A base hit in which the batter safely reaches first base without any errors, fielder's choices, or other advancements caused by the defensive team's actions.
Applicable Roles: Batters
Formula: Count of the # of singles
Use Cases: Evaluating base-hit ability.
Advantages: Simple and widely used.
Limitations: Does not account for power.
Real-Life Examples: Number of singles in a player’s career.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A base hit in which the batter safely reaches second base without any errors, fielder's choices, or other defensive misplays.
Applicable Roles: Batters
Formula: Count of the # of doubles
Use Cases: Measuring extra-base hit ability.
Advantages: Simple and valuable measure.
Limitations: Does not capture full offensive value.
Real-Life Examples: Counting doubles hit in a season.
Visualization Methods: Bar graphs, histograms
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A base hit in which the batter safely reaches third base without any errors, fielder's choices, or other defensive misplays.
Applicable Roles: Batters
Formula: Count of the # of triples
Use Cases: Evaluating speed and power combined.
Advantages: Reflects hitting for extra bases.
Limitations: Rare and situational.
Real-Life Examples: Players with 10+ triples in a season.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures a player's speed over 90 feet, typically from home to first.
Applicable Roles: Runners
Formula: Time taken to cover 90 feet on a sprint
Use Cases: Analyzing in-game sprinting speed and reaction times.
Advantages: Offers precise in-game speed metrics for baserunning.
Limitations: Does not account for external factors like field conditions.
Real-Life Examples: A 90-foot split under 3.9 seconds is considered elite.
Visualization Methods: Scatter plots or histograms comparing split times.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
A fielder's contribution to a putout.
Applicable Roles: Fielders
Formula: Count of the number of assists
Use Cases: Evaluating defensive involvement.
Advantages: Highlights defensive plays.
Limitations: Does not account for errors.
Real-Life Examples: Fielders with the most assists in a season.
Visualization Methods: Bar graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A completed batting appearance that does not result in a walk, hit-by-pitch, or sacrifice.
Applicable Roles: Batters
Formula: PA - BB - HBP - SH - SF
Use Cases: Evaluating offensive contributions.
Advantages: Simple and widely used.
Limitations: Does not include all plate appearances.
Real-Life Examples: Number of at-bats in a season.
Visualization Methods: Bar graphs, line graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Percentage of spin on a pitch that contributes to movement.
Applicable Roles: Pitchers
Formula: (Spin Rate × Cosine of Spin Axis) / Total Spin Rate
Use Cases: Analyzing the effectiveness of a pitch's spin.
Advantages: Provides insights into how much of the spin contributes to movement.
Limitations: Does not account for pitch velocity or release point.
Real-Life Examples: Pitches with high active spin often generate more swings and misses.
Visualization Methods: 3D scatter plots comparing spin rates and active spin percentages.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
The number of games a pitcher appears in.
Applicable Roles: Pitchers
Formula: Count of games appeared in.
Use Cases: Measuring pitcher usage.
Advantages: Simple and clear measure.
Limitations: Doesn't indicate innings pitched or effectiveness.
Real-Life Examples: Tracking a reliever's workload across a season.
Visualization Methods: Bar graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A measure of the strength of a fielder's throwing arm, particularly for outfielders.
Applicable Roles: Fielders
Formula: Average throw velocity in mph on competitive plays
Use Cases: Assessing a fielder's defensive throwing capability.
Advantages: Highlights outfielders with strong arms for assists and base-runner deterrence.
Limitations: Does not account for accuracy or decision-making.
Real-Life Examples: A player with an ARM above 90 mph is considered elite.
Visualization Methods: Bar charts or scatter plots of throw velocity.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Batting Average (AVG) is a statistic that measures a batter's success rate in achieving hits during official at-bats. It is calculated as the number of hits divided by the number of at-bats.
Applicable Roles: Batters
Formula: H ÷ AB
Use Cases: Evaluating a batter's hitting efficiency.
Advantages: Simple and intuitive.
Limitations: Does not account for power or walks.
Real-Life Examples: Comparing league-leading averages.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Indicates how often a player puts the ball in play when they make contact.
Applicable Roles: Pitchers, Batters
Formula: (H - HR) ÷ (AB - K - HR + SF)
Use Cases: Measuring batting average on balls in play, assessing luck for hitters/pitchers
Advantages: Highlights luck factors in hitting and pitching
Limitations: Doesn't account for quality of contact
Real-Life Examples: Determining if a player’s high average is due to luck
Visualization Methods: Histograms, time-series graphs
Creator: Voros McCracken
Sources: FanGraphs, Baseball-Reference, Baseball Savant
Year Introduced: Early 2000s
Formula Complexity: Medium
A measure of how a ballpark affects offensive performance compared to the league average.
Applicable Roles: Teams
Formula: (Team's Home Stats / Team's Road Stats) * League Average
Use Cases: Analyzing how a ballpark influences hitting and pitching outcomes.
Advantages: Helps understand how park conditions affect player performance.
Limitations: Does not account for weather or opponent strength.
Real-Life Examples: A factor above 1.00 indicates a hitter-friendly park, while below 1.00 indicates a pitcher-friendly park.
Visualization Methods: Heatmaps showing park effects on various stats.
Creator: Baseball-Reference, Fangraphs
Sources: Baseball-Reference, Fangraphs
Year Introduced: Not Widely Established
Formula Complexity: Moderate
A well-hit ball defined by a specific combination of exit velocity and launch angle.
Applicable Roles: Batters
Formula: EV >= 98 mph and launch angle between 26-30 degrees
Use Cases: Assessing the quality of a hitter's contact.
Advantages: Strongly correlates with offensive success.
Limitations: Does not account for field or defensive factors.
Real-Life Examples: Barrels per PA of 10% or higher indicates elite contact.
Visualization Methods: Barrel rate heatmaps or scatter plots.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Indicates the percentage of batted balls considered "barrels," or ideal hits.
Applicable Roles: Batters
Formula: Barrels ÷ Batted Ball Events
Use Cases: Assessing quality of contact made by a hitter
Advantages: High correlation with offensive success
Limitations: Limited to batted ball data
Real-Life Examples: Identifying hitters who consistently hit the ball hard
Visualization Methods: Bar graphs, scatter plots
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2015
Formula Complexity: Medium
When a batter is awarded first base due to four balls.
Applicable Roles: Batters
Formula: Count of the # of walks
Use Cases: Evaluating plate discipline.
Advantages: Reflects patience at the plate.
Limitations: Ignores power or contact ability.
Real-Life Examples: Players with high walk totals.
Visualization Methods: Bar graphs, time-series plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Indicates how often a batter walks relative to plate appearances.
Applicable Roles: Pitchers, Batters
Formula: BB ÷ PA
Use Cases: Walk rate evaluation for hitters and pitchers
Advantages: Simple, measures walk discipline
Limitations: Ignores context like pitch framing
Real-Life Examples: Comparing control of pitchers or patience of hitters
Visualization Methods: Bar graphs, time-series graphs
Creator: Traditional Statistic
Sources: Baseball Savant, FanGraphs
Year Introduced: Unknown
Formula Complexity: Low
Batter Excellent: 6.0% and below
Batter Great: 6.1% – 7.0%
Batter Above Avg: 7.1% – 8.0%
Batter Avg: 8.1% – 9.0%
Batter Below Avg: 9.1% – 10.0%
Batter Poor: 10.1% – 11.0%
Batter Awful: 11.1% and above
Pitcher Excellent: 6.0% and below
Pitcher Great: 6.1% – 7.0%
Pitcher Above Avg: 7.1% – 8.0%
Pitcher Avg: 8.1% – 9.0%
Pitcher Below Avg: 9.1% – 10.0%
Pitcher Poor: 10.1% – 11.0%
Pitcher Awful: 11.1% and above
Measures the average number of walks a pitcher allows per nine innings pitched.
Applicable Roles: Pitchers
Formula: (BB / IP) * 9
Use Cases: Evaluating pitcher control and ability to avoid free passes.
Advantages: Easy to understand; reflects control.
Limitations: Neglects batted ball outcomes or strikeout rate.
Real-Life Examples: Highlighting control artists like Greg Maddux.
Visualization Methods: Line graphs, bar charts
Creator: Traditional Statistic
Sources: Baseball Reference, MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Batter Excellent: 1.5 and below
Batter Great: 1.6 – 2.0
Batter Above Avg: 2.1 – 2.5
Batter Avg: 2.6 – 3.0
Batter Below Avg: 3.1 – 3.5
Batter Poor: 3.6 – 4.0
Batter Awful: 4.1 and above
Pitcher Excellent: 1.5 and below
Pitcher Great: 1.6 – 2.0
Pitcher Above Avg: 2.1 – 2.5
Pitcher Avg: 2.6 – 3.0
Pitcher Below Avg: 3.1 – 3.5
Pitcher Poor: 3.6 – 4.0
Pitcher Awful: 4.1 and above
Any ball put into play, including outs, hits, and errors.
Applicable Roles: Batters
Formula: Recorded on every contact made by the bat
Use Cases: Tracking the frequency and outcome of balls in play.
Advantages: Provides a base for analyzing contact quality.
Limitations: Does not indicate outcome quality like hits or home runs.
Real-Life Examples: High BBE rates can indicate an aggressive hitting style.
Visualization Methods: Line or bar charts tracking BBE trends.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
The total number of batters a pitcher faces.
Applicable Roles: Pitchers
Formula: Sum of all batters faced.
Use Cases: Measuring pitcher workload and effectiveness.
Advantages: Simple, inclusive of all results.
Limitations: Doesn't differentiate outcomes.
Real-Life Examples: Evaluating a starter's endurance across innings.
Visualization Methods: Bar graphs, line graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A pitching violation allowing baserunners to advance one base.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Evaluating pitching discipline.
Advantages: Highlights rule compliance.
Limitations: Rare and situational.
Real-Life Examples: A pitcher committing multiple balks in a critical game.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A subset of barrels with an even higher probability of a home run.
Applicable Roles: Batters
Formula: EV >= 100 mph and launch angle between 25-30 degrees
Use Cases: Identifying home run-level contact quality.
Advantages: Highlights elite power hitting and precision.
Limitations: Does not account for park effects or weather.
Real-Life Examples: A high percentage of blasts indicates elite slugging ability.
Visualization Methods: Scatter plots comparing blasts per PA across players.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Any sprint above 30 feet per second.
Applicable Roles: Runners
Formula: Sprints measured at 30+ ft/sec over a minimum distance
Use Cases: Identifying elite baserunning and fielding speed.
Advantages: Quantifies peak in-game speed effectively.
Limitations: Does not consider distance or game context.
Real-Life Examples: A player with more than 10 bolts in a season is considered elite.
Visualization Methods: Scatter plots comparing bolts across games.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Tracks the number of runners left on base by a pitcher when they leave the game.
Applicable Roles: Pitchers
Formula: Count of runners left on base upon pitcher substitution.
Use Cases: Evaluating a reliever's inherited situation and a starter's ability to leave clean bases.
