#165 Oberlin (1-8)

avg: 837.33  •  sd: 113.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
101 Butler Loss 6-9 894.16 Mar 1st Huckleberry Flick 2025
116 Cincinnati Loss 3-9 595.23 Mar 1st Huckleberry Flick 2025
164 Indiana Loss 4-6 476.88 Mar 1st Huckleberry Flick 2025
209 Miami (Ohio) Win 12-3 1101.43 Mar 2nd Huckleberry Flick 2025
124 Xavier Loss 4-10 534.5 Mar 2nd Huckleberry Flick 2025
59 Davenport Loss 4-5 1534.67 Mar 15th Davenport Spring Skirmish
132 Grand Valley Loss 4-8 500.07 Mar 15th Davenport Spring Skirmish
87 Michigan State Loss 6-8 1107.68 Mar 15th Davenport Spring Skirmish
96 Michigan Tech Loss 2-6 736.29 Mar 15th Davenport Spring Skirmish
**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)