Real-Time Analytics

# Real-Time Analytics Real-time analytics enable coaches and analysts to make data-driven decisions during matches through live performance metrics. ## Implementation ### Live Performance Dashboard ```python import streamlit as st import pandas as pd from datetime import datetime # Real-time data stream def update_metrics(): current_data = fetch_live_data() # Calculate rolling metrics possession = current_data['possession_pct'].iloc[-1] xg = current_data['xg'].sum() pressure = current_data['pressures'].rolling(5).mean().iloc[-1] return { 'possession': possession, 'xg': xg, 'pressure_index': pressure } # Display live metrics st.title('Live Match Analytics') metrics = update_metrics() st.metric('Possession', f"{metrics['possession']:.1f}%") st.metric('Expected Goals', f"{metrics['xg']:.2f}") ``` ### Substitution Recommendation ```r library(shiny) library(dplyr) # Real-time fatigue monitoring monitor_fatigue <- function(tracking_data) { current_minute <- max(tracking_data$minute) fatigue_scores <- tracking_data %>% filter(minute >= current_minute - 10) %>% group_by(player_id) %>% summarise( distance = sum(distance), sprints = sum(velocity > 7), fatigue_index = distance / max_distance + (sprints / 10) ) %>% arrange(desc(fatigue_index)) return(fatigue_scores) } ``` ## Applications - Tactical adjustment recommendations - Substitution timing optimization - Opposition weakness detection - Fatigue management alerts