#69 Maryland (11-8)

avg: 1539.96  •  sd: 53.12  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
52 Appalachian State Win 15-13 1848.23 Feb 11th Queen City Tune Up1
134 Carnegie Mellon Win 12-8 1677.47 Feb 11th Queen City Tune Up1
27 South Carolina Loss 10-14 1449.47 Feb 11th Queen City Tune Up1
13 Tufts Loss 8-15 1503.41 Feb 11th Queen City Tune Up1
22 Washington University Loss 12-13 1780.32 Feb 12th Queen City Tune Up1
51 Virginia Win 13-9 2054.03 Feb 12th Queen City Tune Up1
104 Florida State Win 12-11 1470.01 Feb 25th Easterns Qualifier 2023
33 Duke Loss 11-12 1665.66 Feb 25th Easterns Qualifier 2023
49 Notre Dame Win 13-12 1768.26 Feb 25th Easterns Qualifier 2023
25 North Carolina-Wilmington Loss 9-12 1538.89 Feb 25th Easterns Qualifier 2023
85 Alabama Win 13-12 1571.99 Feb 26th Easterns Qualifier 2023
45 Georgetown Loss 13-15 1482.51 Feb 26th Easterns Qualifier 2023
131 Georgia State Win 12-10 1480.7 Feb 26th Easterns Qualifier 2023
205 SUNY-Cortland** Win 13-4 1528.38 Ignored Mar 11th Oak Creek Invite 2023
106 Liberty Loss 8-11 977.3 Mar 11th Oak Creek Invite 2023
83 RIT Win 12-11 1575.36 Mar 11th Oak Creek Invite 2023
187 SUNY-Geneseo Win 13-7 1553.5 Mar 11th Oak Creek Invite 2023
204 Maine Win 13-8 1427.38 Mar 12th Oak Creek Invite 2023
76 Princeton Loss 9-12 1137.9 Mar 12th Oak Creek Invite 2023
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)