#147 Delaware (6-5)

avg: 1187.94  •  sd: 89.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 James Madison Loss 7-10 893.14 Mar 16th Oak Creek Invite 2019
142 Princeton Win 13-12 1334.71 Mar 16th Oak Creek Invite 2019
18 Michigan** Loss 1-13 1308.77 Ignored Mar 16th Oak Creek Invite 2019
163 SUNY-Geneseo Loss 10-13 778.43 Mar 16th Oak Creek Invite 2019
150 Cornell Win 15-9 1693.57 Mar 17th Oak Creek Invite 2019
54 Virginia Tech Loss 8-15 1054.64 Mar 17th Oak Creek Invite 2019
405 Jefferson** Win 12-1 789.23 Ignored Mar 24th Hucktastic Spring 2019
139 Pennsylvania Win 10-7 1619.34 Mar 24th Hucktastic Spring 2019
359 Villanova-B** Win 13-3 1042.64 Ignored Mar 24th Hucktastic Spring 2019
206 West Chester Win 9-8 1091.25 Mar 24th Hucktastic Spring 2019
115 Villanova Loss 7-9 1017.05 Mar 24th Hucktastic Spring 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)