The Diggs Equation — Forecasting Josh Allen Passes and Stefon Diggs Catches
In a previous iteration of this article, we explored how to use data, data science, and formulas to abstract and model data around a problem that was first brought to awareness through a divergence of thought and visualization.
The goal of thinking about this concept was to improve the approach or at least understand the approach, so we worked through it in the last article “The Diggs Equation — Will Josh Allen Pass to Stefon Diggs?”. We found that the probability both naive and simulated was larger than the actual forecasted percentage, and now we will explore how that could be reevaluated.
Through this next stage keep in mind that if the calculations for all these players and all of their dichotomies or statistical relationships were continuously being calculated for thousands of users, it would be very expensive predictive system. New advancements in machine learning, through foundation models, could allow for a stateless model, where in fact the algorithm for one of these relations could be replicated to predict the same type of relation, and spread across all the QB (Quarterback) to WR(Wide Receiver) calculations.
