#17 Vermont (7-4)

avg: 2013.2  •  sd: 209.39  •  top 16/20: 62.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 Oregon Loss 8-10 1755.06 Feb 16th Presidents Day Invite 2019
23 California Win 9-8 2042.92 Feb 16th Presidents Day Invite 2019
10 Northeastern Win 8-5 2561.55 Feb 17th Presidents Day Invite 2019
7 Western Washington Win 7-6 2324.67 Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 3-10 1819.06 Feb 17th Presidents Day Invite 2019
21 Cal Poly-SLO Loss 8-9 1818.59 Feb 18th Presidents Day Invite 2019
4 California-Santa Barbara Loss 6-9 1861.92 Feb 18th Presidents Day Invite 2019
81 Ohio** Win 13-0 1868.3 Ignored Mar 9th Mash Up 2019
212 SUNY-Fredonia** Win 13-0 1078.81 Ignored Mar 9th Mash Up 2019
154 Smith** Win 13-1 1457.04 Ignored Mar 9th Mash Up 2019
283 Rhode Island** Win 13-0 132.25 Ignored Mar 10th Mash Up 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)