#75 Nevada-Reno (13-7)

avg: 1360.69  •  sd: 62.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
192 Montana Win 15-13 1080.38 Jan 25th Pacific Confrontational Invite 2020
19 Oregon State Loss 11-15 1446.95 Jan 25th Pacific Confrontational Invite 2020
168 Lewis & Clark Win 15-7 1565.3 Jan 25th Pacific Confrontational Invite 2020
85 Humboldt State Loss 11-12 1182.34 Jan 25th Pacific Confrontational Invite 2020
192 Montana Win 15-10 1319.81 Jan 26th Pacific Confrontational Invite 2020
142 Washington-B Win 15-6 1638.44 Jan 26th Pacific Confrontational Invite 2020
76 Puget Sound Loss 8-9 1222.71 Feb 8th Stanford Open 2020
54 California-Davis Win 11-9 1727.27 Feb 8th Stanford Open 2020
148 Sonoma State Win 13-7 1565.34 Feb 8th Stanford Open 2020
27 Western Washington Loss 7-10 1329.4 Feb 8th Stanford Open 2020
89 Carleton College-GoP Win 6-5 1403.33 Feb 9th Stanford Open 2020
36 California-Santa Cruz Loss 7-10 1239.17 Feb 9th Stanford Open 2020
126 Chico State Loss 1-9 522.43 Feb 9th Stanford Open 2020
192 Montana Win 11-6 1412.9 Feb 29th Big Sky Brawl 2020
59 Whitman Win 11-10 1565.12 Feb 29th Big Sky Brawl 2020
149 Brigham Young-B Win 11-8 1373.04 Feb 29th Big Sky Brawl 2020
109 Washington State Win 11-9 1428.3 Feb 29th Big Sky Brawl 2020
298 Western Washington-B Win 11-6 894.9 Feb 29th Big Sky Brawl 2020
74 Montana State Win 7-5 1690.08 Mar 1st Big Sky Brawl 2020
44 Utah State Loss 11-12 1463.6 Mar 1st Big Sky Brawl 2020
**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)