Advantages: Provides a clear metric for pitchers leaving difficult situations for relievers.
Limitations: Does not consider the reliever's performance after substitution.
Real-Life Examples: Relievers entering high-leverage situations with multiple BQRs.
Visualization Methods: Bar charts showing bequeathed runners by pitchers.
Creator: Traditional Statistic
Sources: MLB Statcast
Year Introduced: Not Widely Established
Formula Complexity: Low
Tracks the number of inherited runners who score after the original pitcher leaves the game.
Applicable Roles: Pitchers
Formula: Count of bequeathed runners who eventually score.
Use Cases: Evaluating reliever effectiveness at preventing inherited runners from scoring.
Advantages: Highlights reliever performance in high-pressure situations.
Limitations: Does not account for the quality of defense behind the reliever.
Real-Life Examples: Relievers with high BQR-S can indicate ineffective outings.
Visualization Methods: Bar charts comparing BQR-S to total inherited runners.
Creator: Traditional Statistic
Sources: MLB Statcast
Year Introduced: Not Widely Established
Formula Complexity: Low
Occurs when a reliever enters with a lead and allows it to be tied or lost.
Applicable Roles: Pitchers
Formula: Count of failed save opportunities.
Use Cases: Evaluating relievers' reliability.
Advantages: Highlights key game moments.
Limitations: Situational and context-dependent.
Real-Life Examples: A closer's struggles in high-leverage situations.
Visualization Methods: Line graphs, time-series charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1969
Formula Complexity: Low
Evaluates how baserunning affects a team’s ability to score runs.
Applicable Roles: Base Runners
Formula: Evaluates baserunning contributions to run creation.
Use Cases: Comprehensive baserunning evaluation
Advantages: Captures baserunning value
Limitations: Ignores defensive and hitting contributions
Real-Life Examples: Ranking players by baserunning skill
Visualization Methods: Line graphs, area charts
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: 2009
Formula Complexity: Medium
Measures player value using Baseball Reference methodology for WAR.
Applicable Roles: Pitchers, Batters, Fielders
Formula: Provided by Baseball Reference; similar to fWAR but with different weights.
Use Cases: Comprehensive player value evaluation based on Baseball-Reference metrics
Advantages: Comprehensive value measure from Baseball-Reference
Limitations: Relies on specific Baseball-Reference adjustments
Real-Life Examples: Evaluating player contributions for Hall of Fame discussions
Visualization Methods: Scatter plots, bar graphs
Creator: Baseball-Reference
Sources: Baseball-Reference, FanGraphs
Year Introduced: 2010
Formula Complexity: High
The likelihood of a fielder making a play on a batted ball, based on factors such as distance, time, and direction.
Applicable Roles: Fielders
Formula: Calculated using time-to-catch, ball trajectory, and fielder position
Use Cases: Evaluating the difficulty of defensive plays.
Advantages: Quantifies defensive skill and play difficulty objectively.
Limitations: Does not account for effort or external conditions (e.g., wind).
Real-Life Examples: A catch probability of 5% or less represents an "impossible play."
Visualization Methods: Heatmaps or play difficulty visualizations.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: High
Ability of a catcher to convert borderline pitches into called strikes.
Applicable Roles: Catchers
Formula: Calculated by comparing actual strike calls to expected strike calls based on pitch location
Use Cases: Evaluating a catcher's defensive contribution.
Advantages: Highly valuable in assessing catcher defense beyond blocking or throwing.
Limitations: Does not account for umpire bias or game context.
Real-Life Examples: Catchers with elite framing can save several runs per season.
Visualization Methods: Charts comparing catcher strike rates on borderline pitches.
Creator: Baseball Prospectus
Sources: Statcast
Year Introduced: 2011
Formula Complexity: Moderate
Measures the average earned runs allowed by a team when a specific catcher is behind the plate.
Applicable Roles: Catchers
Formula: ERA calculated for catchers
Use Cases: Evaluating catcher impact on team pitching performance.
Advantages: Highlights catcher-pitcher interaction and impact on runs.
Limitations: Influenced by pitcher skill; lacks contextual adjustments.
Real-Life Examples: Used to evaluate how much a catcher contributes to pitching success.
Visualization Methods: Bar graphs, tables
Creator: Unknown
Sources: Baseball Reference
Year Introduced: Unknown
Formula Complexity: Medium
Evaluates a catcher’s framing ability and their impact on pitch outcomes.
Applicable Roles: Catchers
Formula: Estimated runs saved based on catcher framing ability.
Use Cases: Evaluating catchers’ ability to frame pitches for called strikes
Advantages: Reflects catcher pitch-framing ability
Limitations: Doesn't include other catcher defense skills
Real-Life Examples: Ranking catchers based on pitch framing ability
Visualization Methods: Heatmaps, scatter plots
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2015
Formula Complexity: High
A game in which a pitcher pitches all innings for their team.
Applicable Roles: Pitchers
Formula: Count of complete games.
Use Cases: Measuring endurance and effectiveness.
Advantages: Simple, clear measure.
Limitations: Rare in modern baseball.
Real-Life Examples: A pitcher throwing a complete-game shutout.
Visualization Methods: Bar graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures a batter's ability to make contact when swinging.
Applicable Roles: Batters
Formula: Contact ÷ Total Swings
Use Cases: Measuring a hitter’s ability to make contact
Advantages: Simple, measures contact ability
Limitations: Ignores quality of contact
Real-Life Examples: Comparing hitters who make consistent contact
Visualization Methods: Bar graphs, scatter plots
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Number of times a runner is thrown out attempting to steal a base.
Applicable Roles: Runners
Formula: Count of the # of times a runner is caught stealing.
Use Cases: Evaluating baserunning aggressiveness.
Advantages: Highlights baserunning risks.
Limitations: Context not considered.
Real-Life Examples: Players with high CS may need to adjust.
Visualization Methods: Pie charts, time-series graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Percentage of runners thrown out attempting to steal.
Applicable Roles: Catchers, Fielders
Formula: CS ÷ (CS + SB)
Use Cases: Measuring catcher defensive skills.
Advantages: Reflects catcher arm strength.
Limitations: Ignores game context.
Real-Life Examples: Catchers with a high caught stealing percentage.
Visualization Methods: Scatter plots, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Shows how often a pitcher generates either a called strike or a whiff per pitch.
Applicable Roles: Pitchers
Formula: (Called Strikes + Whiffs) ÷ Total Pitches
Use Cases: Evaluating pitchers’ ability to generate called and swinging strikes
Advantages: Measures pitch quality across counts
Limitations: Not widely adopted yet
Real-Life Examples: Analyzing pitchers’ ability to generate called and swinging strikes
Visualization Methods: Scatter plots, bar graphs
Creator: Nick Pollack
Sources: PitcherList
Year Introduced: 2018
Formula Complexity: Medium
The distance a fielder travels to make a play on a batted ball.
Applicable Roles: Fielders
Formula: Distance from initial position to where the ball is fielded
Use Cases: Measuring range and athleticism of fielders.
Advantages: Useful for identifying fielders with excellent range.
Limitations: Does not account for initial positioning or reaction time.
Real-Life Examples: A fielder covering 100+ feet to make a play showcases elite range.
Visualization Methods: Line graphs or field plots showing fielder movement.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
A defensive metric aggregating UZR, DRS, and other advanced stats to evaluate player fielding performance.
Applicable Roles: Fielders
Formula: Aggregate metrics
Use Cases: Evaluating overall defensive performance for players.
Advantages: Combines multiple metrics for comprehensive evaluation.
Limitations: May double-count contributions when metrics overlap.
Real-Life Examples: Identifying elite fielders like Andrelton Simmons.
Visualization Methods: Zone maps, bar graphs
Creator: Fangraphs
Sources: Fangraphs
Year Introduced: Unknown
Formula Complexity: High
Measures the percentage of balls in play (excluding home runs) converted into outs by a team's defense. It evaluates overall team defensive performance.
Applicable Roles: Fielders, Teams
Formula: 1 - (H + ROE) / (PA - BB - SO - HBP - HR)
Use Cases: Evaluating team defense, comparing defensive effectiveness across teams, assessing fielding and pitching support.
Advantages: Simple calculation; provides a high-level view of team defense effectiveness.
Limitations: Does not differentiate between individual players; heavily influenced by pitching and ballpark factors.
Real-Life Examples: Teams with high DER are often better at converting balls in play into outs, like the 2016 Chicago Cubs.
Visualization Methods: Line charts over a season, comparison bar charts, or scatter plots with other defensive metrics.
Creator: Bill James
Sources: Baseball Prospectus
Year Introduced: 1977
Formula Complexity: Low
The difference between runs scored and runs allowed by a team.
Applicable Roles: Teams
Formula: Runs Scored - Runs Allowed
Use Cases: Evaluating team performance and predicting success.
Advantages: Simple, direct measure of performance.
Limitations: Ignores game context and run distribution.
Real-Life Examples: Teams with the highest run differential often perform well.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A defensive play resulting in two outs.
Applicable Roles: Fielders
Formula: Count of the # of double plays executed.
Use Cases: Evaluating team defensive efficiency.
Advantages: Highlights infield coordination.
Limitations: Rare and situational.
Real-Life Examples: Teams leading the league in double plays.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Evaluates a player’s fielding effectiveness relative to average.
Applicable Roles: Fielders
Formula: Computed using advanced fielding metrics.
Use Cases: Defensive evaluation using run-saving ability
Advantages: Reliable for defense evaluation
Limitations: Different systems may disagree on results
Real-Life Examples: Assessing a player's defensive contributions
Visualization Methods: Defensive zone maps, bar graphs
Creator: John Dewan
Sources: FanGraphs
Year Introduced: 2003
Formula Complexity: High
The distance a batted ball travels, measured in feet.
Applicable Roles: Batters, Fielders
Formula: Measured from home plate to where the ball lands
Use Cases: Analyzing power, trajectory, and fielding difficulty.
Advantages: Useful for evaluating a player's power and trajectory.
Limitations: Does not account for wind or park factors.
Real-Life Examples: Hit distances over 400 feet indicate elite power.
Visualization Methods: Scatter plots comparing hit distances across games.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
A defensive misplay allowing a runner to advance or reach base.
Applicable Roles: Fielders
Formula: Count of the # of errors by a fielder
Use Cases: Measuring defensive lapses.
Advantages: Identifies mistakes.
Limitations: Subjective scoring decisions.
Real-Life Examples: Players with the fewest errors in a season.
Visualization Methods: Bar graphs, time-series plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Runs scored against a pitcher that are not due to errors or passed balls.
Applicable Roles: Pitchers
Formula: Count of earned runs allowed.
Use Cases: Evaluating pitcher effectiveness.
Advantages: Separates runs allowed from fielding errors.
Limitations: Doesn't factor in context or quality of opposition.
Real-Life Examples: A pitcher allowing 3 earned runs over 7 innings.
Visualization Methods: Histograms, time-series graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Late 1800s
Formula Complexity: Low
A pitcher's average number of earned runs allowed per nine innings.
