#121 Puget Sound (13-6)

avg: 1281.02  •  sd: 72.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
312 Portland State Win 13-6 1213.97 Jan 26th Flat Tail Open 2019 Mens
446 Lewis & Clark-B** Win 13-1 600 Ignored Jan 26th Flat Tail Open 2019 Mens
192 Gonzaga Loss 7-13 465.01 Jan 26th Flat Tail Open 2019 Mens
3 Oregon** Loss 3-15 1588.99 Ignored Jan 26th Flat Tail Open 2019 Mens
99 Lewis & Clark Loss 11-15 977.61 Jan 27th Flat Tail Open 2019 Mens
162 Washington State Win 15-5 1709.49 Jan 27th Flat Tail Open 2019 Mens
376 Indiana Wesleyan** Win 8-3 953.42 Ignored Mar 9th D III Midwestern Invite 2019
70 St Olaf Win 10-9 1625.54 Mar 9th D III Midwestern Invite 2019
276 North Park Win 10-5 1343.54 Mar 9th D III Midwestern Invite 2019
104 Portland Loss 7-8 1214.16 Mar 10th D III Midwestern Invite 2019
- Grinnell Win 8-4 1258.9 Mar 10th D III Midwestern Invite 2019
177 Winona State Win 11-8 1427.65 Mar 10th D III Midwestern Invite 2019
291 Pacific Lutheran Win 13-8 1199.53 Mar 30th 2019 NW Challenge Tier 2 3
99 Lewis & Clark Loss 11-13 1129.93 Mar 30th 2019 NW Challenge Tier 2 3
326 Western Washington University-B** Win 13-4 1181.73 Ignored Mar 30th 2019 NW Challenge Tier 2 3
192 Gonzaga Win 13-7 1580.08 Mar 30th 2019 NW Challenge Tier 2 3
104 Portland Loss 11-12 1214.16 Mar 31st 2019 NW Challenge Tier 2 3
200 Montana Win 13-11 1213.08 Mar 31st 2019 NW Challenge Tier 2 3
192 Gonzaga Win 10-7 1412.21 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)