This personal blog entry walks through building a Python data pipeline that pulls Statcast pitch-tracking metrics directly from MLB databases to analyze October postseason starting pitchers. The author uses Called Strikes Plus Whiffs (CSW%) as the core metric to evaluate pitching efficiency, enforcing a strict 75-pitch threshold to filter qualifying starts across a playoff series. The post offers a hands-on look at working with domain-specific data filtering, defining boundary conditions in analytics scripts, and turning raw event streams into clean data visualizations.
This post is a public forecasting experiment from a blog you follow. Before the World Series opened, the author locked in five predictions about starting pitching and committed to grading them in public after the final out, misses included. To qualify for ranking, a starter must throw at least 75 pitches across his Series starts, and one prediction is that at least one Game 1 starter will finish the Series under that threshold. The author built a small Statcast app to process every pitch and invites readers to grade the predictions themselves once the Series ends, since the whole event fits inside the app’s 14-day window. While it sits outside the usual technical stack, it is included because it comes directly from a followed source. For a developer, the underlying pattern—shipping a small tool, making predictions observable, and then reviewing them against real data—mirrors the feedback loops that good engineering relies on. It’s a lightweight reminder that craft includes measuring your own calls against outcomes, not just shipping code.
As AI capabilities integrate deeper into backend platforms, treating model memory as an oversized prompt rather than core infrastructure introduces severe architectural inefficiencies. Large prompts consume significant attention budgets, raise API latency, and complicate system orchestration by forcing models to isolate signal from ambient noise. This piece introduces the 'Tax Model' framework from the Sovereign Systems Specification, analyzing recurring operational taxes—the predictable costs associated with retrieving, storing, validating, and moving data across AI systems. For engineers transitioning toward staff-level systems design, framing context management through an explicit tax model helps balance cost, context window limits, and response latencies. Instead of continually ballooning prompts, robust architecture requires disciplined decisions about when information moves, where it resides, and what each state transition costs.