This project involved analyzing data from the Cherry Blossom Ten Mile Run to identify factors influencing runners' performance. By examining variables such as age, gender, training habits, and environmental conditions, the study aimed to uncover patterns and correlations that affect race outcomes.
Data Collection: Compiled comprehensive datasets from race records, including participant demographics, training logs, and race-day conditions.
Statistical Analysis: Employed statistical methods to assess the impact of various factors on race performance, identifying significant predictors.
Visualization: Created visual representations, such as scatter plots and regression lines, to illustrate relationships between variables and performance metrics.
Predictive Modeling: Developed models to forecast race outcomes based on identified key factors, enhancing understanding of performance determinants.
Recommendations: Provided insights and suggestions for runners and organizers to optimize training and event planning, aiming to improve future race performances.