Applicable Roles: Pitchers
Formula: (ER × 9) ÷ IP
Use Cases: Evaluating pitching performance.
Advantages: Widely recognized, easy to interpret.
Limitations: Influenced by team defense.
Real-Life Examples: Comparing league-average ERA across different seasons.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Late 1800s
Formula Complexity: Low
Adjusts a pitcher's ERA based on the league's ERA and ballpark factors, where 100 is league average and higher is better.
Applicable Roles: Pitchers
Formula: (ERA / League ERA) * 100
Use Cases: Comparing pitchers across different leagues and ballparks.
Advantages: Normalizes performance; easy comparison across eras.
Limitations: Relies on accurate park factor adjustments.
Real-Life Examples: Comparing pitchers across high-offense and low-offense ballparks.
Visualization Methods: Bar graphs, scatter plots
Creator: Baseball Reference
Sources: Baseball Reference
Year Introduced: Unknown
Formula Complexity: Medium
Batter Excellent: 170 and above
Batter Great: 150 – 169
Batter Above Avg: 130 – 149
Batter Avg: 100 – 129
Batter Below Avg: 80 – 99
Batter Poor: 60 – 79
Batter Awful: 59 and below
Pitcher Excellent: 170 and above
Pitcher Great: 150 – 169
Pitcher Above Avg: 130 – 149
Pitcher Avg: 100 – 129
Pitcher Below Avg: 80 – 99
Pitcher Poor: 60 – 79
Pitcher Awful: 59 and below
nan
Applicable Roles: Pitchers
The speed of the ball off the bat after contact.
Applicable Roles: Batters
Formula: Speed (mph) of the ball off the bat
Use Cases: Measuring contact quality and power.
Advantages: Correlates strongly with offensive production.
Limitations: Does not account for launch angle or outcome.
Real-Life Examples: An EV above 95 mph is considered elite.
Visualization Methods: Scatter plots or histograms showing EV distribution.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Distance from the pitching rubber to the release point of the ball.
Applicable Roles: Pitchers
Formula: Measured in feet
Use Cases: Assessing how close a pitcher releases the ball to the plate.
Advantages: Can indicate deception or increased perceived velocity.
Limitations: May vary by physical attributes like height or arm length.
Real-Life Examples: Pitchers with longer extensions often have higher perceived velocity.
Visualization Methods: Heatmaps showing release points across different pitchers.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Percentage of swings with high bat speed.
Applicable Roles: Batters
Formula: (Swings with fast bat speed / Total swings) * 100
Use Cases: Assessing a player's bat speed and aggressiveness.
Advantages: Identifies players with quick, high-effort swings.
Limitations: May not correlate directly with success if swings lack control.
Real-Life Examples: A fast-swing rate above 25% indicates a power hitter.
Visualization Methods: Bar charts comparing fast-swing rates.
Creator: Statcast
Sources: Statcast
Year Introduced: 2016
Formula Complexity: Moderate
Percentage of batted balls hit as fly balls.
Applicable Roles: Pitchers, Batters
Formula: (Fly Balls / Total Batted Balls) * 100
Use Cases: Evaluating hitter tendencies for fly balls or pitcher effectiveness in inducing them.
Advantages: Useful for analyzing power hitters or pitchers vulnerable to home runs.
Limitations: Does not account for ballpark factors or line drive misclassifications.
Real-Life Examples: Power hitters with high FB% can sustain high home run totals.
Visualization Methods: Pie charts or bar charts showing FB% by player.
Creator: Traditional Statistic
Sources: MLB Statcast
Year Introduced: Not Widely Established
Formula Complexity: Low
Measures a pitcher’s effectiveness at preventing runs, independent of defense.
Applicable Roles: Pitchers
Formula: ((13 × HR) + (3 × BB) - (2 × K)) ÷ IP + FIP Constant
Use Cases: Pitcher skill evaluation, removing defense and luck effects
Advantages: Focuses on outcomes pitchers control
Limitations: Ignores defense and sequencing
Real-Life Examples: Analyzing a pitcher’s effectiveness based on strikeouts, walks, and home runs
Visualization Methods: Line graphs, scatter plots
Creator: Tom Tango
Sources: FanGraphs
Year Introduced: 1999
Formula Complexity: Medium
Adjusts Fielding Independent Pitching for league and ballpark factors, similar to ERA+.
Applicable Roles: Pitchers
Formula: (FIP / League FIP) * 100
Use Cases: Comparing pitcher performance adjusted for league and ballpark.
Advantages: Adjusts for context, improving cross-league comparisons.
Limitations: Assumes all pitchers have similar defensive support.
Real-Life Examples: Comparing pitchers across hitter-friendly and pitcher-friendly leagues.
Visualization Methods: Bar charts, tables
Creator: Fangraphs
Sources: Fangraphs
Year Introduced: Unknown
Formula Complexity: Medium
A batted ball caught by a fielder before touching the ground.
Applicable Roles: Pitchers, Batters
Formula: Defined by game events.
Use Cases: Evaluating contact quality and defense.
Advantages: Simple, part of traditional stats.
Limitations: Doesn't include batted ball speed or direction.
Real-Life Examples: Analyzing hitters who frequently hit flyouts.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A percentage of plays made successfully in the field.
Applicable Roles: Fielders
Formula: (PO + A) ÷ (PO + A + E)
Use Cases: Evaluating defensive reliability.
Advantages: Simple defensive measure.
Limitations: Does not account for range.
Real-Life Examples: Fielders with high fielding percentages.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A positional adjustment for defensive runs saved (above average).
Applicable Roles: Fielders
Formula: Evaluates defensive contribution above average based on historical data.
Use Cases: Comprehensive defensive value for players
Advantages: Evaluates defensive performance comprehensively
Limitations: Data-intensive, harder to calculate
Real-Life Examples: Comparing defensive ability across positions
Visualization Methods: Bar graphs, scatter plots
Creator: Baseball Info Solutions
Sources: Baseball Info Solutions (via FanGraphs)
Year Introduced: Early 2000s
Formula Complexity: High
A measure of the defensive contribution of a player in terms of runs saved or prevented.
Applicable Roles: Fielders
Formula: Calculated based on the impact of defensive plays on run expectancy
Use Cases: Evaluating the overall defensive impact of players.
Advantages: Comprehensive defensive metric incorporating multiple factors.
Limitations: Dependent on accurate game situation and batted ball data.
Real-Life Examples: A player with an FRV of +10 or higher is considered elite defensively.
Visualization Methods: Charts comparing FRV across players or teams.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: High
Measures player value using FanGraphs methodology for WAR.
Applicable Roles: Pitchers, Batters, Fielders
Formula: Provided by FanGraphs; combines batting, fielding, and baserunning metrics.
Use Cases: Comprehensive player value evaluation based on FanGraphs metrics
Advantages: Combines all facets of player performance
Limitations: Relies on estimations, methodology can vary
Real-Life Examples: Comparing players across all aspects of performance
Visualization Methods: Bar graphs, area charts
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: 2008
Formula Complexity: High
Total number of games a player appears in.
Applicable Roles: All
Use Cases: Assessing player availability and durability.
Advantages: Simple and universal.
Limitations: Doesn't measure contribution level.
Real-Life Examples: Comparing players’ participation.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A single-game performance metric evaluating a starting pitcher’s effectiveness.
Applicable Roles: Pitchers
Formula: 50 + Out Recorded + Strikeouts - (2 x Walks) - Hits Allowed - (4 x Earned Runs)
Use Cases: Measuring a pitcher’s dominance or struggles in a specific game.
Advantages: Provides a quick, holistic evaluation of a starting pitcher’s outing.
Limitations: Not designed for relief pitchers; ignores park effects or opponent quality.
Real-Life Examples: Perfect games or no-hitters will typically have high game scores.
Visualization Methods: Line charts tracking game scores across a season.
Creator: Bill James
Sources: MLB Advanced Media
Year Introduced: 1988
Formula Complexity: Moderate
Percentage of batted balls hit as ground balls.
Applicable Roles: Pitchers, Batters
Formula: (Ground Balls / Total Batted Balls) * 100
Use Cases: Evaluating a pitcher’s ability to induce ground balls or batter tendencies to hit them.
Advantages: Highlights pitchers with ground-ball-heavy profiles or batters avoiding fly balls.
Limitations: Does not account for defensive shifts or ground ball quality.
Real-Life Examples: Pitchers with high GB% often rely on strong infield defense.
Visualization Methods: Pie charts or bar charts showing GB% by player.
Creator: Traditional Statistic
Sources: MLB Statcast
Year Introduced: Not Widely Established
Formula Complexity: Low
Shows the ratio of ground balls to fly balls for a player or team.
Applicable Roles: Pitchers
Formula: Ground Balls ÷ Fly Balls
Use Cases: Ground ball to fly ball ratio analysis for hitters and pitchers
Advantages: Highlights ground ball and fly ball tendencies
Limitations: Doesn't show outcomes of those tendencies
Real-Life Examples: Comparing ground ball pitchers to fly ball pitchers
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
A game in which a pitcher is the last to pitch for their team.
Applicable Roles: Pitchers
Formula: Count of games finished.
Use Cases: Measuring reliever usage in closing roles.
Advantages: Highlights closers and late-game relievers.
Limitations: Doesn't measure performance quality.
Real-Life Examples: A closer finishing 50 games in a season.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A ground ball resulting in two outs.
Applicable Roles: Batters
Formula: Count of the # of times a batter has grouned into a double play.
Use Cases: Measuring negative offensive outcomes.
Advantages: Highlights situational challenges.
Limitations: Context-dependent.
Real-Life Examples: High GIDP totals for slow runners.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A batted ball that is fielded and results in an out on the ground.
Applicable Roles: Pitchers, Batters
Formula: Defined by game events.
Use Cases: Evaluating groundball tendencies for hitters or pitchers.
Advantages: Simple, part of traditional stats.
Limitations: Doesn't measure quality of contact.
Real-Life Examples: Analyzing pitchers who induce many groundouts.
Visualization Methods: Pie charts, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Ratio of groundouts to airouts.
Applicable Roles: Pitchers, Batters
Formula: GO ÷ AO
Use Cases: Evaluating pitching or hitting tendencies.
Advantages: Shows pitching style.
Limitations: Context not considered.
Real-Life Examples: Identifying groundball pitchers.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
The number of games a pitcher starts.
Applicable Roles: Pitchers
Formula: Count of games started.
Use Cases: Evaluating starting pitcher usage.
Advantages: Clear and widely recognized.
Limitations: Doesn't indicate innings pitched or effectiveness.
Real-Life Examples: A pitcher making 30 starts in a season.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A home run hit with the bases loaded.
Applicable Roles: Batters
Formula: Count of a batter's Grand Slams
Use Cases: Highlighting clutch hitting ability.
Advantages: Exciting offensive stat.
Limitations: Rare and situational.
Real-Life Examples: Famous grand slams in playoff games.
