#36 Brown (11-9)

avg: 1580.07  •  sd: 63.79  •  top 16/20: 0.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Georgia Loss 8-9 1405.7 Feb 25th Commonwealth Cup Weekend2 2023
13 Pittsburgh Loss 9-12 1587.98 Feb 25th Commonwealth Cup Weekend2 2023
19 Yale Win 13-4 2386.06 Feb 25th Commonwealth Cup Weekend2 2023
35 Michigan Win 11-10 1744.57 Feb 25th Commonwealth Cup Weekend2 2023
89 Columbia Win 11-5 1678.66 Feb 26th Commonwealth Cup Weekend2 2023
10 Northeastern Loss 7-10 1744.67 Feb 26th Commonwealth Cup Weekend2 2023
13 Pittsburgh Loss 4-8 1368.54 Feb 26th Commonwealth Cup Weekend2 2023
48 Texas Win 11-10 1584.95 Mar 18th Womens Centex1
35 Michigan Loss 6-11 1072.88 Mar 18th Womens Centex1
42 Wisconsin Loss 10-12 1268.36 Mar 18th Womens Centex1
14 Virginia Loss 5-13 1330.66 Mar 19th Womens Centex1
10 Northeastern Loss 8-15 1569.53 Mar 19th Womens Centex1
42 Wisconsin Win 11-10 1631.49 Mar 19th Womens Centex1
33 Ohio State Loss 10-13 1305.76 Mar 19th Womens Centex1
63 Haverford/Bryn Mawr Win 10-1 1876.27 Apr 1st Northeast Classic2
133 New Hampshire** Win 12-2 1348.55 Ignored Apr 1st Northeast Classic2
99 MIT Win 11-0 1583.32 Apr 1st Northeast Classic2
146 SUNY-Geneseo** Win 10-2 1250.15 Ignored Apr 1st Northeast Classic2
76 Bates Win 13-5 1781.37 Apr 2nd Northeast Classic2
133 New Hampshire** Win 10-3 1348.55 Ignored Apr 2nd Northeast Classic2
**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)