#10 Northeastern (22-0)

avg: 2134.33  •  sd: 136.57  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Central Florida** Win 7-2 1730.12 Ignored Jan 28th Florida Winter Classic 2023
47 Florida Win 9-6 1886.46 Jan 28th Florida Winter Classic 2023
46 Florida State Win 10-5 2047.61 Jan 28th Florida Winter Classic 2023
210 Florida Tech** Win 13-0 512.58 Ignored Jan 28th Florida Winter Classic 2023
46 Florida State** Win 12-1 2073.72 Ignored Jan 29th Florida Winter Classic 2023
216 Florida-B** Win 13-0 336.49 Ignored Jan 29th Florida Winter Classic 2023
203 Miami (Florida)** Win 13-0 645.72 Ignored Jan 29th Florida Winter Classic 2023
32 SUNY-Binghamton Win 8-6 1952.08 Feb 25th Commonwealth Cup Weekend2 2023
89 Columbia** Win 13-1 1678.66 Ignored Feb 25th Commonwealth Cup Weekend2 2023
59 Penn State** Win 13-3 1901.24 Ignored Feb 25th Commonwealth Cup Weekend2 2023
14 Virginia Win 13-6 2530.66 Feb 25th Commonwealth Cup Weekend2 2023
69 Case Western Reserve** Win 13-1 1850.46 Ignored Feb 26th Commonwealth Cup Weekend2 2023
36 Brown Win 10-7 1969.74 Feb 26th Commonwealth Cup Weekend2 2023
14 Virginia Win 9-8 2055.66 Feb 26th Commonwealth Cup Weekend2 2023
19 Yale Win 12-10 2024.18 Feb 26th Commonwealth Cup Weekend2 2023
31 California Win 13-8 2154.12 Mar 18th Womens Centex1
18 Colorado State Win 13-11 2040.34 Mar 18th Womens Centex1
33 Ohio State Win 13-4 2233.9 Mar 18th Womens Centex1
36 Brown Win 15-8 2144.88 Mar 19th Womens Centex1
31 California Win 15-6 2257.96 Mar 19th Womens Centex1
18 Colorado State Win 15-7 2411.5 Mar 19th Womens Centex1
44 Pennsylvania Win 13-8 1980.47 Mar 19th Womens Centex1
**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)