Visualization Methods: Video clips, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A measure of how many times a batter safely reaches base by hitting the ball into play, without the aid of an error or fielder's choice. Hits can be categorized into singles, doubles, triples, and home runs.
Applicable Roles: Batters
Formula: Count of a batter's Hits.
Use Cases: Measuring batting success.
Advantages: Simple and universal.
Limitations: Does not measure power or context.
Real-Life Examples: Total hits in a player's career.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Average number of hits allowed by a pitcher per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Hits Allowed / Innings Pitched) * 9
Use Cases: Measuring pitcher effectiveness in limiting hits.
Advantages: Helps compare pitchers’ ability to suppress hits regardless of innings pitched.
Limitations: Does not consider quality of defense or ballpark factors.
Real-Life Examples: An elite pitcher may have an H/9 under 7.
Visualization Methods: Line charts or scatter plots showing H/9 trends over a season.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Percentage of batted balls with an exit velocity of 95+ mph.
Applicable Roles: Batters
Formula: (Batted balls with EV >= 95 mph / Total batted balls) * 100
Use Cases: Measuring a hitter's ability to make hard contact.
Advantages: Strongly correlates with offensive production.
Limitations: Does not account for launch angle or direction.
Real-Life Examples: A hard-hit rate above 40% is elite.
Visualization Methods: Scatter plots or bar charts showing hard-hit percentage.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
A Hit By Pitch (HBP) occurs when a batter is struck by a pitched ball without swinging at it, and the batter is awarded first base as a result.
Applicable Roles: Batters
Formula: Count of a batter's HBP
Use Cases: Evaluating player toughness and on-base rate.
Advantages: Adds value to OBP.
Limitations: Rare and potentially dangerous.
Real-Life Examples: Players with high HBP totals like Craig Biggio.
Visualization Methods: Histograms, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A statistic awarded to a reliever who protects a lead without finishing the game.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Evaluating middle relievers' contributions.
Advantages: Highlights setup pitchers' importance.
Limitations: Situational and context-dependent.
Real-Life Examples: A setup pitcher earning 20+ holds in a season.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1986
Formula Complexity: Low
The time taken to run from home plate to first base.
Applicable Roles: Runners
Formula: Measured in seconds
Use Cases: Assessing speed and quickness out of the batter's box.
Advantages: Provides a metric for in-game baserunning speed.
Limitations: Does not consider external factors like field conditions.
Real-Life Examples: A home-to-first time under 4 seconds is elite.
Visualization Methods: Scatter plots or line graphs tracking trends over time.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
A home run is a hit in which the batter rounds all the bases and scores, along with any runners on base, without being put out or the benefit of a defensive error.
Applicable Roles: Batters
Formula: Count of a batter's home runs.
Use Cases: Measuring power and offensive production.
Advantages: Highlights top power hitters.
Limitations: Ignores situational importance.
Real-Life Examples: Players with 40+ HR in a season.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Projected distance a home run would travel in neutral conditions.
Applicable Roles: Batters
Formula: Calculated using launch angle, exit velocity, and air resistance
Use Cases: Evaluating the power of a home run hitter.
Advantages: Accounts for environmental factors to provide consistent metrics.
Limitations: Does not reflect actual in-game conditions or situational hitting.
Real-Life Examples: Home runs with HR-DIS above 450 feet indicate elite power.
Visualization Methods: Scatter plots or bar charts of projected HR distances.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Average number of home runs allowed by a pitcher per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Home Runs Allowed / Innings Pitched) * 9
Use Cases: Measuring pitcher susceptibility to giving up home runs.
Advantages: Highlights pitchers prone to long balls or excelling at suppressing them.
Limitations: Does not account for ballpark dimensions or weather conditions.
Real-Life Examples: A pitcher with an HR/9 under 1 is considered excellent.
Visualization Methods: Bar charts comparing HR/9 across pitchers or teams.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Indicates the percentage of fly balls that result in home runs.
Applicable Roles: Pitchers, Batters
Formula: HR ÷ FB
Use Cases: Evaluating home run rate consistency for pitchers
Advantages: Measures home run frequency
Limitations: Small sample sizes can skew results
Real-Life Examples: Understanding the percentage of fly balls that result in home runs
Visualization Methods: Pie charts, histograms
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: 2005
Formula Complexity: Low
A walk issued deliberately by the pitcher.
Applicable Roles: Batters
Use Cases: Evaluating strategic pitching decisions.
Advantages: Context-driven stat.
Limitations: Rare and situational.
Real-Life Examples: Players frequently intentionally walked.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
The number of defensive innings a player has been in the field.
Applicable Roles: Fielders, Pitchers
Use Cases: Measuring defensive workload.
Advantages: Reflects time on the field.
Limitations: Does not reflect performance quality.
Real-Life Examples: Players with the most innings played.
Visualization Methods: Bar graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
The total number of innings a pitcher has pitched.
Applicable Roles: Pitchers
Formula: Sum of all innings pitched.
Use Cases: Measuring workload and endurance for pitchers.
Advantages: Clear, easy to track.
Limitations: Doesn't reflect quality of innings pitched.
Real-Life Examples: A starter pitching 200 innings in a season.
Visualization Methods: Line graphs, time-series charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A baserunner already on base when a reliever enters the game.
Applicable Roles: Pitchers
Formula: Count of runners inherited.
Use Cases: Evaluating relievers' performance in high-pressure situations.
Advantages: Highlights relievers' ability to prevent inherited runs.
Limitations: Doesn't account for context like number of outs.
Real-Life Examples: A reliever entering with bases loaded and no outs.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Number of inherited baserunners allowed to score by a relief pitcher.
Applicable Roles: Pitchers
Formula: Inherited Runners Scored / Total Inherited Runners
Use Cases: Evaluating relief pitchers’ effectiveness in preventing inherited runners from scoring.
Advantages: Highlights relievers who excel in high-pressure situations.
Limitations: Does not account for defensive support or inherited base state.
Real-Life Examples: A dominant reliever may have an IR-A close to 0.
Visualization Methods: Pie charts showing percentage of inherited runners allowed to score.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Shows how much power a player generates from their hits.
Applicable Roles: Batters
Formula: SLG - AVG
Use Cases: Measuring a player’s raw power through isolated power calculation
Advantages: Isolates power from batting average
Limitations: Misses context, like base-out states
Real-Life Examples: Identifying power hitters
Visualization Methods: Scatter plots, bar graphs
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Mid-1900s
Formula Complexity: Low
The fielder's reaction, burst, and route efficiency on batted balls.
Applicable Roles: Fielders
Formula: Reaction time + burst speed + route efficiency
Use Cases: Analyzing defensive instincts and speed to field batted balls.
Advantages: Breaks down defensive performance into measurable components.
Limitations: Does not account for environmental factors like wind or field conditions.
Real-Life Examples: A jump rating above 2.0 feet is considered elite.
Visualization Methods: Line or bar charts showing reaction and movement efficiency.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Indicates how often a batter strikes out relative to plate appearances.
Applicable Roles: Pitchers, Batters
Formula: K ÷ PA
Use Cases: Strikeout evaluation for hitters and pitchers
Advantages: Simple, measures strikeout ability
Limitations: Doesn't include situational factors
Real-Life Examples: Evaluating pitchers who rely on strikeouts
Visualization Methods: Line graphs, pie charts
Creator: Traditional Statistic
Sources: Baseball Savant, FanGraphs
Year Introduced: Unknown
Formula Complexity: Low
Measures the difference between strikeout percentage and walk percentage, highlighting plate discipline and command for pitchers.
Applicable Roles: Pitchers
Formula: K% - BB%
Use Cases: Evaluating pitcher effectiveness and command.
Advantages: Simple to calculate; highlights critical control aspects.
Limitations: Overlooks batted ball outcomes and contextual factors.
Real-Life Examples: Used to differentiate elite pitchers from average ones in MLB.
Visualization Methods: Line charts, bar graphs
Creator: Unknown
Sources: Fangraphs
Year Introduced: Unknown
Formula Complexity: Low
Average number of strikeouts a pitcher records per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Total Strikeouts / Innings Pitched) * 9
Use Cases: Evaluating a pitcher’s ability to generate strikeouts.
Advantages: Highlights strikeout proficiency without defensive factors.
Limitations: Does not consider walks, hits, or situational pitching.
Real-Life Examples: A K/9 above 10 is often considered elite for modern pitchers.
Visualization Methods: Bar charts comparing K/9 across pitchers or seasons.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Number of strikeouts per walk issued by a pitcher.
Applicable Roles: Pitchers
Formula: (Total Strikeouts / Total Walks)
Use Cases: Evaluating a pitcher’s control and dominance on the mound.
Advantages: Provides a balanced view of strikeouts and walk prevention.
Limitations: Does not directly consider hits or run prevention.
Real-Life Examples: A K/BB above 4 is considered excellent.
Visualization Methods: Scatter plots showing K/BB trends over time.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
A game in which a pitcher is charged with a loss.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Measuring pitchers' win-loss records.
Advantages: Simple, part of traditional stats.
Limitations: Context-dependent, team-based stat.
Real-Life Examples: A starter losing 10 games despite a low ERA.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures the vertical angle at which the ball leaves the bat after contact, critical for understanding batted ball trajectories.
Applicable Roles: Hitters, Pitchers
Formula: Measured in degrees
Use Cases: Analyzing optimal hitting strategies (e.g., line drives, fly balls) and identifying pitcher tendencies.
Advantages: Correlates directly with hitting outcomes like home runs or line drives.
Limitations: Context-dependent; high angles may not always result in productive hits.
Real-Life Examples: Launch angles between 10-25 degrees are optimal for extra-base hits.
Visualization Methods: Scatter plots, bar charts, heatmaps
Creator: MLB Statcast
Sources: Statcast, Baseball Savant
Year Introduced: 2015
Formula Complexity: Low
Percentage of batted balls classified as line drives.
Applicable Roles: Pitchers, Batters
Formula: (Line Drives / Balls in Play) * 100
Use Cases: Evaluating a batter's ability to hit line drives or a pitcher’s susceptibility to giving them up.
Advantages: Line drives often result in higher batting averages and slugging percentages.
Limitations: Does not account for batted ball speed or direction.
Real-Life Examples: A batter with an LD% above 20% is considered an excellent line-drive hitter.
Visualization Methods: Scatter plots comparing LD% across players or teams.
Creator: Traditional Statistic
Sources: FanGraphs
Year Introduced: Not Widely Established
Formula Complexity: Low
Distance a baserunner leads off the base before a pitch.
Applicable Roles: Runners
Formula: Measured in feet
Use Cases: Analyzing a runner's aggressiveness and risk-taking on the bases.
Advantages: Helps identify baserunners with strong steal potential.
Limitations: Does not account for a pitcher's pick-off skill or defensive positioning.
Real-Life Examples: A lead distance above 10 feet is considered aggressive.
Visualization Methods: Heatmaps showing lead distances for different runners.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
A metric evaluating player performance in high-pressure late-inning situations, such as close games in the 7th inning or later.
