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Footy project

Objective

  1. Use django to create a website
  2. Full premier league squad, with footballer profile etc?
    • allow user to re-direct to fbref, transfermarket webiste with a hyperlink
  3. Being able to plan and form user's custom squad
  4. Being able to 'simulate' or battle against other squad
  5. Custom budget, team-ranking etc?
  6. Use Postgre sql cuz we have A LOT of data

Plan

  1. Website - Django should be fine
  2. Battle..I'm not sure atm but probs use some deep learning shit
  3. Scraping : https://www.youtube.com/watch?v=rfM3Jli81fU https://github.com/hoyishian/footballwebscraper

PostgreSQL vs Sqlite

Migration from SQLite to PostgreSQL can be relatively straightforward, especially if you're starting with a small project.

Here are the general steps involved:

Create a PostgreSQL database: Set up a PostgreSQL instance and create a new database for your project.
Install Django's PostgreSQL adapter: Ensure you have the psycopg2 library installed, which is required for Django to connect to PostgreSQL.
Configure Django settings: Update your settings.py file to use the PostgreSQL database settings.
Run migrations: Use python manage.py migrate to apply your existing migrations to the new PostgreSQL database.
Test your application: Verify that your application works as expected with the PostgreSQL database.

However, there are a few things to keep in mind:

Data types: Ensure that your SQLite data types are compatible with PostgreSQL. There might be minor differences, such as the default precision of floating-point numbers.
Indexes: If you have custom indexes defined in SQLite, you'll need to recreate them in PostgreSQL.
Constraints: Check if any constraints (e.g., UNIQUE, FOREIGN KEY) need to be adjusted or recreated.
Full-text search (if applicable): If you're using full-text search features, you might need to configure them differently in PostgreSQL.

For more complex applications or large datasets, it's recommended to use a tool like pg_dump to create a database dump from SQLite and then import it into PostgreSQL. This can help automate the migration process and reduce the risk of errors.

So use SQLite for now on, and move on to postgre sql later on

Progress

September 2024

8th Sep

9th Sep

  • Working on scraping part
  • csv file storing indiv player profile of premier league is done?
  • We can use compare top 5 similar players in fbref

    Sth is bit off cuz it compares raw stat....I can do sth like add a scaling factor cuz of team and league difference?

I can start working on AI stuff cuz its using apis anyway?

Probs forget about django atm,

10th Sep

  • Finished jupyter notebook of big 5 player standard stats scraper
  • Probs better to set up an obsidian vault for logging i guess
  • Started working on Rating system for an indiv player

13th Sep

  • Worked on Django part.
  • Created view of players_all : Display upto 50 plyers, can be as sth of activity feed
TODO:
  • Other views (Player detail view, Team detail view)
  • Add other info about team: Stadium, team size, supporters, city etc
  • Implement Graph.

Note

PostgreSQL vs Sqlite

Migration from SQLite to PostgreSQL can be relatively straightforward, especially if you're starting with a small project.

Here are the general steps involved:

Create a PostgreSQL database: Set up a PostgreSQL instance and create a new database for your project.
Install Django's PostgreSQL adapter: Ensure you have the psycopg2 library installed, which is required for Django to connect to PostgreSQL.
Configure Django settings: Update your settings.py file to use the PostgreSQL database settings.
Run migrations: Use python manage.py migrate to apply your existing migrations to the new PostgreSQL database.
Test your application: Verify that your application works as expected with the PostgreSQL database.

However, there are a few things to keep in mind:

Data types: Ensure that your SQLite data types are compatible with PostgreSQL. There might be minor differences, such as the default precision of floating-point numbers.
Indexes: If you have custom indexes defined in SQLite, you'll need to recreate them in PostgreSQL.
Constraints: Check if any constraints (e.g., UNIQUE, FOREIGN KEY) need to be adjusted or recreated.
Full-text search (if applicable): If you're using full-text search features, you might need to configure them differently in PostgreSQL.

For more complex applications or large datasets, it's recommended to use a tool like pg_dump to create a database dump from SQLite and then import it into PostgreSQL. This can help automate the migration process and reduce the risk of errors.

So use SQLite for now on, and move on to postgre sql later on

AI deeplearning stuff for better comparison of footballer

Yes, deep learning and AI can be very effective for comparing football players by leveraging large amounts of data (e.g., match statistics, player attributes, GPS data, etc.). Here are some common approaches to using deep learning for player comparison:

1. Player Similarity Models

  • Objective: Quantify how similar two players are based on their performance data, playing style, or physical attributes.
  • Approach:
    • Autoencoders: An autoencoder is a neural network used for unsupervised learning that compresses the player data into a lower-dimensional representation (encoding). You can compare the encoded vectors of different players using a distance metric (e.g., Euclidean distance, cosine similarity).
    • Embeddings: Train a neural network to generate embeddings for players based on their match performance data. Similar players will have embeddings that are closer to each other in the latent space.

