Baseball Analytics
Master Data-Driven Baseball Analysis
Master the art of baseball analytics from sabermetrics fundamentals to advanced Statcast analysis
250
Tutorials6
Examples15
Datasets37
MetricsLearning Paths
Foundations and Data Infrastructure
6 TopicsIntroduction to baseball analytics, data sources, programming tools, and statistical fundamentals
Getting Started
5 TopicsIntroduction to baseball analytics and key concepts
Part I: Foundations and Data Infrastructure
6 TopicsFoundational concepts including sabermetrics, data sources, and analytics tools
Basic Statistics
5 TopicsTraditional baseball statistics and their calculations
Hitting and Offensive Analytics
8 TopicsComprehensive analysis of hitting metrics, plate discipline, power, batted ball data, and offensive performance
Part II: Hitting and Offensive Analytics
8 TopicsComprehensive hitting metrics, plate discipline, and offensive analysis
Advanced Metrics
5 TopicsModern sabermetrics including WAR, FIP, and wRC+
Part III: Pitching Analytics
10 TopicsPitching statistics, arsenal analysis, and pitcher evaluation
Pitching Analytics
10 TopicsIn-depth pitching statistics, pitch design, velocity, movement, and pitcher performance evaluation
Fielding and Defensive Analytics
6 TopicsDefensive metrics, positioning, range evaluation, and comprehensive fielding analysis
Part IV: Fielding and Defensive Analytics
6 TopicsDefensive metrics, positioning, and field analytics
Statcast Data
5 TopicsLaunch angle, exit velocity, and tracking data analysis
Advanced Metrics and Sabermetrics
5 TopicsWAR, win probability, run expectancy, park factors, aging curves, and projection systems
Data Collection
5 TopicsWeb scraping, APIs, and data sources for baseball
Part V: Advanced Metrics and Sabermetrics
6 TopicsWAR, win probability, run expectancy, and advanced statistical methods
Part VI: Team Strategy and Game Theory
6 TopicsLineup construction, bullpen management, and strategic analysis
Visualization
5 TopicsCreating spray charts, heat maps, and baseball visualizations
Machine Learning
5 TopicsPredictive modeling and ML applications in baseball
Part VII: Player Evaluation and Scouting
6 TopicsDraft analytics, player development, and evaluation methods
Part VIII: Contracts, Salary, and Team Building
5 TopicsFinancial aspects of baseball and roster construction
Part IX: Advanced Statistical Methods
5 TopicsMachine learning, Bayesian statistics, and advanced analysis
Part X: Historical Baseball and Era Comparisons
4 TopicsHistorical eras and era adjustments
Part XI: Advanced Hitting Concepts
8 TopicsAdvanced hitting mechanics and analysis
Part XII: Advanced Pitching Concepts
10 TopicsAdvanced pitching mechanics and strategy
Part XIII: Fielding and Defense Deep Dives
6 TopicsComprehensive defensive analysis by position
Part XIV: Baserunning and Speed
5 TopicsSpeed analytics and baserunning strategy
Part XV: In-Game Strategy and Tactics
7 TopicsManagerial decisions and in-game strategy
Part XVI: Player Development and Minor Leagues
6 TopicsProspect development and minor league analysis
Part XVII: International Baseball
5 TopicsInternational leagues and player analysis
Part XVIII: Business, Economics, and Operations
7 TopicsBaseball business and economic analysis
Part XIX: Historical Eras and Evolution
6 TopicsBaseball era analysis and evolution
Part XX: Specialized Topics and Niches
7 TopicsSpecialized baseball analytics topics
Part XXI: Medical, Health, and Safety
5 TopicsInjury analytics and player health
Part XXII: Technology and Innovation
6 TopicsBaseball technology and tracking systems
Part XXIII: Umpiring and Rules
4 TopicsUmpire analytics and rule analysis
Part XXIV: Specific Situations and Strategy
5 TopicsSpecial game situations and strategy
Advanced Methods
8 TopicsAdvanced statistical and machine learning methods for sports analytics
Part XXV: Prospect Analysis and Scouting
5 TopicsComprehensive prospect analysis
Game Theory & Strategy
7 TopicsStrategic decision-making and game theory in baseball
Part XXVI: Coaching and Instruction
4 TopicsCoaching impact and instruction methods
Draft & Prospect Analytics
2 TopicsMLB draft analysis and prospect evaluation
