#157 Johns Hopkins (11-14)

avg: 1005.84  •  sd: 74.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
39 Cincinnati** Loss 2-13 1030.04 Ignored Jan 25th Mid Atlantic Warm Up 2025
66 Dartmouth Loss 3-13 852.25 Jan 25th Mid Atlantic Warm Up 2025
159 George Mason Loss 8-9 872.77 Jan 25th Mid Atlantic Warm Up 2025
180 American Win 10-8 1168.4 Jan 26th Mid Atlantic Warm Up 2025
122 Boston University Loss 4-15 567.78 Jan 26th Mid Atlantic Warm Up 2025
179 Pennsylvania Win 11-4 1511.9 Jan 26th Mid Atlantic Warm Up 2025
159 George Mason Loss 7-9 718.43 Feb 22nd Monument Melee 2025
272 Virginia Commonwealth Win 11-6 1058.03 Feb 22nd Monument Melee 2025
294 Maryland-Baltimore County Win 9-6 818.42 Feb 22nd Monument Melee 2025
180 American Loss 5-13 305.74 Feb 23rd Monument Melee 2025
184 East Carolina Loss 9-10 764.71 Feb 23rd Monument Melee 2025
247 George Washington Win 10-7 1015.55 Feb 23rd Monument Melee 2025
83 SUNY-Buffalo Win 9-5 1849.99 Mar 1st Oak Creek Challenge 2025
113 Lehigh Win 5-3 1622.81 Mar 1st Oak Creek Challenge 2025
132 Rutgers Loss 3-13 516.55 Mar 1st Oak Creek Challenge 2025
71 Case Western Reserve Loss 7-8 1280.06 Mar 2nd Oak Creek Challenge 2025
56 Cornell Loss 5-13 923.94 Mar 2nd Oak Creek Challenge 2025
116 West Chester Win 11-8 1557.78 Mar 2nd Oak Creek Challenge 2025
98 Boston College Loss 7-9 992.37 Mar 29th East Coast Invite 2025
116 West Chester Loss 8-11 826.56 Mar 29th East Coast Invite 2025
83 SUNY-Buffalo Loss 7-10 931.26 Mar 29th East Coast Invite 2025
236 NYU Win 9-8 774.53 Mar 29th East Coast Invite 2025
108 Columbia Loss 7-12 698.68 Mar 30th East Coast Invite 2025
222 Harvard Win 14-7 1292.79 Mar 30th East Coast Invite 2025
132 Rutgers Win 9-6 1535.12 Mar 30th East Coast Invite 2025
**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)