Mark Stolte is a data analyst with expertise in professional and college football and basketball, where he blends his passion for the game with statistical insights and data science to uncover betting advantages. With a background rooted in statistical modeling and a sharp understanding of football strategy, Mark focuses on transforming raw data into actionable intelligence for performance evaluation, scouting, and gambling.
His career has centered around using data to enhance understanding of both player and team dynamics. He’s developed a ridge regression model to predict NFL spreads and moneylines, leveraging historical performance and matchup variables to beat the closing line. In college football, he created an algorithm to identify the most deserving College Football Playoff teams—balancing resume strength, game control, and predictive power. He also built a classification model for college basketball, aimed at segmenting teams based on playing style and efficiency profiles to improve matchup analysis.
Beyond football and basketball, Mark maintains an analytical edge in the gambling space, always seeking models that blend statistical rigor with intuitive strategy. He’s also created fictional governance frameworks for college football and leads long-term franchise mode projects in Madden, using predefined team archetypes to simulate front office decision-making. Mark’s work reflects a forward-thinking view of sports analysis—rooted in clarity, adaptability, and innovation.
