#168 Johns Hopkins (10-14)

avg: 1086.58  •  sd: 56.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
162 American Loss 7-8 979.41 Jan 28th Mid Atlantic Warmup
167 Virginia Commonwealth Win 8-7 1215.1 Jan 28th Mid Atlantic Warmup
41 William & Mary Loss 6-13 1118.88 Jan 28th Mid Atlantic Warmup
83 RIT Loss 7-15 850.36 Jan 29th Mid Atlantic Warmup
167 Virginia Commonwealth Loss 12-13 965.1 Jan 29th Mid Atlantic Warmup
82 Binghamton Loss 9-15 946.06 Feb 18th Blue Hen Open
97 Delaware Loss 9-14 945.13 Feb 18th Blue Hen Open
70 Lehigh Loss 9-15 1011.25 Feb 18th Blue Hen Open
76 Princeton Loss 10-15 1029.66 Feb 19th Blue Hen Open
248 Drexel Win 13-9 1163.11 Feb 19th Blue Hen Open
285 Villanova Win 15-6 1149.69 Feb 19th Blue Hen Open
286 Maryland-Baltimore County Win 12-3 1145.96 Mar 4th Oak Creek Challenge 2023
175 Rowan Win 13-6 1648.16 Mar 4th Oak Creek Challenge 2023
70 Lehigh Loss 8-13 1030.57 Mar 4th Oak Creek Challenge 2023
162 American Win 13-9 1522.98 Mar 5th Oak Creek Challenge 2023
185 West Chester Loss 9-11 755.44 Mar 5th Oak Creek Challenge 2023
345 Salisbury** Win 13-3 713.45 Ignored Mar 5th Oak Creek Challenge 2023
101 Navy Win 13-11 1605.33 Apr 1st Atlantic Coast Open 2023
84 Richmond Loss 9-13 1031.35 Apr 1st Atlantic Coast Open 2023
369 George Mason** Win 13-4 600 Ignored Apr 1st Atlantic Coast Open 2023
36 Penn State** Loss 4-13 1177.62 Ignored Apr 1st Atlantic Coast Open 2023
162 American Loss 3-9 504.41 Apr 2nd Atlantic Coast Open 2023
147 Connecticut Loss 11-14 849.14 Apr 2nd Atlantic Coast Open 2023
172 East Carolina Win 13-12 1188.43 Apr 2nd Atlantic Coast Open 2023
**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)