#73 Williams (11-7)

avg: 1385.1  •  sd: 69.61  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
122 Boston University Loss 7-8 1042.78 Mar 1st UMass Invite 2025
108 Columbia Win 10-9 1344.19 Mar 1st UMass Invite 2025
126 Maine Win 12-5 1738.09 Mar 1st UMass Invite 2025
170 Massachusetts -B Win 12-1 1559.86 Mar 1st UMass Invite 2025
46 Middlebury Loss 9-12 1252.43 Mar 2nd UMass Invite 2025
115 Vermont-B Win 12-5 1794.71 Mar 2nd UMass Invite 2025
176 Ithaca Win 11-7 1398.95 Mar 22nd Salt City Classic
301 Rensselaer Polytech** Win 13-4 976.06 Ignored Mar 22nd Salt City Classic
80 Rochester Loss 7-8 1215.49 Mar 22nd Salt City Classic
128 SUNY-Binghamton Win 11-6 1674.76 Mar 22nd Salt City Classic
24 Ottawa Loss 10-15 1380.24 Mar 23rd Salt City Classic
83 SUNY-Buffalo Loss 11-12 1195.93 Mar 23rd Salt City Classic
206 Christopher Newport** Win 13-1 1378.26 Ignored Mar 29th Easterns 2025
70 Franciscan Win 10-9 1535.69 Mar 29th Easterns 2025
396 Mary Washington** Win 13-0 600 Ignored Mar 29th Easterns 2025
144 Bates Win 13-12 1184.56 Mar 30th Easterns 2025
45 Elon Loss 12-13 1480.05 Mar 30th Easterns 2025
34 Lewis & Clark Loss 6-15 1059.3 Mar 30th Easterns 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)