Applicable Roles: Batters, Pitchers
Formula: Performance metrics (e.g., batting average, slugging percentage, or strikeout rate) weighted by game leverage index.
Use Cases: Evaluating clutch performance, in-game decision-making, and situational effectiveness.
Advantages: Highlights players who excel under pressure, providing insights for late-game strategies.
Limitations: Small sample sizes can lead to variability; not predictive for future performance.
Real-Life Examples: Players like David Ortiz excelling in high-leverage playoff moments; Mariano Rivera's postseason dominance.
Visualization Methods: Line charts of performance trends in late innings, tables comparing clutch vs. non-clutch performance.
Creator: Baseball analysts (varies by implementation)
Sources: MLB game logs, Baseball-Reference, FanGraphs
Year Introduced: 2010
Formula Complexity: Moderate
Number of runners left on base at the end of an inning.
Applicable Roles: Batters, Teams
Use Cases: Evaluating offensive efficiency.
Advantages: Measures scoring opportunities.
Limitations: Doesn't capture full offensive context.
Real-Life Examples: Comparing team LOB totals in a game.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Evaluates team offensive efficiency by excluding non-scoring contributions.
Applicable Roles: Pitchers
Formula: (H + BB + HBP - R) ÷ (H + BB + HBP)
Use Cases: Evaluating pitchers’ ability to strand baserunners
Advantages: Indicates strand rate for pitchers
Limitations: Influenced by sequencing luck
Real-Life Examples: Measuring pitchers’ ability to prevent baserunners from scoring
Visualization Methods: Line graphs, time-series graphs
Creator: Traditional Statistic
Sources: FanGraphs
Year Introduced: 1980s
Formula Complexity: Medium
The number of wins needed by a team (or losses by their closest rival) to clinch a playoff berth or division title.
Applicable Roles: Teams
Formula: Games Remaining + 1 - (Team's Wins - Closest Rival's Losses)
Use Cases: Tracking a team's progress toward securing playoff qualification.
Advantages: Simple and widely used for fan engagement and playoff tracking.
Limitations: Does not reflect a team's likelihood of winning specific games.
Real-Life Examples: A magic number of 1 means a team is one win away from clinching.
Visualization Methods: Simple tables showing team standings and remaining games.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Measures how many baserunners a pitcher allows per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Hits + Walks + Hit Batters) / Innings Pitched * 9
Use Cases: Evaluating a pitcher’s effectiveness at preventing baserunners.
Advantages: Provides insight into pitcher control and efficiency.
Limitations: Does not account for quality of defense or luck factors like BABIP.
Real-Life Examples: Greg Maddux consistently posted low MB/9 values.
Visualization Methods: Scatter plots comparing MB/9 with ERA or WHIP.
Creator: Traditional Statistic
Sources: Baseball-Reference
Year Introduced: Not Widely Established
Formula Complexity: Low
The total number of pitches thrown by a pitcher in a game or season.
Applicable Roles: Pitchers
Formula: Count of pitches thrown.
Use Cases: Evaluating pitcher workload and efficiency.
Advantages: Simple, widely tracked.
Limitations: Doesn't include pitch types or outcomes.
Real-Life Examples: A pitcher throwing 100 pitches in a complete game.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Mid-1900s
Formula Complexity: Low
A defensive action that results in a batter or runner being put out.
Applicable Roles: Fielders
Use Cases: Measuring defensive contribution.
Advantages: Simple and direct metric.
Limitations: Ignores play difficulty.
Real-Life Examples: Out totals for defensive leaders.
Visualization Methods: Scatter plots, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures how often a batter swings at pitches outside the strike zone.
Applicable Roles: Batters
Formula: Swings Outside Zone ÷ Pitches Outside Zone
Use Cases: Measuring how often hitters swing at pitches outside the strike zone
Advantages: Reflects plate discipline outside the zone
Limitations: Limited to approach, not outcomes
Real-Life Examples: Comparing hitters’ tendency to swing at pitches outside the strike zone
Visualization Methods: Zone charts, scatter plots
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Measures defensive effectiveness using Statcast metrics for range and ability.
Applicable Roles: Fielders
Formula: Computed using Statcast's fielding play evaluation.
Use Cases: Evaluating defensive contributions using Statcast data
Advantages: Comprehensive defensive metric
Limitations: Limited to Statcast-equipped parks
Real-Life Examples: Comparing infielders and outfielders by defensive performance
Visualization Methods: Zone charts, scatter plots
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2016
Formula Complexity: High
Indicates how often a player gets on base via hits, walks, or hit-by-pitches.
Applicable Roles: Batters
Formula: (H + BB + HBP) ÷ (AB + BB + HBP + SF)
Use Cases: Evaluating a player’s ability to get on base
Advantages: Simple, measures on-base ability
Limitations: Doesn't consider power
Real-Life Examples: Identifying players who get on base frequently
Visualization Methods: Bar graphs, pie charts
Creator: Traditional Statistic
Sources: Baseball-Reference, FanGraphs
Year Introduced: Early 1900s
Formula Complexity: Low
Batter Excellent: .390 and above
Batter Great: .370 – .389
Batter Above Avg: .340 – .369
Batter Avg: .320 – .339
Batter Below Avg: .310 – .319
Batter Poor: .300 – .309
Batter Awful: .299 and below
Pitcher Excellent: .390 and above
Pitcher Great: .370 – .389
Pitcher Above Avg: .340 – .369
Pitcher Avg: .320 – .339
Pitcher Below Avg: .310 – .319
Pitcher Poor: .300 – .309
Pitcher Awful: .299 and below
An assist recorded by an outfielder.
Applicable Roles: Outfielders
Use Cases: Evaluating outfielder arm strength.
Advantages: Highlights defensive value.
Limitations: Rare and situational.
Real-Life Examples: Outfielders with the most assists.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Shows the percentage of batted balls hit to the opposite field.
Applicable Roles: Batters
Formula: Opposite Field Balls ÷ Total Batted Balls
Use Cases: Evaluating a hitter’s tendency to hit the ball to the opposite field
Advantages: Identifies opposite-field hitters
Limitations: Limited to batted ball direction
Real-Life Examples: Comparing opposite-field hitters
Visualization Methods: Spray charts, heatmaps
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Measures a player's overall offensive productivity, combining OBP and SLG.
Applicable Roles: Batters
Formula: OBP + SLG
Use Cases: Combining OBP and SLG for overall offensive performance evaluation
Advantages: Combines on-base and slugging
Limitations: Treats OBP and SLG equally, despite different impacts
Real-Life Examples: Ranking players by offensive performance
Visualization Methods: Bar graphs, heatmaps
Creator: Traditional Statistic
Sources: Baseball-Reference, FanGraphs
Year Introduced: 1980s
Formula Complexity: Low
Contextualizes OPS by accounting for league averages and ballpark effects.
Applicable Roles: Batters
Formula: 100 × (OPS ÷ league OPS) ÷ park factor
Use Cases: League- and park-adjusted comparison of OPS
Advantages: Normalized for league and park effects
Limitations: Still limited to hitting stats
Real-Life Examples: Comparing hitters across different ballparks and eras
Visualization Methods: Indexed bar charts, scatter plots
Creator: Baseball-Reference
Sources: Baseball-Reference
Year Introduced: 2002
Formula Complexity: Medium
Average number of pitches thrown per game started by a pitcher.
Applicable Roles: Pitchers
Formula: Total Pitches Thrown as Starter / Games Started
Use Cases: Measuring a starting pitcher’s durability and workload per game.
Advantages: Highlights pitchers capable of sustaining high pitch counts.
Limitations: Does not consider pitch effectiveness or fatigue factors.
Real-Life Examples: A durable starter averages around 90-100 P/GS.
Visualization Methods: Bar charts comparing P/GS across starting pitchers.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Average number of pitches thrown per inning pitched.
Applicable Roles: Pitchers
Formula: Total Pitches / Innings Pitched
Use Cases: Measuring a pitcher’s efficiency in managing pitch counts.
Advantages: Highlights pitchers capable of working deeper into games.
Limitations: Does not account for quality of opponents or game situations.
Real-Life Examples: A highly efficient pitcher averages fewer than 15 P/IP.
Visualization Methods: Line charts tracking P/IP trends over a season.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Average number of pitches a batter sees per plate appearance.
Applicable Roles: Batters
Formula: Total Pitches Seen / Plate Appearances
Use Cases: Evaluating plate discipline and ability to work counts.
Advantages: Helps identify patient hitters; provides insights into batting strategy.
Limitations: Does not account for the outcome of plate appearances or specific situations.
Real-Life Examples: Joey Votto’s ability to see many pitches in each plate appearance.
Visualization Methods: Line charts showing P/PA trends over seasons.
Creator: Traditional Statistic
Sources: Fangraphs, Baseball-Reference
Year Introduced: Unknown
Formula Complexity: Low
A completed turn at bat, including hits, walks, and outs.
Applicable Roles: Batters
Use Cases: Evaluating offensive opportunities.
Advantages: Includes all batting events.
Limitations: Does not measure quality of results.
Real-Life Examples: Total PAs in a season.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Average number of plate appearances before a batter strikes out.
Applicable Roles: Batters
Formula: Plate Appearances / Strikeouts
Use Cases: Measuring a batter’s strikeout avoidance.
Advantages: Simple to calculate; helps identify contact-oriented players.
Limitations: Ignores other outcomes like walks, hits, or quality of contact.
Real-Life Examples: Tony Gwynn’s low strikeout rate relative to his plate appearances.
Visualization Methods: Bar graphs comparing PA/SO across players in the same league.
Creator: Traditional Statistic
Sources: Fangraphs, Baseball-Reference
Year Introduced: Unknown
Formula Complexity: Low
A pitch that gets past the catcher and allows runners to advance.
Applicable Roles: Catchers
Use Cases: Measuring defensive lapses by catchers.
Advantages: Highlights catcher performance.
Limitations: Rare and situational.
Real-Life Examples: Catchers with the fewest passed balls.
Visualization Methods: Time-series plots, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Horizontal and vertical movement of a pitch due to spin.
Applicable Roles: Pitchers
Formula: Measured in inches
Use Cases: Assessing the effectiveness and uniqueness of a pitch's trajectory.
Advantages: Crucial for pitch design and evaluating deception.
Limitations: Does not account for pitch location or release point.
Real-Life Examples: High-movement pitches often generate weak contact or swings and misses.
Visualization Methods: 3D visualizations of pitch movement trajectories.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Time between pitch releases.
Applicable Roles: Pitchers
Formula: Measured in seconds
Use Cases: Analyzing a pitcher's pace and rhythm.
Advantages: Can impact batter timing and defensive readiness.
Limitations: Does not account for game situations or external delays.
Real-Life Examples: Pitchers with faster tempos often maintain better defensive alignment.
Visualization Methods: Timelines showing pitch tempos across games or innings.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
A successful throw to a base to get a baserunner out.
Applicable Roles: Pitchers
Formula: Count of successful pickoffs.
