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$86.9K

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How much do from home baseball statistician jobs pay per year?

As of Sep 2, 2026, the average yearly pay for from home baseball statistician in the United States is $86,921.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,500.00 and $98,500.00 per year, depending on experience, location, and employer.

What does a from home baseball statistician do?

A From Home Baseball Statistician is responsible for collecting, analyzing, and interpreting baseball data from games and player performances, all while working remotely. They input game statistics, verify data accuracy, and may create reports or visualizations for coaches, teams, broadcasters, or sports websites. Their work helps inform decisions about player performance, team strategies, and fan engagement. This role requires strong attention to detail, knowledge of baseball rules, and proficiency with data entry and statistical software.

What are the key skills and qualifications needed to thrive as a from home baseball statistician?

To thrive as a From Home Baseball Statistician, you need a strong analytical background, attention to detail, and a solid understanding of baseball rules and statistics, often supported by a degree in mathematics, statistics, or a related field. Experience with statistical software (such as R, Python, or Excel), databases, and familiarity with sports analytics platforms is typically required. Excellent organizational skills, time management, and clear written communication set top performers apart in this remote role. These skills ensure accurate data collection and analysis, enabling teams and organizations to make informed decisions based on reliable baseball statistics.

How does a from home baseball statistician typically collaborate with coaches and analysts while working remotely?

As a From Home Baseball Statistician, you'll frequently collaborate with coaches, analysts, and other team members through digital channels such as video calls, shared databases, and instant messaging platforms. Clear communication and timely updates are essential, especially when delivering real-time or post-game statistical reports. You may also participate in virtual meetings to discuss data trends, respond to specific data requests, and help interpret findings that inform coaching strategies. Building strong remote working relationships and staying responsive are key to ensuring your analyses are integrated into decision-making processes.
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From Home Baseball Statistician jobs near you
Infographic showing various From Home Baseball Statistician job openings in the United States as of August 2026, with employment types broken down into 43% Full Time, and 57% Part Time. Highlights an 100% In-person job distribution, with an average salary of $86,921 per year, or $41.8 per hour.

Quantitative Analyst Associate (2027)

Philadelphia Phillies - Baseball Operations

Philadelphia, PA • On-site

Part-time

Posted 22 days ago

Job description

Title: Quantitative Analyst Associate
Department: Baseball Research & Development
Reports to: Lead/Senior Quantitative Analyst
Status: Hourly Part-Time Seasonal
Position Overview:
As a Quantitative Analyst (QA) Associate, you help shape The Phillies Baseball Operations strategies by processing, analyzing, and interpreting large and complex data. You do more than just crunch the numbers; you carefully plan the design of your own studies by asking and answering the right questions, while also working collaboratively with other analysts and software engineers on larger projects.
Using analytical rigor, you work with your team as you mine through data and see opportunities for The Phillies to improve. After communicating the results of your studies and experiments to Baseball Operations leadership and executive staff, you collaborate with front office executives, scouts, coaches, and trainers to incorporate your findings into Phillies practices. Identifying the challenge is only half the job; you also work to figure out and implement the solution.
Responsibilities:
  • Conduct statistical research projects and manage the integration of their outputs into our proprietary tools and applications (e.g., performance projections, player valuations, draft assessments, injury analyses, etc.)
  • Communicate with front office executives, scouts, coaches, and medical staff to design and interpret statistical studies
  • Assist the rest of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball, statistics, data visualization, and programming
  • Continually enhance your knowledge of baseball and data science through reading, research, and discussion with your teammates and the rest of the front office
  • Provide input to database architecture to ensure efficient application of baseball data

Required Qualifications:
  • Deep understanding of statistics, including supervised and unsupervised learning, regularization, model assessment and selection, model inference and averaging, ensemble methods, etc.
  • Meaningful experience programming, using analytical software (Python, R, or similar), and interacting with databases
  • Proven willingness to both teach others and learn new techniques
  • Willingness to work as part of a team on complex projects
  • Proven leadership and self-direction

Preferred Qualifications:
  • Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
  • 0-5+ years of relevant work experience
  • Experience drawing conclusions from data, communicating those conclusions to decision makers, and recommending actions

To be considered, all candidates must submit a response for the prompt below:
In player evaluation, some metrics are highly predictive of future performance but provide limited information about why a player will succeed or fail. Other metrics may be less predictive on their own but can help identify specific strengths, weaknesses, or opportunities for improvement.
Assume you have access to several years of professional baseball data, including traditional statistics, pitch- or play-level tracking data, scouting evaluations, player demographics, injury history, and minor-league level and park context.
In 250 words or less, describe how you would determine which information should be included in a player projection model and which information should instead be used primarily to explain, diagnose, or contextualize the projection. Discuss how you would evaluate a metric that improves historical model accuracy but may not remain stable over time, may duplicate information contained in other variables, or may be difficult to obtain consistently for all players.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

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