#278 Christopher Newport (8-4)

avg: 764.63  •  sd: 90.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
171 RIT Loss 8-13 585.49 Feb 23rd Oak Creek Challenge 2019
188 East Carolina Win 11-8 1395.97 Feb 23rd Oak Creek Challenge 2019
166 Virginia Commonwealth Loss 4-8 527.02 Feb 23rd Oak Creek Challenge 2019
206 West Chester Loss 3-11 366.25 Feb 23rd Oak Creek Challenge 2019
158 Lehigh Loss 11-15 747.91 Feb 24th Oak Creek Challenge 2019
338 Wake Forest Win 15-7 1133.6 Feb 24th Oak Creek Challenge 2019
379 Shenandoah Win 13-2 944.59 Mar 30th Atlantic Coast Open 2019
384 Pennsylvania-B Win 12-7 822.04 Mar 30th Atlantic Coast Open 2019
428 American-B Win 11-8 338.45 Mar 30th Atlantic Coast Open 2019
417 Georgetown-B Win 13-10 429.75 Mar 30th Atlantic Coast Open 2019
384 Pennsylvania-B Win 15-7 901.53 Mar 31st Atlantic Coast Open 2019
371 George Washington-B Win 13-4 964.13 Mar 31st Atlantic Coast Open 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)