Evan Miyakawa

Evan Miyakawa

Data Scientist

Zelus Analytics


My name is Evan Miyakawa, and I am a Ph.D. statistician, working as a Data Scientist for Zelus Analytics. I obtained my doctorate in Statistics from Baylor in 2022. I previously received my bachelors degree in Mathematics from Taylor University in 2017. In my spare time, I run a college basketball analytics website at EvanMiya.com.


  • Statistical Computation Methods
  • Bayesian Statistics
  • Statistical Machine Learning
  • Big Data
  • Sports Analytics


  • Ph.D. in Statistics, 2022

    Baylor University

  • M.S. in Statistics, 2018

    Baylor University

  • B.S. in Mathematics, 2017

    Taylor University














Data Scientist

Zelus Analytics

Nov 2021 – Present Remote
I work as a data scientist for the NBA group at Zelus Analytics, serving multiple NBA clients by creating advanced statistical models for evaluating things like player performance and insights from tracking data.

Summer Associate

Institute for Defense Analyses

Jun 2021 – Aug 2021 Alexandria, VA
I worked on classified projects sponsored by the Department of Defense,related to quantifying predictive uncertainty in machine learning forecasts.

Graduate Assistant

Baylor University

Aug 2019 – Jan 2022 Waco, TX
My graduate assistantship included statistical consulting with various projects at Baylor, including a (soon to be published) paper implementing the recycled predictions bootstrap method with professors in the Economics and Health Services Research departments. I am also involved in classified consulting work for Eli Lilly and Company’s statistics department.

Chief Data Wrangler

Wakefield Research Partners

Jun 2019 – Present Waco, TX
I provide company-wide technical support in R, and I automate data generation processes.

Introduction to Statistics Teacher

Baylor University

Aug 2018 – May 2019 Waco, TX
I taught two semesters of Introduction to Stats for undergraduate students. I also tutored students studying statistics at the undergraduate and graduate levels.

Data Analyst Intern

Global Media Outreach

Jun 2016 – Aug 2016 Plano, TX
I conducted statistical analysis and provided reports on the effectiveness of advertisement campaigns, the performance of online communication volunteers, and donation trends. My research on advertisement campaigns led to over $40,000 saved by the organization in just two months.

Papers and Presentations

TRICARE for Life: The Impact of Reduced Cost-Sharing on Health Care Use

This paper is being submitted for review, co‑authored by Dr. Forest Kim and Dr. Neil Fleming at Baylor University. I contributed to this paper by applying the recycled predictions bootstrap method in order to find the impact of a policy change on expenditures for patients of various health insurance types.

Contributions to the Practical Application of Bayesian Methods to Survival Analysis in Clinical Trials

My dissertation deals with several distinct concepts, including Bayesian computational methods, model selection in network meta-analysis with clinical trial data, and quantifying forecast uncertainty in machine learning.


  • The first chapter serves as a “one-stop shop” for bayesian computational methods. Common MCMC methods are studied, and a simulation study compares the performance of several popular Bayesian computing platforms in R.
  • The second chapter we assesses the performance of several model selection criteria for a complex family of network meta- analysis models for survival data.
  • We also propose a technique for study outlier detection and present simulation results that demonstrate its effectiveness. Finally, the third chap- ter covers methods for constructing prediction intervals for forecasts for various machine learning algorithms.
  • The third chapter proposes a new technique for measuring forecast uncertainty that can be used on a wide set of machine learning models.

Bayesian Computational Strategies - Preliminary Dissertation Presentation

I presented a talk on common Bayesian computational methods and implementations, which contrasts each and gives general guidelines for use. The paper form of this presentation is being written and will be a chapter in my dissertation.


  • Gives overview of Gibbs Sampling and Hamiltonian Monte Carlo methods.
  • The presentation addresses strengths and weaknesses of STAN, JAGS, OpenBUGS, Nimble, and Greta, and compares their respective computation time and accuracy.