Dat-520-milestone-two erin-fitzgerald v2 PDF

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Running head: MILESTONE 2

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Impact of Batting and Pitching in Winning Erin Fitzgerald Southern New Hampshire University DAT-520-Q3065 Decision Methods and Modeling 20TW3 February 18, 2020

MILESTONE 2

1 Impact of Batting and Pitching in Winning

In Milestone 2, the aim is to understand whether the winning teams are created based on batting or pitching. Two teams use a ball and a bat to play the game of baseball. One of the team’s fields and the other bats interchangeably throughout the game. Pitching is the process where one of the players throws the ball and the opponent tries to hit it by batting. For the milestone, the Lahman data set shall be utilized to provide the variables that will try to answer the question in the study (SeanLahman.com, 2020). The data will be split for the two leagues and will be collected from 1901 to 2017. Two leagues shall be used to provide data for the study. These leagues are the American and National leagues. It should be understood that even though the sport is the same, the rules differ between the National and the American League (Graham, 2012). A scatter graph is used because it provides correlations. Correlation is the measure of association between variables (Schober, Boer, and Schwarte, 2018). American League Batting The variable that will represent batting is the batting average (BA). BA is attained by dividing the total amount of hits (H) by the total number of at-bats (AB), which will be done for the different leagues identified in the study. Earned Run Average (ERA) will represent the pitching variable. Below is a graph that matches the winning and the batting for the American League teams from 1901 to 2017.

MILESTONE 2

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Batting Average V. Win Percentage 18 16 14

f(x) = − 5.76 x + 34.4 R² = 0.48

12 10 8 6 4 2 0 3

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3.6

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Batting Average Graph showing the American League’s Batting Average against Winning Percentage Correlation coefficient: r = .690 Linear Regression Line: y = -5.758x + 34.4 From the graph above, the batting average impacts the teams winning percentage and is a significant aspect of winning in baseball. The correlation coefficient identified is 0.690 that indicates that there is an adequate positive correlation between the winning percentage and the BA. For simplification purposes the data shows is that the higher the team’s BA the greater the winning percentage of the team. Pitching For pitching, the variable that represents it is the Earned Run Average (ERA), which is identified as the average number of the runs that are permitted in a full nine earning game. Below is a graphical representation that likens the American League’s ERA with the matching winning percentage for the period the league has been in existence from 1901 to 2017.

MILESTONE 2

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ERA V. Winning Percentage

Winning Percentage

18 16 14 f(x) = − 0.55 x + 14.98 R² = 0.24

12 10 8 6 4 2 0

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ERA Graph showing the American League’s ERA against Winning Percentage Correlation coefficient: r = 0.486 Linear Regression Line: y = - 0.5452x + 14.977 Similarly, the information from the graph indicates that the ERA is a significant factor when it comes to winning in the National League and has a direct influence on the team's winning percentage. Besides, the correlation from the graph is positive at 0.486 but weaker than that of the batting which is at 0.690. Thus, comparable to the batting data the higher a team’s ERA the greater the winning percentage. However, the batting has a superior impact on the winning percentage than pitching in the National League.

National League Batting

MILESTONE 2

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Similar to the data for the American League, the National league used BA that was calculated by dividing the number of hits (H) by the at-bats (AB). The data were plotted on a scatter graph as shown below.

Batting Average v. Win Percentage Winning Percentage

0.16 0.14 0.12 f(x) = − 0.02 x + 0.12 R² = 0

0.1 0.08 0.06 0.04 0.02 0 0.2

0.22

0.24

0.26

0.28

0.3

0.32

0.34

0.36

0.38

Batting Average Graph showing the National League’s Batting Average against Winning Percentage Correlation coefficient: r = 0.045 Linear Regression Line: y = -0.0247x + 0.1183 Information from the graph regarding the teams in the National League indicates that the ERA variable is an important aspect of the winning percentage hence the winning potential of the teams. In the league, the teams have a positive correlation coefficient of 0.045. Hence it indicates that the higher the ERA the higher the winning percentage of a team. Pitching

MILESTONE 2

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A similar process such as the one used earlier for the American League was followed when developing the pitching graph for the National League. The graph below was plotted using Earned Run Average (ERA) against the winning percentage.

Winning Percentage

ERA V. Winning Percentage 0.16 0.14 0.12

f(x) = 0 x + 0.1 R² = 0.07

0.1 0.08 0.06 0.04 0.02 0

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Earned Run Everage Graph showing the National League’s ERA against Winning Percentage Correlation coefficient: r = 0.258 Linear Regression Line: y = 0.003x + 0.0968 In the National League ERA contributes to the winning percentages of teams positively. From the graph, it has a correlation coefficient of 0.258. For every ERA gained in the game, there is an increase in winning percentage. From the data, the pitching graph has a higher coefficient of 0.258 than the batting coefficient of 0.045, which indicates that pitching has a higher impact on winning in the National League compared to batting. Conclusion Overall, both batting and pitching have an impact on winning in both the American League and the National League. For the American League batting has a higher impact on

MILESTONE 2 winning because it has a stronger correlation coefficient of 0.690 than that observed for pitching of 0.486. In the National League, the evidence is different from the American League. Ecidence shows that pitching has a higher impact on the winning percentage compared to batting in the National League. The difference in value is significantly higher with pitching having a correlation coefficient of 0.28 while batting having 0.045. From the information, the trends are different depending on the leagues, and therefore, the study concludes that for the American League, batting is more important in winning, while in National League, pitching is essential in winning.

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MILESTONE 2

1 References

Graham, C. (2012). Baseball enigma: The optimal batting order. In Proceedings of MIT Sloan Sports AnalyticsConference. Retrieved from http://www.sloansportsconference.com/wpcontent/uploads/2012/02/1-Baseball-Enigma-MIT-Sloan-2012-C-Graham.pdf Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768 SeanLahman.com. (2020). Download Lahman’s Baseball Database. Retrieved from http://www.seanlahman.com/baseball-archive/statistics/...