#111 Bird (18-8)

avg: 1022.53  •  sd: 62.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
201 Pixel Win 13-9 957.65 Jul 8th Heavyweights 2023
50 Steamboat Win 13-11 1619.34 Jul 8th Heavyweights 2023
235 Mad City Vibes** Win 13-4 829.8 Ignored Jul 8th Heavyweights 2023
117 Spectre Win 11-10 1135.45 Jul 9th Heavyweights 2023
129 Bandwagon Win 12-11 1044.21 Jul 9th Heavyweights 2023
38 UNION Loss 9-12 1201.33 Jul 9th Heavyweights 2023
164 Pandamonium Win 11-7 1241.2 Jul 15th Cheep Thrills 2023
215 Lake Superior Disc Win 13-8 923.66 Jul 15th Cheep Thrills 2023
237 Ca$h Cow$ Win 12-6 803.69 Jul 15th Cheep Thrills 2023
245 Underdogs Win 10-8 336.87 Jul 15th Cheep Thrills 2023
2 Drag'n Thrust** Loss 5-13 1546.21 Ignored Jul 16th Cheep Thrills 2023
188 Melt Loss 11-12 485.23 Jul 16th Cheep Thrills 2023
196 Great Minnesota Get Together Win 13-3 1158.75 Jul 16th Cheep Thrills 2023
94 Risky Business Loss 8-11 722.67 Aug 19th Cooler Classic 34
188 Melt Win 10-6 1106.39 Aug 19th Cooler Classic 34
56 No Touching! Loss 6-13 724.26 Aug 19th Cooler Classic 34
121 Jabba Win 13-6 1584.87 Aug 19th Cooler Classic 34
94 Risky Business Loss 7-14 505.39 Aug 20th Cooler Classic 34
64 Minnesota Star Power Win 12-11 1408.35 Aug 20th Cooler Classic 34
56 No Touching! Loss 7-12 803.75 Aug 20th Cooler Classic 34
155 Madison United Mixed Ultimate Win 10-9 942.14 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
196 Great Minnesota Get Together Win 13-6 1158.75 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
235 Mad City Vibes** Win 13-4 829.8 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
92 Mad Udderburn Win 12-10 1338.28 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
112 Pushovers Win 13-9 1440.56 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
78 Northern Comfort Loss 10-15 735.15 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
**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)