Evolution of Tracking Data

# Evolution of Tracking Data Tracking data provides granular position information at multiple frames per second, enabling advanced spatial and temporal analysis. ## Current State ### Processing Tracking Data ```python import pandas as pd import numpy as np # Load tracking data (25 fps) tracking = pd.read_csv('match_tracking.csv') # Calculate velocity tracking['velocity'] = np.sqrt( tracking['x'].diff()**2 + tracking['y'].diff()**2 ) * 25 # frames per second # Identify sprints sprints = tracking[tracking['velocity'] > 7.0] # m/s ``` ### Spatial Analysis ```r library(dplyr) library(sp) # Calculate space control calculate_control <- function(x, y, velocity) { R <- velocity * time_to_control + 4 # radius of control return(pi * R^2) } tracking_data <- tracking_data %>% mutate(control_area = calculate_control(x_pos, y_pos, velocity)) ``` ## Future Innovations - Bone and limb tracking for biomechanics - Eye tracking for attention analysis - Ball spin and trajectory prediction - Real-time tactical formation detection