#175 Maryland-Baltimore County (7-5)

avg: 928  •  sd: 78.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 American Win 11-7 1198.64 Feb 24th Monument Melee
280 Drexel Win 13-5 1073.63 Feb 24th Monument Melee
208 Virginia Commonwealth Loss 7-11 318.48 Feb 24th Monument Melee
224 American Win 10-8 994.41 Feb 25th Monument Melee
206 George Washington Win 9-8 928.3 Feb 25th Monument Melee
166 Villanova Loss 6-10 462.38 Feb 25th Monument Melee
85 Carnegie Mellon Loss 8-11 952.72 Mar 2nd Oak Creek Challenge 2024
169 Rutgers Win 10-9 1076.64 Mar 2nd Oak Creek Challenge 2024
152 West Chester Win 11-9 1275.78 Mar 2nd Oak Creek Challenge 2024
85 Carnegie Mellon Loss 9-13 899.76 Mar 3rd Oak Creek Challenge 2024
130 Towson Loss 10-13 788.69 Mar 3rd Oak Creek Challenge 2024
165 RIT Win 11-10 1090.29 Mar 3rd Oak Creek Challenge 2024
**Blowout Eligible


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)