Many sports data are continuous and can be treated as functional data. Examples include soccer players' maximum accelerations throughout their careers, rowing stroke rates and speeds during races, and the EMG curves of muscles with their corresponding body accelerations, among many others. This talk explores how functional data analysis (FDA) can be applied in sports analytics through three projects. First, I will talk about an analysis of soccer players' maximum acceleration in relation to age and provide insights into predicting future player performance. Next, I will discuss how a function-on-function regression model can be used to study the effect of an individual or team's stroke rate on their speed in rowing races. Finally, I will introduce a sparse estimation method for historical functional linear models and demonstrate its use in exploring the effect of muscle activation on body acceleration.