#268 Allegheny (5-5)

avg: 716.62  •  sd: 81.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
385 Princeton-B Win 13-3 804.07 Feb 24th Bring the Huckus 9
- Muhlenberg Loss 10-11 905.09 Feb 24th Bring the Huckus 9
251 Susquehanna Loss 6-12 185.81 Feb 24th Bring the Huckus 9
269 Northeastern-B Win 8-7 838.8 Feb 24th Bring the Huckus 9
328 Kent State Win 13-5 1069.15 Mar 3rd DiscThrow Inferno 2K18
228 Wooster Loss 8-10 573.56 Mar 3rd DiscThrow Inferno 2K18
108 Franciscan Loss 8-13 805.74 Mar 3rd DiscThrow Inferno 2K18
328 Kent State Win 11-8 834.76 Mar 4th DiscThrow Inferno 2K18
416 Kettering** Win 13-2 455.32 Ignored Mar 4th DiscThrow Inferno 2K18
210 Miami (Ohio) Loss 8-13 420.87 Mar 4th DiscThrow Inferno 2K18
**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)