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.
Extracting long-term personal activity data from public APIs often reveals hidden capabilities and undocumented behaviors in RESTful endpoints. By analyzing nine years of platform data from Dev.to, developer Ken Walger uncovered several practical API tricks for efficient data ingestion. Notably, while the API documentation lists a default page limit of 80 records for the followers endpoint, sending a higher per_page parameter (such as 1000) successfully retrieves larger batches, dramatically reducing required HTTP requests from hundreds down to nineteen. Furthermore, examining timestamped follower records allows developers to reconstruct detailed historical growth metrics without having previously recorded them. For developers seeking to hone their API consumption and backend data pipeline skills, this writeup offers valuable practical lessons. It highlights the importance of probing API pagination limits dynamically and leveraging implicit historical metadata embedded within API payloads. Mastering these subtle integration mechanics helps engineers build faster, more resilient API integrations while minimizing rate limit overhead.