#7 Pittsburgh (10-6)

avg: 1987.46  •  sd: 56.14  •  top 16/20: 99.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Maryland Loss 9-10 1559.28 Feb 3rd Queen City Tune Up 2018 College Open
8 Massachusetts Loss 8-11 1598.16 Feb 3rd Queen City Tune Up 2018 College Open
150 North Carolina-Asheville** Win 11-0 1731.08 Ignored Feb 3rd Queen City Tune Up 2018 College Open
10 Virginia Tech Win 10-8 2185.97 Feb 3rd Queen City Tune Up 2018 College Open
39 Northwestern Win 11-2 2228.7 Feb 3rd Queen City Tune Up 2018 College Open
18 Brigham Young Win 13-9 2271.95 Mar 3rd Stanford Invite 2018
32 California Win 13-9 2114.37 Mar 3rd Stanford Invite 2018
1 North Carolina Loss 8-13 1849.18 Mar 3rd Stanford Invite 2018
19 Colorado Win 13-10 2179.09 Mar 4th Stanford Invite 2018
5 Washington Loss 10-13 1723.26 Mar 4th Stanford Invite 2018
6 Brown Loss 10-12 1808.59 Mar 4th Stanford Invite 2018
20 Cal Poly-SLO Win 11-8 2208.73 Mar 4th Stanford Invite 2018
37 Central Florida Win 15-8 2199.56 Mar 31st Easterns 2018
2 Carleton College Loss 10-15 1774.59 Mar 31st Easterns 2018
16 North Carolina-Wilmington Win 13-12 2009.51 Mar 31st Easterns 2018
65 California-Santa Barbara Win 15-8 2027.18 Mar 31st Easterns 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)