Welcome to my Data Portfolio page! On this site, I outline my specific coding skills, data projects I've worked on, and my research experiences.

All of my experience has contained one of the following skills, whether for a class, for research, or other projects in general.

• R
• Python
• Excel
• SQL
• SAS
• Java & C
• Power BI
• Databricks

R and Python are my strongest languages as I have the most experience with those two, but I am confident in my abilities to apply my technical abilities in any tools listed.

• We built a model that simulates each pitch of a half-inning, generating typical baseball outcomes and returning the number of runs scored, given an input of many unique count/baserunner/lineup/outs situations.
• My role was to code the structure of the function (with live input from group mates), and building a script that could be run to reproduce the simulations over thousands of innings.
• We kept track of the runs scored each inning, also noting the leadoff hitter for each inning to perform further statistical analysis. The framework was created so that it could be reproduced by individual teams with specific players.

• Created our own expected points function that takes field position, down, and distance as input, and will output the number of expected points in many simulations (positive value for team with possession, negative for opponent).
• I took the lead by creating the framework of the overall function and creating the play-by-play simulation until a one team scored.
• We used this creation to answer the question of when to go for it or when to attempt a field goal on 4th down with an average college football kicker.

• This was a project done in the Fall of 2022 as a part of our UVA Sports Analytics and Statistics Laboratory with Samuel Brown and James Carney.
• What started as a project to analyze whether there is a trend among certain hitters who get better stike calls in the MLB turned into a project on general MLB strike zone analysis.
• We created an R-shiny app displaying our findings which can be found HERE. It contains stike zones for each hitter (actual and called) and multiple comparisons using our self-created statistic Expected Strikes Above Average (ESAA) / Expected Strikes per Pitch (ESPP) which is the same just normalized by pitch.

Undergraduate Research Intern (May 2023 - July 2023)

• Analyzed the role of healthcare facilities in the spread of MRSA, to apply to other anti-microbial resistant (AMR) infections as well. A link to my final poster at the completion of the program can be found HERE.
• Used Python data manipulation to clean/wrangle large datasets and build a travel matrix from Virginia insurance claims from 2016-2021 for input to infection patch model.
• Ran PatchSim software simulations on high powered computing software Rivanna. The estimated case counts could be matched to ground truth numbers which would be used to estimate the true MRSA exposure, infection, and recovery rates to understand the spread in future containment policies.

Undergraduate Researcher (January 2023 - Present)

• Work with UVA Data Science Professor Natalie Kupperman along with other undergraduate students on various sports data projects.
• Helped develop "ACL Reconstruction Reference Value Dashboard" for clinicians to reference when analyzing patient recoveries after ACL Reconstruction surgery. The link to the project description and the dashboard can be found HERE.
• Currently assisting with the implementation of analytical software with our UVA basketball and football programs.

Undergraduate Researcher (January 2023 - Present)

• Work with UVA Data Science Professor Stephen Baek among his research group on various computer vision data projects. Currently working on research project doing "Kinematic Modeling and the Computational Analysis of the Human Underwater Undulatory Kick."
• Using 3D computer vision to analyze the movement of a swimmer during the underwater portion of the race (first 15m of race during start and after turns). Original data gathered is one swimmer from the UVA Swim team and the goal is to be able to apply this research to any body type by editing the swimmer in our simulations.

Undergraduate Researcher (September 2021 - Present)

• Work in small groups to analyze data for projects at the college and professional level of sports using R and Python.
• Investigated whether it is of greater benefit for teams to fair catch all punts in the NFL; decision making on when appropriate.
• Researched borderline pitches in the MLB using Baseball Savant pitch location data and greated a new expected strikes statistic. The description of the project is found above (MLB Called Strikes Analysis).