#59 Oregon State (11-8)

avg: 1562.19  •  sd: 62.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 Humboldt State Win 13-8 1554.59 Jan 26th Flat Tail Open 2019 Mens
383 Washington-C** Win 13-0 903.69 Ignored Jan 26th Flat Tail Open 2019 Mens
99 Lewis & Clark Win 13-4 1958.77 Jan 26th Flat Tail Open 2019 Mens
116 Nevada-Reno Win 15-8 1858.53 Jan 26th Flat Tail Open 2019 Mens
192 Gonzaga Win 14-9 1496.41 Jan 27th Flat Tail Open 2019 Mens
3 Oregon Loss 8-15 1624.18 Jan 27th Flat Tail Open 2019 Mens
280 Idaho** Win 15-4 1354.23 Ignored Mar 9th Palouse Open 2019
311 Central Washington** Win 15-5 1225.02 Ignored Mar 9th Palouse Open 2019
162 Washington State Win 15-7 1709.49 Mar 9th Palouse Open 2019
326 Western Washington University-B Win 15-9 1097.21 Mar 10th Palouse Open 2019
168 Whitworth Win 15-7 1687.23 Mar 10th Palouse Open 2019
50 Stanford Win 14-12 1853.7 Mar 23rd 2019 NW Challenge Mens Tier 1
10 Washington Loss 7-13 1486.97 Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Loss 11-12 1640.9 Mar 24th 2019 NW Challenge Mens Tier 1
42 British Columbia Loss 10-12 1435.48 Mar 24th 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 9-12 1284.4 Mar 24th 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 8-13 1648.3 Mar 25th 2019 NW Challenge Mens Tier 1
58 Whitman Loss 10-13 1251.51 Mar 25th 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 8-13 1638.57 Mar 25th 2019 NW Challenge Mens Tier 1
**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)