#249 Cedarville (4-10)

avg: 612.8  •  sd: 86.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
164 Ohio Loss 10-15 523.26 Mar 15th Grand Rapids Invite 2025
123 Wisconsin-Milwaukee Loss 10-13 818.85 Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 9-14 1012.48 Mar 15th Grand Rapids Invite 2025
142 Grand Valley Win 10-9 1191.48 Mar 15th Grand Rapids Invite 2025
11 Davenport** Loss 4-15 1380.95 Ignored Mar 16th Grand Rapids Invite 2025
143 Michigan Tech Loss 6-15 462.91 Mar 16th Grand Rapids Invite 2025
221 Wisconsin-B Loss 9-11 461.5 Mar 16th Grand Rapids Invite 2025
344 South Carolina-B Win 13-8 625.76 Mar 29th Needle in a Ho Stack 2025
255 Wake Forest Loss 8-10 323.82 Mar 29th Needle in a Ho Stack 2025
94 Tennessee-Chattanooga** Loss 3-12 679.56 Ignored Mar 29th Needle in a Ho Stack 2025
187 North Carolina-B Loss 7-9 585.05 Mar 29th Needle in a Ho Stack 2025
199 North Carolina State-B Loss 5-15 217.07 Mar 30th Needle in a Ho Stack 2025
345 Georgia College Win 11-8 488.03 Mar 30th Needle in a Ho Stack 2025
344 South Carolina-B Win 11-2 729.6 Mar 30th Needle in a Ho Stack 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)