
Mooching was a subtle method of deep-water fishing in midsummer. It called for drifting slowly “while raising and lowering [one’s] … line to probe random depths along the way… The strategy explored varying depths, temperatures, and currents. Strikes might occur at any time, though fish were most often expected when the lure began either fleeing upwards or fluttering helplessly into the darkness. But there was no definite pattern…
“So I motored offshore, coasted to a stop, and dropped a wide-bladed Colorado spinner over the side” and let it sink to the bottom as I drifted on the breeze. “A regular throbbing along the line meant my spinner was … free of weeds. I willingly abandoned deliberate control… Raising and lowering the line became automatic.”
It was an old-fashioned technique that guaranteed no sure-fire results but – with patience – it could turn up a good fish or two on hot days when nothing else bore fruit. Still, there was an obvious obstacle: Trusting to the vagaries of the wind and a haphazard sample of fishing depths ran directly contrary to the logic of deliberate planning.
No doubt today’s trust in big-data’s AI tools will make mooching’s very different model seem even less appealing than in Rob’s time. Even so, perhaps life confronts us with at least a few variables we would be wisest to admit that we cannot control. Do you agree?
Pp. 159-60
(Illustration generated by AI)
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