#243 Kent State (7-15)

avg: 906.75  •  sd: 55.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
125 Butler Loss 1-13 751.62 Mar 1st Huckin in the Hills XI
304 Cleveland State Win 5-4 793.12 Mar 1st Huckin in the Hills XI
217 Akron Loss 10-11 889.04 Mar 2nd Huckin in the Hills XI
226 West Virginia Win 11-8 1329.09 Mar 2nd Huckin in the Hills XI
178 Ohio Loss 1-13 566.75 Mar 2nd Huckin in the Hills XI
145 Oberlin Loss 8-12 843.64 Mar 15th Spring Spook
218 Miami (Ohio) Loss 6-7 886.72 Mar 15th Spring Spook
217 Akron Win 12-10 1252.16 Mar 16th Spring Spook
321 Cincinnati -B Win 15-7 1201.89 Mar 16th Spring Spook
145 Oberlin Loss 9-15 769.32 Mar 16th Spring Spook
80 Case Western Reserve** Loss 1-13 962.39 Ignored Apr 12th Ohio D I Mens Conferences 2025
165 Dayton Loss 5-12 611.18 Apr 12th Ohio D I Mens Conferences 2025
260 Toledo Win 13-10 1165.35 Apr 12th Ohio D I Mens Conferences 2025
178 Ohio Loss 7-12 646.24 Apr 12th Ohio D I Mens Conferences 2025
217 Akron Loss 9-11 764.83 Apr 13th Ohio D I Mens Conferences 2025
346 Wright State Win 15-3 1070.23 Apr 13th Ohio D I Mens Conferences 2025
178 Ohio Loss 10-14 768.04 Apr 13th Ohio D I Mens Conferences 2025
77 Ohio State** Loss 6-15 988.07 Ignored Apr 26th Ohio Valley D I College Mens Regionals 2025
29 Pittsburgh** Loss 2-15 1278.81 Ignored Apr 26th Ohio Valley D I College Mens Regionals 2025
113 West Chester Loss 5-15 792.64 Apr 26th Ohio Valley D I College Mens Regionals 2025
218 Miami (Ohio) Win 15-9 1527.2 Apr 27th Ohio Valley D I College Mens Regionals 2025
178 Ohio Loss 6-15 566.75 Apr 27th Ohio Valley D I College Mens Regionals 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)