#289 Drexel (2-16)

avg: 769.67  •  sd: 58.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
226 American Win 9-8 1158.12 Feb 24th Monument Melee
147 Maryland-Baltimore County Loss 5-13 715.87 Feb 24th Monument Melee
204 Virginia Commonwealth Win 8-6 1408.47 Feb 24th Monument Melee
170 Villanova Loss 4-15 651.96 Feb 25th Monument Melee
217 George Washington Loss 7-13 500.97 Feb 25th Monument Melee
202 George Mason Loss 5-10 542.43 Feb 25th Monument Melee
85 Cornell Loss 6-13 975.01 Mar 2nd Oak Creek Challenge 2024
148 Johns Hopkins Loss 2-13 715.12 Mar 2nd Oak Creek Challenge 2024
91 SUNY-Buffalo** Loss 2-13 943.75 Ignored Mar 2nd Oak Creek Challenge 2024
217 George Washington Loss 6-13 458.5 Mar 3rd Oak Creek Challenge 2024
150 West Chester Loss 7-13 756.99 Mar 3rd Oak Creek Challenge 2024
148 Johns Hopkins Loss 3-13 715.12 Mar 3rd Oak Creek Challenge 2024
170 Villanova Loss 9-12 906.6 Apr 13th East Penn D I Mens Conferences 2024
150 West Chester Loss 3-11 714.53 Apr 13th East Penn D I Mens Conferences 2024
97 Lehigh** Loss 4-12 926.34 Ignored Apr 13th East Penn D I Mens Conferences 2024
56 Temple** Loss 4-13 1143.67 Ignored Apr 13th East Penn D I Mens Conferences 2024
170 Villanova Loss 4-11 651.96 Apr 13th East Penn D I Mens Conferences 2024
92 Pennsylvania** Loss 2-13 939.36 Ignored Apr 14th East Penn 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)