#103 Lehigh (15-4)

avg: 1170.49  •  sd: 65.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Boston University Win 7-6 1151.85 Feb 24th Bring The Huckus 2024
127 Dartmouth Win 8-5 1401.29 Feb 24th Bring The Huckus 2024
54 Haverford/Bryn Mawr Loss 3-10 961.45 Feb 24th Bring The Huckus 2024
165 SUNY-Geneseo Win 10-5 1172.19 Feb 24th Bring The Huckus 2024
117 Boston University Loss 4-7 530.69 Feb 25th Bring The Huckus 2024
98 Rochester Loss 7-8 1092.18 Feb 25th Bring The Huckus 2024
165 SUNY-Geneseo Win 10-7 987.96 Feb 25th Bring The Huckus 2024
218 William & Mary-B** Win 10-3 488.23 Ignored Mar 2nd Cherry Blossom Classic 2024
194 Miami (Florida)** Win 9-1 896.4 Ignored Mar 2nd Cherry Blossom Classic 2024
53 American Loss 6-9 1166.24 Mar 3rd Cherry Blossom Classic 2024
144 Catholic Win 7-5 1177.12 Mar 3rd Cherry Blossom Classic 2024
151 George Washington Win 10-6 1287.39 Mar 3rd Cherry Blossom Classic 2024
120 Johns Hopkins Win 10-6 1504.15 Mar 3rd Cherry Blossom Classic 2024
142 Boston College Win 8-5 1308.91 Mar 24th King of New York 2024
142 Boston College Win 10-6 1351.47 Mar 24th King of New York 2024
142 Boston College Win 8-5 1308.91 Mar 24th King of New York 2024
164 Ithaca Win 6-4 999.61 Mar 24th King of New York 2024
165 SUNY-Geneseo Win 9-5 1127.35 Mar 24th King of New York 2024
210 SUNY-Cortland** Win 9-3 687.63 Ignored Mar 24th King of New York 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)