In professional sports, there is a huge interest in attempting to leverage historic statistics to both predict future outcomes (wins/losses) and explore opportunities for tuning or improving a team or individual’s performance. This data-driven approach to sports has gained a large following over the last decade and entered mass media in the form of fantasy leagues, movies (e.g. Moneyball), and websites/podcasts (e.g. FiveThirtyEight). In this analysis, we will be using a classic baseball data set with the goal of building several different models capable of predicting team wins over a season given other team stats during that season (i.e. homeruns, strikeouts, base hits, etc).
We will first explore the data looking for issues or challenges (i.e. missing data, outliers, possible coding errors, multicollinearlity, etc). Once we have a handle on the data, we will apply any necessary cleaning steps. Once we have a reasonable dataset to work with, we will build and evaluate three different linear models that predict seasonal wins. Our dataset includes both training data and evaluation data - we will train using the main training data, then evaluate models based on how well they perform against the holdout evaluation data. Finally we will select a final model that offers the best compromise between accuracy and simplicity.
Describe the size and the variables in the moneyball training data set. Consider that too much detail will cause a manager to lose interest while too little detail will make the manager consider that you aren’t doing your job. Some suggestions are given below. Please do NOT treat this as a check list of things to do to complete the assignment. You should have your own thoughts on what to tell the boss. These are just ideas.
The moneyball training set contains 17 columns - including the target variable “TARGET_WINS” - and 2276 rows, covering baseball team performance statistics from the years 1871 to 2006 inclusive. The data has been adjusted to match the performance of a typical 162 game season. The data-set was entirely numerical and contained no categorical variables.
Below, we created a chart that describes each variable in the dataset and the theoretical effect it will have on the number of wins projected for a team.