#335 College of New Jersey (3-8)

avg: 541.21  •  sd: 71.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
118 MIT** Loss 3-13 687.73 Ignored Mar 9th Atlantic City 9
77 Colby Loss 6-13 872.69 Mar 9th Atlantic City 9
122 Yale Loss 6-13 679.52 Mar 9th Atlantic City 9
252 SUNY-Cortland Loss 11-12 720.28 Mar 9th Atlantic City 9
301 Salisbury Loss 8-13 156.97 Mar 30th Garden State 9
292 Navy Loss 8-12 261.75 Mar 30th Garden State 9
343 Dickinson Loss 12-13 387.3 Mar 30th Garden State 9
356 West Virginia Win 11-7 915.66 Mar 30th Garden State 9
426 Sacred Heart Win 13-6 607.92 Mar 31st Garden State 9
266 Penn State-B Loss 8-12 357.18 Mar 31st Garden State 9
416 Temple-B Win 12-8 566.06 Mar 31st Garden State 9
**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)