Baseball Analytics with Statcast

Project Summary

Graduate Student: Jacob Coleman, 3rd year Ph.D. student in Statistical Science

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

Themes and Categories
Year

Graduate Student: Jacob Coleman, 3rd year Ph.D. student in Statistical Science

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

  • Data management, summarization, and exploration with R package dplyr
  • Data visualization through R package ggplot2
  • Worked with state-of-the-art data pulled from online source 

Summary

In this Data Exploration, students were introduced to baseball dataset Statcast, downloaded from baseballsavant.mlb.com, that included every pitch thrown in the first week of the 2016 season, with 19 characteristics. The students were tasked with using R packages dplyr and ggplot2 to answer data exploration and summarizion questions. The exercises challenged them to use information about the data as well as newly acquired computation skills.

The Statcast data is owned by MLB Advanced Media, L.P. and was downloaded from a search performed on baseballsa- vant.mlb.com for all pitches from 4/1/16 to 4/7/16. Statcast is a relatively new dataset (introduced in 2015), including all pitch characteristics from its precurser PitchF/X (such as pitch movement, type, start and end velocity, etc.). Statcast also added tracking of the ball during the entirety of the play, as well as tracking for all fielders. Full Statcast data is not yet available to the public, but Baseball Savant allows the public to have access to Statcast-added batted ball variables such as launch angle and batted ball speed.

Dplyr is an extremely powerful tool for exploring data, using simple structure to perform complex data management tasks. Students were introducted to dplyr in a previous lecture, and used the Statcast data to gain hands-on experience working with data. Their tasks ranged from simple summaries to sophisticated manipulation (as real data is rarely in perfect form for desired analysis). They also integrated the R package ggplot2 to visualize some of their findings and draw futher conclusions. 

 

Related Projects

This data expedition focused on the mechanisms animals use to orient using environmental stimuli, the methods that scientists use to test hypotheses about orientation, and the statistical methods used with circular orientation data. Students collected their own data set during the class period, performed hypothesis testing on their data using circular statistics in R, and aggregated their data to formally test the hypothesis that isopods orient with light using an RShiny online application.

This exercise served as a capstone to a series of four class sessions on orientation and navigation, where students read primary scientific literature that used circular statistics in their methods. This data exercise was used to give students the opportunity to collect their own data, discover why linear statistics wouldn’t be sufficient to analyze them, and then implement their own analysis. The goal of this course was to give students a better understanding of circular statistics, with hands-on application in forming and testing a hypothesis.

In this two-day, virtual data expedition project, students were introduced to the APIM in the context of stress proliferation, linked lives, the spousal relationship, and mental and physical health outcomes.

Stress proliferation is a concept within the stress process paradigm that explains how one person’s stressors can influence others (Thoits 2010). Combining this with the life course principle of linked lives explains that because people are embedded in social networks, stress not only can impact the individual but can also proliferate to people close to them (Elder Jr, Shanahan and Jennings 2015). For example, one spouse’s chronic health condition may lead to stress-provoking strain in the marital relationship, eventually spilling over to affect the other spouse’s mental health. Additionally, because partners share an environment, experiences, and resources (e.g., money and information), as well as exert social control over each other, they can monitor and influence each other’s health and health behaviors. This often leads to health concordance within couples; in other words, because individuals within the couple influence each other’s health and well-being, their health tends to become more similar or more alike (Kiecolt-Glaser and Wilson 2017, Polenick, Renn and Birditt 2018). Thus, a spouse’s current health condition may influence their partner’s future health and spouses may contemporaneously exhibit similar health conditions or behaviors.

However, how spouses influence each other may be patterned by the gender of the spouse with the health condition or exhibiting the health behaviors. Recent evidence suggests that a wife’s health condition may have little influence on her husband’s future health conditions, but that a husband’s health condition will most likely influence his wife’s future health (Kiecolt-Glaser and Wilson 2017).

Fluid mechanics is the study of how fluids (e.g., air, water) move and the forces on them. Scientists and engineers have developed mathematical equations to model the motions of fluid and inertial particles. However, these equations are often computationally expensive, meaning they take a long time for the computer to solve.

 

To reduce the computation time, we can use machine learning techniques to develop statistical models of fluid behavior. Statistical models do not actually represent the physics of fluids; rather, they learn trends and relationships from the results of previous simulation experiments. Statistical models allow us to leverage the findings of long, expensive simulations to obtain results in a fraction of the time. 

 

In this project, we provide students with the results of direct numerical simulations (DNS), which took many weeks for the computer to solve. We ask students to use machine learning techniques to develop statistical models of the results of the DNS.