#126 Maine (8-3)

avg: 1138.09  •  sd: 77.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
122 Boston University Win 9-7 1447.12 Mar 1st UMass Invite 2025
108 Columbia Win 7-6 1344.19 Mar 1st UMass Invite 2025
73 Williams Loss 5-12 785.1 Mar 1st UMass Invite 2025
170 Massachusetts -B Win 9-8 1084.86 Mar 1st UMass Invite 2025
120 Connecticut Loss 5-8 725.42 Mar 2nd UMass Invite 2025
68 Wesleyan Loss 8-13 935.15 Mar 2nd UMass Invite 2025
301 Rensselaer Polytech** Win 10-4 976.06 Ignored Mar 29th Ocean State Invite 2025
151 Rhode Island Win 11-10 1176.19 Mar 29th Ocean State Invite 2025
283 Roger Williams Win 11-5 1037.23 Mar 29th Ocean State Invite 2025
278 Central Connecticut State** Win 15-1 1069.54 Ignored Mar 30th Ocean State Invite 2025
151 Rhode Island Win 11-6 1597.89 Mar 30th Ocean State Invite 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)