#186 Richmond (2-9)

avg: 261.23  •  sd: 79.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Cedarville Loss 6-11 343.84 Feb 18th Commonwealth Cup Weekend1 2023
145 Virginia-B Loss 2-8 55.94 Feb 18th Commonwealth Cup Weekend1 2023
201 Wake Forest Loss 8-9 -62.33 Feb 18th Commonwealth Cup Weekend1 2023
181 George Washington Loss 6-7 182.11 Feb 19th Commonwealth Cup Weekend1 2023
215 Elon Win 8-4 317.48 Feb 19th Commonwealth Cup Weekend1 2023
175 Michigan-B Loss 6-11 -154.31 Feb 19th Commonwealth Cup Weekend1 2023
73 St. Olaf** Loss 1-13 626.89 Ignored Mar 25th Needle in a Ho Stack2
94 Boston College** Loss 3-11 438.5 Ignored Mar 25th Needle in a Ho Stack2
114 Union (Tennessee)** Loss 1-13 299.39 Ignored Mar 25th Needle in a Ho Stack2
201 Wake Forest Win 6-4 428.28 Mar 25th Needle in a Ho Stack2
56 Tennessee Loss 6-13 740.42 Mar 26th Needle in a Ho Stack2
**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)