#245 Underdogs (2-16)

avg: 74.21  •  sd: 67.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
111 Bird Loss 8-10 759.86 Jul 15th Cheep Thrills 2023
196 Great Minnesota Get Together Loss 7-10 169.08 Jul 15th Cheep Thrills 2023
188 Melt Loss 6-13 10.23 Jul 15th Cheep Thrills 2023
164 Pandamonium** Loss 3-13 174.31 Ignored Jul 16th Cheep Thrills 2023
237 Ca$h Cow$ Win 11-9 473.59 Jul 16th Cheep Thrills 2023
215 Lake Superior Disc Loss 4-11 -172.5 Jul 16th Cheep Thrills 2023
132 Mousetrap** Loss 5-13 298.86 Ignored Aug 19th Cooler Classic 34
78 Northern Comfort** Loss 3-13 588.75 Ignored Aug 19th Cooler Classic 34
182 The Force Loss 5-15 39.22 Aug 19th Cooler Classic 34
209 Mastodon Loss 9-10 337.95 Aug 19th Cooler Classic 34
240 PanIC Loss 11-12 16.66 Aug 20th Cooler Classic 34
227 Arms Race Loss 7-10 -63.69 Aug 20th Cooler Classic 34
235 Mad City Vibes Loss 6-15 -370.2 Aug 20th Cooler Classic 34
228 Dinosaur Fancy Loss 9-15 -207.33 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
174 Boomtown Pandas Loss 7-15 113.09 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
64 Minnesota Star Power** Loss 4-15 683.35 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
215 Lake Superior Disc Loss 5-15 -172.5 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
249 Midnight Nut Busters Win 15-11 342.21 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)