#396 Maryland-B (7-17)

avg: 107.67  •  sd: 62.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
215 Akron** Loss 2-8 397.92 Ignored Mar 1st Huckin in the Hills XI
397 Ohio-B Win 5-4 219.65 Mar 1st Huckin in the Hills XI
226 West Virginia** Loss 1-13 348.77 Ignored Mar 1st Huckin in the Hills XI
307 Cleveland State Loss 7-9 374.58 Mar 2nd Huckin in the Hills XI
417 Denison Win 10-5 156.75 Mar 2nd Huckin in the Hills XI
338 Penn State-Behrend Loss 7-11 81.86 Mar 2nd Huckin in the Hills XI
401 George Washington-B Loss 7-11 -453.79 Mar 29th Fishbowl 2025
383 James Madison-B Loss 4-9 -367.52 Mar 29th Fishbowl 2025
343 Virginia-B Loss 9-11 253.64 Mar 29th Fishbowl 2025
226 West Virginia Loss 6-13 348.77 Mar 29th Fishbowl 2025
406 William & Mary-B Win 8-6 256.76 Mar 30th Fishbowl 2025
406 William & Mary-B Loss 6-12 -623.04 Mar 30th Fishbowl 2025
413 American-B Win 13-4 285.62 Apr 12th Colonial Dev Mens Conferences 2025
339 Delaware-B Loss 4-13 -68.01 Apr 12th Colonial Dev Mens Conferences 2025
401 George Washington-B Win 13-5 613.1 Apr 12th Colonial Dev Mens Conferences 2025
404 Georgetown-B Win 12-9 325.65 Apr 12th Colonial Dev Mens Conferences 2025
413 American-B Win 8-6 -13.89 Apr 13th Colonial Dev Mens Conferences 2025
404 Georgetown-B Loss 8-9 -144.72 Apr 13th Colonial Dev Mens Conferences 2025
339 Delaware-B Loss 6-11 -14.71 Apr 26th Atlantic Coast Dev College Mens Regionals 2025
205 North Carolina State-B** Loss 4-13 440.51 Ignored Apr 26th Atlantic Coast Dev College Mens Regionals 2025
334 South Carolina-B Loss 7-9 281.11 Apr 26th Atlantic Coast Dev College Mens Regionals 2025
264 Virginia Tech-B Loss 7-9 527 Apr 26th Atlantic Coast Dev College Mens Regionals 2025
343 Virginia-B Loss 4-11 -97.15 Apr 27th Atlantic Coast Dev College Mens Regionals 2025
343 Virginia-B Loss 7-9 223.51 Apr 27th Atlantic Coast Dev College Mens Regionals 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)