open Tournament 2018
146 Nevada-Reno Loss 14-15 1024.3 Jan 20th Flat Tail Open Tournament 2018
165 Humboldt State Loss 6-15 466.68 Jan 20th Flat Tail Open Tournament 2018
275 Washington-B Win 15-9 1215.6 Jan 20th Flat Tail Open Tournament 2018
158 Lewis & Clark Loss 6-7 977.13 Jan 21st Flat Tail Open Tournament 2018
55 Oregon State Loss 6-13 918.18 Jan 21st Flat Tail Open Tournament 2018
72 Portland Loss 9-15 908.01 Mar 3rd 18th Annual PLU BBQ Open
121 Puget Sound Loss 10-13 928.77 Mar 3rd 18th Annual PLU BBQ Open
364 Seattle Win 13-3 931.78 Mar 3rd 18th Annual PLU BBQ Open
354 Washington-C Win 13-1 954.31 Mar 3rd 18th Annual PLU BBQ Open
158 Lewis & Clark Loss 8-14 566.09 Mar 4th 18th Annual PLU BBQ Open
226 Western Washington-B Win 15-8 1402.05 Mar 4th 18th Annual PLU BBQ Open
121 Puget Sound Loss 6-13 656.91 Mar 24th NW Challenge 2018
127 Montana Loss 7-13 649.52 Mar 24th NW Challenge 2018
158 Lewis & Clark Loss 5-13 502.13 Mar 24th NW Challenge 2018
275 Washington-B Win 13-8 1196.28 Mar 24th NW Challenge 2018
206 Washington State Win 13-7 1481.1 Mar 25th NW Challenge 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)