#9 Massachusetts (16-4)

avg: 2065.5  •  sd: 40.3  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
66 Penn State Win 13-3 2135.24 Feb 9th Queen City Tune Up 2019 Men
119 Clemson** Win 13-3 1883.55 Ignored Feb 9th Queen City Tune Up 2019 Men
47 Maryland Win 13-8 2152.49 Feb 9th Queen City Tune Up 2019 Men
79 Tulane** Win 13-2 2056.42 Ignored Feb 9th Queen City Tune Up 2019 Men
64 Ohio Win 15-3 2139.4 Feb 10th Queen City Tune Up 2019 Men
11 North Carolina State Win 17-16 2152.57 Feb 10th Queen City Tune Up 2019 Men
1 North Carolina Loss 8-15 1667.11 Feb 10th Queen City Tune Up 2019 Men
4 Pittsburgh Win 13-12 2309.92 Mar 9th Classic City Invite 2019
55 Florida State Win 12-11 1736.67 Mar 9th Classic City Invite 2019
48 Kennesaw State Win 13-10 1974.63 Mar 9th Classic City Invite 2019
25 South Carolina Win 9-8 1911.69 Mar 10th Classic City Invite 2019
11 North Carolina State Win 11-8 2393.18 Mar 10th Classic City Invite 2019
26 North Carolina-Wilmington Win 10-7 2170.64 Mar 10th Classic City Invite 2019
17 Minnesota Win 13-11 2179.89 Mar 30th Easterns 2019 Men
45 California-Santa Barbara Win 13-11 1892.09 Mar 30th Easterns 2019 Men
44 Virginia Win 13-6 2271.41 Mar 30th Easterns 2019 Men
3 Oregon Loss 13-15 1974.81 Mar 30th Easterns 2019 Men
4 Pittsburgh Loss 12-13 2059.92 Mar 31st Easterns 2019 Men
1 North Carolina Loss 12-15 1931.43 Mar 31st Easterns 2019 Men
20 Tufts Win 11-9 2113.35 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)