#145 Liberty (7-11)

avg: 1214.02  •  sd: 54.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 William & Mary Win 9-8 1398.75 Jan 27th Winta Binta Vinta Fest 2018
66 Virginia Loss 3-11 1171.7 Jan 27th Winta Binta Vinta Fest 2018
150 Virginia Commonwealth Win 8-6 1486.73 Jan 27th Winta Binta Vinta Fest 2018
49 Duke** Loss 0-13 1351.48 Ignored Jan 27th Winta Binta Vinta Fest 2018
135 William & Mary Loss 5-12 673.75 Jan 28th Winta Binta Vinta Fest 2018
80 James Madison Loss 5-12 1047.06 Jan 28th Winta Binta Vinta Fest 2018
88 Georgetown Loss 8-9 1453.62 Jan 28th Winta Binta Vinta Fest 2018
31 Penn State** Loss 5-14 1486.19 Ignored Feb 24th Commonwealth Cup 2018
99 Princeton Loss 6-15 920.12 Feb 24th Commonwealth Cup 2018
218 Elon Win 13-4 1344.95 Feb 24th Commonwealth Cup 2018
64 Columbia Loss 7-12 1263.44 Feb 25th Commonwealth Cup 2018
123 MIT Loss 8-12 918.07 Feb 25th Commonwealth Cup 2018
139 Michigan State Win 9-7 1533.6 Feb 25th Commonwealth Cup 2018
179 Smith Win 12-10 1241.03 Mar 10th Mash Up 2018
218 Elon Win 12-8 1186.1 Mar 10th Mash Up 2018
74 Massachusetts Loss 8-15 1146.23 Mar 10th Mash Up 2018
179 Smith Win 11-7 1469.8 Mar 11th Mash Up 2018
74 Massachusetts Loss 9-15 1195.56 Mar 11th Mash Up 2018
**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)