#136 Wesleyan (15-8)

avg: 1368.91  •  sd: 63.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Bryant Win 12-8 1781.93 Feb 10th UMass Invite 2024
272 Rowan Win 13-9 1275.35 Feb 10th UMass Invite 2024
108 Vermont-B Loss 7-10 1085.37 Feb 10th UMass Invite 2024
55 Williams Loss 9-12 1404.2 Feb 10th UMass Invite 2024
199 Connecticut College Loss 10-11 1000.75 Feb 11th UMass Invite 2024
272 Rowan Win 11-8 1222.39 Feb 11th UMass Invite 2024
131 Yale Loss 9-13 960.8 Feb 11th UMass Invite 2024
213 Ithaca Loss 9-10 950.64 Mar 30th Northeast Classic 2024
181 SUNY-Cortland Win 9-8 1318.21 Mar 30th Northeast Classic 2024
108 Vermont-B Loss 8-9 1350.03 Mar 30th Northeast Classic 2024
187 College of New Jersey Win 9-8 1288.42 Mar 31st Northeast Classic 2024
181 SUNY-Cortland Loss 8-12 752.06 Mar 31st Northeast Classic 2024
401 Siena** Win 13-1 639.98 Ignored Apr 13th Hudson Valley D III Mens Conferences 2024
205 Vassar Win 13-4 1705.54 Apr 13th Hudson Valley D III Mens Conferences 2024
284 Marist Win 13-6 1409.2 Apr 13th Hudson Valley D III Mens Conferences 2024
139 Army Win 15-12 1648.84 Apr 14th Hudson Valley D III Mens Conferences 2024
260 Hartford Win 15-6 1509.61 Apr 14th Hudson Valley D III Mens Conferences 2024
249 Colgate Win 15-6 1560.22 Apr 27th Metro East D III College Mens Regionals 2024
213 Ithaca Win 15-6 1675.64 Apr 27th Metro East D III College Mens Regionals 2024
267 SUNY-Geneseo Win 15-5 1471.49 Apr 27th Metro East D III College Mens Regionals 2024
252 Hamilton Win 15-7 1530.85 Apr 28th Metro East D III College Mens Regionals 2024
117 Rochester Loss 10-13 1099.19 Apr 28th Metro East D III College Mens Regionals 2024
245 Skidmore Win 15-8 1542.88 Apr 28th Metro East D III College Mens Regionals 2024
**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)