#125 Ohio (6-2)

avg: 1345.82  •  sd: 109.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
239 Miami (Ohio)** Win 13-5 1129.18 Ignored Feb 24th 7th Annual Bens Barmitzvah 2018
163 Xavier Loss 9-10 984.23 Feb 24th 7th Annual Bens Barmitzvah 2018
121 Dayton Win 12-5 1963.47 Feb 24th 7th Annual Bens Barmitzvah 2018
219 Ohio State-B** Win 13-1 1341.16 Ignored Feb 25th 7th Annual Bens Barmitzvah 2018
121 Dayton Win 10-8 1626.13 Feb 25th 7th Annual Bens Barmitzvah 2018
146 DePaul Loss 7-8 1074.94 Mar 31st Indy Invite College Women 2018
238 Washington University-B** Win 8-0 1170.12 Ignored Mar 31st Indy Invite College Women 2018
- Wooster Win 8-4 1174.8 Mar 31st Indy Invite College Women 2018
**Blowout Eligible

FAQ

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)