#116 Nevada-Reno (10-8)

avg: 1293.72  •  sd: 77.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Oregon State Loss 8-15 997.38 Jan 26th Flat Tail Open 2019 Mens
402 Oregon State-B** Win 13-2 803 Ignored Jan 26th Flat Tail Open 2019 Mens
241 Washington-B Win 13-3 1488.48 Jan 26th Flat Tail Open 2019 Mens
58 Whitman Loss 7-13 1022.12 Jan 26th Flat Tail Open 2019 Mens
162 Washington State Win 15-11 1490.65 Jan 27th Flat Tail Open 2019 Mens
99 Lewis & Clark Win 15-11 1739.93 Jan 27th Flat Tail Open 2019 Mens
- Sonoma State Win 13-6 1110.53 Feb 9th Stanford Open 2019
75 Air Force Loss 6-13 877.54 Feb 9th Stanford Open 2019
216 Occidental Win 11-5 1527.34 Feb 9th Stanford Open 2019
50 Stanford Win 6-5 1757.74 Feb 10th Stanford Open 2019
78 Carleton College-GoP Win 8-7 1582.72 Feb 10th Stanford Open 2019
58 Whitman Loss 3-8 979.65 Feb 10th Stanford Open 2019
16 Southern California** Loss 1-13 1376.15 Ignored Mar 23rd Trouble in Vegas 2019
65 Florida Loss 11-13 1306.9 Mar 23rd Trouble in Vegas 2019
170 Colorado-Denver Win 13-11 1312.75 Mar 24th Trouble in Vegas 2019
74 Arizona Loss 4-13 879.09 Mar 24th Trouble in Vegas 2019
76 Utah Win 12-9 1819.09 Mar 24th Trouble in Vegas 2019
133 Utah State Loss 5-7 917.13 Mar 24th Trouble in Vegas 2019
**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)