How Data Science is Changing Football?
Data analysis is now used to guide everything from player transfers and training intensity to selecting opponents and suggesting the optimum kick direction at any location on the field. The FIFA Football Data Ecosystem contains information on all of the game’s actions, including passes, shoots, and official judgments.
Hwang Hee-Chan of South Korea removed his jersey after striking the game-winning goal against Portugal during the FIFA World Cup in Qatar. He was seen donning a vest that collects GPS information from individual players. Infact, data fixation is nothing new in the world of sports.
Moneyball by Michael Lewis, published in 2003, changed the game. It described how Billy Beane, director of the Major League Baseball team Oakland Athletics, selected players in 2002 based on data and the use of statistics regarding their performance and developed a winning baseball team on a tight budget. As soon as the Moneyball culture began to permeate every part of our lives, including elections, healthcare, business, and national planning, Silicon Valley stepped in to add to it with the data science fairy dust.
The Portland Trail Blazers hired Protrade Sports, a company that used prior college data to evaluate the likelihood that a player would be a performer, during the 2005 National Basketball Association draught. But the complexity of the data increased as technology evolved. For instance, SportVision has tracked the direction and speed of each pitch in Major League Baseball since 2006 using motion-capture technology. Josh Kalk, a researcher of physics and mathematics, was hired by the Tampa Bay Rays to examine how pitchers’ release points alter for various pitches. Nate Silver, the author of The Signal and the Noise (2012), was a key figure in promoting data science in both business and sports.
We soon had more data than we knew what to do with. Additionally, we always gather data. Well, data science has also begun to gain traction in football. It should be the case. But there’s no doubt that football is far more complicated than baseball or cricket. Baseball is a stop-start game by nature; it has a discrete set of activities. The continual nature of football makes it an inherently fluid and low-scoring “invasion game” (a game in which teams “invade” an opponent’s zone in order to score a goal or point). Football has a higher level of abstraction than baseball from a statistical and analytical perspective. As a result, the data revolution in football is proceeding more slowly.
However, historically speaking, data-based football tactics can be traced back to the 1950s, a time before personal computers, when Charles Reep, an ex-army accountant, attended matches in England and made rudimentary notes about elements like pitch positions and passing sequences. At Wolverhampton Wanderers Football Club, Reep used data analysis to recommend strategies and tactics. He helped develop a direct and sharp playing style that discouraged sideways passes and helped the team win three league championships in five years.
The 2009 publication Soccernomics by Simon Kuper and Stefan Szymanski, dubbed “the most-intelligent text ever written on soccer” by the San Francisco Chronicle, might be thought of as Moneyball’s soccer counterpart in the post-Moneyball era. It explored the reasons why Brazil and Germany win, why England loses, and why the US, Japan, Australia, Turkey, and even Iraq would rule football one day. Goalkeepers, according to the writers, are underrated in the transfer market whereas Brazilian players are overrated.
The GPS data collected during practice and competitions even reveal the likelihood of injury using an algorithm. The number of passes, shots, interceptions and even more sophisticated analytics like anticipated goals and threats can all be generated using cutting-edge technology. The FIFA Football Data Ecosystem also has information about every play made during a game, including passes, shoots, substitutions, and official decisions, among many other things.
The post How Data Science is Changing Football? appeared first on Analytics Insight.
Data analysis is now used to guide everything from player transfers and training intensity to selecting opponents and suggesting the optimum kick direction at any location on the field. The FIFA Football Data Ecosystem contains information on all of the game’s actions, including passes, shoots, and official judgments.
Hwang Hee-Chan of South Korea removed his jersey after striking the game-winning goal against Portugal during the FIFA World Cup in Qatar. He was seen donning a vest that collects GPS information from individual players. Infact, data fixation is nothing new in the world of sports.
Moneyball by Michael Lewis, published in 2003, changed the game. It described how Billy Beane, director of the Major League Baseball team Oakland Athletics, selected players in 2002 based on data and the use of statistics regarding their performance and developed a winning baseball team on a tight budget. As soon as the Moneyball culture began to permeate every part of our lives, including elections, healthcare, business, and national planning, Silicon Valley stepped in to add to it with the data science fairy dust.
The Portland Trail Blazers hired Protrade Sports, a company that used prior college data to evaluate the likelihood that a player would be a performer, during the 2005 National Basketball Association draught. But the complexity of the data increased as technology evolved. For instance, SportVision has tracked the direction and speed of each pitch in Major League Baseball since 2006 using motion-capture technology. Josh Kalk, a researcher of physics and mathematics, was hired by the Tampa Bay Rays to examine how pitchers’ release points alter for various pitches. Nate Silver, the author of The Signal and the Noise (2012), was a key figure in promoting data science in both business and sports.
We soon had more data than we knew what to do with. Additionally, we always gather data. Well, data science has also begun to gain traction in football. It should be the case. But there’s no doubt that football is far more complicated than baseball or cricket. Baseball is a stop-start game by nature; it has a discrete set of activities. The continual nature of football makes it an inherently fluid and low-scoring “invasion game” (a game in which teams “invade” an opponent’s zone in order to score a goal or point). Football has a higher level of abstraction than baseball from a statistical and analytical perspective. As a result, the data revolution in football is proceeding more slowly.
However, historically speaking, data-based football tactics can be traced back to the 1950s, a time before personal computers, when Charles Reep, an ex-army accountant, attended matches in England and made rudimentary notes about elements like pitch positions and passing sequences. At Wolverhampton Wanderers Football Club, Reep used data analysis to recommend strategies and tactics. He helped develop a direct and sharp playing style that discouraged sideways passes and helped the team win three league championships in five years.
The 2009 publication Soccernomics by Simon Kuper and Stefan Szymanski, dubbed “the most-intelligent text ever written on soccer” by the San Francisco Chronicle, might be thought of as Moneyball’s soccer counterpart in the post-Moneyball era. It explored the reasons why Brazil and Germany win, why England loses, and why the US, Japan, Australia, Turkey, and even Iraq would rule football one day. Goalkeepers, according to the writers, are underrated in the transfer market whereas Brazilian players are overrated.
The GPS data collected during practice and competitions even reveal the likelihood of injury using an algorithm. The number of passes, shots, interceptions and even more sophisticated analytics like anticipated goals and threats can all be generated using cutting-edge technology. The FIFA Football Data Ecosystem also has information about every play made during a game, including passes, shoots, substitutions, and official decisions, among many other things.
The post How Data Science is Changing Football? appeared first on Analytics Insight.
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