#227 Delaware-B (0-11)

avg: 356.49  •  sd: 107.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
67 Yale** Loss 0-13 783.49 Ignored Mar 9th Delaware The Main Event 2019
52 Columbia** Loss 1-13 903.27 Ignored Mar 9th Delaware The Main Event 2019
161 Drexel Loss 6-9 392.42 Mar 9th Delaware The Main Event 2019
121 Towson** Loss 3-13 424.96 Ignored Mar 9th Delaware The Main Event 2019
163 SUNY-Binghamton Loss 5-7 454.29 Mar 10th Delaware The Main Event 2019
241 Cornell-B Loss 5-6 103.44 Mar 10th Delaware The Main Event 2019
161 Drexel Loss 6-9 392.42 Mar 10th Delaware The Main Event 2019
67 Yale** Loss 2-15 783.49 Ignored Mar 30th West Chester Ram Jam 2019
133 Haverford Loss 5-10 404.74 Mar 30th West Chester Ram Jam 2019
56 Pennsylvania** Loss 2-14 887.36 Ignored Mar 30th West Chester Ram Jam 2019
41 Harvard** Loss 1-14 967.65 Ignored Mar 30th West Chester Ram Jam 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)