#66 Penn State (11-9)

avg: 1535.24  •  sd: 58.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Clemson Win 11-9 1532.76 Feb 9th Queen City Tune Up 2019 Men
47 Maryland Loss 8-10 1393.66 Feb 9th Queen City Tune Up 2019 Men
9 Massachusetts Loss 3-13 1465.5 Feb 9th Queen City Tune Up 2019 Men
79 Tulane Loss 7-11 989.53 Feb 9th Queen City Tune Up 2019 Men
61 Tennessee Loss 9-15 1038.71 Feb 10th Queen City Tune Up 2019 Men
131 Chicago Win 14-13 1391.49 Feb 10th Queen City Tune Up 2019 Men
33 Johns Hopkins Loss 11-13 1502.33 Mar 16th Oak Creek Invite 2019
108 North Carolina-Charlotte Win 11-8 1690.68 Mar 16th Oak Creek Invite 2019
54 Virginia Tech Win 13-11 1848.29 Mar 16th Oak Creek Invite 2019
204 SUNY-Buffalo Win 13-5 1571.8 Mar 16th Oak Creek Invite 2019
32 William & Mary Loss 12-15 1446.19 Mar 17th Oak Creek Invite 2019
73 Temple Win 15-10 1934.47 Mar 17th Oak Creek Invite 2019
101 Connecticut Win 15-11 1737.4 Mar 17th Oak Creek Invite 2019
102 Georgetown Win 13-8 1847.34 Mar 30th Atlantic Coast Open 2019
62 Duke Win 12-11 1676 Mar 30th Atlantic Coast Open 2019
35 Middlebury Loss 10-12 1488.37 Mar 30th Atlantic Coast Open 2019
114 Liberty Win 13-7 1857.64 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Loss 4-15 1131.17 Mar 31st Atlantic Coast Open 2019
91 Mary Washington Loss 10-11 1257.51 Mar 31st Atlantic Coast Open 2019
137 North Carolina-B Win 15-12 1533.65 Mar 31st Atlantic Coast Open 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)