How to Become a Baseball Statistician
Build a strong foundation in statistical analysis., Develop a comprehensive understanding of baseball statistics., Apply statistical findings into data that clients will find useful., Explore avenues that can help you find a job as a baseball...
Step-by-Step Guide
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Step 1: Build a strong foundation in statistical analysis.
By pursuing a practical, university-based education, aspiring baseball statisticians can develop a thorough understanding of complex statistical concepts and applications.
Most degrees include intensive study of:
Probability and statistics.
Applied statistical methods.
Quantitative analysis.
Variance theory.
Time-series analysis.
Statistical computer analysis. -
Step 2: Develop a comprehensive understanding of baseball statistics.
Most baseball statisticians cultivated their expertise beginning at an early age.
Most understand how the game's statistics are compiled and how data is interrelated.
Some of the fundamental concepts in baseball statistics include:
Official scoring:
This is the cornerstone of all statistical analysis of the game.
Judgments made by the official scorer in each game are binding.
Certain decisions, like whether a batted ball is determined to be a base hit of a fielder's error, have an impact on the career numbers of the batter, fielder and pitcher involved in the play.
Simple statistics:
Conventional statistics form the basis of all statistical analysis in the game today.
Simple data, like at-bats, hits and strikeouts, are unambiguous.
But some of the fundamental statistics are interrelated and together form important starting points for further analysis.
For example, dividing the number of base hits into the number of official at-bats renders the player's batting average, one of the game's seminal figures.
Complex concepts:
Pioneers in the statistical analysis of baseball have compared and combined certain conventional data to form groundbreaking methods of analyzing players.
Sabermetrics concepts have slowly gained mainstream acceptance because of their powerful results.
A leading Sabermetrics principle is OPS, which combines a player's on-base and slugging percentages into a single figure.
This number purports to show the player's value to his team and has gained widespread acceptance as a potent statistic. , Experts who produce baseball statistics can tailor their findings to their audience.
Statisticians who work for media organizations and teams tend to present raw, in-depth findings.
But analysts who work for agents often generate nuanced figures that help their clients land big contracts for players.
In some cases, statisticians manipulate numbers or conjure statistics to highlight a player's value.
Some examples include:
Analyzing players of similar ages:
Statistician Marc Rubin helped the agent of Atlanta Braves star Chipper Jones land a big contract his client as he neared the end of his career.
Rubin analyzed Jones' productivity and compared it to hall-of-famer Dave Winfield, who signed a lucrative deal at the end of his career.
The analysis helped Jones ink a massive, 4-year extension.
Extrapolate performance outside tabulated data:
Rubin helped Dan Wheeler, a pitcher, earn a $2.15 million contract despite carrying a record of 1 win and 9 losses and an earned run average over
5.00.
By sifting through game data, Rubin discovered that Wheeler often entered games when his team was trailing.
Most of the time, he would maintain the deficit, giving his team a chance to win.
By expanding his conceptual thinking, Rubin uncovered a statistically significant abstraction that helped Wheeler during his salary-arbitration hearing. , There is no such thing as a standard curriculum to becoming a successful baseball statistician, but 2 pioneers of modern baseball statistics cut similar paths toward achieving notoriety in the field.
Devise a new method of baseball statistical analysis.
When Steve Mann developed the "run productivity average" in the 1970s, the formula's effectiveness in gauging player value was irrefutable.
Houston Astros general manager Tal Smith was so impressed by the power of the technique that he hired Mann as the team statistician in
1979.
Promote yourself.
Sabermetrics guru Bill James developed several potent statistical applications before he achieved mainstream success.
James tirelessly wrote about his techniques, eventually self-publishing the bulk of his work.
Mann got his start in Houston by self-marketing:
He wrote a letter to Smith.
You don't have to take such an expensive or brazen approach to promote your work.
Start a personal blog on baseball statistics.
Write about your statistical analysis and methods you've developed to effectively evaluate talent.
Approach minor-league teams.
Armed with an intriguing, singular analytical approach, contact a local minor-league affiliate and sell your technique.
