#85 Risky Business (13-11)

avg: 1126.47  •  sd: 52.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Public Enemy Loss 6-13 1138.53 Jun 24th Texas 2 Finger 2023
142 Goosebumps Win 13-4 1443.61 Jun 24th Texas 2 Finger 2023
228 Scoober Heroes** Win 13-4 910.05 Ignored Jun 24th Texas 2 Finger 2023
123 Amber Loss 9-10 839.64 Jun 25th Texas 2 Finger 2023
228 Scoober Heroes Win 11-5 910.05 Jun 25th Texas 2 Finger 2023
151 Dallas Nightfall Win 7-4 1298.52 Jun 25th Texas 2 Finger 2023
103 Bird Win 11-8 1403.15 Aug 19th Cooler Classic 34
54 No Touching! Loss 7-13 786.55 Aug 19th Cooler Classic 34
182 Melt Win 12-10 858.18 Aug 19th Cooler Classic 34
86 Mad Udderburn Win 14-10 1519.84 Aug 20th Cooler Classic 34
27 Chicago Parlay Loss 8-15 1072.7 Aug 20th Cooler Classic 34
103 Bird Win 14-7 1620.42 Aug 20th Cooler Classic 34
19 Public Enemy** Loss 2-13 1138.53 Ignored Sep 9th 2023 Mixed Texas Sectional Championship
142 Goosebumps Loss 11-12 718.61 Sep 9th 2023 Mixed Texas Sectional Championship
154 Moontower Win 11-9 1043.81 Sep 9th 2023 Mixed Texas Sectional Championship
30 Waterloo Loss 4-13 1015.31 Sep 9th 2023 Mixed Texas Sectional Championship
228 Scoober Heroes Win 13-6 910.05 Sep 10th 2023 Mixed Texas Sectional Championship
142 Goosebumps Win 11-10 968.61 Sep 10th 2023 Mixed Texas Sectional Championship
30 Waterloo Loss 6-15 1015.31 Sep 10th 2023 Mixed Texas Sectional Championship
21 Love Tractor Loss 7-12 1186.52 Sep 23rd 2023 South Central Mixed Regional Championship
142 Goosebumps Win 8-7 968.61 Sep 23rd 2023 South Central Mixed Regional Championship
134 Sin Nombre Win 8-4 1427.64 Sep 23rd 2023 South Central Mixed Regional Championship
1 shame. Loss 7-14 1584.32 Sep 24th 2023 South Central Mixed Regional Championship
28 Flight Club Loss 7-12 1114.76 Sep 24th 2023 South Central Mixed Regional 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)