#197 Christopher Newport (3-8)

avg: 565.13  •  sd: 89.31  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
155 Appalachian State Loss 10-13 521.93 Mar 16th Bonanza 2019
47 Williams** Loss 3-10 926.41 Ignored Mar 16th Bonanza 2019
156 Wisconsin-Milwaukee Loss 6-10 348.86 Mar 16th Bonanza 2019
82 Georgetown** Loss 2-13 666.46 Ignored Mar 16th Bonanza 2019
157 Virginia Commonwealth Loss 8-15 277.1 Mar 17th Bonanza 2019
130 Connecticut Loss 0-13 392.45 Mar 30th Atlantic Coast Open 2019
71 William & Mary** Loss 3-13 726.62 Ignored Mar 30th Atlantic Coast Open 2019
182 George Mason Win 9-7 907.65 Mar 30th Atlantic Coast Open 2019
147 George Washington Loss 4-13 281.09 Mar 30th Atlantic Coast Open 2019
259 East Carolina Win 15-10 526.76 Mar 31st Atlantic Coast Open 2019
147 George Washington Win 9-6 1299.66 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)