Experiencing Machine Learning with Premier League Data

I've been studying machine learning recently. To gain practical experience, I decided to work with Premier League data and apply machine learning techniques to it.

For this project, I used match result data from the 2024-2025 season. The data was downloaded from this site.

As the main features, I selected the following:

The target variable is the match result (win, draw, or loss).

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report import joblib # 1. Load data df = pd. read_csv ( ' epl2024.csv ' ) # 2. Extract only necessary columns features = [ ' HS ', ' AS ', ' HST ', ' AST ', ' HF ', ' AF ', ' HC ', ' AC ', ' HY ', ' AY ' ] # Shots, fouls, cards, etc. target = ' FTR ' # Match result (H/D/A) # 3.

Create feature and target data X = df [ features ] y = df [ target ] # 4. Label encoding (convert target variable to numbers) le = LabelEncoder () y_encoded = le. fit_transform ( y ) # H->0, D->1, A->2, etc. # 5. Split into training and test data X_train, X_test, y_train, y_test = train_test_split ( X, y_encoded, test_size = 0.2, random_state = 42 ) # 6.

Create and train model model = RandomForestClassifier ( n_estimators = 100, random_state = 42 ) model. fit ( X_train, y_train ) # 7. Prediction and evaluation y_pred = model. predict ( X_test ) print ( " Accuracy: ", accuracy_score ( y_test, y_pred )) print ( " \n Classification Report: \n ", classification_report ( y_test, y_pred, target_names = le. classes_ )) # 8. Save the trained model and label encoder joblib. dump ( model, ' epl_rf_model.pkl ' ) joblib. dump ( le, ' label_encoder.pkl ' ) Enter fullscreen mode Exit fullscreen mode Looking at Prediction Results and Evaluation When I ran the code, I got the following output:

Accuracy: 0.5921052631578947 Classification Report: precision recall f1-score support A 0.62 0.75 0.68 28 D 0.15 0.12 0.14 16 H 0.76 0.69 0.72 32 accuracy 0.59 76 macro avg 0.51 0.52 0.51 76 weighted avg 0.58 0.59 0.58 76 Enter fullscreen mode Exit fullscreen mode The accuracy (correct prediction rate) was 59%. Looking at the details more closely, I can see that away wins (A) and home wins (H) have both high precision and recall, so the model predicts them fairly accurately. On the other hand, draws (D) are difficult for the model to predict.

Now that I've created the model, let me use it to make predictions on new data:

import pandas as pd import numpy as np import joblib model = joblib. load ( ' epl_rf_model.pkl ' ) le = joblib. load ( ' label_encoder.pkl ' ) # New match data (as a DataFrame with feature names) new_match = pd. DataFrame ( [[ 15, 8, 1, 3, 10, 12, 5, 4, 2, 1 ]], columns = [ ' HS ', ' AS ', ' HST ', ' AST ', ' HF ', ' AF ', ' HC ', ' AC ', ' HY ', ' AY ' ] ) predicted_num = model. predict ( new_match ) predicted_label = le. inverse_transform ( predicted_num ) print ( f " Prediction result: { predicted_label [ 0 ] } " ) Enter fullscreen mode Exit fullscreen mode What Was the Prediction Result for the New Match? The output was as follows:

Prediction result: A Enter fullscreen mode Exit fullscreen mode In this example, the home team had only 1 shot on target while the away team had 3 shots on target, so the model predicted an away team victory.

Even though I had almost no knowledge of machine learning, working hands-on with actual data deepened my understanding. Next, I'd like to try more advanced analysis, such as predicting next season's standings (hoping my favorite Manchester United will finish in first place...!)