Use Cases: Highlighting pitchers' control of the running game.
Advantages: Simple, game-impacting stat.
Limitations: Rare and situational.
Real-Life Examples: A pitcher with a high number of pickoffs in a season.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A defensive action that completes an out.
Applicable Roles: Fielders
Use Cases: Measuring defensive involvement.
Advantages: Reflects positioning skill.
Limitations: Ignores defensive range.
Real-Life Examples: Fielders with the most putouts.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Percentage of batted balls resulting in infield fly balls.
Applicable Roles: Pitchers, Batters
Formula: (Pop-ups / Total Batted Balls) * 100
Use Cases: Measuring a batter's tendency to produce weak contact or a pitcher's ability to induce it.
Advantages: Indicates poor contact quality for hitters or strong contact management for pitchers.
Limitations: Does not account for fielding or outcomes beyond weak contact.
Real-Life Examples: A high PO% might be around 10-15% for pitchers.
Visualization Methods: Bar charts comparing PO% across players or teams.
Creator: Traditional Statistic
Sources: FanGraphs
Year Introduced: Not Widely Established
Formula Complexity: Low
The time it takes for a catcher to deliver the ball to second base during a stolen base attempt.
Applicable Roles: Catchers
Formula: Time from glove to throw completion at second base
Use Cases: Evaluating catcher quickness and throwing efficiency.
Advantages: Useful for assessing a catcher's ability to prevent stolen bases.
Limitations: Does not account for pitch type or pitcher's delivery time.
Real-Life Examples: A pop time under 1.9 seconds is considered elite.
Visualization Methods: Scatter plots comparing pop times across catchers.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Shows the percentage of batted balls hit to the pull side.
Applicable Roles: Batters
Formula: Pulled Balls ÷ Total Batted Balls
Use Cases: Evaluating a hitter’s tendency to pull the ball
Advantages: Identifies pull hitters
Limitations: Doesn't differentiate productive or unproductive pull tendencies
Real-Life Examples: Identifying pull hitters
Visualization Methods: Spray charts, bar graphs
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Percentage of two-strike pitches resulting in a strikeout.
Applicable Roles: Pitchers
Formula: (Number of Two-Strike Strikeouts / Total Two-Strike Pitches) × 100
Use Cases: Evaluating a pitcher's ability to finish off hitters with two strikes.
Advantages: Highlights the effectiveness of a pitcher's strikeout pitches.
Limitations: Does not account for pitch count or context of the strikeout.
Real-Life Examples: Pitchers with high putaway percentages often excel in strikeouts.
Visualization Methods: Bar charts showing putaway percentages by pitch type.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
How fast a pitch appears to a batter based on release point and velocity.
Applicable Roles: Pitchers
Formula: Calculated based on release extension and actual velocity
Use Cases: Evaluating a pitch's effectiveness from a batter's perspective.
Advantages: Useful for understanding how a pitch plays above its velocity.
Limitations: Does not consider spin or movement.
Real-Life Examples: Pitches with high perceived velocity often generate more swings and misses.
Visualization Methods: Graphs showing perceived velocity compared to actual velocity for various pitches.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
A formula predicting a team's winning percentage based on runs scored and runs allowed.
Applicable Roles: Teams
Formula: (Runs Scored^2) / (Runs Scored^2 + Runs Allowed^2)
Use Cases: Evaluating how well a team is performing relative to its run differential.
Advantages: Provides an objective measure of team performance.
Limitations: Does not account for situational hitting, bullpen usage, or luck.
Real-Life Examples: A team with a Pythagorean Winning Percentage above .600 is considered strong.
Visualization Methods: Scatter plots comparing actual vs. expected winning percentage.
Creator: Bill James
Sources: Baseball-Reference
Year Introduced: 1980s
Formula Complexity: Low
A start in which a pitcher completes at least six innings and allows three or fewer earned runs.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Evaluating starting pitcher consistency.
Advantages: Simple, clear benchmark.
Limitations: Doesn't account for situational context.
Real-Life Examples: A starter with 20+ quality starts in a season.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1985
Formula Complexity: Low
A player scores by safely crossing home plate.
Applicable Roles: Batters, Teams
Use Cases: Measuring offensive production.
Advantages: Simple and universal.
Limitations: Does not account for individual contribution.
Real-Life Examples: Total runs scored by a team in a season.
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Average number of runs allowed by a pitcher per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Total Runs Allowed / Innings Pitched) * 9
Use Cases: Measuring overall pitching performance in preventing runs.
Advantages: A simpler metric compared to ERA but includes unearned runs.
Limitations: Does not separate earned from unearned runs or account for defensive factors.
Real-Life Examples: A solid RA9 might be around 3.50 for a strong pitcher.
Visualization Methods: Scatter plots comparing RA9 across teams or seasons.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
Evaluates a player's contribution to run scoring above average.
Applicable Roles: Fielders
Formula: Runs Above Average (RAA) = Runs Produced - Average Runs
Use Cases: Run contribution above average for players
Advantages: Measures total runs above average for a player
Limitations: Focused, but doesn't account for league differences
Real-Life Examples: Quantifying offensive runs above average
Visualization Methods: Line graphs, scatter plots
Creator: Baseball Prospectus
Sources: Baseball Prospectus
Year Introduced: Unknown
Formula Complexity: Medium
A set of criteria defining eligibility for rate stats like ERA or OBP.
Applicable Roles: Batters, Pitchers
Use Cases: Ensuring statistical fairness.
Advantages: Standardizes comparisons.
Limitations: Can exclude players with fewer appearances.
Real-Life Examples: Minimum PAs to qualify for batting title.
Visualization Methods: Histograms, bar graphs
Creator: MLB
Sources: MLB
Year Introduced: Mid-1900s
Formula Complexity: Low
Number of runs scored due to a batter's at-bat.
Applicable Roles: Batters
Use Cases: Evaluating clutch hitting ability.
Advantages: Measures offensive contribution.
Limitations: Influenced by teammates’ performance.
Real-Life Examples: RBIs by cleanup hitters in a lineup.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A statistic estimating a player's total contribution to their team’s run production.
Applicable Roles: Batters
Formula: ((Hits + Walks) * Total Bases) / (At-Bats + Walks)
Use Cases: Measuring a batter’s offensive contribution.
Advantages: Simple and widely understood; evaluates offensive value holistically.
Limitations: Ignores context, such as the quality of opposing pitching or defense.
Real-Life Examples: Babe Ruth’s legendary offensive seasons showcase high RC values.
Visualization Methods: Bar charts comparing RC across players or teams in a season.
Creator: Bill James
Sources: Baseball-Reference, Fangraphs
Year Introduced: 1979
Formula Complexity: Moderate
Estimates run creation based on base-out situations.
Applicable Roles: Pitchers, Batters
Formula: Expected runs for each base/out situation before and after an event.
Use Cases: Context-neutral evaluation of offensive contribution
Advantages: Contextualizes contributions based on base-out state
Limitations: Situational, not park or league adjusted
Real-Life Examples: Evaluating a player’s situational hitting
Visualization Methods: Heatmaps, bar graphs
Creator: Traditional Statistic
Sources: FanGraphs
Year Introduced: 2005
Formula Complexity: High
A defensive statistic measuring the number of plays a fielder participates in per game.
Applicable Roles: Fielders
Formula: (Innings Played × (Putouts + Assists)) / Defensive Games
Use Cases: Comparing defensive effectiveness, identifying players who make more plays than average for their position.
Advantages: Simple and easy to calculate; highlights defensive activity levels.
Limitations: Does not account for defensive positioning, quality of opponents, or ballpark effects.
Real-Life Examples: Ozzie Smith's consistently high RF values as a shortstop.
Visualization Methods: Bar graphs comparing RF among players at the same position.
Creator: Bill James
Sources: MLB game logs, Baseball-Reference
Year Introduced: 1980
Formula Complexity: Low
A batter reaches base due to a defensive error.
Applicable Roles: Batters
Use Cases: Evaluating defensive mistakes.
Advantages: Highlights fielding errors.
Limitations: Situational and rare.
Real-Life Examples: Comparing ROE trends among teams.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Average number of runs scored by a pitcher's team per nine innings pitched.
Applicable Roles: Pitchers
Formula: (Total Runs Scored While Pitcher Was Active / Innings Pitched) * 9
Use Cases: Measuring the offensive support a pitcher receives during their starts.
Advantages: Helps evaluate team contributions to a pitcher’s win potential.
Limitations: Does not measure pitching quality or defensive performance.
Real-Life Examples: A pitcher with strong team support might have RS/9 above 5.
Visualization Methods: Line graphs showing RS/9 trends for pitchers over a season.
Creator: Traditional Statistic
Sources: MLB Official Stats
Year Introduced: Not Widely Established
Formula Complexity: Low
A win awarded to a reliever who enters the game in a non-starting role.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Highlighting relievers' contributions to team wins.
Advantages: Simple, easy to track.
Limitations: Context-dependent, team-based stat.
Real-Life Examples: A reliever earning 10+ wins in a season.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
When a runner advances a base without a hit.
Applicable Roles: Runners
Use Cases: Evaluating baserunning ability.
Advantages: Highlights speed and skill.
Limitations: Does not account for opportunities.
Real-Life Examples: Total stolen bases in a player’s career.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
The percentage of successful stolen base attempts.
Applicable Roles: Runners
Formula: SB ÷ (SB + CS)
Use Cases: Measuring efficiency of stolen bases.
Advantages: Reflects baserunning skill.
Limitations: Ignores other baserunning metrics.
Real-Life Examples: High SB% among elite base stealers.
Visualization Methods: Bar graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A fly ball out that allows a runner to score.
Applicable Roles: Batters
Use Cases: Evaluating situational hitting.
Advantages: Highlights team-oriented plays.
Limitations: Rare and situational.
Real-Life Examples: Successful sacrifice flies in a season.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A bunt intended to advance runners and sacrifice the batter.
Applicable Roles: Batters
Use Cases: Evaluating situational hitting.
Advantages: Highlights team-oriented plays.
Limitations: Context-dependent and rare.
Real-Life Examples: Successful sacrifice bunts in a season.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
The defensive alignment adjustments made based on batter tendencies.
Applicable Roles: Fielders
Formula: Based on player positioning and ball-in-play data
Use Cases: Assessing team strategy and its impact on defensive outcomes.
Advantages: Quantifies the effectiveness of defensive positioning.
Limitations: Does not account for unpredictable batter behavior or errors.
Real-Life Examples: A shift success rate above 70% indicates strategic efficiency.
Visualization Methods: Heatmaps showing shifted vs. standard positioning.
Creator: Statcast
Sources: Fangraphs, Statcast
Year Introduced: 2015
Formula Complexity: Low
A complete game in which the pitcher allows no runs.
Applicable Roles: Pitchers
Formula: Count of complete games with zero runs allowed.
Use Cases: Evaluating dominant pitching performances.
Advantages: Reflects single-game dominance.
Limitations: Rare, requires strong defensive support.
