DATA621 | Project I

Abstract
To see how regression will help us evaluate baseball team performance, we will explore, analyze and model a historical baseball data set containing approximately 2200 records, to determine a team’s performance based on statistics of their performance. Each record represents a professional baseball team from the years 1871 to 2006 inclusive, and the data include the performance of the team for the given year, with all of the statistics adjusted to match the performance of a 162 game season.

While correlation does not equal causation it is suggested that a focus on some of the variables such as a focus on either single hits or triple or more hits to the exclusion of doubles might be worth pursuing. Also the data suggests that a focus on home runs allowed may not be worth giving up a number of more normal hits.

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Introduction
Because baseball is so numbers-heavy, there are many different statistics to consider when searching for the best predictors of team success. There are offensive statistics (offense meaning when a team is batting) and defensive statistics (defense meaning when a team is in the field). These categories can be broken up into many more subcategories. However, for the purpose of the this project we will use the available data to build a multiple linear regression model on the training data to predict the number of wins for the team.

Data Used
VARIABLE NAME                          DEFINITION
THEORETICAL EFFECT
INDEX                                               Identification Variable (do not use)
None
TARGET_WINS                           Number of wins
Outcome Variable
TEAM_BATTING_H                       Base Hits by batters (1B,2B,3B,HR)
Positive Impact on Wins
TEAM_BATTING_2B Doubles by batters (2B)
Positive Impact on Wins
TEAM_BATTING_3B Triples by batters (3B)
Positive Impact on Wins
TEAM_BATTING_HR Homeruns by batters (4B)
Positive Impact on Wins
TEAM_BATTING_BB Walks by batters
Positive Impact on Wins
TEAM_BATTING_HBPBatters hit by pitch (get a free base)
Positive Impact on Wins
TEAM_BATTING_SO Strikeouts by batters
Negative Impact on Wins
TEAM_BASERUN_SB Stolen bases
Positive Impact on Wins
the data was provided in csv file. The files contain approximately 2200 records. Each record represents a professional baseball team from the years 1871 to 2006 inclusive. Each record has the performance of the team for the given year, with all of the statistics adjusted to match the performance of a 162 game season.



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VARIABLE NAME                          DEFINITION
THEORETICAL EFFECT
TEAM_BASERUN_CS Caught stealing
Negative Impact on Wins
TEAM_FIELDING_E                   Errors
Negative Impact on Wins
TEAM_FIELDING_DP Double Plays
Positive Impact on Wins
TEAM_PITCHING_BBWalks allowed
Negative Impact on Wins
TEAM_PITCHING_H Hits allowed
Negative Impact on Wins
TEAM_PITCHING_HRHomeruns allowed
Negative Impact on Wins
TEAM_PITCHING_SO Strikeouts by pitchers
Positive Impact on Wins
Data exploration
Variable Selection

Outliers

Correlations among predictors

Data Preparation

Build Models

Select Model

Appendix

References
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