#221 Christopher Newport (6-13)

avg: 995.31  •  sd: 58.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
53 William & Mary Loss 7-13 1152.76 Jan 25th Mid Atlantic Warm Up 2025
282 Navy Win 13-6 1363.93 Jan 25th Mid Atlantic Warm Up 2025
167 Pennsylvania Win 13-8 1705.75 Jan 25th Mid Atlantic Warm Up 2025
111 Vermont-B Loss 1-13 818.32 Jan 25th Mid Atlantic Warm Up 2025
59 James Madison Loss 8-14 1133.69 Jan 26th Mid Atlantic Warm Up 2025
115 RIT Loss 10-13 1056.08 Jan 26th Mid Atlantic Warm Up 2025
140 George Mason Loss 7-13 747.39 Jan 26th Mid Atlantic Warm Up 2025
76 Williams Loss 1-13 992.52 Mar 29th Easterns 2025
67 Franciscan** Loss 0-13 1038.25 Ignored Mar 29th Easterns 2025
290 Mary Washington Win 11-4 1334.64 Mar 29th Easterns 2025
78 Richmond Loss 3-13 982.46 Mar 29th Easterns 2025
102 North Carolina-Asheville Loss 5-15 859.86 Mar 30th Easterns 2025
142 Davidson Loss 8-15 726.63 Mar 30th Easterns 2025
102 North Carolina-Asheville Loss 1-11 859.86 Apr 12th Atlantic Coast D III Mens Conferences 2025
78 Richmond Loss 2-11 982.46 Apr 12th Atlantic Coast D III Mens Conferences 2025
290 Mary Washington Win 11-6 1281.33 Apr 12th Atlantic Coast D III Mens Conferences 2025
365 High Point Win 11-7 827.78 Apr 12th Atlantic Coast D III Mens Conferences 2025
142 Davidson Loss 7-15 691.44 Apr 13th Atlantic Coast D III Mens Conferences 2025
282 Navy Win 13-12 888.93 Apr 13th Atlantic Coast D III Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)