Building a Custom MLB Baseball Player Rating Model from Scratch Using Python and Scikit-Learn

Introduction

The game of baseball is a complex and nuanced sport, with numerous factors contributing to a player’s overall performance. In recent years, the use of advanced analytics and machine learning techniques has become increasingly prevalent in the sport, allowing teams to gain a competitive edge. This blog post will guide readers through the process of building a custom MLB baseball player rating model from scratch using Python and scikit-learn.

Overview of the Project

Our goal is to create a rating system that can accurately predict a player’s performance on the field. This will involve collecting and preprocessing data, selecting relevant features, training a machine learning model, and evaluating its performance.

Data Collection and Preprocessing

The first step in building our model is to collect and preprocess the necessary data. In this case, we’ll be using publicly available data from websites such as FanGraphs or Baseball-Reference.com.

We’ll start by collecting data on each player’s historical performance, including metrics such as batting average, home runs, RBIs, and more. We’ll also collect information on their physical attributes, such as height, weight, and body fat percentage.

Once we have our data, we’ll need to preprocess it to ensure that it’s in a suitable format for use in our model. This will involve handling missing values, scaling numerical features, and encoding categorical variables.

Feature Selection

With our data preprocessed, the next step is to select the relevant features that will contribute to our model’s performance. In this case, we’ll be using a combination of traditional baseball statistics, as well as some more advanced metrics such as wRAA and wOBA.

We’ll also consider the concept of “basketball-on-base” (BoB) which is a measure of a player’s ability to get on base via walk, hit by pitch or fielding error. This metric can provide valuable insights into a player’s plate discipline and ability to reach base without hitting the ball in play.

Model Selection

Now that we have our data preprocessed and our features selected, it’s time to choose a model to train. In this case, we’ll be using a combination of scikit-learn’s GradientBoostingClassifier and RandomForestClassifier.

We’ll start by training a simple baseline model on the raw data, then proceed to add more complex features and techniques to improve its performance.

Model Evaluation

Once our model is trained, we’ll need to evaluate its performance using a variety of metrics. In this case, we’ll be using accuracy, precision, recall, F1 score, mean squared error, and R-squared.

We’ll also consider the concept of “cross-validation” which is a technique used to assess the model’s performance on unseen data. This will give us a more accurate picture of its true ability to generalize to new situations.

Practical Example

Let’s say we have a player with the following statistics:

  • Batting Average: .300
  • Home Runs: 20
  • RBIs: 80
  • wRAA: +100
  • wOBA: .400

Using our model, we can calculate their predicted rating as follows:

[EXAMPLE_START:python]

Predicted rating based on player statistics

predicted_rating = (0.3 * 100) + (20 * 50) + (80 * 10)
print(predicted_rating)
[EXAMPLE_END]

Conclusion

Building a custom MLB baseball player rating model from scratch using Python and scikit-learn is a complex task that requires careful consideration of many factors. In this blog post, we’ve outlined the steps involved in building such a model, including data collection and preprocessing, feature selection, model selection, and model evaluation.

As we can see, the key to success lies in selecting the right features and techniques, as well as using proper evaluation metrics. By following these guidelines, readers can create their own custom rating models that can provide valuable insights into player performance.

So, the next time you’re watching a game or reading about a player’s stats, remember that there’s often more to the story than meets the eye. The intersection of data science and sports is an exciting space, and one that holds much promise for the future.