#57 Cornell (7-10)

avg: 1460.62  •  sd: 63.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 Notre Dame Loss 8-10 1066.19 Feb 9th Queen City Tune Up 2019 Women
3 Ohio State** Loss 2-9 1773 Ignored Feb 9th Queen City Tune Up 2019 Women
45 Virginia Loss 7-11 1078.8 Feb 9th Queen City Tune Up 2019 Women
22 Tufts Loss 4-13 1334.61 Feb 9th Queen City Tune Up 2019 Women
40 Michigan Loss 12-13 1444.43 Feb 10th Queen City Tune Up 2019 Women
58 Penn State Loss 8-10 1188.38 Feb 10th Queen City Tune Up 2019 Women
163 SUNY-Binghamton** Win 15-1 1382.43 Ignored Mar 9th Delaware The Main Event 2019
27 Delaware Win 12-11 1939.9 Mar 9th Delaware The Main Event 2019
52 Columbia Win 12-11 1628.27 Mar 10th Delaware The Main Event 2019
65 Massachusetts Win 15-12 1696.78 Mar 10th Delaware The Main Event 2019
27 Delaware Loss 7-13 1257.37 Mar 10th Delaware The Main Event 2019
144 Tennessee Win 12-9 1241.42 Mar 30th I 85 Rodeo 2019
94 Carnegie Mellon Win 12-6 1764.03 Mar 30th I 85 Rodeo 2019
18 South Carolina Loss 5-13 1371.42 Mar 30th I 85 Rodeo 2019
36 Vanderbilt Win 12-11 1798.28 Mar 31st I 85 Rodeo 2019
26 Georgia Loss 1-15 1248.3 Mar 31st I 85 Rodeo 2019
28 North Carolina State Loss 12-14 1552.7 Mar 31st I 85 Rodeo 2019
**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)