#189 East Carolina (10-8)

avg: 862.04  •  sd: 82.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
206 George Washington Win 10-6 1299.46 Feb 24th Monument Melee
184 George Mason Loss 6-8 575.55 Feb 24th Monument Melee
166 Villanova Loss 6-9 539.97 Feb 24th Monument Melee
208 Virginia Commonwealth Loss 7-10 395.71 Feb 25th Monument Melee
184 George Mason Win 8-7 1001.05 Feb 25th Monument Melee
206 George Washington Win 11-10 928.3 Feb 25th Monument Melee
210 Charleston Win 8-3 1376.87 Mar 16th Southerns 2024
191 Georgia-B Loss 7-9 576.74 Mar 16th Southerns 2024
376 Wisconsin-Eau Claire-B** Win 13-2 248.06 Ignored Mar 16th Southerns 2024
210 Charleston Win 9-2 1376.87 Mar 17th Southerns 2024
191 Georgia-B Loss 7-9 576.74 Mar 17th Southerns 2024
322 Luther Win 12-6 827.37 Mar 17th Southerns 2024
77 Cedarville Loss 3-13 755.5 Mar 23rd Needle in a Ho Stack 2024
216 North Carolina State-B Win 8-7 885.32 Mar 23rd Needle in a Ho Stack 2024
245 Georgia College Win 12-1 1247.36 Mar 24th Needle in a Ho Stack 2024
116 Liberty Loss 2-12 582.96 Mar 24th Needle in a Ho Stack 2024
235 North Carolina-B Win 11-6 1234.16 Mar 24th Needle in a Ho Stack 2024
158 Kennesaw State Loss 6-10 514.56 Mar 24th Needle in a Ho Stack 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)