
DHD Automated Player Database
Session level player profiles with automated updates after each training day: rolling trends, session over session deltas, and visuals for bat speed, contact quality, and batted ball performance.
Open dashboard
Eight week player development internship at Diamond Hitting Development. Athletes trained across Blast Motion, Rapsodo, and 4D Motion, but session data lived on separate devices with no shared history. I built the R pipeline that merged those systems into one swing level dataset, then shipped Shiny apps for daily review, peer comparison, and training programs tied to each athlete's gaps.
Athletes and coaches looked up individual sessions on Rapsodo or HitTrax iPads. There was no shared database, so progress lived in device silos.
50+ athletes and coaches used the automated player database to review session trends, track progress over time, and see where each athlete sat relative to others in the DHD pool.
The comparison tool reached 75+ users benchmarking themselves against DHD athletes and other elite competitors. From those gaps, athletes could download a free lifting program I helped write that targeted their biggest weakness versus peers.

Session level player profiles with automated updates after each training day: rolling trends, session over session deltas, and visuals for bat speed, contact quality, and batted ball performance.
Open dashboard
Peer and elite benchmarking with gap views, plus a path to download a lifting program aimed at each athlete's weakest area versus peers.
Open dashboardTraining review moved from device silos to a shared database. Athletes tracked progress, saw how they compared to peers, and left with a program aimed at their biggest gap.