How Analytics Became the Language of Modern Sports: MLB and the NHL
- Karinna Leonard
- 2 days ago
- 7 min read
Updated: 2 days ago
An integral part of modern sports culture is analytics. Whether through fantasy leagues, front offices, or casual fans catching up on a game, data now shapes how sports are discussed and understood. With MLB spring training underway and the NHL season entering its final stretch as playoff races intensify and end-of-season awards loom, this is a perfect moment to examine the evolution of analytics in sports.
Analytics in Major League Baseball
Baseball has long been a sport grounded in tradition and nostalgia, which made the early adoption of analytics a slow and sometimes controversial process. Yet over the past several decades, data-driven approaches have completely reshaped how teams evaluate players, construct rosters, and understand performance.
If you’ve seen Moneyball, you already have a sense of why analytics succeed in sports. While the film dramatizes certain aspects and downplays the fact that data-driven evaluation had already gained traction through sabermetrics, its central argument still rings true. Traditional scouting evaluations can overlook player value that careful analysis reveals, making analytics a key component of modern baseball success.
The Origins of Sabermetrics
The story begins with Bill James, who is widely regarded as the father of sabermetrics. Born in 1949 and raised in a small town in Kansas, James filled his time by combining a passion for baseball with an analytical mindset, poring over baseball box scores from newspapers. After graduating from the University of Kansas with a double-major in English and Economics, James eventually decided to abandon academia in 1974 to focus solely on baseball.
From there he joined the Society for American Baseball Research (SABR), and began submitting his own articles to Baseball Bulletin and Baseball Digest. While they initially published some of his work, SABR eventually started to reject his pieces as he dove deeper into baseball science. Undeterred, James launched his own annual Baseball Abstract in 1977, detailing innovative metrics such as Runs Created and the Pythagorean winning percentage, which quantified performance beyond traditional stats.
Over time, James's work gained mainstream recognition and people started to believe in his methods. In 2002, the Boston Red Sox hired him as "Senior Baseball Operations Advisor," and in 2006 the Oakland Athletics famously leveraged sabermetrics to build a competitive roster despite budget constraints—a story that inspired Moneyball.
From Experimentation to Industry Standard
From these beginnings, baseball analytics exploded. Modern front offices now incorporate advanced metrics across every aspect of player evaluation, including hitting, pitching, fielding, base running, and contextual measures like WAR (Wins Above Replacement).
High-tech tools like Statcast use thousands of high speed cameras in every ballpark to track nearly every moment on the field, capturing data ranging from exit velocity and launch angle to sprint speed. Baseball Savant, MLB's public analytics platform, allows fans to explore this data in detail, while sites like FanGraphs provide analytical context. Today, analysts and fans alike can examine everything from a pitcher's arm slot to the exact path of a batter's swing, offering unprecedented insight into how the game is played.
Analytics in the National Hockey League
The NHL presents a far more complicated story, as debate persists over when advanced analytics truly entered the league. This raises important questions: what does “advanced” actually mean? Is the beginning of the analytics era marked by the moment data was first collected, or by the point at which it began to influence league-wide decision making?
As a historically traditional sport, hockey was especially resistant to analytical approaches. Many within the hockey world expressed clear neophobia toward what they viewed as “hockey nerds” attempting to push the sport into a world governed by numbers rather than on-ice play. Nevertheless, those “outsiders” persisted, helping shape the modern version of the game that exists today.
Hockey's "Summer of Analytics"
While there are multiple entry points into hockey’s analytics history, one of the most widely recognized turning points is what has come to be known as hockey's "summer of analytics" in 2014. Unlike baseball’s more centralized evolution, hockey’s analytics movement was shaped by a broad group of independent thinkers. Figures such as Tim Barnes (also known as Vic Ferrari), Eric Tulsky, Tore Purdy, Tyler Dellow, and others helped lay the groundwork for modern hockey analytics. Even earlier, Roger Neilson was recognized for his innovative use of videotape to analyze team and player performance, signaling an early embrace of data-informed decision-making.
Despite these contributions, analytics remained largely external to NHL front offices until the 2014 offseason, when teams began formally hiring analytics researchers. One of the most significant signals of this shift was the Toronto Maple Leafs' decision to hire Kyle Dubas as Assistant General Manager. Dubas arrived from the OHL, where he was the General Manager for the Sault Ste. Marie Greyhounds and had been employing advanced statistical tracking through manually collected data. With greater organizational resources, the Leafs were able to partner with SAS Analytics, transforming how data was integrated into their roster construction and organizational strategy.
