#104 Portland (15-4)

avg: 1339.16  •  sd: 55.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
62 Duke Loss 9-13 1132.44 Feb 9th Stanford Open 2019
261 Cal Poly-SLO-B Win 13-5 1421.14 Feb 9th Stanford Open 2019
21 California Win 10-9 1968.46 Feb 9th Stanford Open 2019
71 Michigan Tech Loss 8-13 994.83 Mar 8th D III Midwestern Invite 2019
303 Ohio Northern Win 12-9 997.57 Mar 9th D III Midwestern Invite 2019
- Grinnell** Win 13-2 1294.09 Ignored Mar 9th D III Midwestern Invite 2019
138 Missouri S&T Win 7-5 1558.23 Mar 9th D III Midwestern Invite 2019
376 Indiana Wesleyan** Win 13-0 953.42 Ignored Mar 10th D III Midwestern Invite 2019
70 St Olaf Loss 6-8 1200.04 Mar 10th D III Midwestern Invite 2019
186 Macalester Win 11-9 1280.83 Mar 10th D III Midwestern Invite 2019
121 Puget Sound Win 8-7 1406.02 Mar 10th D III Midwestern Invite 2019
162 Washington State Win 13-9 1528.06 Mar 30th 2019 NW Challenge Tier 2 3
280 Idaho Win 11-5 1354.23 Mar 30th 2019 NW Challenge Tier 2 3
241 Washington-B Win 11-5 1488.48 Mar 30th 2019 NW Challenge Tier 2 3
200 Montana Win 11-7 1451.13 Mar 30th 2019 NW Challenge Tier 2 3
289 Brigham Young-B Win 11-8 1076.63 Mar 30th 2019 NW Challenge Tier 2 3
241 Washington-B Win 13-3 1488.48 Mar 31st 2019 NW Challenge Tier 2 3
121 Puget Sound Win 12-11 1406.02 Mar 31st 2019 NW Challenge Tier 2 3
99 Lewis & Clark Loss 11-13 1129.93 Mar 31st 2019 NW Challenge Tier 2 3
**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)