#361 Saint Joseph's University (3-9)

avg: 340.73  •  sd: 111.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
389 Hofstra Win 8-7 285.95 Mar 3rd Atlantic City 7 2018
166 MIT** Loss 4-13 466.51 Ignored Mar 3rd Atlantic City 7 2018
281 Navy Loss 8-13 169.24 Mar 4th Atlantic City 7 2018
369 Brown-B Win 9-6 728.72 Mar 4th Atlantic City 7 2018
172 Colby** Loss 2-13 435.77 Ignored Mar 4th Atlantic City 7 2018
302 Salisbury Loss 3-13 -25.47 Mar 24th Jersey Devil 7
179 SUNY-Binghamton** Loss 2-13 418.07 Ignored Mar 24th Jersey Devil 7
132 Columbia University** Loss 4-13 588.17 Ignored Mar 24th Jersey Devil 7
254 New Hampshire Win 11-4 1360.58 Mar 25th Jersey Devil 7
323 Rowan-B Loss 5-9 -35.49 Mar 25th Jersey Devil 7
132 Columbia University** Loss 1-13 588.17 Ignored Mar 25th Jersey Devil 7
302 Salisbury Loss 4-12 -25.47 Mar 25th Jersey Devil 7
**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)