#115 American (10-3)

avg: 992.6  •  sd: 69.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
132 George Mason Win 10-9 965.98 Feb 8th TowsonTown Throwdown 2020
180 George Washington-B Win 13-4 1076.24 Feb 8th TowsonTown Throwdown 2020
216 Christopher Newport** Win 15-5 681.54 Ignored Feb 8th TowsonTown Throwdown 2020
63 Towson Loss 5-10 750.19 Feb 8th TowsonTown Throwdown 2020
159 Maryland-Baltimore County Win 13-2 1197.06 Feb 9th TowsonTown Throwdown 2020
88 Richmond Loss 7-9 857.61 Feb 9th TowsonTown Throwdown 2020
237 Towson-B** Win 8-0 351.74 Ignored Feb 9th TowsonTown Throwdown 2020
104 Delaware Win 7-6 1174.03 Feb 29th Cherry Blossom Classic 2020
203 Dickinson** Win 11-4 838.34 Ignored Feb 29th Cherry Blossom Classic 2020
168 Virginia Commonwealth Win 9-2 1145.55 Feb 29th Cherry Blossom Classic 2020
164 Pittsburgh-B Win 12-9 907.61 Mar 1st Cherry Blossom Classic 2020
109 Carnegie Mellon Win 10-9 1150.99 Mar 1st Cherry Blossom Classic 2020
69 New Hampshire Loss 7-14 708.92 Mar 1st Cherry Blossom Classic 2020
**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)