#31 Brown (13-6)

avg: 1874.19  •  sd: 62.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
88 Virginia Tech Win 15-8 1862.07 Feb 24th Commonwealth Cup Weekend 2 2024
42 Purdue Loss 8-10 1438.13 Feb 24th Commonwealth Cup Weekend 2 2024
35 Ohio Loss 7-11 1331.24 Feb 24th Commonwealth Cup Weekend 2 2024
75 Columbia Win 6-5 1499.91 Feb 25th Commonwealth Cup Weekend 2 2024
49 Georgia Tech Win 10-5 2206.1 Feb 25th Commonwealth Cup Weekend 2 2024
61 Penn State Win 11-3 2103.49 Feb 25th Commonwealth Cup Weekend 2 2024
96 Chicago** Win 13-2 1851 Ignored Mar 16th Womens Centex 2024
19 Colorado State Loss 9-13 1694.64 Mar 16th Womens Centex 2024
46 Texas Win 11-6 2223.69 Mar 16th Womens Centex 2024
108 Middlebury** Win 13-4 1746.44 Ignored Mar 16th Womens Centex 2024
19 Colorado State Loss 10-15 1659.6 Mar 17th Womens Centex 2024
36 Texas-Dallas Win 11-6 2340.19 Mar 17th Womens Centex 2024
21 Ohio State Win 11-10 2207.16 Mar 17th Womens Centex 2024
41 SUNY-Binghamton Win 9-8 1828.59 Mar 30th East Coast Invite 2024
4 North Carolina** Loss 3-11 2022.41 Ignored Mar 30th East Coast Invite 2024
61 Penn State Win 9-7 1782.83 Mar 30th East Coast Invite 2024
12 Michigan Loss 8-15 1746.8 Mar 30th East Coast Invite 2024
39 Virginia Win 9-8 1863.89 Mar 31st East Coast Invite 2024
58 Cornell Win 11-6 2074.22 Mar 31st East Coast Invite 2024
**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)