Shot volume is a fundamental indicator of attacking involvement in soccer because shots occur more frequently than goals and provide a more stable measure of opportunity. This thesis develops a statistical framework for predicting player-level shot output in Premier League matches using player match logs and match-level team context. The model predicts shots per 90 minutes from long-term shooting rate, recent form, non-penalty expected goals, shot-creating actions, team possession, team attacking form, opponent defensive form, venue, and position. Out-of-sample validation compares a shooting-only baseline, expanded linear model, count models, and a quadratic linear specification. The final approach translates predicted shots per 90 into expected match-level shot totals using expected minutes and then maps those totals to Poisson probabilities. A case study for Bukayo Saka against Chelsea illustrates how the method produces interpretable probabilities for over/under shotlines. An interactive Shiny app extends the framework into a sportsbook-style prototype that allows users to select players, view model projections and Poisson shot-count probabilities, and simulate Over/Under betting decisions.