Fan Engagement Analytics

# Fan Engagement Analytics Analytics can transform how fans interact with football through personalized content, engagement tracking, and enhanced viewing experiences. ## Engagement Metrics ### Social Media Analysis ```python import pandas as pd from textblob import TextBlob # Analyze fan sentiment def analyze_sentiment(tweets_df): sentiments = [] for tweet in tweets_df['text']: analysis = TextBlob(tweet) sentiments.append(analysis.sentiment.polarity) tweets_df['sentiment'] = sentiments # Aggregate by time period engagement = tweets_df.groupby('timestamp').agg({ 'sentiment': 'mean', 'retweets': 'sum', 'likes': 'sum' }) return engagement # Track match excitement excitement_peaks = engagement[engagement['likes'] > engagement['likes'].quantile(0.9)] ``` ### Viewing Behavior ```r library(dplyr) library(survival) # Analyze viewer retention viewer_data <- streaming_data %>% group_by(user_id, match_id) %>% summarise( watch_time = max(timestamp) - min(timestamp), completion_rate = watch_time / match_duration, engagement_score = n_distinct(interaction_type) ) # Predict churn churn_model <- glm( churned ~ completion_rate + engagement_score + team_performance, data = viewer_data, family = binomial ) ``` ## Personalization - Content recommendation engines - Predictive notifications for key moments - Fantasy football optimization - AR/VR enhanced viewing experiences