#128 PowderHogs (5-11)

avg: 1048.49  •  sd: 74.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
184 False Summit Win 12-11 907.88 Jun 24th Colorado Summer Solstice 2023
57 Fungi Loss 7-11 1025.65 Jun 24th Colorado Summer Solstice 2023
171 Sonoran Dog Win 11-6 1374.9 Jun 24th Colorado Summer Solstice 2023
54 ISO Atmo Loss 7-15 919.35 Jun 25th Colorado Summer Solstice 2023
159 Choice City Hops Win 10-7 1274.31 Jun 25th Colorado Summer Solstice 2023
49 Shrimp Loss 1-13 955.23 Jun 25th Colorado Summer Solstice 2023
78 Drought Loss 8-13 877.95 Aug 19th Ski Town Classic 2023
60 Switchback Loss 7-10 1086.11 Aug 19th Ski Town Classic 2023
113 Utah Hatu Loss 8-13 660.98 Aug 19th Ski Town Classic 2023
142 Fat Stacks Win 13-10 1297.37 Aug 20th Ski Town Classic 2023
68 Brawl Win 13-8 1914.71 Aug 20th Ski Town Classic 2023
60 Switchback Loss 8-13 979.62 Aug 20th Ski Town Classic 2023
65 Sawtooth Loss 12-14 1222.19 Sep 9th 2023 Mens Big Sky Sectional Championship
53 Sundance Kids Loss 7-15 924.03 Sep 9th 2023 Mens Big Sky Sectional Championship
113 Utah Hatu Loss 12-15 856.65 Sep 9th 2023 Mens Big Sky Sectional Championship
142 Fat Stacks Loss 12-15 668.73 Sep 10th 2023 Mens Big Sky 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)