#1 North Carolina (17-0)

avg: 2811.43  •  sd: 169.31  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
25 Duke** Win 14-2 2168.74 Ignored Jan 21st Carolina Kickoff womens and nonbinary
83 Temple** Win 15-1 1577.42 Ignored Jan 21st Carolina Kickoff womens and nonbinary
28 South Carolina** Win 15-1 2141.36 Ignored Jan 22nd Carolina Kickoff womens and nonbinary
142 North Carolina-B** Win 14-1 1168.24 Ignored Jan 22nd Carolina Kickoff womens and nonbinary
21 North Carolina State** Win 14-2 2241.53 Ignored Jan 22nd Carolina Kickoff womens and nonbinary
46 Florida State** Win 13-3 1959.02 Ignored Feb 11th Queen City Tune Up1
26 Minnesota** Win 14-1 2164.32 Ignored Feb 11th Queen City Tune Up1
60 William & Mary** Win 15-0 1759.58 Ignored Feb 11th Queen City Tune Up1
27 Notre Dame** Win 15-5 2156.17 Ignored Feb 11th Queen City Tune Up1
4 Tufts Win 12-5 2900.29 Feb 12th Queen City Tune Up1
5 Vermont Win 11-7 2713.96 Feb 12th Queen City Tune Up1
6 Brigham Young** Win 11-4 2754.83 Mar 11th Stanford Invite Womens
12 California-Santa Barbara** Win 10-4 2532.78 Ignored Mar 11th Stanford Invite Womens
9 Washington** Win 12-5 2656.54 Ignored Mar 11th Stanford Invite Womens
3 Colorado Win 13-3 2940.96 Mar 12th Stanford Invite Womens
11 Oregon** Win 13-3 2569.78 Ignored Mar 12th Stanford Invite Womens
4 Tufts Win 12-8 2741.45 Mar 12th Stanford Invite Womens
**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)