Dubas's hiring helped normalize the presence of analytics within NHL front offices and set a precedent that other organizations soon followed. In a 2024 article, John Matisz noted that prior to this hiring surge, “about 15 people were working in analytics roles. The headcount for analytics staffers has since exploded to nearly 150." The rapid growth of this number underscores how quickly analytics grew from the margins.
Making Sense of the Numbers
As teams began to adopt analytics, it became increasingly important to understand what those early metrics were actually measuring, and how they could be used to improve performance. One of the most influential figures in this effort was Tim Barnes, who originally shared his work under the pseudonym Vic Ferrari. Through his blog, “Irreverent Oilers Fans”, Barnes helped popularize public discussion of hockey analytics and even coined the term "Corsi," a metric that remains widely used today.
Corsi is essentially a formula that measures shot differential by tracking all shot attempts—shots on goal, missed shots, and blocked shots—at both ends of the ice while a player is on the ice. Rather than focusing on goals alone, the metric is designed to estimate puck possession and territorial control. A positive Corsi percentage (CF%) indicates that a team or player controls the play more often than not, while a negative CF% suggests the opposite.
The statistic can be broken down into several components, including Corsi For (CF), Corsi Against (CA), overall Corsi (C = CF - CA), Corsi For percentage (CF%), relative Corsi For (CF% Rel), and Corsi per 60 Minutes at even strength (C/60). Together, these variations allow analysts to contextualize performance across teammates, opponents, and ice time.
Corsi also served as the foundation for related metrics that further refined possession-based analytics. Fenwick functions similarly but removes blocked shots to focus on unimpeded attempts, while expected goals (xG) models weight shot chances based on factors such as location and shot type. Additional tools like WOWY (with or without you) further isolate individual player impact. Collectively, these statistics shifted hockey analysis away from outcomes alone and toward repeatable process, laying the groundwork for the more advanced metrics used today.
From Front Offices to Fans
One of the most prominent examples of analytics' expansion Is Eric Tulsky, whose path into hockey analytics was anything but conventional. A Harvard graduate with a PhD in chemistry from UC Berkeley, Tulsky transitioned from a career in nanotechnology into hockey analytics through the emerging blogosphere. By translating complex mathematical concepts into accessible analysis for other readers, he gained recognition and was quietly brought on by the Carolina Hurricanes as a part-time consultant in 2014.
The following year, he joined the organization in a full-time role, working closely with the front office. Tulsky's rise through the organization culminated in his appointment to General Manager of the franchise in 2024. His leadership has emphasized interdisciplinary hiring and reinforced the value of analytics as a scientific tool within hockey operations.
As analytics became a staple across NHL front offices, the league itself began investing in data infrastructure. In 2014, the NHL partnered with SAP to improve data accessibility and enhance the fan experience. NHL.com/stats launched in February of the following year and has continued to evolve, providing increasingly detailed public data.
At the same time, companies such as Sportlogiq began tracking NHL games. During the 2015-2016 NHL season, they introduced shot-location tracking, finding that approximately 30% of all shot attempts originated from the slot. By the 2018-2019 season, that figure had risen to roughly 40%, reflecting a league wide adjustment driven by analytical insight.
The emergence of private data companies raised concerns about what might be lost as analytics moved behind closed doors. In 2022, Seattle Kraken analyst Alison Lukan emphasized this, saying, "We must remember that...most innovators started their work in the public sphere and are now in-house with teams or supporting organizations. If we take away public innovation, we affect public passion for the game while also hindering the ability to identify the talent at a human level that will continue to evolve analytics in hockey.”
Lukan's comment highlighted a growing tension within the sport. As data became more valuable to teams, access for fans risked shrinking, threatening the public pipeline that had long fueled hockey's analytical innovation.
That tension helped shape the league's next step, and in October 2023, the NHL launched EDGE advanced stats. Powered by Sportradar, this league-wide tracking system transformed analytic metrics by using infrared emitters embedded in player jerseys and pucks to collect real-time movement data. It creates the ability to track metrics such as total skating distance, speed bursts, and shot speed. The system revolutionized the game by providing fans with unprecedented insight into how the action unfolds.
What began as skepticism toward analytics has evolved into a thriving data environment, with public analytics communities continuing to flourish through platforms like Puck Pedia, Hockey DB, Puckalytics, and Natural Stat Trick.
Across both Major League Baseball and the National Hockey League, analytics have moved from fringe experimentation to an essential part of how the games are played, managed, and consumed. Though each league took a different path to get there, data now shapes everything from roster construction to in-game strategy.
What many once feared would strip sports of their humanity has instead deepened how that humanity is understood. Numbers now provide context, helping teams find competitive edges, leagues evolve, and fans better understand what they are watching. Once met with skepticism and outright resistance, analytics have become a permanent and defining lens through which modern sports are understood.
Edited by: Kelly Cassette
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