#139 Wesleyan (4-7)

avg: 700.23  •  sd: 74.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
96 Harvard Loss 4-11 421.25 Mar 4th No Sleep Till Brooklyn 2023
- SUNY-Stony Brook Win 6-4 978.49 Mar 4th No Sleep Till Brooklyn 2023
55 Cornell** Loss 3-9 746.43 Ignored Mar 5th No Sleep Till Brooklyn 2023
97 NYU Loss 3-9 410.45 Mar 5th No Sleep Till Brooklyn 2023
- Colgate** Win 13-3 600 Ignored Mar 14th High Tide Sanctioned week 2
185 Messiah Win 10-5 836.53 Apr 1st Shady Encounters
97 NYU Loss 3-6 463.75 Apr 1st Shady Encounters
67 Mount Holyoke Loss 5-8 814.04 Apr 1st Shady Encounters
95 Temple Loss 0-9 429.26 Apr 1st Shady Encounters
151 Rutgers Win 9-4 1205.72 Apr 2nd Shady Encounters
61 Vermont-B Loss 3-13 686.98 Apr 2nd Shady Encounters
**Blowout Eligible


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)