Example libraries: TensorFlow, PyTorch

2. Clustering-Based Methods

  • Objective: Group players into clusters that exhibit similar behavior or performance. This allows you to categorize players into groups like "playmakers," "strikers," etc.
  • Approach:
    • K-means clustering on features: After extracting relevant features from the players' data, use clustering algorithms (e.g., K-means, DBSCAN) to group similar players.
    • Deep clustering methods: Use deep learning models (e.g., deep clustering networks) to learn more meaningful cluster representations automatically.

3. Reinforcement Learning (RL)

  • Objective: Learn optimal strategies and player roles by modeling football games as a reinforcement learning environment.
  • Approach:
    • Use RL algorithms like Deep Q-Networks (DQN) or Policy Gradient to model how players perform actions on the field and how their decisions affect match outcomes.
    • You can also compare players based on their "impact" on the game, such as how frequently they make high-value decisions or contributions to the team’s success.

4. Player Role Classification

  • Objective: Use classification algorithms to categorize players by role (e.g., midfielder, defender, forward) based on their play style and performance.
  • Approach:
    • Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs): Use player match data (like pass maps, heat maps, and time-series statistics) to classify their role. These models can automatically learn patterns in player behavior.

5. Time-Series Models for Player Trajectory Analysis

  • Objective: Analyze how a player’s performance evolves over time and forecast future performance.
  • Approach:
    • Use RNNs, LSTM (Long Short-Term Memory), or GRU (Gated Recurrent Unit) models to analyze time-series data like player stats per match. These models can help predict a player's future form based on past data.

6. Performance Forecasting

  • Objective: Predict a player's future performance or market value based on historical data.
  • Approach:
    • Use regression-based neural networks (e.g., fully connected layers) to predict key performance indicators (KPIs) like goals, assists, pass accuracy, etc.
    • Add feature importance analysis to see which attributes (e.g., stamina, speed, passing) contribute most to success in the model.

7. Generative Adversarial Networks (GANs) for Player Style Transfer

  • Objective: Imagine how a player would perform if they had another player’s style (e.g., "What if Player X had Player Y's dribbling skills?").
  • Approach:
    • GANs can be used to generate new player profiles by "mixing" different players' attributes. For instance, you could generate a player who combines the best attributes of a creative midfielder and a clinical striker.

Data Sources

To build a successful model, you will need access to comprehensive data. You can use:

  • Event-based data: Actions during matches like passes, shots, tackles (e.g., from sources like Opta, StatsBomb).
  • Tracking data: Players’ movement on the field, which provides deeper insight into positioning and decision-making.
  • GPS or wearable data: Provides physical performance metrics like distance covered, sprint speed, and fatigue.

Tools and Libraries

  • Python Libraries:
    • Scikit-learn for traditional ML algorithms.
    • TensorFlow or PyTorch for deep learning models.
  • Visualization: Matplotlib, Seaborn, Plotly for visualizing comparisons between players.

Example: Using a Neural Network for Player Comparison

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Example: Create a neural network to learn player features
model = Sequential([
    Dense(64, activation='relu', input_shape=(num_features,)),
    Dense(32, activation='relu'),
    Dense(16, activation='relu'),
    Dense(2, activation='linear')  # Output a 2D embedding space for player comparison
])

model.compile(optimizer='adam', loss='mse')

# X_train is the feature matrix for player stats
model.fit(X_train, y_train, epochs=50)

# Get player embeddings
player_embeddings = model.predict(X_train)

# Compare players using their embeddings
from sklearn.metrics.pairwise import cosine_similarity

similarities = cosine_similarity(player_embeddings)

Conclusion

Using deep learning/AI for football player comparison can reveal valuable insights about playing styles, performance trends, and player development. The choice of method depends on the data you have available and the kind of comparison you're interested in (e.g., skill similarity, role, or impact on the game).

Pandas to do above deeplearnign shit

Yes, you can absolutely use a Pandas DataFrame as a data source for training machine learning or deep learning models. Pandas is a great tool for preprocessing and organizing your data before feeding it into a model.

Here’s how you can go about it:

1. Data Preprocessing with Pandas

If you have player data stored in DataFrames, you'll typically need to prepare it before it can be used with machine learning models. Here are some common preprocessing steps using Pandas:

a. Handling Missing Data:

# Fill missing values with the mean or drop rows/columns with NaN values
df = df.fillna(df.mean())  # or df.dropna()

b. Normalization:

Scaling your features is important, especially when using distance-based models like k-means or neural networks.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

c. Feature Encoding:

If you have categorical data (e.g., position, team), you’ll need to encode it numerically.

# One-hot encoding for categorical features
df = pd.get_dummies(df, columns=['position', 'team'])

d. Splitting Data:

Split the DataFrame into features (X) and target (y), especially if you're doing supervised learning.

X = df.drop(columns=['target_column'])  # Features
y = df['target_column']  # Target variable

2. Using Pandas DataFrame for Machine Learning

Once you have preprocessed your data in a Pandas DataFrame, you can convert it into NumPy arrays or directly use the DataFrame with most ML frameworks like scikit-learn and TensorFlow.

