#111 Maryland (3-8)

avg: 1169.98  •  sd: 79.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
45 Notre Dame Loss 7-13 1016.04 Feb 8th Queen City Tune Up 2020 Open
21 North Carolina State** Loss 4-13 1220.92 Ignored Feb 8th Queen City Tune Up 2020 Open
- Rutgers Loss 4-8 632.86 Feb 8th Queen City Tune Up 2020 Open
117 Appalachian State Loss 10-12 905.54 Feb 9th Queen City Tune Up 2020 Open
41 Alabama Loss 7-9 1320.66 Feb 29th Easterns Qualifier 2020
73 Carnegie Mellon Loss 6-7 1241.46 Feb 29th Easterns Qualifier 2020
92 Duke Win 8-7 1387.44 Feb 29th Easterns Qualifier 2020
32 Dartmouth Loss 6-10 1187.28 Feb 29th Easterns Qualifier 2020
107 Ohio Win 12-11 1313.06 Mar 1st Easterns Qualifier 2020
91 Indiana Win 12-9 1615.49 Mar 1st Easterns Qualifier 2020
55 Virginia Tech Loss 6-12 887.64 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)