#240 Vermont-C (5-3)

avg: 690.7  •  sd: 81.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
338 Alabama-B** Win 13-4 606.25 Ignored Feb 29th Cutlass Classic 2020
95 Connecticut Loss 7-11 778.61 Feb 29th Cutlass Classic 2020
293 Charleston Win 12-9 740.97 Feb 29th Cutlass Classic 2020
128 Clemson Loss 9-15 600.41 Feb 29th Cutlass Classic 2020
251 East Carolina Loss 7-9 339.93 Feb 29th Cutlass Classic 2020
338 Alabama-B** Win 15-5 606.25 Ignored Mar 1st Cutlass Classic 2020
348 Radford Win 15-8 516.46 Mar 1st Cutlass Classic 2020
241 Wake Forest Win 10-6 1181.73 Mar 1st Cutlass Classic 2020
**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)