Sabermetrics is the apllication of statistical analysis to baseball data in order to measure in-game activity. The term Sabermetrics comes from saber (Society for American Baseball Research) and metrics (as in econometrics).
The movie Moneyball focuses on the “quest for the secret of success in baseball”. It follows a low-budget team, the Oakland Athletics, who believed that underused statistics, such as a player’s ability to get on base, better predict the ability to score runs than typical statistics like home runs, RBIs (runs batted in), and batting average. Obtaining players who excelled in these underused statistics turned out to be much more affordable for the team.
In 2003, Michael Lewis published Moneyball about Billy Beane, the Oakland Athletics General Manager since 1997. The book was centered around Billy Beane's use of Sabemetrics to identify and recruit under-valued baseball players. With this strategy, his team could achieve as many wins as teams with more than double the payroll. The figures below show the relationship between team salaries and number of wins for years: 1997, 2001, 2003, 2013. The green dot represents the Oakland Athletics, the blue dot represents the New York Yankees, and the red dot represents The Boston Red Sox. We can see that the Oakland Athletics went from the underperforming team in 1997, to became a highly competitive team with a comparable number of wins to the New York Yankees. The Oakland Athletics made it to the play-offs in 4 successive years: 2000,2001,2002,2003.
Getting the data and setting up your machine
For this blog post, I will use Lahman’s Baseball Database and Python programming language to explain some of the techniques used in Sabermetrics. This Database contains complete batting and pitching statistics from 1871 to 2013, plus fielding statistics, standings, team stats, managerial records, post-season data, and more. You can download the data from this this link. I will be using two files from this dataset:
Salaries.csv and Teams.csv. To execute the code from this blog, I will use 5 Python libraries: Numpy, Scipy, Pandas and Matplotlib and statsmodels.
Numpy- open source extension module for Python.It provides fast pre-compiled functions for numerical routines.
It adds support to Python for large, multi-dimensional arrays and matrices. Besides that it supplies a large library of high-level mathematical functions to operate on these arrays.
Scipy-SciPy is widely used in scientific and technical computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Pandas-Pandas is a library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas is free software released under the three-clause BSD license.
Matplotlib- matplotlib is a plotting library for NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like wxPython, Qt, or GTK+.
Statsmodels- Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator
Load the data into Jupyter Notebook:
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