Impara Introduction to Predictive Modeling | Predictive Modeling in Sports

Predictive modeling is a key technique in sports analytics that allows you to forecast future events or outcomes based on historical data. The process typically follows a structured workflow designed to ensure reliable and actionable results. The main stages in a predictive modeling workflow are:

Data preparation: collect, clean, and organize your sports data so it is ready for analysis;
Model selection: choose the right algorithm or statistical method for your prediction task;
Model evaluation: assess how well your model performs using appropriate metrics and validation techniques.

Each of these steps is critical for building models that can provide accurate and useful predictions in real-world sports scenarios.


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import pandas as pd
from sklearn.model_selection import train_test_split

# Sample sports match data
data = {
    "team_a_score": [80, 75, 90, 60, 68],
    "team_b_score": [78, 82, 85, 70, 72],
    "team_a_win": [1, 0, 1, 0, 0]
}

df = pd.DataFrame(data)

# Split data into training and test sets
train_df, test_df = train_test_split(df, test_size=0.4, random_state=42)

print("Training set:")
print(train_df)
print("\nTest set:")
print(test_df)

The train-test split is a fundamental step in predictive modeling. By dividing your data into a training set and a test set, you ensure that your model is trained on one portion of the data and evaluated on another, unseen portion. This helps you measure how well your model is likely to perform on new, real-world data, preventing overfitting and giving you a realistic estimate of its predictive power.


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import pandas as pd

# Hardcoded player statistics for a match
data = {
    "points": [18, 22, 15, 30, 25],
    "rebounds": [8, 5, 7, 10, 6],
    "assists": [4, 7, 3, 8, 5],
    "win": [1, 1, 0, 1, 0]
}

df = pd.DataFrame(data)

# Features: points, rebounds, assists
X = df[["points", "rebounds", "assists"]]

# Labels: win (1 = win, 0 = loss)
y = df["win"]

print("Features (X):")
print(X)
print("\nLabels (y):")
print(y)

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Sezione 3. Capitolo 1

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Data preparation: collect, clean, and organize your sports data so it is ready for analysis;
Model selection: choose the right algorithm or statistical method for your prediction task;
Model evaluation: assess how well your model performs using appropriate metrics and validation techniques.

Each of these steps is critical for building models that can provide accurate and useful predictions in real-world sports scenarios.


              12345678910111213141516171819
            
import pandas as pd
from sklearn.model_selection import train_test_split

# Sample sports match data
data = {
    "team_a_score": [80, 75, 90, 60, 68],
    "team_b_score": [78, 82, 85, 70, 72],
    "team_a_win": [1, 0, 1, 0, 0]
}

df = pd.DataFrame(data)

# Split data into training and test sets
train_df, test_df = train_test_split(df, test_size=0.4, random_state=42)

print("Training set:")
print(train_df)
print("\nTest set:")
print(test_df)


              12345678910111213141516171819202122
            
import pandas as pd

# Hardcoded player statistics for a match
data = {
    "points": [18, 22, 15, 30, 25],
    "rebounds": [8, 5, 7, 10, 6],
    "assists": [4, 7, 3, 8, 5],
    "win": [1, 1, 0, 1, 0]
}

df = pd.DataFrame(data)

# Features: points, rebounds, assists
X = df[["points", "rebounds", "assists"]]

# Labels: win (1 = win, 0 = loss)
y = df["win"]

print("Features (X):")
print(X)
print("\nLabels (y):")
print(y)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 1