What Baseball statistics have an impact on a team’s overall ranking?
For at least the last century and a half, baseball has been known as America’s sport. People of all ages turn out each season to see the nine innings of excitement, and of course, the break in the middle of the seventh inning to sing “Take Me Out to The Ball Game.” Baseball is won by the team who gets the most number of runs in a single game and as they begin to win more games, they get higher in the rankings. Today, I will show that the number of hits a team gets in a game is less correlated with the rank of a team than runs or home runs are with the rank of the team and therefore has less of an impact on the team’s overall ranking.
According to Sports N’ Hobbies, rank is determined, for the most part, by wins and losses of a team. However, I would like to examine the relationship of other variables like the number of hits, the number of runs, and the number of home runs to the ranking of a team. To examine my findings, I am using the Lahman’s Baseball Database, specifically the Teams.csv file. In this file, it discusses teams from 1871 to present and different statistics about them like the number of wins and losses they had in a season. For the purposes of my research, I am specifically looking at the rank, the number of wins, the number of home runs, the number of hits and the number of runs a team has because I believe those will have the largest correlations. Once I sorted my dataset, I was able to see that the teams with the highest ranking (ranked 1) seemed to have more wins, runs, hits, and home runs than the teams with the worst ranking (ranked 12). This told me that there must be some correlation between the rank of the team and how many runs, hits, and homeruns a team was getting.
Through my initial findings, I made a hypothesis. I hypothesized that when teams move higher in the rankings (closer to 1), the number of hits, home runs, and runs increase. Additionally, between the three variables I hypothesized that runs would have the largest negative correlation with the ranking because a team must get more runs than the other team to win. To confirm or reject my hypothesis, I needed to calculate the correlation between each of the variables. I found that hits had a negative correlation with rankings of 0.266 and home runs had a negative correlation with rankings of 0.370. Additionally, I found that runs have a negative correlation with rankings of 0.422. This means that there is more of a correlation between runs and rankings than either of the two variables. Furthermore, there is more of a correlation between home runs and ranking than hits and ranking. Though hits are necessary for a team to score a run, they do not count in the decision of who wins the game like runs and home runs do.
These correlations demonstrate the relationship between the ranking of a team and the specific statistic. Through my analysis of correlation, I have learned that hits do not affect the ranking of a team as much as runs or home runs do. If I were to continue my research into what affects a teams ranking, I would also like to look at the correlation between rankings and losses as well as the correlation between rankings and at bats. I hypothesize that since at bats are the first step to getting a run and a lot of at bats are very unsuccessful, at bats would have less of an impact on the ranking of a team than hits.
With my analysis of the dataset and these specific variables, I can conclude that there is more of a relationship between the ranking of a team and runs, and more of a relationship between the ranking of a team and home runs as compared to the relationship of the ranking of the team and the number of hits. A negative correlation of 0.266 between the ranking of a team and the number of hits is not a very strong correlation. Therefore, I am able to determine that there is not a strong relationship between the number of hits and the ranking of the team. Even though it is most of the time essential for a team to get a hit in order to to get a run, it is not needed so, who won or lost a game is not necessarily affected by the number of hits.
Throughout the paper, I have shown that there is a weaker relationship between hits and the ranking of a team than runs and the ranking of the team or home runs and the ranking of the team. Next time I go to a baseball game, I am going to watch a player get a hit and think about how weak the relationship of that hit is with the overall ranking of the team. It might even make me resist clapping because of how weak the relationship is between hits and the ranking of the team compared to the other variables that I studied. So, next time you go to a game, remember those relationships and Play Ball!
With my analysis of the dataset and these specific variables, I can conclude that there is more of a relationship between the ranking of a team and runs, and more of a relationship between the ranking of a team and home runs as compared to the relationship of the ranking of the team and the number of hits. A negative correlation of 0.266 between the ranking of a team and the number of hits is not a very strong correlation. Therefore, I am able to determine that there is not a strong relationship between the number of hits and the ranking of the team. Even though it is most of the time essential for a team to get a hit in order to to get a run, it is not needed so, who won or lost a game is not necessarily affected by the number of hits.
Throughout the paper, I have shown that there is a weaker relationship between hits and the ranking of a team than runs and the ranking of the team or home runs and the ranking of the team. Next time I go to a baseball game, I am going to watch a player get a hit and think about how weak the relationship of that hit is with the overall ranking of the team. It might even make me resist clapping because of how weak the relationship is between hits and the ranking of the team compared to the other variables that I studied. So, next time you go to a game, remember those relationships and Play Ball!
