baseball-research-advisor

Name: baseball-research-advisor
Availability: InStock
Author: nseluga

Review baseball analytics reports for methodological rigor and statistical assumptions.

Updated Jul 2, 2026

Search Tags:#research evaluation #critical review #methodology analysis #baseball analytics #statistical assumptions

Ready to run

npx skills add https://github.com/nseluga/os --skill baseball-research-advisor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: baseball-research-advisor
Source: https://github.com/nseluga/os/tree/main/skills/baseball-research-advisor
Command: npx skills add https://github.com/nseluga/os --skill baseball-research-advisor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a critical review of baseball analytics methodologies, ensuring decisions are logically defended and assumptions are explicitly handled.

Core Features & Use Cases

Critical Analysis: Peer review of baseball analytics methodology, modeling decisions, and statistical assumptions.
Reference Standards: Evaluates against a set of established research papers in baseball analytics.
Challenging Decisions: Demands explicit justification for data choice, feature engineering, modeling approach, evaluation methodology, and statistical assumptions.
Review Output: Provides strengths, weaknesses, questions to defend, alternative approaches, confidence assessment, and decision defensibility scoring.

Quick Start

Review the methodology of the baseball analytics report found in 'report.pdf'.

Frequently Asked Questions about baseball-research-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I critically review the methodology used in a baseball analytics report?▼

To critically review baseball analytics methodologies, you must assess methodological rigor, data handling, model choices, and statistical assumptions. This process demands explicit justification for data selection, feature engineering, and evaluation methodology to ensure logical defensibility.

What should be included in a peer review of baseball analytics modeling decisions?▼

A peer review of baseball analytics modeling decisions should evaluate statistical assumptions against established research papers. It must identify strengths, weaknesses, alternative approaches, and provide a decision defensibility score to challenge data choices.

Can I evaluate statistical assumptions in baseball analytics against reference standards?▼

You can evaluate statistical assumptions by comparing them against a repository of high-quality research papers in baseball analytics. This reference standard ensures your methodology analysis challenges data choices and feature engineering effectively.

What outputs do I get from assessing the rigor of baseball analytics research?▼

Assessing baseball analytics research yields a review output detailing strengths, weaknesses, questions to defend, alternative approaches, and a confidence assessment. It also includes a decision defensibility scoring metric.

Does reviewing baseball analytics methodologies require reference papers?▼

Reviewing baseball analytics methodologies requires access to a repository of high-quality research papers. These references act as standards to evaluate methodological rigor, challenge modeling decisions, and validate statistical assumptions.

When do I need to challenge data handling and feature engineering in baseball models?▼

You need to challenge data handling and feature engineering when evaluating modeling approaches to ensure logical defensibility. This critical review demands explicit justification for data choices and statistical assumptions.