Editor’s note: The depth and breadth of the concepts explored in this post required a longer post than normal.
For generations, children and adults would flock to their local card shop to buy packs of baseball cards, which would have a wealth of information about a player’s performances with statistics like pitcher wins and losses and earned run average (ERA). For instance, in Bob Gibson’s 1968 National League most valuable player (MVP) season he amassed a 22-9 record with a 1.12 ERA. Pretty impressive, right? It was and still is an amazing stat line over the course of a full season, but for a select number of baseball fanatics traditional baseball statistics did not tell the whole story, rather, those statistics were just the tip of the iceberg.
In 1974, baseball analyst Bill James, Pete Palmer and Dick Cramer co-founded the Statistical Analysis Research Committee for the Society for American Baseball Research (SABR). James and other colleagues began amassing a deep baseball statistical database, where they tracked data and baseball outcomes that had never been tracked before in “the search for objective knowledge about baseball,” as described by James in 1980, paying homage to SABR. This movement in mathematically and statistically-driven baseball research analysis was known as sabermetrics. Although the ideas of sabermetrics had been around long before James, he was the first to put it all together and bring to the masses in his 1982 book, Bill James Baseball Abstract. The book started out, “If you sometimes get the feeling between here and the back cover that you are coming in on the middle of a discussion, it is because you are.” In the years that followed, James and his crowd-sourced network of statistical enthusiasts kept expanding and plotting data and baseball outcomes, which encompassed much more than the statistics found in a boxsore or a scorecard. They wanted to get more exact, precise and more complex with their statistical analysis, so they charted where baseballs were being hit.
Despite the growing popularity of sabermetrics, it was being ignored by the mainstream baseball community, as they were playing the game same way they always had, listening and adhering to gut feelings, rather than objective, statistical analysis. It is important to understand sabermetrics and advanced baseball analytics is as much about statistics, as it is about trends and strategy, both in-game and baseball operations (player scouting and development).
These statistical trends were not consciously applied to strategy and decision-making until Cleveland Indians’ assistant general manager Mark Shapiro asked for a statistical database that could rank rival clubs’ top players and prospects, in terms of contracts, performance and trends. Therefore, DiamondView was created in 2000, according to Cleveland Plain Dealer. The Indians most notably used DiamondView when slugger and fan favorite Jim Thome wanted a new contract following the 2002 season. Using the software, the Indians projected Thome was not going to keep up his production over the long term to warrant a big contract. When the Indians made Thome a “generous, long-term contract” offer, Thome rejected it, opting instead to sign a six-year, $85 million contract with the Philadelphia Phillies.
In 2003, Michael Lewis published Moneyball, a book chronicling the unfathomable success of one of baseball smallest market teams, the Oakland Athletics, during their playoff years of 2002 and 2003. Desperate for resources and a way to outsmart the bigger market teams, the book centers around general manager Billy Beane and the divided front office of how the team started to value on-base percentage and slugging percentage as more accurate indicators of production than traditional baseball statistics.
After Moneyball, sabermetrics and advanced baseball analytics took off and became over time a valuable integration of every MLB front office in the quest to find more undervalued statistics to flip the playing field and increase the statistical probabilities of outcomes.
Marred in a stretch of 20 consecutive losing seasons, the Pittsburgh Pirates turned to advanced baseball analytics such as defensive runs saved, pitch framing (the percentage a catcher can frame a ball to be called a strike) and fielding-independent pitching (FIP) (explanations of these and other baseball analytics can be found here) to maximize the value offseason transactions, player scouting and development, given one of the lowest payrolls and unattractive destinations in all of baseball.
In 2015, MLB unveiled Statcast in all thirty of its ballparks. Casella (2015) writes, “Statcast, a state-of-the-art tracking technology, is capable of gathering and displaying previously immeasurable aspects of the game.” Now with Statcast and its high-resolution optical cameras and radar equipment, aspects like spin rate, exit velocity and first step are all now being tracked. As we figure out which variables are important to maximize production, we must ask ourselves, “what do sabermetrics and baseball analytics want? What is the endgame?”
According to Kelly (2010), “Extrapolated, technology wants what life wants: Increasing efficiency,[i]ncreasing opportunity,[i]ncreasing emergence,[i]ncreasing complexity,[i]ncreasing diversity,[i]ncreasing specialization,[i]ncreasing ubiquity,[i]ncreasing freedom,[i]ncreasing mutualism,[i]ncreasing beauty,[i]ncreasing sentience,[i]ncreasing structure [and] [i]ncreasing evolvability” (p. 270).
Similarly, sabermetrics, including the statistical drive them, and baseball analytics have the same wants and desires as life or technology. Through the development of sabermetrics and baseball analytics we can understand how with each era in sabermetrics and baseball analytics, baseball’s desires and wants have grown in the areas Kelly (2010) described.
In the modern game of baseball, big data is rampant and the organizations that know how to understand, utilize and communicate these analytics and statistical trends in a relatable way to management and players reign supreme.
Sawchik (2015) explains starting in the 2012 offseason, the Pittsburgh Pirates began meshing their personnel, who were mostly baseball traditionalists, to their sabermetric thoughts and theories in the front office to increase defensive efficiency and opportunities for their maligned pitchers to get outs.
Starting in the 2013 campaign, the Pirates shifting their defense, in accordance to the specific opposing hitter’s spray chart (specialization) and even shifting different depending on the count (complexity and diversity). Even though by this point almost every front office was using baseball analytics in some fashion (emergence and ubiquity), the Pirates chose to focus on pitch framing, defensive shifts, the two-seam fastball and pitching inside to maximum their chances of success (freedom). It was not until the techniques yielded results that there was a buy-in from both traditionalist management and players (mutualism). Part of that success was due to minor league coaches embracing those techniques, so players coming up through the system could see the success of those techniques and get comfortable using them as they dynamically changing and evolving to account for new trends in the data (structure, sentience, beauty and evolvability). These sabermetric trends and advanced baseball analytics were significant, contributing factors to the Pirates bucking their 21-year losing season streak and payoff drought.
What trends in other industries reimagine operations and contrast the dominant narrative of a static, monolithic entity?