#215 Akron (10-11)

avg: 997.92  •  sd: 69.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
397 Ohio-B** Win 12-2 694.65 Ignored Mar 1st Huckin in the Hills XI
396 Maryland-B** Win 8-2 707.67 Ignored Mar 1st Huckin in the Hills XI
226 West Virginia Win 9-3 1548.77 Mar 1st Huckin in the Hills XI
127 Butler Loss 4-13 735.19 Mar 2nd Huckin in the Hills XI
244 Kent State Win 11-10 1016.15 Mar 2nd Huckin in the Hills XI
153 Kentucky Loss 4-13 648.49 Mar 15th Spring Spook
263 Toledo Win 8-3 1420.43 Mar 15th Spring Spook
218 Miami (Ohio) Win 13-7 1553.71 Mar 16th Spring Spook
153 Kentucky Loss 6-15 648.49 Mar 16th Spring Spook
244 Kent State Loss 10-12 653.03 Mar 16th Spring Spook
348 Wright State Win 12-6 1034.81 Apr 12th Ohio D I Mens Conferences 2025
218 Miami (Ohio) Win 8-5 1449.79 Apr 12th Ohio D I Mens Conferences 2025
37 Cincinnati** Loss 3-13 1196.25 Ignored Apr 12th Ohio D I Mens Conferences 2025
76 Ohio State Loss 4-10 970.01 Apr 12th Ohio D I Mens Conferences 2025
244 Kent State Win 11-9 1140.36 Apr 13th Ohio D I Mens Conferences 2025
218 Miami (Ohio) Loss 8-15 431.37 Apr 13th Ohio D I Mens Conferences 2025
74 Temple Loss 2-15 984.07 Apr 26th Ohio Valley D I College Mens Regionals 2025
37 Cincinnati Loss 7-15 1196.25 Apr 26th Ohio Valley D I College Mens Regionals 2025
167 Dayton Loss 9-15 676.47 Apr 26th Ohio Valley D I College Mens Regionals 2025
179 Ohio Loss 14-15 1026.21 Apr 27th Ohio Valley D I College Mens Regionals 2025
218 Miami (Ohio) Win 13-12 1121.18 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)