#211 Lake Superior Disc (6-12)

avg: 446.79  •  sd: 66.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
159 Pandamonium Loss 3-13 185.85 Jul 15th Cheep Thrills 2023
103 Bird Loss 8-13 541.38 Jul 15th Cheep Thrills 2023
182 Melt Loss 10-11 495.06 Jul 15th Cheep Thrills 2023
235 Ca$h Cow$ Loss 5-10 -337.36 Jul 16th Cheep Thrills 2023
189 Great Minnesota Get Together Win 13-10 895.03 Jul 16th Cheep Thrills 2023
244 Underdogs Win 11-4 684.14 Jul 16th Cheep Thrills 2023
241 PanIC Win 10-6 637.96 Aug 19th Cooler Classic 34
131 Stackcats Loss 6-13 290.78 Aug 19th Cooler Classic 34
170 Boomtown Pandas Loss 5-13 127.04 Aug 19th Cooler Classic 34
116 Jabba Loss 6-13 392.19 Aug 19th Cooler Classic 34
159 Pandamonium Loss 8-11 420.24 Aug 20th Cooler Classic 34
131 Stackcats Loss 5-15 290.78 Aug 20th Cooler Classic 34
54 No Touching! Loss 8-15 779.28 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
176 The Force Loss 12-13 526.87 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
249 Midnight Nut Busters Win 14-7 558.67 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
226 Dinosaur Fancy Win 12-10 559.9 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
170 Boomtown Pandas Loss 5-15 127.04 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
244 Underdogs Win 15-5 684.14 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)