Sports analytics

Baseball was one of the first sports to embrace sports analytics with Earnshaw Cook publishing Percentage Baseball in 1964. This was the first publication citing sports analytics to garner national media attention.[4] In 1981, Bill James helped bring SABR (Society for American Baseball Research),[5] one of the leading sports analytical organizations for baseball, into national prominence when Sports Illustrated featured James in the article He Does It By The Numbers by Daniel Okrent (1981).[6]

In 1984, New York Mets manager Davey Johnson became the first known member of a known sports organization to advocate for the use of sports analytics. During his time with the Baltimore Orioles, Johnson had tried to convince the organization to use his FORTRAN baseball computer simulation to determine the team's optimal starting lineup. As manager of the Mets, Johnson tasked a team employee with writing a dBASE II application to run sophisticated statistical models in order to better understand the capabilities and tendencies of the team's opponents.[7] By the close of the twentieth century, sports analytics had gained significant acceptance by the management of many Major League Baseball clubs, notably the Oakland A's, Boston Red Sox and Cleveland Indians.

At the same time, baseball fans and sports media had begun to adopt sports analytics as a way to understand and report the game. In 1996, Baseball Prospectus[8] sought to build upon Bill James' work when it launched the Baseball Prospectus website in order to present sabermetric research and related findings as well as publish advanced metrics such as EqA, the Davenport Translations (DT's), and VORP. Baseball Prospectus has grown into a multi-channel sports media organization employing a team of statisticians and writers who publish New York Times Best Selling books and host weekly radio shows and podcasts.

The MLB has set the benchmark in sports analytics for a number of years, with some of the game's brightest minds having never set foot into the heat of a major or minor league baseball game. Theo Epstein of the Chicago Cubs is one of those minds who has never suited up in a professional baseball game; instead, Epstein relies on his Yale University education and the numbers behind the game to make many of his decisions.[9] Epstein, known for his role in ending two of baseball's most famous streaks (the Boston Red Sox curse of the Great Bambino in 2004, and as recently as the 2016 World Series, helping end the 108-year drought between World Series wins for the Chicago Cubs), is a member of a growing community in major league baseball who do not rely on years of major league playing experience. This community has been able to grow thanks to the in-depth collection of statistics that has existed in baseball for decades. With analytics being relatively common in MLB, there is a breadth of statistics that have become vital in the analysis of the game, which include:

• Batting average is one of the most commonly discussed statistics in baseball. A player's batting average is determined by dividing the number of hits by the number of at bats for that player. This statistic shows a player's tendencies and which pitch usually strikes them out and can help them identify pitches they struggle with at the plate.[10]
• On-base percentage is the percentage of times a player reaches base on either a hit, walk, or by being hit by a pitch. This is a significant offensive stat, as it looks beyond hits and, more importantly, illustrates how often a batter can avoid being put out at the plate. This is a more in-depth offensive statistic than batting average, as it takes into account walks and being hit by a pitch, both of which are indicators of how a player handles an at bat. Sabermetrics can help change a player's approach in order to raise their own base percentage, increasing productivity and, ultimately, their overall worth as a player.[11]
• Slugging average is the calculation that determines the number of bases a player earns on hits. To determine this stat, the number of bases earned is divided by the number of at bats. This is a good measure for measuring a batter's power, as the higher their slugging average is, the more likely they are to hit for extra bases (i.e. a double, triple or home run). For sluggers, analytics can help them improve decision-making at the plate. Now, hitters can study the tendencies of the pitchers they are going to face, thereby familiarizing themselves before they are up to bat.[12]
• WHIP stands for Walks plus Hits allowed per Inning Pitched and tends to be viewed as a strong way to measure the success of a pitcher, as it illustrates how many baserunners the pitcher allows on both hits and walks. This is also a method for looking at a pitcher's efficiency. Now, pitchers can study the upcoming lineup they are going to face and focus on tendencies of the batter, such as where they stand on the plate, what pitches they tend to chase, and what part of the field they like to hit.[13][14]

Houston Rockets' Daryl Morey was the first NBA general manager to implement advanced metrics as a key aspect of player evaluation.[15] In the years that followed Morey's hiring, the NBA moved quickly to adopt advanced metrics-based player evaluation practices. In 2012, John Hollinger left ESPN to become VP of Basketball Operations for the Memphis Grizzlies.

Beyond professional basketball front offices, major sports media websites such as Basketball Reference are dedicated to the collection, synthesis, and dissemination of advanced metrics to pro and college basketball organizations, sports media members, and fans.

North Carolina, under coach Frank McGuire, was the first known basketball organization to utilize advanced possession metrics to gain a competitive advantage. Since then, sports analytics enthusiasts in basketball have created weighted statistics that measure each player and each team's on-court efficiency. Most basketball-specific advanced metrics feature a per-minute measurement to ensure that a player's incremental team contributions are measured irrespective of usage volume.

In 2003, the sports analytics-focused website Football Outsiders pioneered football's first comprehensive advanced metric, DVOA (defense-adjusted value over average),[16] which compares a player's success on each play to the league average based on a number of variables including down, distance, location on field, current score gap, quarter, and strength of opponent. Football Outsiders' work has since been widely cited by analytical members the sports media establishment. A few years later, Pro Football Focus launched a comprehensive statistical database, which soon featured a sophisticated player grading system.[17] Advanced Football Analytics (originally Advanced NFL Stats) has its EPA (expected points added) and WPA (win probability added) for NFL players.

