#72 Swans (12-12)

avg: 1016.86  •  sd: 84.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Dynasty Win 15-9 1167.45 Aug 18th Oshadega Ultimate Invite 2018
51 BroCats Loss 12-14 968.89 Aug 18th Oshadega Ultimate Invite 2018
90 Omen Win 15-13 1115.21 Aug 18th Oshadega Ultimate Invite 2018
76 Slag Dump Loss 9-15 475.41 Aug 19th Oshadega Ultimate Invite 2018
65 Mango Tree Win 15-8 1608.98 Aug 19th Oshadega Ultimate Invite 2018
132 DingWop Win 15-13 823.37 Aug 25th Rutabaga Rebellion
59 Mallard Loss 11-15 716.89 Aug 25th Rutabaga Rebellion
117 THE BODY Win 15-9 1246.42 Aug 25th Rutabaga Rebellion
70 Imperial Loss 9-15 515.08 Aug 25th Rutabaga Rebellion
70 Imperial Loss 9-11 781.36 Aug 26th Rutabaga Rebellion
117 THE BODY Win 10-9 855.94 Aug 26th Rutabaga Rebellion
132 DingWop Win 13-5 1209.19 Sep 8th Northwest Plains Mens Sectional Championship 2018
32 General Strike Loss 11-13 1117.66 Sep 8th Northwest Plains Mens Sectional Championship 2018
103 houSE Win 13-7 1381.07 Sep 8th Northwest Plains Mens Sectional Championship 2018
161 Ironside** Win 13-2 807.16 Ignored Sep 8th Northwest Plains Mens Sectional Championship 2018
32 General Strike Loss 14-15 1221.5 Sep 9th Northwest Plains Mens Sectional Championship 2018
39 Mad Men Win 13-9 1697.53 Sep 9th Northwest Plains Mens Sectional Championship 2018
59 Mallard Win 15-14 1223.06 Sep 9th Northwest Plains Mens Sectional Championship 2018
95 Scythe Win 14-12 1100.02 Sep 22nd North Central Mens Regional Championship 2018
39 Mad Men Loss 7-13 721.44 Sep 22nd North Central Mens Regional Championship 2018
60 DeMo Loss 10-13 768.44 Sep 22nd North Central Mens Regional Championship 2018
8 Sub Zero** Loss 3-13 1229.77 Ignored Sep 22nd North Central Mens Regional Championship 2018
49 CaSTLe Loss 8-13 717.99 Sep 23rd North Central Mens Regional Championship 2018
21 Prairie Fire Loss 7-13 950.65 Sep 23rd North Central Mens Regional Championship 2018
**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)