#22 Tufts (8-8)

avg: 1750.18  •  sd: 54.25  •  top 16/20: 10.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 5-11 1745.34 Feb 3rd Queen City Tune Up 2018 College Open
12 North Carolina State Loss 7-8 1793.86 Feb 3rd Queen City Tune Up 2018 College Open
28 Carnegie Mellon Win 10-6 2214.81 Feb 3rd Queen City Tune Up 2018 College Open
9 Georgia Win 10-9 2074.28 Feb 3rd Queen City Tune Up 2018 College Open
62 Vermont Win 11-6 2012.53 Feb 3rd Queen City Tune Up 2018 College Open
3 Oregon Loss 6-13 1588.77 Mar 3rd Stanford Invite 2018
19 Colorado Loss 9-11 1601.75 Mar 3rd Stanford Invite 2018
13 Wisconsin Loss 10-11 1792.12 Mar 3rd Stanford Invite 2018
43 British Columbia Loss 10-12 1356.52 Mar 4th Stanford Invite 2018
11 Emory Loss 8-12 1479.52 Mar 4th Stanford Invite 2018
117 Pennsylvania Win 13-5 1871.31 Mar 24th Atlantic Coast Open 2018
34 William & Mary Win 12-11 1773.2 Mar 24th Atlantic Coast Open 2018
177 Virginia Commonwealth Win 13-7 1579.76 Mar 24th Atlantic Coast Open 2018
28 Carnegie Mellon Win 10-8 1981.32 Mar 25th Atlantic Coast Open 2018
86 Duke Win 14-9 1872.85 Mar 25th Atlantic Coast Open 2018
34 William & Mary Loss 9-10 1523.2 Mar 25th Atlantic Coast Open 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)