#148 Johns Hopkins (13-11)

avg: 1315.12  •  sd: 47.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
141 Boston University Loss 8-9 1217.31 Jan 27th Mid Atlantic Warm Up
77 Carnegie Mellon Loss 8-13 1111.31 Jan 27th Mid Atlantic Warm Up
166 RIT Win 11-9 1517.03 Jan 27th Mid Atlantic Warm Up
104 Liberty Loss 8-9 1365.72 Jan 27th Mid Atlantic Warm Up
226 American Win 12-8 1474.28 Jan 28th Mid Atlantic Warm Up
204 Virginia Commonwealth Loss 10-11 982.98 Jan 28th Mid Atlantic Warm Up
166 RIT Win 15-10 1721.43 Jan 28th Mid Atlantic Warm Up
85 Cornell Loss 4-13 975.01 Mar 2nd Oak Creek Challenge 2024
289 Drexel Win 13-2 1369.67 Mar 2nd Oak Creek Challenge 2024
91 SUNY-Buffalo Loss 7-13 986.22 Mar 2nd Oak Creek Challenge 2024
289 Drexel Win 13-3 1369.67 Mar 3rd Oak Creek Challenge 2024
217 George Washington Win 11-6 1605.2 Mar 3rd Oak Creek Challenge 2024
150 West Chester Loss 8-11 948.92 Mar 3rd Oak Creek Challenge 2024
262 Virginia Tech-B Win 15-8 1464.09 Mar 30th Atlantic Coast Open 2024
166 RIT Loss 13-14 1142.82 Mar 30th Atlantic Coast Open 2024
307 Mary Washington Win 14-8 1219.19 Mar 30th Atlantic Coast Open 2024
204 Virginia Commonwealth Win 9-7 1387.32 Mar 30th Atlantic Coast Open 2024
244 Dickinson Win 15-4 1579.09 Mar 31st Atlantic Coast Open 2024
217 George Washington Win 15-8 1623.31 Mar 31st Atlantic Coast Open 2024
226 American Win 14-8 1569.16 Apr 20th Colonial D I Mens Conferences 2024
147 Maryland-Baltimore County Loss 7-8 1190.87 Apr 20th Colonial D I Mens Conferences 2024
64 Maryland Loss 8-12 1236.73 Apr 20th Colonial D I Mens Conferences 2024
175 Delaware Loss 11-13 993.04 Apr 21st Colonial D I Mens Conferences 2024
217 George Washington Win 13-9 1477.07 Apr 21st Colonial D I Mens Conferences 2024
**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)