Real-Life Examples: A pitcher throwing 5 shutouts in a season.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
An advanced pitching metric estimating run prevention while accounting for defense and park factors.
Applicable Roles: Pitchers
Formula: Complex regression model based on strikeouts, walks, and ground balls.
Use Cases: Evaluating a pitcher’s skill-based effectiveness in preventing runs.
Advantages: Accounts for defense-independent performance better than ERA or FIP.
Limitations: Requires advanced data and modeling; complex to calculate manually.
Real-Life Examples: A SIERA around 3.00 indicates elite performance.
Visualization Methods: Heatmaps or comparative charts for SIERA values among pitchers.
Creator: TangoTiger, Matt Swartz
Sources: FanGraphs
Year Introduced: 2011
Formula Complexity: High
Calculates a player's power by weighting different hit types (e.g., singles, doubles, etc.).
Applicable Roles: Batters
Formula: (1B + 2 × 2B + 3 × 3B + 4 × HR) ÷ AB
Use Cases: Measuring a player’s power contribution through extra-base hits
Advantages: Reflects power hitting
Limitations: Overvalues extra-base hits
Real-Life Examples: Measuring a player's power-hitting ability
Visualization Methods: Line graphs, bar graphs
Creator: Traditional Statistic
Sources: Baseball-Reference, FanGraphs
Year Introduced: Early 1900s
Formula Complexity: Low
Batter Excellent: .550 and above
Batter Great: .500 – .549
Batter Above Avg: .450 – .499
Batter Avg: .400 – .449
Batter Below Avg: .350 – .399
Batter Poor: .300 – .349
Batter Awful: .299 and below
Pitcher Excellent: .550 and above
Pitcher Great: .500 – .549
Pitcher Above Avg: .450 – .499
Pitcher Avg: .400 – .449
Pitcher Below Avg: .350 – .399
Pitcher Poor: .300 – .349
Pitcher Awful: .299 and below
Occurs when a pitcher gets three strikes on a batter.
Applicable Roles: Pitchers, Batters
Formula: Count of strikeouts.
Use Cases: Measuring pitchers' dominance or hitters' struggles.
Advantages: Simple, widely tracked.
Limitations: Doesn't account for pitch quality.
Real-Life Examples: A pitcher striking out 300 batters in a season.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Evaluates a player's raw speed and baserunning contributions.
Applicable Roles: Batters, Base Runners
Formula: Combines stolen base data, triples, and speed score components.
Use Cases: Evaluating player speed and baserunning ability
Advantages: Quantifies base running ability
Limitations: Ignores situational baserunning context
Real-Life Examples: Evaluating baserunning ability
Visualization Methods: Time-series graphs, scatter plots
Creator: Bill James
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Medium
Below
Applicable Roles: Pitchers
Formula: Measured using advanced tracking tools
Use Cases: Used to evaluate pitch effectiveness, especially breaking pitches.
Advantages: Provides insights into pitch movement and deception.
Limitations: Doesn't account for factors like arm angle, release point, or environmental conditions.
Real-Life Examples: High-spin fastballs often appear to "rise" to hitters; low-spin fastballs drop more dramatically.
Visualization Methods: RPM distribution charts; comparisons by pitch type
Creator: TrackMan
Sources: MLB Advanced Media; Baseball Savant; Statcast
Year Introduced: 2015
Formula Complexity: Low
Percentage of batted balls hit with an ideal combination of exit velocity and launch angle.
Applicable Roles: Batters
Formula: Calculated based on Statcast data
Use Cases: Identifying hitters with consistent hard and optimal contact.
Advantages: Helps highlight players with elite bat-to-ball skills.
Limitations: Does not account for situational hitting or pitch quality.
Real-Life Examples: A squared-up rate above 10% is considered strong.
Visualization Methods: Scatter plots showing squared-up balls by players.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Feet per second in a runner’s fastest one-second window.
Applicable Roles: Runners
Formula: Measured in feet per second
Use Cases: Measuring a player's peak running speed.
Advantages: Useful for evaluating baserunning and defensive range.
Limitations: Does not account for reaction time or route efficiency.
Real-Life Examples: A sprint speed above 30 ft/s is considered elite.
Visualization Methods: Line graphs tracking trends in sprint speed over time.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Measures the difference between a player's market value and their actual cost.
Applicable Roles: Front Office
Formula: Player Value - Salary
Use Cases: Evaluating trades, free-agent signings, budget allocation
Advantages: Highlights cost-efficiency; aids in roster planning
Limitations: Relies on accurate player valuation methods and salary projections
Real-Life Examples: Calculating the value of a young player on a cost-controlled contract compared to a free agent
Visualization Methods: Bar graphs, scatter plots, tables
Creator: Fangraphs
Sources: Fangraphs, MLB front offices
Year Introduced: 2010
Formula Complexity: Medium
Awarded to a pitcher who preserves a win while meeting specific conditions.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Evaluating closers' effectiveness.
Advantages: Highlights impact of late-game pitching.
Limitations: Context-dependent, team-based stat.
Real-Life Examples: A closer earning 40 saves in a season.
Visualization Methods: Line graphs, bar charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1969
Formula Complexity: Low
The percentage of save opportunities converted into saves.
Applicable Roles: Pitchers
Formula: (Saves ÷ Save Opportunities) × 100
Use Cases: Evaluating efficiency of closers.
Advantages: Highlights consistent late-game pitching.
Limitations: Doesn't measure quality of saves.
Real-Life Examples: A closer converting 90% of save opportunities.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1969
Formula Complexity: Low
Tracks every instance in which a pitcher has a chance to earn a save.
Applicable Roles: Pitchers
Formula: Count of save opportunities.
Use Cases: Measuring save chances for closers.
Advantages: Reflects opportunities provided by team.
Limitations: Context-dependent, doesn't measure performance.
Real-Life Examples: Analyzing closers with high save opportunity rates.
Visualization Methods: Scatter plots, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1969
Formula Complexity: Low
Percentage of batted balls with a launch angle between 8-32 degrees.
Applicable Roles: Batters
Formula: Measured directly by Statcast data
Use Cases: Assessing a hitter's ability to hit balls in the optimal range for power and average.
Advantages: Correlates well with offensive production.
Limitations: Does not reflect pitch quality or defensive positioning.
Real-Life Examples: A sweet spot percentage above 35% is considered elite.
Visualization Methods: Bar charts comparing sweet spot percentages across hitters.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Moderate
Distance covered by the bat head during the swing.
Applicable Roles: Batters
Formula: Measured in feet or inches
Use Cases: Analyzing swing mechanics and efficiency.
Advantages: Helps evaluate swing tendencies and potential contact quality.
Limitations: Does not account for pitch speed or location.
Real-Life Examples: Shorter swing lengths are often associated with higher contact rates.
Visualization Methods: Visualization of swing paths overlaid with pitch trajectories.
Creator: Biomechanics Labs
Sources: Statcast
Year Introduced: 2015
Formula Complexity: High
A term for swings and misses that result in a batter being badly fooled.
Applicable Roles: Pitchers
Formula: No formal formula; a qualitative measure
Use Cases: Evaluating pitch deception and effectiveness.
Advantages: Highlights the dominance of certain pitches or sequences.
Limitations: Subjective and not formally quantified.
Real-Life Examples: Used as an anecdotal measure of pitch dominance.
Visualization Methods: Video highlights showing swings and misses labeled as "swords."
Creator: Informal
Year Introduced: Informal
Formula Complexity: Low
Indicates how often a pitcher generates swinging strikes.
Applicable Roles: Pitchers
Formula: Swinging Strikes ÷ Total Pitches
Use Cases: Evaluating how often pitchers generate swinging strikes
Advantages: Highlights swing-and-miss frequency
Limitations: Doesn't show count or situation context
Real-Life Examples: Analyzing pitchers’ ability to induce swings and misses
Visualization Methods: Scatter plots, line graphs
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Total number of bases achieved from hits.
Applicable Roles: Batters
Formula: 1B + (2 × 2B) + (3 × 3B) + (4 × HR)
Use Cases: Evaluating offensive contribution.
Advantages: Simple and comprehensive.
Limitations: Ignores walks and HBP.
Real-Life Examples: Total bases in a slugger’s season.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Total number of defensive opportunities.
Applicable Roles: Fielders
Formula: PO + A + E
Use Cases: Evaluating defensive workload.
Advantages: Comprehensive defensive metric.
Limitations: Does not reflect difficulty.
Real-Life Examples: Fielders with the most total chances.
Visualization Methods: Scatter plots, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A metric estimating earned runs while accounting for batted ball types and outcomes.
Applicable Roles: Pitchers
Formula: Calculated using batted ball data (line drives, fly balls, ground balls).
Use Cases: Measuring pitching performance based on batted ball profiles.
Advantages: Accounts for defense-independent factors, providing a realistic ERA estimate.
Limitations: Requires advanced data and is not as widely available as FIP or xERA.
Real-Life Examples: A tERA near 3.50 suggests above-average pitching.
Visualization Methods: Comparative charts for tERA vs. ERA across pitchers.
Creator: StatCorner
Sources: StatCorner
Year Introduced: 2009
Formula Complexity: High
Quantifies the run value added or lost by a fielder's throws.
Applicable Roles: Fielders
Formula: Calculated using the impact of throws on run expectancy
Use Cases: Evaluating the quality and impact of defensive throws.
Advantages: Provides an objective measure of throwing effectiveness.
Limitations: Does not account for fielder positioning or external factors.
Real-Life Examples: A player with a throwing value above +5 is considered above average.
Visualization Methods: Charts comparing throwing value across fielders or positions.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: High
A defensive play resulting in three outs.
Applicable Roles: Fielders
Use Cases: Highlighting rare defensive feats.
Advantages: Extremely rare.
Limitations: Limited to a single play.
Real-Life Examples: Teams achieving triple plays in a season.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A run scored due to an error or passed ball.
Applicable Roles: Pitchers
Formula: Count of runs scored due to defensive errors.
Use Cases: Highlighting defensive impact on pitchers' stats.
Advantages: Separates pitching and defensive performance.
Limitations: Situational, depends on official scoring.
Real-Life Examples: A pitcher allowing multiple unearned runs in a game.
Visualization Methods: Pie charts, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures a player's ability to generate hits in high-value situations.
Applicable Roles: Fielders
Formula: Computed using hit location and defensive play data.
Use Cases: Defensive performance evaluation for fielders
Advantages: Measures defensive value
Limitations: Requires multiple years for reliability
Real-Life Examples: Comparing defensive value of outfielders
Visualization Methods: Zone charts, scatter plots
Creator: Mitchel Lichtman
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: High
Speed of a pitch as it crosses home plate.
Applicable Roles: Pitchers
Formula: Measured in miles per hour (MPH)
Use Cases: Evaluating the raw speed of pitches.
Advantages: Crucial for determining pitch effectiveness, especially fastballs.
Limitations: Does not account for movement or deception.
Real-Life Examples: Pitches with high velocity often generate late swings or misses.
