#169 Johns Hopkins (9-10)

avg: 1060.68  •  sd: 56.71  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
179 SUNY-Binghamton Win 10-9 1143.07 Feb 24th Oak Creek Challenge 2018
270 American Win 13-2 1307.3 Feb 24th Oak Creek Challenge 2018
182 NYU Win 10-7 1388.42 Feb 24th Oak Creek Challenge 2018
117 Pennsylvania Loss 13-15 1057.14 Feb 24th Oak Creek Challenge 2018
250 Maryland-Baltimore County Win 13-11 997.54 Feb 25th Oak Creek Challenge 2018
135 Brandeis Loss 11-14 861.53 Feb 25th Oak Creek Challenge 2018
194 George Washington Win 12-9 1309.79 Feb 25th Oak Creek Challenge 2018
91 Penn State Loss 8-13 877.74 Mar 17th Oak Creek Invite 2018
134 Princeton Win 13-9 1593.46 Mar 17th Oak Creek Invite 2018
119 Bates Loss 12-14 1043.67 Mar 17th Oak Creek Invite 2018
34 William & Mary Loss 5-13 1048.2 Mar 17th Oak Creek Invite 2018
109 Williams Loss 10-15 842.61 Mar 18th Oak Creek Invite 2018
115 Villanova Win 12-11 1401.67 Mar 18th Oak Creek Invite 2018
103 Delaware Loss 10-15 870.58 Mar 18th Oak Creek Invite 2018
167 North Carolina-B Loss 8-12 625.09 Mar 24th Atlantic Coast Open 2018
177 Virginia Commonwealth Loss 10-13 694.09 Mar 24th Atlantic Coast Open 2018
83 Middlebury Loss 11-13 1177.77 Mar 24th Atlantic Coast Open 2018
368 Edinboro** Win 8-0 911.46 Ignored Mar 25th Atlantic Coast Open 2018
243 Rowan Win 13-11 1012.85 Mar 25th Atlantic Coast Open 2018
**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)