#102 Lehigh (9-12)

avg: 1276.26  •  sd: 73.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
40 Haverford/Bryn Mawr Win 7-5 2171.52 Feb 22nd Bring The Huckus 2025
90 Ithaca Loss 7-8 1251.07 Feb 22nd Bring The Huckus 2025
47 Wesleyan Loss 2-12 1220.96 Feb 22nd Bring The Huckus 2025
143 SUNY-Geneseo Win 9-3 1600.08 Feb 22nd Bring The Huckus 2025
40 Haverford/Bryn Mawr Loss 4-10 1243.37 Feb 23rd Bring The Huckus 2025
117 Swarthmore Win 8-7 1320.07 Feb 23rd Bring The Huckus 2025
143 SUNY-Geneseo Win 10-7 1389.75 Feb 23rd Bring The Huckus 2025
149 Delaware Loss 3-4 850.99 Mar 1st Cherry Blossom Classic 2025
172 George Mason Win 5-3 1221.61 Mar 1st Cherry Blossom Classic 2025
100 SUNY-Buffalo Loss 5-6 1191.52 Mar 1st Cherry Blossom Classic 2025
111 SUNY-Binghamton Win 6-5 1359.66 Mar 1st Cherry Blossom Classic 2025
134 Catholic Loss 2-7 458.71 Mar 2nd Cherry Blossom Classic 2025
210 Johns Hopkins Win 9-4 1098.81 Mar 2nd Cherry Blossom Classic 2025
168 Miami (Florida) Win 9-5 1339.15 Mar 2nd Cherry Blossom Classic 2025
47 Wesleyan Loss 6-9 1402.4 Mar 29th East Coast Invite 2025
106 Temple Loss 7-9 983.12 Mar 29th East Coast Invite 2025
78 Mount Holyoke Loss 6-10 995.01 Mar 29th East Coast Invite 2025
24 Minnesota** Loss 2-15 1534.28 Ignored Mar 29th East Coast Invite 2025
76 Columbia Loss 7-9 1228.57 Mar 30th East Coast Invite 2025
119 Yale Win 12-6 1760.52 Mar 30th East Coast Invite 2025
27 Northeastern** Loss 1-8 1492.61 Ignored Mar 30th East Coast Invite 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)