Example 1: Using a Pandas DataFrame with Scikit-Learn

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Random Forest classifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy}")

Example 2: Using a Pandas DataFrame with TensorFlow/Keras

If you're using a deep learning model, TensorFlow/Keras can easily take NumPy arrays, which you can extract from your Pandas DataFrame:

import tensorflow as tf
from sklearn.model_selection import train_test_split

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, test_size=0.2)

# Build a simple neural network
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(1, activation='linear')  # For regression; use 'softmax' for classification
])

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(X_train, y_train, epochs=10)

# Evaluate the model
loss = model.evaluate(X_test, y_test)
print(f"Test Loss: {loss}")

3. Using Deep Learning to Compare Players

Once you’ve structured your player data (statistics, performance metrics) in Pandas, you can feed this into a deep learning model that compares players by:

  • Generating player embeddings: This is useful if you want to visualize players in a lower-dimensional space based on similarity.
  • Predicting player performance: Use the model to predict future performance metrics like goals, assists, etc., based on past data.
  • Clustering players: Group similar players based on their attributes.

For example, suppose your DataFrame has columns like goals, assists, passes, and tackles. You can train a neural network to learn a compact representation of each player, and then compare players based on their learned embeddings.

Example: Using Neural Networks to Generate Player Embeddings

import tensorflow as tf

# Assuming 'df' contains your player data, you can directly use it
X = df.values  # Convert the DataFrame to a NumPy array

# Define the model (Autoencoder for dimensionality reduction)
input_layer = tf.keras.layers.Input(shape=(X.shape[1],))
encoded = tf.keras.layers.Dense(64, activation='relu')(input_layer)
encoded = tf.keras.layers.Dense(32, activation='relu')(encoded)
encoded = tf.keras.layers.Dense(16, activation='relu')(encoded)

# Output the encoded (compressed) representation
encoder = tf.keras.Model(input_layer, encoded)

# Compile the model
encoder.compile(optimizer='adam', loss='mse')

# Train the model to learn the player representations
encoder.fit(X, X, epochs=50)

# Get the compressed player embeddings
player_embeddings = encoder.predict(X)

# Now you can use these embeddings to compare players, e.g., with cosine similarity
from sklearn.metrics.pairwise import cosine_similarity
similarities = cosine_similarity(player_embeddings)

print(similarities)  # Players with higher similarity scores are more alike

In this example, the autoencoder learns a compact representation of players based on their stats, and you can then compare players by measuring the similarity between these embeddings.

Conclusion

Pandas is a powerful tool for organizing and preprocessing your football player data. Once the data is ready, you can use it with deep learning models to compare players, predict future performance, or cluster players based on their attributes. The flexibility of Pandas combined with machine learning libraries makes it ideal for this task.

Least distance to find the most similar player

Using a minimum distance approach to find similar players is a good start, especially since you have a structured dataset. Here are some points to consider for your approach:

  1. Feature Selection

    You need to carefully select which features (columns) from your CSV file will contribute to the "similarity" between players. For example, stats like Gls, Ast, xG, and xAG may be more relevant than Age or Nation depending on the type of similarity you're looking for (playstyle vs. general comparison). Normalize the selected features since some stats (e.g., goals, assists) have different scales compared to others (e.g., progressive carries). This will prevent any one feature from disproportionately influencing the distance.

  2. Distance Metric

    Euclidean Distance: A commonly used metric that works well if all features are equally important. Cosine Similarity: Useful if you care more about the direction (playstyle) than the magnitude (overall player rating). Mahalanobis Distance: Takes correlations between variables into account, which can help if some stats are more related than others.

  3. Dimensionality Reduction (Optional)

    Use techniques like Principal Component Analysis (PCA) or t-SNE to reduce the number of dimensions and uncover patterns in the data. This helps with visualization and may improve the model by removing noisy features.

  4. Clustering (Optional)

    Consider applying clustering techniques like K-Means or DBSCAN to group players with similar styles. Once players are clustered, you can find players from the same cluster as your reference player.

  5. Weighting Features

    Some stats might be more relevant than others depending on what "similar" means to you. For example, if you're comparing strikers, goals and assists might be weighted higher, while for midfielders, passing stats like PrgP (progressive passes) and xAG (expected assists) might matter more.

  6. Cross-validation and Fine-tuning

    You can also experiment by splitting players into different categories (e.g., position groups like forwards, midfielders, defenders) and adjust your model to account for positional differences. Testing and cross-validating against known similar players (e.g., players known to have similar playstyles) can also help fine-tune the model.

  7. Visualization

    Once you compute the distances, visualize the players in a scatter plot or radar chart. This can help you interpret the similarity.

Here’s a brief outline of the next steps:

Normalize the player stats.
Select a distance metric (Euclidean, cosine, etc.).
Implement the distance calculation and find the nearest player(s) based on that metric.
(Optional) Use clustering or dimensionality reduction if you want more advanced pattern detection.

Would you like to see a basic implementation or suggestions on a particular part of this process?