#56 Maryland (12-8)

avg: 1698.67  •  sd: 76.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
71 Central Florida Loss 8-9 1432.32 Feb 22nd 2025 Commonwealth Cup Weekend 2
76 Columbia Loss 6-7 1382.91 Feb 22nd 2025 Commonwealth Cup Weekend 2
34 Ohio Loss 4-10 1358.56 Feb 22nd 2025 Commonwealth Cup Weekend 2
145 Boston College** Win 14-6 1586.95 Ignored Feb 23rd 2025 Commonwealth Cup Weekend 2
60 Purdue Win 12-5 2257.52 Feb 23rd 2025 Commonwealth Cup Weekend 2
37 American Loss 2-5 1296.84 Mar 1st Cherry Blossom Classic 2025
91 Brown-B Win 11-5 1962.75 Mar 1st Cherry Blossom Classic 2025
113 George Washington Win 8-0 1811.98 Mar 1st Cherry Blossom Classic 2025
227 Pennsylvania-B Win 5-2 923.52 Mar 1st Cherry Blossom Classic 2025
37 American Loss 3-9 1296.84 Mar 2nd Cherry Blossom Classic 2025
168 Miami (Florida)** Win 11-1 1410.09 Ignored Mar 2nd Cherry Blossom Classic 2025
100 SUNY-Buffalo Win 10-1 1916.52 Mar 2nd Cherry Blossom Classic 2025
77 Penn State Win 9-6 1915.82 Mar 2nd Cherry Blossom Classic 2025
119 Yale Win 9-8 1306.21 Mar 29th East Coast Invite 2025
77 Penn State Win 6-4 1862.86 Mar 29th East Coast Invite 2025
78 Mount Holyoke Win 11-4 2091.17 Mar 29th East Coast Invite 2025
23 Pennsylvania Loss 7-10 1856.18 Mar 29th East Coast Invite 2025
28 Georgia Loss 4-9 1461.57 Mar 30th East Coast Invite 2025
20 Virginia Loss 7-11 1802.03 Mar 30th East Coast Invite 2025
64 South Carolina Win 10-7 2024.33 Mar 30th East Coast Invite 2025
**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)