#75 Wesleyan (16-6)

avg: 1599.36  •  sd: 94.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Connecticut Loss 6-7 1235.44 Mar 1st UMass Invite 2025
36 Middlebury Loss 9-12 1473.61 Mar 1st UMass Invite 2025
206 Tufts-B Loss 6-9 623.74 Mar 1st UMass Invite 2025
111 Vermont-B Loss 8-10 1155.65 Mar 1st UMass Invite 2025
105 Boston University Win 11-8 1815.27 Mar 2nd UMass Invite 2025
108 Columbia Win 11-8 1809.06 Mar 2nd UMass Invite 2025
134 Maine Win 13-8 1820.73 Mar 2nd UMass Invite 2025
133 Bates Win 13-8 1821.6 Mar 29th Easterns 2025
33 Elon Win 13-11 2071.5 Mar 29th Easterns 2025
36 Middlebury Loss 8-13 1322.81 Mar 29th Easterns 2025
33 Elon Loss 13-15 1628.48 Mar 30th Easterns 2025
67 Franciscan Win 15-12 1938.74 Mar 30th Easterns 2025
40 Lewis & Clark Win 14-11 2120.93 Mar 30th Easterns 2025
311 Hartford Win 13-6 1231.65 Apr 12th Hudson Valley D III Mens Conferences 2025
415 New Haven** Win 13-0 258.52 Ignored Apr 12th Hudson Valley D III Mens Conferences 2025
235 Skidmore Win 13-6 1527 Apr 12th Hudson Valley D III Mens Conferences 2025
235 Skidmore Win 15-8 1491.81 Apr 13th Hudson Valley D III Mens Conferences 2025
192 Vassar Win 15-7 1693.07 Apr 13th Hudson Valley D III Mens Conferences 2025
254 Colgate Win 13-7 1419.78 Apr 26th Metro East D III College Mens Regionals 2025
267 SUNY-Geneseo Win 12-6 1388.95 Apr 26th Metro East D III College Mens Regionals 2025
192 Vassar Win 13-5 1693.07 Apr 26th Metro East D III College Mens Regionals 2025
81 Rochester Win 15-11 1936.29 Apr 27th Metro East D III College Mens Regionals 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)