#2 Brown (20-2)

avg: 2229.16  •  sd: 53.21  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
17 Minnesota Win 13-7 2508.58 Feb 8th Florida Warm Up 2019
29 Texas-Dallas Win 12-10 2010.03 Feb 8th Florida Warm Up 2019
22 Georgia Win 13-9 2253.06 Feb 8th Florida Warm Up 2019
150 Cornell Win 13-7 1735.62 Feb 9th Florida Warm Up 2019
49 Northwestern Win 12-10 1875.81 Feb 9th Florida Warm Up 2019
69 Emory** Win 13-5 2108.46 Ignored Feb 9th Florida Warm Up 2019
31 Texas A&M Win 15-9 2263.89 Feb 9th Florida Warm Up 2019
22 Georgia Win 15-11 2215.66 Feb 10th Florida Warm Up 2019
15 Central Florida Win 15-9 2505.8 Feb 10th Florida Warm Up 2019
19 Colorado State Win 13-9 2318.11 Mar 2nd Stanford Invite 2019
7 Carleton College-CUT Win 11-8 2484.25 Mar 2nd Stanford Invite 2019
10 Washington Loss 11-12 1919.51 Mar 2nd Stanford Invite 2019
8 Colorado Win 11-9 2344.65 Mar 3rd Stanford Invite 2019
5 Cal Poly-SLO Win 12-10 2382.58 Mar 3rd Stanford Invite 2019
1 North Carolina Loss 8-13 1735.76 Mar 3rd Stanford Invite 2019
47 Maryland Win 13-9 2074.89 Mar 30th Easterns 2019 Men
24 Auburn Win 13-11 2025.62 Mar 30th Easterns 2019 Men
28 Northeastern Win 13-9 2194.4 Mar 30th Easterns 2019 Men
11 North Carolina State Win 13-10 2355.71 Mar 30th Easterns 2019 Men
4 Pittsburgh Win 12-11 2309.92 Mar 31st Easterns 2019 Men
1 North Carolina Win 15-10 2685.53 Mar 31st Easterns 2019 Men
7 Carleton College-CUT Win 15-12 2419.13 Mar 31st Easterns 2019 Men
**Blowout Eligible

FAQ

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
  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
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)