#268 Akron (5-6)

avg: 871.21  •  sd: 92.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
378 SUNY-Buffalo-B Win 13-4 901.69 Mar 9th Spring Spook 2024
347 Wright State Win 13-5 1127.36 Mar 9th Spring Spook 2024
237 Carthage Win 9-3 1589.65 Mar 9th Spring Spook 2024
157 Miami (Ohio) Loss 8-14 753.73 Mar 10th Spring Spook 2024
194 Ohio Loss 9-13 724.06 Mar 10th Spring Spook 2024
79 Case Western Reserve** Loss 1-13 995.69 Ignored Apr 20th Ohio D I Mens Conferences 2024
292 Kent State Loss 10-11 622.63 Apr 20th Ohio D I Mens Conferences 2024
182 Dayton Win 9-8 1315.4 Apr 20th Ohio D I Mens Conferences 2024
292 Kent State Loss 9-10 622.63 Apr 21st Ohio D I Mens Conferences 2024
347 Wright State Win 7-6 652.36 Apr 21st Ohio D I Mens Conferences 2024
282 Toledo Loss 14-15 693.55 Apr 21st Ohio D I Mens Conferences 2024
**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)