#150 Nashville Mudcats (7-19)

avg: 926.2  •  sd: 53.48  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
116 Atlanta Arson Loss 3-11 543.89 Jun 24th Huntsville Huckfest
124 Battleship Win 11-10 1185.92 Jun 24th Huntsville Huckfest
129 Foxtrot Win 11-10 1167.89 Jun 24th Huntsville Huckfest
207 Villains Win 11-5 1199.94 Jun 24th Huntsville Huckfest
146 Ronin Win 11-5 1551.5 Jun 25th Huntsville Huckfest
116 Atlanta Arson Loss 4-4 1143.89 Jun 25th Huntsville Huckfest
118 Raptor Loss 9-11 862.38 Jun 25th Huntsville Huckfest
56 Little Red Wagon Loss 6-11 951.22 Jun 25th Huntsville Huckfest
35 baNC Loss 7-13 1086.81 Jul 8th Club Terminus 2023
120 El Niño Loss 8-10 829.04 Jul 8th Club Terminus 2023
88 Black Lung Loss 8-13 806.58 Jul 8th Club Terminus 2023
119 Tennessee Folklore Loss 8-11 734.98 Jul 9th Club Terminus 2023
93 Charleston Heat Stroke Loss 10-13 963.22 Jul 9th Club Terminus 2023
138 Queen City Kings Win 13-5 1595.15 Jul 9th Club Terminus 2023
37 Alliance** Loss 5-13 1027.37 Ignored Aug 5th Trestlemania V
134 Dyno Loss 3-10 426.16 Aug 5th Trestlemania V
144 Music City Mafia Loss 6-7 840.1 Aug 5th Trestlemania V
93 Charleston Heat Stroke Loss 7-10 901.7 Aug 5th Trestlemania V
37 Alliance** Loss 3-13 1027.37 Ignored Aug 6th Trestlemania V
124 Battleship Win 12-11 1185.92 Aug 6th Trestlemania V
198 Capitol City Chaos Win 9-6 1114.76 Aug 6th Trestlemania V
93 Charleston Heat Stroke Loss 2-7 691.36 Aug 6th Trestlemania V
144 Music City Mafia Loss 11-13 736.26 Sep 9th 2023 Mens East Coast Sectional Championship
64 Hooch Loss 8-13 952.41 Sep 9th 2023 Mens East Coast Sectional Championship
100 Python Loss 8-13 717.86 Sep 9th 2023 Mens East Coast Sectional Championship
167 Pulp Loss 13-15 624.13 Sep 10th 2023 Mens East 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)