MLB Team ERA vs OPS Chart

Calculation

ERA = (Earned Runs ÷ Innings Pitched) × 9

ERA measures a pitcher's effectiveness by calculating how many earned runs they allow per nine innings pitched. Only runs scored without the benefit of defensive errors are counted as "earned."

Historical Context

ERA was first developed and used in the early 1900s to provide a more accurate assessment of a pitcher's performance apart from their team's fielding abilities. Henry Chadwick, considered the "Father of Baseball," is often credited with developing many early baseball statistics including elements that evolved into ERA. The statistic gained prominence in the 1910s under American League President Ban Johnson and became official in 1912.

The "Dead Ball Era" (1900-1919) saw ERAs commonly below 2.50, while the modern MLB has seen average ERAs typically range from 3.80-4.50. Hall of Famer Ed Walsh holds the career record with a remarkable 1.82 ERA, while the single-season record belongs to Dutch Leonard's 0.96 ERA in 1914.

Calculation

OPS = On-Base Percentage + Slugging Percentage

Where:

• On-Base Percentage (OBP) = (Hits + Walks + Hit By Pitch) ÷ (At Bats + Walks + Hit By Pitch + Sacrifice Flies)
• Slugging Percentage (SLG) = Total Bases ÷ At Bats
• Total Bases = Singles + (Doubles × 2) + (Triples × 3) + (Home Runs × 4)

Historical Context

While its component statistics (OBP and SLG) have been tracked since baseball's early days, OPS as a combined metric emerged in the 1970s and gained mainstream popularity during the sabermetric revolution of the 1980s. Branch Rickey and Allan Roth pioneered OBP in the 1940s, recognizing the importance of reaching base beyond just hits.

Pete Palmer and John Thorn helped popularize OPS in their 1984 book "The Hidden Game of Baseball," and it became more widely adopted when the statistics were included on the backs of Topps baseball cards starting in 2004. Babe Ruth holds the career record at 1.164, while Barry Bonds' 1.422 mark in 2004 is the single-season record.

Calculation

PCT = Wins ÷ (Wins + Losses)

Winning Percentage (PCT) measures how often a team wins by dividing total wins by total games played. Despite being called a "percentage," it's traditionally expressed as a three-decimal number (e.g., .586 rather than 58.6%).

Historical Context

The three-decimal format for winning percentage became standardized in baseball during the early 20th century as sports statistics were formalized. This unique reporting method—displaying the decimal without multiplying by 100—allows for greater precision when comparing closely matched teams over a long season, enabling meaningful distinctions even when teams are separated by just a few games.

The highest single-season winning percentage belongs to the 1906 Chicago Cubs (.763), while the 1884 St. Louis Maroons of the Union Association achieved a remarkable .832 mark. The Cincinnati Red Stockings of 1869 posted baseball's only perfect record at 67-0, though this was before the modern Major League era.

Calculation

GB = [(Leader's Wins - Team's Wins) + (Team's Losses - Leader's Losses)] ÷ 2

Games Behind (GB) measures how far a team trails the division leader in the standings. The formula averages the differences in wins and losses between the two teams, providing a standardized measure of distance in the standings.

Historical Context

The GB statistic has been a standings mainstay since the early days of organized baseball, becoming a crucial tool during the development of league structures and pennant races in the late 19th and early 20th centuries. First-place teams are always listed with a dash (—) rather than zero to indicate their leading position.

GB can include half-games when teams have played different numbers of games, reflecting the reality of baseball's day-to-day schedule. While simple, GB has occasionally been criticized for not accounting for remaining schedules or the mathematical probability of catching leaders, leading to alternative metrics like "elimination number" or "magic number" that better quantify playoff chances.

Calculation

fWAR = (Batting Runs + Baserunning Runs + Fielding Runs + Positional Adjustment + League Adjustment + Replacement Runs) / Runs Per Win

fWAR is FanGraphs' implementation of the Wins Above Replacement metric. It combines a player's total offensive and defensive contributions, converting them into a single number that represents their value in team wins compared to a "replacement player" (a minor league call-up or freely available player).

Historical Context

The concept of WAR emerged in the 1970s and 1980s through the work of sabermetricians like Bill James and Pete Palmer, who sought to create all-encompassing player evaluation metrics. FanGraphs developed their specific implementation (fWAR) in the mid-2000s, incorporating more advanced defensive metrics and regularly updating the methodology.

WAR revolutionized player evaluation by providing a framework to compare players across positions and eras with a single number. It has gained mainstream acceptance over the past decade, being regularly cited during MLB broadcasts and in Hall of Fame discussions. Most famously, Mike Trout's historically high WAR totals early in his career helped cement his reputation as one of baseball's greatest players despite playing on less successful Angels teams.

Calculation

wRC+ = ((wRAA/PA + League R/PA) + (League R/PA - Park Factor * League R/PA))/League wRC/PA * 100

wRC+ measures a player's total offensive value by runs, adjusted for ballpark and league context. It's scaled so that 100 is always league average, making it easy to compare players across different parks, leagues, and eras.

• wRAA = Weighted Runs Above Average
• PA = Plate Appearances
• Park Factor = Adjustment for home ballpark effects

Historical Context

wRC+ evolved from Bill James' Runs Created statistic, which he developed in the 1970s. FanGraphs refined this into wRC+ in the 2000s, adding park and league adjustments to create a more contextual offensive metric. The "plus" indicates that it's indexed to league average (100).

wRC+ has become the preferred offensive metric among sabermetricians because it captures a player's complete offensive contribution in one number while accounting for the varying offensive environments across different ballparks and eras. For example, it helps demonstrate that a player hitting .280 in San Francisco's pitcher-friendly Oracle Park might be more impressive than someone hitting .300 in Colorado's hitter-friendly Coors Field.