Ying Yee Yu¹, Kyler Harris¹, ¹USA, ²University of California, USA

Middle- and high-school soccer coaches decide who plays, for how long, and at what cost to a student’s rest and schoolwork, yet they make those decisions from memory and a paper roster while validated monitoring tools remain confined to professional academies. This paper presents FairPlay Lab, a cross-platform mobile application that turns the records a volunteer coach can realistically keep — training attendance, a seven-day perceived-exertion average, match minutes, and tallied pitch events — into three transparent indicators: a squad fairness index, a hero-ball versus team-ball cohesion split, and a per-athlete academic crash-risk band. Every recommendation is deliberately rule-based rather than learned, and each substitution suggestion opens into an itemized explanation card naming the factors that produced it. An analysis of the shipped decision surfaces shows the rotation engine responds most strongly to perceived exertion, confines all recommendations to a 24–42 minute window, and contains a discontinuity at the high-load threshold that inverts the intended penalty.

youth soccer, playing-time equity, coaching decision support, explainable analytics, rating of perceived exertion, training load monitoring, passing networks, team cohesion, athlete wellbeing, mobile application, offline-first architecture, rule-based scoring