#86 Bates (17-2)

avg: 1317.07  •  sd: 54.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
196 NYU Win 10-8 1103.86 Mar 2nd No Sleep till Brooklyn 2024
46 Williams Loss 8-13 1028.8 Mar 2nd No Sleep till Brooklyn 2024
278 SUNY-Stony Brook** Win 12-2 1097.13 Ignored Mar 2nd No Sleep till Brooklyn 2024
167 Columbia Loss 8-10 695.59 Mar 3rd No Sleep till Brooklyn 2024
236 MIT** Win 12-3 1280.57 Ignored Mar 3rd No Sleep till Brooklyn 2024
146 Yale Win 10-9 1185.05 Mar 3rd No Sleep till Brooklyn 2024
267 Massachusetts-Lowell Win 9-5 1058.05 Mar 23rd Ocean State Invite
182 Worcester Polytechnic Institute Win 11-2 1490.83 Mar 23rd Ocean State Invite
112 Boston College Win 9-6 1608.45 Mar 23rd Ocean State Invite
181 Northeastern-B Win 7-1 1498.07 Mar 23rd Ocean State Invite
182 Worcester Polytechnic Institute Win 10-4 1490.83 Mar 24th Ocean State Invite
181 Northeastern-B Win 10-4 1498.07 Mar 24th Ocean State Invite
112 Boston College Win 11-9 1439.09 Mar 24th Ocean State Invite
182 Worcester Polytechnic Institute Win 9-4 1490.83 Mar 30th New England Open 2024 Open Division
295 Harvard-B** Win 13-5 969.58 Ignored Mar 30th New England Open 2024 Open Division
207 Colby Win 9-4 1395.22 Mar 30th New England Open 2024 Open Division
225 Brandeis Win 13-5 1330.24 Mar 31st New England Open 2024 Open Division
182 Worcester Polytechnic Institute Win 8-6 1191.32 Mar 31st New England Open 2024 Open Division
103 Bowdoin Win 11-9 1465.2 Mar 31st New England Open 2024 Open Division
**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)