Part XXVII: Psychology and Mental Performance
4 TopicsMental skills and psychology in baseball
Contracts & Salary
1 TopicsPlayer valuation, arbitration, and contract analysis
Quick Start Guide
Learn Basics
Start with fundamental statisticsAdvanced Metrics
Explore sport-specific analyticsPractice with Data
Use real datasets and examplesApply Knowledge
Build your own analytics projectsRecent Tutorials
Minor League Analytics
Draft & Prospect AnalyticsMLB Draft Analytics
Draft & Prospect AnalyticsHistorical Era Adjustments
Game Theory & StrategyGame Theory in Baseball Management
Game Theory & StrategyCatcher Framing Deep Dive
Game Theory & StrategyKey Baseball Metrics
BABIP BABIP
Batting average on balls put into play. Used to identify luck or unsustainable performance; league average is around .300. Variables: H = Hits, HR = Home Runs, AB = At-Bats, K = Strikeouts, SF = Sacrifice Flies
\text{BABIP} = \frac{H - HR}{AB - K - HR + SF}
Isolated Power ISO
Measures raw power by removing singles from slugging percentage. Shows extra bases per at-bat. Variables: SLG = Slugging Percentage, AVG = Batting Average, 2B = Doubles, 3B = Triples, HR = Home Runs
\text{ISO} = \text{SLG} - \text{AVG} = \frac{2B + 2(3B) + 3(HR)}{AB}
OPS Plus OPS+
Park and league-adjusted OPS. 100 is league average, with each point representing a percentage above or below. Variables: OBP = On-Base Percentage, SLG = Slugging Percentage, lgOBP = League OBP, lgSLG = League SLG (adjusted for park)
\text{OPS+} = 100 \times \left( \frac{\text{OBP}}{\text{lgOBP}} + \frac{\text{SLG}}{\text{lgSLG}} - 1 \right)
Weighted Runs Above Average wRAA
Offensive runs above average based on wOBA. Foundation for the batting component of WAR. Variables: wOBA = Player wOBA, lgwOBA = League wOBA, wOBA Scale = ~1.15 (converts to runs), PA = Plate Appearances
\text{wRAA} = \frac{(\text{wOBA} - \text{lgwOBA})}{\text{wOBA Scale}} \times PA
wOBA wOBA
Weights each method of reaching base by its actual run value. Considered the best single measure of offensive contribution. Linear weights are updated annually. Variables: BB = Walks, HBP = Hit By Pitch, 1B = Singles, 2B = Doubles, 3B = Triples, HR = Home Runs (weights are approximate, vary by year)
\text{wOBA} = \frac{0.69(BB) + 0.72(HBP) + 0.88(1B) + 1.24(2B) + 1.56(3B) + 2.00(HR)}{AB + BB + SF + HBP}
wRC+ wRC+
Park and league-adjusted runs created. 100 is league average; each point above/below represents a percentage better/worse than average. Variables: wRAA = Weighted Runs Above Average, PA = Plate Appearances, lgR/PA = League Runs per PA, lgwRC/PA = League wRC per PA
\text{wRC+} = \left( \frac{\text{wRAA}}{\text{PA}} + \text{lgR/PA} \right) \times \frac{1}{\text{Park Factor}} \times \frac{1}{\text{lgwRC/PA}} \times 100
Defensive Runs Saved DRS
Comprehensive defensive metric from Baseball Info Solutions. Measures runs saved across all defensive components. Variables: rPM = Plus/Minus Runs, rGDP = GDP Runs, rARM = Outfield Arm Runs, rHR = Home Run Robbing Runs, rGFP = Good Fielding Play Runs
\text{DRS} = \text{rPM} + \text{rGDP} + \text{rARM} + \text{rHR} + \text{rGFP}
Ultimate Zone Rating UZR
Advanced defensive metric measuring runs saved above average based on play-by-play data and zone information. Variables: RngR = Range Runs, ErrR = Error Runs, DPR = Double Play Runs
\text{UZR} = \sum(\text{Zone Runs}) = \text{RngR} + \text{ErrR} + \text{DPR}
Expected Fielding Independent Pitching xFIP
FIP with home runs regressed to league-average HR/FB rate. Better at predicting future performance than FIP. Variables: FB = Fly Balls, lgHR/FB = League HR per Fly Ball rate (~10-11%), others same as FIP
\text{xFIP} = \frac{13(FB \times \text{lgHR/FB}) + 3(BB + HBP) - 2(K)}{IP} + C
FIP FIP
Estimates ERA based only on strikeouts, walks, HBP, and home runs—outcomes the pitcher controls. C is a constant to put FIP on ERA scale (usually around 3.10). Variables: HR = Home Runs, BB = Walks, HBP = Hit By Pitch, K = Strikeouts, IP = Innings Pitched, C = FIP Constant (~3.10)
\text{FIP} = \frac{13(HR) + 3(BB + HBP) - 2(K)}{IP} + C
Datasets & Resources
Ready to Master Baseball Analytics?
Start with the basics and work your way up to advanced machine learning applications.