#172 Memphis Pharaohs (7-13)

avg: 825.27  •  sd: 62.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
37 Alliance Loss 5-11 1027.37 Jun 24th Huntsville Huckfest
198 Capitol City Chaos Win 11-8 1061.81 Jun 24th Huntsville Huckfest
89 Second Nature Loss 8-12 858.44 Jun 24th Huntsville Huckfest
144 Music City Mafia Loss 7-11 498.21 Jun 24th Huntsville Huckfest
56 Little Red Wagon Loss 7-13 940.38 Jun 24th Huntsville Huckfest
134 Dyno Win 10-6 1522.32 Jun 25th Huntsville Huckfest
129 Foxtrot Win 9-8 1167.89 Jun 25th Huntsville Huckfest
116 Atlanta Arson Loss 1-13 543.89 Aug 26th Ragna Rock 2023
149 Rawhide Loss 6-13 327.02 Aug 26th Ragna Rock 2023
103 Scythe Loss 8-13 706.93 Aug 26th Ragna Rock 2023
105 Dreadnought Loss 5-12 587.83 Aug 27th Ragna Rock 2023
214 Meadowlark Win 13-5 1148.73 Aug 27th Ragna Rock 2023
237 Arkansas Win 11-6 915.5 Aug 27th Ragna Rock 2023
103 Scythe Loss 6-13 603.08 Aug 27th Ragna Rock 2023
146 Ronin Loss 10-13 623.36 Sep 9th 2023 Mens Gulf Coast Sectional Championship
37 Alliance** Loss 4-13 1027.37 Ignored Sep 9th 2023 Mens Gulf Coast Sectional Championship
124 Battleship Loss 8-11 695.31 Sep 9th 2023 Mens Gulf Coast Sectional Championship
198 Capitol City Chaos Win 13-9 1114.76 Sep 9th 2023 Mens Gulf Coast Sectional Championship
198 Capitol City Chaos Win 13-9 1114.76 Sep 10th 2023 Mens Gulf Coast Sectional Championship
89 Second Nature Loss 7-13 742.07 Sep 10th 2023 Mens Gulf Coast 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)