#83 Penn State (4-7)

avg: 1309.42  •  sd: 70.03  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
41 Alabama Loss 10-12 1361.88 Feb 8th Queen City Tune Up 2020 Open
107 Ohio Win 10-8 1450.73 Feb 8th Queen City Tune Up 2020 Open
22 Georgia Loss 9-13 1398.39 Feb 8th Queen City Tune Up 2020 Open
50 Purdue Loss 8-13 1001.22 Feb 9th Queen City Tune Up 2020 Open
55 Virginia Tech Win 9-8 1591.95 Feb 29th Easterns Qualifier 2020
26 South Carolina Loss 7-11 1278.04 Feb 29th Easterns Qualifier 2020
24 Vermont Loss 4-13 1183.02 Feb 29th Easterns Qualifier 2020
161 Georgia State Win 11-5 1588.49 Feb 29th Easterns Qualifier 2020
107 Ohio Win 13-9 1606.63 Mar 1st Easterns Qualifier 2020
91 Indiana Loss 12-15 969.63 Mar 1st Easterns Qualifier 2020
67 Wisconsin-Milwaukee Loss 11-15 1010.48 Mar 1st Easterns Qualifier 2020
**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)