#235 Drexel (5-20)

avg: 570.94  •  sd: 77.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Boston University Loss 6-10 525.17 Jan 28th Mid Atlantic Warmup
51 James Madison** Loss 2-13 837.21 Ignored Jan 28th Mid Atlantic Warmup
72 RIT** Loss 4-13 750.15 Ignored Jan 28th Mid Atlantic Warmup
74 Binghamton Loss 8-10 1055.95 Jan 28th Mid Atlantic Warmup
135 Boston University Loss 7-9 742 Jan 29th Mid Atlantic Warmup
180 American Loss 7-13 270.57 Jan 29th Mid Atlantic Warmup
65 Princeton Loss 6-13 774.33 Feb 18th Blue Hen Open
143 Virginia Commonwealth Loss 8-12 545.56 Feb 18th Blue Hen Open
215 Villanova Loss 7-10 268.36 Feb 18th Blue Hen Open
158 NYU Loss 4-13 331.35 Feb 18th Blue Hen Open
151 Johns Hopkins Loss 9-13 539.03 Feb 19th Blue Hen Open
158 NYU Loss 5-11 331.35 Feb 19th Blue Hen Open
154 George Washington Loss 5-11 346.65 Mar 4th Oak Creek Challenge 2023
90 Virginia Tech** Loss 3-13 645.75 Ignored Mar 4th Oak Creek Challenge 2023
166 Yale Win 6-4 1262.53 Mar 4th Oak Creek Challenge 2023
266 Maryland-Baltimore County Win 11-4 1037.69 Mar 5th Oak Creek Challenge 2023
162 Rowan Loss 8-10 657.9 Mar 5th Oak Creek Challenge 2023
189 West Chester Loss 2-13 184.89 Mar 5th Oak Creek Challenge 2023
99 Temple Loss 6-13 597.41 Mar 18th Spring Fling adelphia
246 Pennsylvania Loss 11-13 281.45 Mar 18th Spring Fling adelphia
190 Pittsburgh-B Loss 11-12 656.66 Mar 18th Spring Fling adelphia
342 SUNY-Fredonia** Win 13-5 -441.93 Ignored Mar 18th Spring Fling adelphia
99 Temple Loss 5-11 597.41 Mar 19th Spring Fling adelphia
246 Pennsylvania Win 9-8 635.29 Mar 19th Spring Fling adelphia
342 SUNY-Fredonia** Win 15-2 -441.93 Ignored Mar 19th Spring Fling adelphia
**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)