Nodes vs The Moon: AFL game on Sunday

In this game, which would you prioritise, the Nodes or the Moon placement? The chart shows North Node helping the ASC team (1.5º away) however, Moon's antiscion will conjunct DSC team's significator (Jupiter) in about 2.36º.

From my understanding, the closer the better, so I'm going with ASC team in this case.

What is your opinion on this?

Moon placement (since in the method I use the Moon provides the key information)

hey dr.farr,

i am still excited about your method on assigning asc and dsc

In the experimental model I have been testing these past 2 years (and which has so far been running 64.5 % wins/35.5% losses) I do not assign ascendant and descendant at all-rather I use the Moon in its Manilius Decan for the time of the game, and see what MODALITY the decan sign is (cardinal, fixed, mutable) in order to select the winner. In all cases I use the team with the highest winning % and check to see if it is the visitor or home team: if the Moon is in a fixed sign and the home team has more wins than the visitor, then I select the home team; if the Moon is in a mutable sign and the visitor team has more wins than the home team, then I select the visitor team; if the Moon is in a cardinal sign, then whichever team (visitor or home) has the higher number of wins is selected. If BOTH teams have the same % of wins, then that game = no action (no selection is made)
UNLESS the Moon is in a cardinal sign, in which case I select the home team OR unless the Moon is in a fixed sign, in which case I select the home team.

I gave up on trying to assign asc/desc because whatever assignment I used did not yield results better than chance, after several hundred experimental games using various assignments: I tried visitor=9th house vs home=4th house; I used higher wins team = 10th house vs lower wins team = 4th house; I used home = 1st house vs visitor = 7th house; I tried higher wins team = 1st house vs lower wins team = 7th house: none of these attempts at assigning asc/desc, yielded above-chance results, when tested over time.


Some important notes regarding the above experiments and results:

-in my model, I test only professional sports teams; further I have only tested basketball, football, hockey, and baseball (and the baseball seasons have, so far, dragged down the return on wagering capital, from an average of 45% net return per week-in basketball, hockey and football-down to a net 30% return per week when factoring in the results from baseball season: I have not yet been satisfied with the model's results relative to baseball and am still working on adapting the model to baseball)

-I have not tested the model in soccer, rugby or cricket

-my experiments with the model have been for regular season games only-I do not use the model for playoff games

-my results with the model have been with MONEY LINE WAGERING ONLY, not with point spreads! I have no idea what the model's results would be against point spreads; however, money line wagering is now universally available for all major professional sports, so that's not a problem

-the model is not ENTIRELY mechanical: in the category "almost even", I do NOT mean that each time has the same win %-I mean that IN MY JUDGEMENT the teams are close to being equal to each other, and so I put the teams in the "almost even" category; raw win %'s, then, are NOT STRICTLY used when I decide this "almost even" category

Turns out Dr Farr was right. Moon is in Pisces - mutable sign, and visitor team had more wins than home team - so visitor team won. In this case, visitor team happened to be the ASC team as well - they play in white and black, while the home team (DSC) played in purple.

Interesting methods from dr. Farr, combined with stats, also with ehab atari and sven555 planet dignity and deblity methods, and adding planet that represent team by kit color by fensi88.. I believe it would be better result for acurracy.