#35 Ohio (12-7)

avg: 1798.14  •  sd: 93.86  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
31 Brown Win 11-7 2341.08 Feb 24th Commonwealth Cup Weekend 2 2024
88 Virginia Tech Win 12-10 1535.38 Feb 24th Commonwealth Cup Weekend 2 2024
42 Purdue Win 11-9 1950 Feb 24th Commonwealth Cup Weekend 2 2024
38 South Carolina Win 8-3 2363.12 Feb 25th Commonwealth Cup Weekend 2 2024
90 Carnegie Mellon Win 10-5 1864.75 Feb 25th Commonwealth Cup Weekend 2 2024
39 Virginia Loss 7-8 1613.89 Feb 25th Commonwealth Cup Weekend 2 2024
25 Pittsburgh Loss 6-7 1837.92 Feb 25th Commonwealth Cup Weekend 2 2024
63 Cincinnati Loss 5-9 968.93 Mar 2nd Huckleberry Flick
204 Dayton** Win 12-4 772.26 Ignored Mar 2nd Huckleberry Flick
87 Indiana Win 9-8 1427.91 Mar 2nd Huckleberry Flick
63 Cincinnati Win 10-4 2097.99 Mar 3rd Huckleberry Flick
87 Indiana Win 11-2 1902.91 Mar 3rd Huckleberry Flick
143 Oberlin** Win 12-3 1450.51 Ignored Mar 3rd Huckleberry Flick
90 Carnegie Mellon Win 13-6 1890.85 Mar 30th East Coast Invite 2024
58 Cornell Win 8-5 1981.13 Mar 30th East Coast Invite 2024
2 Vermont** Loss 4-15 2189.63 Ignored Mar 30th East Coast Invite 2024
20 Northeastern Loss 5-13 1501.67 Mar 30th East Coast Invite 2024
7 Tufts Loss 6-11 1973.48 Mar 31st East Coast Invite 2024
29 Wisconsin Loss 8-10 1650.95 Mar 31st East Coast Invite 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)