Visualization Methods: Line graphs comparing velocity trends over a season.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: Low
Awarded to a pitcher on the winning team who meets certain conditions.
Applicable Roles: Pitchers
Formula: Defined by MLB rules.
Use Cases: Evaluating pitcher contributions to team success.
Advantages: Simple, part of traditional stats.
Limitations: Context-dependent, team-based stat.
Real-Life Examples: A pitcher earning 20+ wins in a season.
Visualization Methods: Line graphs, event markers
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A comprehensive stat evaluating overall player contribution to their team’s success, combining batting, baserunning, and fielding.
Applicable Roles: Pitchers, Batters, Fielders
Formula: Varies by provider; combines offensive, defensive, and pitching contributions.
Use Cases: Overall player value comparison, free agent market evaluation, MVP debates
Advantages: Comprehensive measure of player value
Limitations: Complex calculation, different methodologies across sources
Real-Life Examples: Comparing Mike Trout's all-around value to other players
Visualization Methods: Bar graphs, scatter plots
Creator: Baseball-Reference, FanGraphs
Sources: FanGraphs, Baseball-Reference
Year Introduced: 2008
Formula Complexity: High
The probability of a team winning at any point in a game based on the current game state.
Applicable Roles: Teams, Players
Formula: Calculated using historical data for similar situations
Use Cases: Analyzing in-game decisions and player contributions to wins.
Advantages: Provides context to plays, especially in high-leverage situations.
Limitations: Depends heavily on accurate historical data and game state modeling.
Real-Life Examples: A Win Expectancy shift of 20% indicates a significant play.
Visualization Methods: Line graphs showing Win Expectancy changes during a game.
Creator: Traditional Statistic
Sources: Baseball Savant, Fangraphs
Year Introduced: Not Widely Established
Formula Complexity: High
Measures the number of baserunners a pitcher allows per inning.
Applicable Roles: Pitchers
Formula: (Walks + Hits) ÷ Innings Pitched
Use Cases: Evaluating pitcher efficiency and control.
Advantages: Simple, easy to calculate.
Limitations: Doesn't account for defensive quality.
Real-Life Examples: A pitcher maintaining a WHIP under 1.00 for a season.
Visualization Methods: Line graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: 1979
Formula Complexity: Low
Batter Excellent: 1.00 and below
Batter Great: 1.01 – 1.10
Batter Above Avg: 1.11 – 1.20
Batter Avg: 1.21 – 1.30
Batter Below Avg: 1.31 – 1.40
Batter Poor: 1.41 – 1.50
Batter Awful: 1.51 and above
Pitcher Excellent: 1.00 and below
Pitcher Great: 1.01 – 1.10
Pitcher Above Avg: 1.11 – 1.20
Pitcher Avg: 1.21 – 1.30
Pitcher Below Avg: 1.31 – 1.40
Pitcher Poor: 1.41 – 1.50
Pitcher Awful: 1.51 and above
A hit or event that ends a game with a win for the home team.
Applicable Roles: Batters, Teams
Use Cases: Measuring clutch performance.
Advantages: Highlights dramatic game endings.
Limitations: Rare and situational.
Real-Life Examples: Walk-off home runs in postseason play.
Visualization Methods: Line graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Measures overall offensive productivity per plate appearance using linear weights.
Applicable Roles: Batters
Formula: ((0.69 × uBB) + (0.72 × HBP) + (0.88 × 1B) + (1.24 × 2B) + (1.56 × 3B) + (2.01 × HR)) ÷ PA
Use Cases: Offensive player evaluation, understanding overall hitting contribution
Advantages: Weighted for run value of events
Limitations: Ignores baserunning and defensive value
Real-Life Examples: Evaluating a player’s offensive production compared to league average
Visualization Methods: Heatmaps, scatter plots
Creator: Tom Tango
Sources: FanGraphs
Year Introduced: 2007
Formula Complexity: Medium
A pitch that the catcher cannot catch, allowing baserunners to advance.
Applicable Roles: Pitchers
Formula: Count of wild pitches.
Use Cases: Highlighting control issues in pitchers.
Advantages: Reflects pitchers' command.
Limitations: Context-dependent, relies on catcher’s framing.
Real-Life Examples: A pitcher with high wild pitch totals in a season.
Visualization Methods: Scatter plots, bar graphs
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A stat measuring how much a player contributes to their team's chances of winning, based on game context.
Applicable Roles: Batters, Pitchers
Formula: Change in win probability before and after a player's plate appearance or pitch.
Use Cases: Evaluating a player’s situational impact on games.
Advantages: Accounts for high-leverage situations; captures clutch performance.
Limitations: Can overvalue small sample sizes; situational and context-dependent.
Real-Life Examples: David Ortiz’s postseason heroics showcase high WPA values.
Visualization Methods: Win probability graphs highlighting key moments in games.
Creator: Tom Tango
Sources: Fangraphs, Baseball-Reference
Year Introduced: 2004
Formula Complexity: High
Measures the proportion of games won by a pitcher or team.
Applicable Roles: Pitchers, Teams
Formula: Wins ÷ (Wins + Losses)
Use Cases: Evaluating team or pitcher performance.
Advantages: Simple, widely understood.
Limitations: Context-dependent, doesn't measure quality.
Real-Life Examples: A team maintaining a winning percentage above .600.
Visualization Methods: Line graphs, pie charts
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
A measure of a batter’s offensive value relative to league average.
Applicable Roles: Batters
Formula: ((wOBA - League wOBA) / wOBA Scale) * Plate Appearances
Use Cases: Measuring offensive contribution compared to the league average.
Advantages: Accounts for league and park effects; precise and widely used.
Limitations: Requires advanced metrics like wOBA; less intuitive than traditional stats.
Real-Life Examples: Mike Trout consistently ranks highly in wRAA across seasons.
Visualization Methods: Line charts comparing wRAA across players or tracking it over time.
Creator: TangoTiger
Sources: Fangraphs, Baseball-Reference
Year Introduced: 2002
Formula Complexity: High
A context-neutral offensive statistic scaled to league average of 100.
Applicable Roles: Batters
Formula: (wRAA + (league R/PA) × PA) ÷ (league R/PA × PA) × 100
Use Cases: Weighted runs created, adjusted for league and park factors
Advantages: Park and league adjusted hitting value
Limitations: Doesn't include baserunning or defense
Real-Life Examples: Comparing offensive contributions across leagues
Visualization Methods: Bar graphs, heatmaps
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: 2010
Formula Complexity: Medium
Expected batting average based on quality of contact and outcomes.
Applicable Roles: Batters
Formula: Calculated using exit velocity, launch angle, and on-field data
Use Cases: Evaluating how well a hitter should perform based on contact quality.
Advantages: Accounts for quality of contact, independent of defense.
Limitations: Does not consider situational hitting or baserunning.
Real-Life Examples: An xBA above .300 is elite.
Visualization Methods: Heatmaps or scatter plots comparing xBA to actual BA.
Creator: Statcast
Sources: Statcast
Year Introduced: 2015
Formula Complexity: High
Any hit that is a double, triple, or home run.
Applicable Roles: Batters
Formula: 2B + 3B + HR
Use Cases: Evaluating power-hitting ability.
Advantages: Highlights offensive strength.
Limitations: Misses context, like RBIs.
Real-Life Examples: Players with many extra-base hits.
Visualization Methods: Bar graphs, scatter plots
Creator: Traditional Statistic
Sources: MLB
Year Introduced: Early 1900s
Formula Complexity: Low
Predicts future ERA based on a pitcher's strikeout, walk, and home run rates.
Applicable Roles: Pitchers
Formula: Modeled using batted ball quality and strikeout/walk data.
Use Cases: Expected ERA based on Statcast data
Advantages: Accounts for quality of contact
Limitations: Predictive but not descriptive
Real-Life Examples: Identifying overperforming or underperforming pitchers
Visualization Methods: Line graphs, scatter plots
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2020
Formula Complexity: High
A predictive stat measuring a pitcher's performance based on strikeouts, walks, and expected home run rate.
Applicable Roles: Pitchers
Formula: FIP formula, replacing home runs with league-average HR/FB rate.
Use Cases: Evaluating and predicting future pitcher performance independent of defense.
Advantages: Provides better predictive value than FIP; accounts for luck in home runs allowed.
Limitations: Still relies on assumptions about league-average HR/FB rates.
Real-Life Examples: Pitchers with low xFIP often sustain high performance over time.
Visualization Methods: Scatter plots comparing xFIP with ERA or FIP.
Creator: Dave Studenmund
Sources: Fangraphs
Year Introduced: 2006
Formula Complexity: Moderate
Predicts offensive performance using batted ball quality and plate discipline.
Applicable Roles: Batters
Formula: Based on expected outcomes from batted ball quality.
Use Cases: Expected slugging percentage from Statcast metrics
Advantages: Predictive, based on quality of contact
Limitations: Ignores baserunning
Real-Life Examples: Predicting future slugging percentages
Visualization Methods: Line graphs, heatmaps
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2020
Formula Complexity: Medium
Predicts batting outcomes using Statcast’s expected metrics.
Applicable Roles: Batters
Formula: Predicted using Statcast's expected batted ball data.
Use Cases: Expected offensive performance based on Statcast inputs
Advantages: Predictive, combines contact quality and discipline
Limitations: Ignores sequencing and baserunning
Real-Life Examples: Predicting expected offensive production
Visualization Methods: Heatmaps, bar graphs
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: 2015
Formula Complexity: High
Measures the expected weighted on-base average for balls in play, based on quality of contact (exit velocity and launch angle).
Applicable Roles: Batters, Pitchers
Formula: Expected value
Use Cases: Evaluating hitter and pitcher performance based on contact quality.
Advantages: Accounts for quality of contact; removes randomness from analysis.
Limitations: Does not include strikeouts or walks; context-specific.
Real-Life Examples: Highlighting hitters who excel at generating hard contact.
Visualization Methods: Heatmaps, scatter plots
Creator: Baseball Savant
Sources: Baseball Savant
Year Introduced: Unknown
Formula Complexity: High
Measures how often a batter swings at pitches inside the strike zone.
Applicable Roles: Batters
Formula: Swings Inside Zone ÷ Pitches Inside Zone
Use Cases: Measuring how often hitters swing at pitches in the strike zone
Advantages: Reflects aggression on strikes
Limitations: Doesn't show quality of contact
Real-Life Examples: Analyzing hitters’ aggression on strikes
Visualization Methods: Zone charts, bar graphs
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low
Indicates how often a pitcher throws pitches within the strike zone.
Applicable Roles: Pitchers, Batters
Formula: Pitches Inside Strike Zone ÷ Total Pitches
Use Cases: Evaluating pitchers’ ability to throw strikes
Advantages: Simple, shows pitch location frequency
Limitations: Contextless, doesn't show results
Real-Life Examples: Evaluating pitchers’ ability to throw strikes
Visualization Methods: Scatter plots, heatmaps
Creator: FanGraphs
Sources: FanGraphs
Year Introduced: Early 2000s
Formula Complexity: Low