#33 Johns Hopkins (17-4)

avg: 1731.17  •  sd: 56.3  •  top 16/20: 1.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
88 Tennessee-Chattanooga Win 13-7 1976.72 Feb 16th Easterns Qualifier 2019
44 Virginia Win 12-11 1796.41 Feb 16th Easterns Qualifier 2019
145 Dayton Win 13-11 1418.52 Feb 16th Easterns Qualifier 2019
101 Connecticut Win 11-8 1721.85 Feb 16th Easterns Qualifier 2019
81 Georgia Tech Win 15-12 1747.81 Feb 17th Easterns Qualifier 2019
38 Purdue Win 15-13 1921.22 Feb 17th Easterns Qualifier 2019
44 Virginia Loss 9-15 1155.93 Feb 17th Easterns Qualifier 2019
204 SUNY-Buffalo Win 13-8 1467.96 Mar 16th Oak Creek Invite 2019
66 Penn State Win 13-11 1764.08 Mar 16th Oak Creek Invite 2019
108 North Carolina-Charlotte Win 13-8 1821.23 Mar 16th Oak Creek Invite 2019
54 Virginia Tech Win 13-8 2115.6 Mar 16th Oak Creek Invite 2019
101 Connecticut Win 15-13 1570.42 Mar 17th Oak Creek Invite 2019
47 Maryland Win 15-12 1956.82 Mar 17th Oak Creek Invite 2019
18 Michigan Loss 12-15 1608.27 Mar 17th Oak Creek Invite 2019
120 James Madison Win 13-7 1840.33 Mar 30th Atlantic Coast Open 2019
101 Connecticut Loss 8-9 1231.24 Mar 30th Atlantic Coast Open 2019
83 Rutgers Win 13-8 1929.13 Mar 30th Atlantic Coast Open 2019
171 RIT Win 13-6 1681.65 Mar 30th Atlantic Coast Open 2019
66 Penn State Win 15-4 2135.24 Mar 31st Atlantic Coast Open 2019
35 Middlebury Loss 10-12 1488.37 Mar 31st Atlantic Coast Open 2019
118 MIT Win 15-7 1887.73 Mar 31st Atlantic Coast Open 2019
**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)