Grantland lead football writer Bill Barnwell created the first metrics focused on predicting the future performance of an individual player, the Speed Score, which he referenced in a piece written for Pro Football Prospectus. After analyzing data pertaining to running back success, Barnwell discovered that the most successful running backs at the NFL level were both fast and heavy, therefore, Speed Score weights 40-yard dash times by assigning a premium to bigger, often stronger, running backs.[18]

One of the driving forces for the use of sports analytics in the NFL has been the growth of fantasy football. Fantasy sports writer C. D. Carter and peers at XN Sports, NumberFire, and the long-form fantasy football analysis site, Rotoviz.com, have established an informal subculture of fantasy football sports writers who refer to themselves as "degens". The degen movement is responsible for the creation of numerous American football efficiency metrics that better explain past football performances and attempt to predict future player production. Height-adjusted Speed Score,[19] College Dominator Rating,[20] Target Premium,[21] Catch Radius,[22] Net Expected Points (NEP),[23] and Production Premium[24] were recently created and disseminated by degen writers and mathematicians. Building on the work of these writers, sites such as PlayerProfiler.com distill a wide variety of established advanced metrics into a single player snapshot designed to be palatable to the casual sports fan.[24]

The NHL has kept statistics since its inception, yet it is a relatively new adopter of analytics-based decision making. The Toronto Maple Leafs were the first team in the NHL to hire a member of management with a largely analytical background when they hired assistant general manager Kyle Dubas in 2014. Dubas, similar to Theo Epstein in MLB, has never suited up in a professional game and relies on the numbers generated by players on a nightly basis both now and in the past to make decisions.[25]

• The Corsi statistic is an advanced statistic that has been widely adopted throughout the NHL, as teams, fans and media alike rely on the Corsi statistic to track shot attempt differential.[26] Corsi has been recognized as the most informative single statistic in the game of hockey as it can provide insight into both the offensive and defensive play of a team as well as the amount of time a team has possession of the puck.[27]

The PGA Tour collects vast amounts of data throughout the season. These statistics track each shot a player takes in tournament play, collecting information on how far the ball travels and exactly where each shot is played from and where it finishes. These data have been used for a number of years by players and their coaches during practice sessions as well as during tournament preparation, highlighting the areas in which that player needs to improve before teeing it up in tournament play.

• Shotlink data collection has revolutionized the way that data is collected in the game of golf. Introduced on a full-time basis in 2003, Shotlink relies on a number of strategically placed on-course laser rangefinders and cameras to collect precise data from every shot that is struck on the PGA Tour.[28] With these data, players are able to see the areas of their game that need improving, and on a broader year-to-year basis, players can review course statistics from previous years to allow for relevant tournament preparation. On top of the year-to-year statistics provided players and fans can also easily access these statistics at an up to the minute rate, giving these data an extremely high velocity. Shotlink has also made its mark on the world of golf course design as designers have constant access to up to the minute statistics of professional golfers, allowing for these designers to create courses that can provide a challenge for the world's best players.[28]

Soccer uses tracking data, such as the positional data of the players and ball, for teams to obtain information about players’ conditioning.[29] This data has also been used for evaluating attacking performance to estimate goals scored using Artificial Intelligence.[30] Other approaches have included dribbling and passing.[31] Research is also undergoing at Nagoya University to investigate the potential of using the defender-orientated ball recovery and being attacked as metrics, with it being used successfully with data from the Japanese J1 League to predict the strategies used by the teams.[32]

The Astros rely heavily on analytics when making decisions. The team has employees with titles like, director of decision sciences, medical risk manager and mathematic modeler.[36] Unlike other professional teams who typically use analytics solely for player transactions and signings, the Astros have begun to use analytics to make decisions on how they will play on the field, "applying the defensive shift more than any other team in the MLB last season."[36] Using this approach, the Houston Astros captured their first World Series victory in franchise history in 2017.[37]

One of the early adopters of SportVU, the San Antonio Spurs have been using analytics to gain a competitive advantage on opponents for a number of years. Collectively as a team the Spurs have honed in on the importance of the three pointer and as a result constantly rank among the league lead in three point attempts. The teams understanding of the importance of the "three" extends beyond the offensive side of the court as they are relentless at defending the three pointer in the defensive end of the court.[36]

In 2009 the Chicago Blackhawks turned to an outside company to produce analytical assessments for them.[36] Subsequently, the Blackhawks have achieved unparalleled success in the NHL, winning three Stanley Cups in six seasons. With this success has come a number of difficult decisions for Blackhawks management as they are often only able to hang onto a core group of players following each cup run, while other key players receive offers that the Blackhawks simply cannot match under the NHL's salary cap. However, by using this analytics based system, the team has continuously been able to fill these gaps by finding players who are undervalued by other teams but will fit well with the Blackhawks' style of play. Many times, a team put together like this will seem underwhelming but perform higher than expectations. This strategy could be adopted by teams with limited financial freedom to put together a competitive team.[38] This process has been refined by the Blackhawks who provide yet another example of the longevity that can be associated with analytic base decision making.[39]