Do Sports Analytics
Decision-making quality — from 7 books.
Mike West, Editor · Leading With Data · sportscoach.guide/guides/sports-analytics
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Do Sports Analytics
Decision-making quality — from 7 books.
Mike West, Editor · Leading With Data · sportscoach.guide/guides/sports-analytics
The problem
Decision-Making Quality & Support Value erodes when it is left to instinct instead of method.
You were taught the moves piecemeal, never the whole model.
Do Sports Analytics · Mike West, Editor · Leading With Data
The model
- 01Analysis System & Method Design
- 02Data Quality, Reliability & Objectivity
- 03Information Validity, Relevance & Insight Quality
- 04Metric Stability & Predictiveness
- 05Feedback & Communication Quality
- 06Stakeholder Buy-in, Engagement & Adoption
- 07Contextual, Relational & Micropolitical Conditions
- 08Analyst Competence & Micropolitical Literacy
How it grows
- New to the role0–1 yr
- Running the function1–3 yr
- Shaping the whole3+ yr
Do Sports Analytics · Mike West, Editor · Leading With Data
Step 1 of 5 · Analysis System & Method Design
Anchor every tracked variable to a named decision it feeds; delete variables that map to none.
Misconception
Practitioners believe that buying the most sophisticated tracking platform or model is the design decision that matters most.
Reality
The design that determines value is the fit between your captured variables and the decisions the coach actually makes each week; a spreadsheet mapped to real questions beats an unused optical-tracking suite.
Do Sports Analytics · Mike West, Editor · Leading With Data
Step 2 of 5 · Data Quality, Reliability & Objectivity
Quantify reliability before quantifying performance; publish the error margin alongside the metric.
Misconception
Analysts assume that once data comes from an automated tracking or provider feed, objectivity and accuracy are guaranteed.
Reality
Automated feeds carry their own systematic errors — calibration drift, event-definition ambiguity, clock misalignment — and human tagging embeds coder subjectivity; both require explicit reliability checks.
Do Sports Analytics · Mike West, Editor · Leading With Data
Step 3 of 5 · Information Validity, Relevance & Insight Quality
An insight earns its place only if a named person can do something different because of it.
Misconception
Practitioners equate statistical validity and model accuracy with relevance to the coach's decision.
Reality
A metric can be statistically impeccable and utterly useless if it measures something no one can act on; relevance is judged by the decision it changes, not by its R-squared.
Do Sports Analytics · Mike West, Editor · Leading With Data
Step 4 of 5 · Metric Stability & Predictiveness
Establish the stabilization threshold for each metric before using it to project.
Misconception
Analysts treat a metric that describes past performance well as automatically predictive of future performance.
Reality
Descriptive fit and predictive stability are different properties; a metric only forecasts if it repeats across samples, and many spectacular single-season numbers are largely luck that regresses to the mean.
Do Sports Analytics · Mike West, Editor · Leading With Data
Step 5 of 5 · Feedback & Communication Quality
Timeliness within the coaching cycle often matters more than analytical completeness.
Misconception
Analysts believe communication quality is about polishing visuals and packaging the same content more attractively.
Reality
Communication quality is co-created meaning: it depends on shared language, timing that matches the coaching cycle, and interaction that lets the receiver interrogate the finding — not on prettier slides.
Do Sports Analytics · Mike West, Editor · Leading With Data
A framework to use
Six-Step Model for Gathering Quality Information
This framework is part of the full guide. See the guide →
Do Sports Analytics · Mike West, Editor · Leading With Data
The cheat sheet
- Analysis System & Method DesignAnchor every tracked variable to a named decision it feeds; delete variables that map to none.
- Data Quality, Reliability & ObjectivityQuantify reliability before quantifying performance; publish the error margin alongside the metric.
- Information Validity, Relevance & Insight QualityAn insight earns its place only if a named person can do something different because of it.
- Feedback & Communication QualityTimeliness within the coaching cycle often matters more than analytical completeness.
- Stakeholder Buy-in, Engagement & AdoptionTrust is earned through incremental wins before it can carry counterintuitive findings.
- Contextual, Relational & Micropolitical ConditionsRead the power map before proposing anything that challenges an established practice.
- Analyst Competence & Micropolitical LiteracyTechnical fluency is the entry ticket; micropolitical literacy is the differentiator.
- Athlete/Team Performance Improvement & OutcomesAttribution requires a traceable pathway, not a coincidence of analytics and success.
Do Sports Analytics · Mike West, Editor · Leading With Data
Sources
- An Introduction to Performance Analysis of Sport — etc.
- Analytic Methods in Sports Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other… — Thomas A. Severini
- Coaching Knowledges Understanding the Dynamics of Sport Performance — Jim Denison
- Football Analytics with Python R — Eric A. EagerRichard A. Erickson
- Game of Edges The Analytics Revolution and the Future of Professional Sports — Bruce Schoenfeld
- Professional Practice in Sport Performance Analysis — Andrew Butterworth
- Sports Analytics in Practice with R — Ted Kwartler
Do Sports Analytics · Mike West, Editor · Leading With Data