Allie Kelso, Caitlyn Nolan, Nora Hanson, Andy Fullerton,
Hannah Dineen, Katie Mclaughlin, and Samantha Attar
Professor Wilson
BI223
Evaluating Player Speed in Relationship to Age: Trends in Top Baseball Players
Introduction:
The following analysis compares the speed of the league’s fastest athletes as their
career progresses. The performance statistics we will analyze are stolen bases and
extra-base hits (triples). Due to the effects of age, we hypothesized that as an athlete
gets older, their speed decreases. We would expect to see a decrease in the amount of
stolen bases and triples hit as a player’s career progresses.
Methods:
Using the Lahman data frame, we determined the top ten players of all time in both
steals and triples. We used dplyr to manipulate the data and used ggplot to create
figures that reflected the fluctuating trends in stolen bases and triples over time. We then
performed a correlation test in order to determine if there is a significant relationship
between a player’s age and their career numbers in stolen bases and triples.
Findings:
Figure 1. Number of triples hit per year by the top 10 triple hitters over a 20 year span.
Figure 2. Number of stolen bases per year for the top 10 base stealers over a 20 year
time span.
Based on the data we collected, we found that as a player ages, their speed decreases.
Because of this, the amount of stolen bases and triples hit by a player decreases over
time as well.
As seen in Figure 1 (3B), there is a general downward trend as the top ten triple hitters
aged and could no longer stretch their run to the same extent as it had previously in their
careers. The outliers we see in the upper half of the graph indicating a higher number of
triples hit are younger players beginning their careers, thus able to stretch their runs and
therefore hitting more triples. However, despite the outliers, the general downward trend
of the graph indicates that all players slow as they age, regardless of where they began
their careers.
In Figure 2 (SB), a similar trend can be viewed. At the beginning of the players’ careers,
their stolen base numbers are high. The curve then slopes heavily downwards as the
players age, with outliers that can be attributed to younger players starting their careers
with the ability to steal more bases in their youth. Eventually these younger players turn
the curve back to a positive slope as more players have the ability to steal bases, and
then, towards the far right of the graph, the curve begins a downwards trajectory once
more as these players also age, diminishing both their speed and stolen bases.With
these findings, we are unable to reject our null hypothesis and therefore, conclude that
there is a negative correlation between speed and age.
Discussion/ Overview/ Implications:
The general negative slope depicted in both graphs indicates that as players age, their
speed decreases. Typically, at the beginning of a professional baseball career, a player
is in their low to mid twenties, which is the peak of a male’s athleticism. Because of this,
younger players are able to run faster and train harder, therefore making them more
agile than older players. This, in turn, allows them to steal more bases. Similarly, at a
younger age, although their batting abilities may be the same as an older player’s, their
athleticism can allow them to stretch a double into a triple.