#64 Minnesota Star Power (10-7)

avg: 1283.35  •  sd: 58.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
71 Sego Win 15-10 1690.34 Jul 15th TCT Select Flight West 2023
41 BW Ultimate Loss 10-12 1262.15 Jul 15th TCT Select Flight West 2023
54 Lights Out Win 12-11 1468.13 Jul 15th TCT Select Flight West 2023
47 Donuts Loss 8-9 1318.38 Jul 16th TCT Select Flight West 2023
39 Lotus Loss 7-13 971.42 Jul 16th TCT Select Flight West 2023
117 Spectre Win 13-9 1429.02 Aug 19th Cooler Classic 34
30 Chicago Parlay Loss 6-13 1063.97 Aug 19th Cooler Classic 34
92 Mad Udderburn Win 10-8 1362.82 Aug 19th Cooler Classic 34
117 Spectre Win 14-6 1610.45 Aug 20th Cooler Classic 34
111 Bird Loss 11-12 897.53 Aug 20th Cooler Classic 34
92 Mad Udderburn Win 13-10 1428.3 Aug 20th Cooler Classic 34
174 Boomtown Pandas Win 15-3 1313.09 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
78 Northern Comfort Loss 14-15 1063.75 Sep 9th 2023 Mixed Northwest Plains Sectional Championship
245 Underdogs** Win 15-4 674.21 Ignored Sep 9th 2023 Mixed Northwest Plains Sectional Championship
164 Pandamonium Win 15-5 1374.31 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
132 Mousetrap Win 15-12 1199.35 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
56 No Touching! Loss 13-14 1199.26 Sep 10th 2023 Mixed Northwest Plains Sectional Championship
**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)