We will be using two files from this dataset: Salaries.csv and Teams.csv. You can download the data from this this link. 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. Getting the data and setting up your machineįor this tutorial, we will use the Lahman’s Baseball Database. I will use 3 Python libraries: Pandas for data manipulation and analysis, statsmodels for building the statistical models and Matplotlib for data visualization. In this post, I will use Lahman’s Baseball Database and Python programming language to explain some of the techniques used in Sabermetrics. In 2011, the movie Moneyball based on Lewis' book was released starring Brad Pitt in the role of Beane. The Oakland Athletics made it to the play-offs in 4 successive years: 2000,2001,2002,2003. 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 green dot represents the Oakland Athletics, the blue dot represents the New York Yankees, and the red dot represents The Boston Red Sox. The figures below show the relationship between team salaries and number of wins for years: 1997, 2001, 2003, 2013. With this strategy, his team could achieve as many wins as teams with more than double the payroll. The book was centered around Billy Beane's use of Sabemetrics to identify and recruit under-valued baseball players. In 2003, Michael Lewis published Moneyball about Billy Beane, the Oakland Athletics General Manager since 1997. The term Sabermetrics comes from saber (Society for American Baseball Research) and metrics (as in econometrics). Sabermetrics is the apllication of statistical analysis to baseball data in order to measure in-game activity. Baseball Analytics: An Introduction to Sabermetrics using Python
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