#356 Harvard-B (3-12)

avg: 28.93  •  sd: 77.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
222 Harvard** Loss 2-13 109.9 Ignored Mar 9th MIT Invite
218 MIT** Loss 1-13 139.3 Ignored Mar 9th MIT Invite
305 Northeastern-C Loss 5-7 30.7 Mar 9th MIT Invite
340 MIT-B Win 8-6 459.85 Mar 9th MIT Invite
296 Florida-B Loss 4-6 30.76 Mar 15th Tally Classic XIX
269 Jacksonville State Loss 2-11 -64.7 Mar 15th Tally Classic XIX
343 Nova Southeastern Loss 8-9 8.33 Mar 15th Tally Classic XIX
367 South Florida-B Win 7-6 54.54 Mar 15th Tally Classic XIX
112 Bowdoin** Loss 2-13 604.58 Ignored Mar 29th New England Open 2025
305 Northeastern-C Loss 8-10 96.17 Mar 29th New England Open 2025
207 Northeastern-B** Loss 2-13 166.44 Ignored Mar 29th New England Open 2025
299 Massachusetts-Lowell Loss 4-8 -179.8 Mar 29th New England Open 2025
333 Connecticut-B Loss 3-9 -382.13 Mar 30th New England Open 2025
365 Wentworth Loss 10-12 -269.85 Mar 30th New England Open 2025
346 Western New England Win 7-0 719.36 Mar 30th New England Open 2025
**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)