#76 Williams (15-7)

avg: 1592.52  •  sd: 66.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
105 Boston University Loss 7-8 1324.66 Mar 1st UMass Invite 2025
108 Columbia Win 10-9 1568.45 Mar 1st UMass Invite 2025
134 Maine Win 12-5 1924.57 Mar 1st UMass Invite 2025
164 Massachusetts -B Win 12-1 1820.7 Mar 1st UMass Invite 2025
36 Middlebury Loss 9-12 1473.61 Mar 2nd UMass Invite 2025
111 Vermont-B Win 12-5 2018.32 Mar 2nd UMass Invite 2025
160 Ithaca Win 11-7 1697.59 Mar 22nd Salt City Classic
347 Rensselaer Polytech** Win 13-4 1061.28 Ignored Mar 22nd Salt City Classic
81 Rochester Loss 7-8 1430.12 Mar 22nd Salt City Classic
146 SUNY-Binghamton Win 11-6 1830.3 Mar 22nd Salt City Classic
30 Ottawa Loss 10-15 1419.27 Mar 23rd Salt City Classic
97 SUNY-Buffalo Loss 11-12 1349.63 Mar 23rd Salt City Classic
221 Christopher Newport Win 13-1 1595.31 Mar 29th Easterns 2025
67 Franciscan Win 10-9 1763.25 Mar 29th Easterns 2025
290 Mary Washington** Win 13-0 1334.64 Ignored Mar 29th Easterns 2025
133 Bates Win 13-12 1450.44 Mar 30th Easterns 2025
33 Elon Loss 12-13 1717.66 Mar 30th Easterns 2025
40 Lewis & Clark Loss 6-15 1207.59 Mar 30th Easterns 2025
312 Amherst** Win 15-3 1228.94 Ignored Apr 19th South New England D III Mens Conferences 2025
363 Clark** Win 15-3 968.58 Ignored Apr 19th South New England D III Mens Conferences 2025
362 Western New England** Win 15-3 969 Ignored Apr 19th South New England D III Mens Conferences 2025
281 Worcester Polytechnic** Win 15-6 1363.99 Ignored Apr 20th South New England D III Mens Conferences 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)