Existing predictive modeling in sports analytics often hinges on atheoretical assumptions winnowed from a large and diverse pool of game metrics. Feature subset selection by way of a genetic algorithm to identify and assess the combinatorial advantage for a group of metrics is a viable option to otherwise arbitrary model construction. However, this approach concedes similar arbitrariness as there is no general strategy or common practice design among the tightly coupled nucleus of genetic operators. The resulting dizzying ecosystem of choice is especially difficult to overcome and leaves a residual uncertainty regarding true strength of output, specifically for practical implementations. This study transposes ideas from extreme environmental change into a quasi-deterministic extension of standard GA functionality that seeks to punctuate converged populations with individuals from auxiliary metas. This strategy has the effect of challenging what might otherwise be considered shallow fitness, thereby promoting greater trust in output against innumerable alternatives.
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