The pay probably will be minimal, but you'll be gaining a profile inside a Major League organization that could lead to a role with the big league club. -
Step 3: Apply statistical findings into data that clients will find useful.
-
Step 4: Explore avenues that can help you find a job as a baseball statistician.
Detailed Guide
By pursuing a practical, university-based education, aspiring baseball statisticians can develop a thorough understanding of complex statistical concepts and applications.
Most degrees include intensive study of:
Probability and statistics.
Applied statistical methods.
Quantitative analysis.
Variance theory.
Time-series analysis.
Statistical computer analysis.
Most baseball statisticians cultivated their expertise beginning at an early age.
Most understand how the game's statistics are compiled and how data is interrelated.
Some of the fundamental concepts in baseball statistics include:
Official scoring:
This is the cornerstone of all statistical analysis of the game.
Judgments made by the official scorer in each game are binding.
Certain decisions, like whether a batted ball is determined to be a base hit of a fielder's error, have an impact on the career numbers of the batter, fielder and pitcher involved in the play.
Simple statistics:
Conventional statistics form the basis of all statistical analysis in the game today.
Simple data, like at-bats, hits and strikeouts, are unambiguous.
But some of the fundamental statistics are interrelated and together form important starting points for further analysis.
For example, dividing the number of base hits into the number of official at-bats renders the player's batting average, one of the game's seminal figures.
Complex concepts:
Pioneers in the statistical analysis of baseball have compared and combined certain conventional data to form groundbreaking methods of analyzing players.
Sabermetrics concepts have slowly gained mainstream acceptance because of their powerful results.
A leading Sabermetrics principle is OPS, which combines a player's on-base and slugging percentages into a single figure.
This number purports to show the player's value to his team and has gained widespread acceptance as a potent statistic. , Experts who produce baseball statistics can tailor their findings to their audience.
Statisticians who work for media organizations and teams tend to present raw, in-depth findings.
But analysts who work for agents often generate nuanced figures that help their clients land big contracts for players.
In some cases, statisticians manipulate numbers or conjure statistics to highlight a player's value.
Some examples include:
Analyzing players of similar ages:
Statistician Marc Rubin helped the agent of Atlanta Braves star Chipper Jones land a big contract his client as he neared the end of his career.
Rubin analyzed Jones' productivity and compared it to hall-of-famer Dave Winfield, who signed a lucrative deal at the end of his career.
The analysis helped Jones ink a massive, 4-year extension.
Extrapolate performance outside tabulated data:
Rubin helped Dan Wheeler, a pitcher, earn a $2.15 million contract despite carrying a record of 1 win and 9 losses and an earned run average over
5.00.
By sifting through game data, Rubin discovered that Wheeler often entered games when his team was trailing.
Most of the time, he would maintain the deficit, giving his team a chance to win.
By expanding his conceptual thinking, Rubin uncovered a statistically significant abstraction that helped Wheeler during his salary-arbitration hearing. , There is no such thing as a standard curriculum to becoming a successful baseball statistician, but 2 pioneers of modern baseball statistics cut similar paths toward achieving notoriety in the field.
Devise a new method of baseball statistical analysis.
When Steve Mann developed the "run productivity average" in the 1970s, the formula's effectiveness in gauging player value was irrefutable.
Houston Astros general manager Tal Smith was so impressed by the power of the technique that he hired Mann as the team statistician in
1979.
Promote yourself.
Sabermetrics guru Bill James developed several potent statistical applications before he achieved mainstream success.
James tirelessly wrote about his techniques, eventually self-publishing the bulk of his work.
Mann got his start in Houston by self-marketing:
He wrote a letter to Smith.
You don't have to take such an expensive or brazen approach to promote your work.
Start a personal blog on baseball statistics.
Write about your statistical analysis and methods you've developed to effectively evaluate talent.
Approach minor-league teams.
Armed with an intriguing, singular analytical approach, contact a local minor-league affiliate and sell your technique.
The pay probably will be minimal, but you'll be gaining a profile inside a Major League organization that could lead to a role with the big league club.
About the Author
Ryan Tucker
Specializes in breaking down complex crafts topics into simple steps.
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