Predicting performance outcomes has the potential to transform training approaches, inform coaching strategies, and deepen our understanding of the factors that contribute to athletic success. Traditional non-automated data analysis in sports are often difficult to scale. To address this gap, this study analyzes factors influencing athletic performance by leveraging passively collected sensor data from smartwatches and ecological momentary assessments (EMA). The study aims to differentiate between 14 collegiate volleyball players who go on to perform well or poorly, using data collected prior to the beginning of the season. This is achieved through an integrated feature set creation approach. The model, validated using leave-one-subject-out cross-validation, achieved promising predictive performance (F1 score = 0.75). Importantly, by utilizing data collected before the season starts, our approach offers an opportunity for players predicted to perform poorly to improve their projected outcomes through targeted interventions by virtue of daily model predictions. The findings from this study not only demonstrate the potential of machine learning in sports performance prediction but also shed light on key features along with subjective psycho-physiological states that are predictive of, or associated with, athletic success.