The game of soccer is changing at a rapid pace due to the implementation of big data analytics in recent years. The statistical and analytical data has been used to gain a competitive advantage in the performance of the sport. Much like in the movie Moneyball, Soccer has started to gain valuable insights in to how to effectively utilize key indicators to better prepare for games. According to Bernard Marr, the first know expert to pioneer notational analysis in this sport was Charles Reep. An accountant by trade, Reep’s analysis focused on the passing moves that led to goals. Reep founded the importance of 3 to 4 uninterrupted passes which led to most goals. The technology Prozone, has been used to support both pre and post-game analysis of each player. “Today, all 20 Premier League football stadiums in the UK are equipped with a set of 8-10 digital cameras that track every player on the pitch. Ten data points are collected every second for each of the 22 players on the pitch, generating 1.4 million data points per game. Prozone analysts will then code the data to identify every tackle, shot or pass in order to enable managers and performance analysts gain insights of what exactly happened in each game, on and off the ball,” (Marr, 2015). Soccer clubs are using these pioneering data gathering tools to make more informed decisions on signing players, player evaluation for improvement, and scouting. Another aspect of this data is the information available to sports betting players who wish to gain insight before creating a team typically used for the gambling app “draft kings.” For example, according to engineering Professor Luis Amaral, the AFR system (Average Footballer Rating) for each player, values all the players on a particular team which then would indicate the team’s strength (Milliken, 2018). This paper will focus on the changes that big data analytics has brought to the sport of soccer. As coaches an organization realize the value of quantitative data over.
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Big Data’s Impact on the Transfer Market
Big data helps Soccer Clubs better understand which players to sign. Clubs have often times punched over their weight in the pursuit of players that do not live up to the money spent. A good example of clubs using big data analytics to find undervalued players is Red Star Belgrade. According to a Forbes Article, Lorenzo Ebecilio was playing for a little watched Cypriot team when Red Star Belgrade was searching for an attacking midfielder. The 21st Club, a soccer consultative intelligence company, consulted Red Star Belgrade to take a look at Lorenzo Ebecilio from the intelligence that they were able to gather through intelligence data. Red Star Belgrade signed Ebecilio who recently scored in the prestigious tournament the Champions League. How was this data found? The 21st Club has created an analytics engine called (PIRLO) Predictive Intelligence Research and Learning Outputs. Clubs will ask this organization to find out the gaps between their club and the clubs in the top of the division. The 21st club will come back with extensive research on changing the style of play, the coach, and player recruitment. Chaudhuri of the 21st club says the objective is to also tackle biases implemented when signing a player. Mr. Chaudhuri found that because a player scored a single world cup goal, the players transfer fee went up 15% (Kidd, 2018).
Another example of big data analytics at work is the use of “StatDNA” used by Arsenal Football Club. According to a New York Time article by Rory Smith, Arsenal’s previous manager Arsene Wenger decided to implement big data analytics in determining the next signing he should consider for his team. The case study used in the article shows the failures of two particular signings. Marouane Chamakh, a dilettante Moroccan striker who scored only 14 goals in three seasons, and Park Chu-Young, who transferred from Monaco and only played seven minutes in two years. Both players left Arsenal. After their departure, Arsenal Wenger decided it was time to seek other options. Arsenal had used StatDNA before but Ivan Gazidis, Arsenal’s chief executive, and Hendrick Almstadt, head of business development proposed the idea of buying StatDNA outright to thwart rivals. A presentation ensued and they went along with the purchase. Clubs are trying to minimize the loses incurred and an arms race has ensued amongst clubs as to who has the best data available to gather intelligence. StatDNA is a data-led approach that has landed defender Gabriel Paulista in 2015 and encouraged Mr. Wenger to purchase Gonzalo Higuain over the objections of the scouting department because of vital information gathered. The data-led approach has not been perfect however, it has created doubt about signing Kevin de Bruyne, of now Manchester City, because of a possible inability to handle the Premier League. StatDNA is jealously guarded by Arsenal, and those involved believe that it is the most advanced in the field because of Arsenal’s exclusiveness. Other clubs have to seek data from external companies, such as Opta. StatDNA is more advanced in that it can code 14 matches in a couple of hours where other commercial data companies can only code a single match. StatDNA is more in depth than most, it focuses not only on offensive matters but defensive responsibilities. For example, it asses how frequently defenders fail to spot an opponent running past them or losing a one v. one battle. It also has created a metric where it measures the seriousness of a mistake made on the field (Smith, 2017).
What does Big Data Analytics do for clubs wishing to sign players? The case examples above observe the advantages of big data analytics and the advantages gained by contracting with sports data led companies, or outright owning a company like Arsenal Football Club. The main take away from this segment of the player transfer market is the ability to minimize the risk taken on by a club when they invest in a player. In the case of Red Star Belgrade, the club does not have the means to make a substantial investment for a particular player. They were able to achieve a bargain by finding an undervalued player by analyzing key elements that were over looked by the transfer market. According to the Mirror, Arsenal Foot Ball club spent 14 million pounds for Marouane Chamakh with minimal upside to show for it (Lewis, 2013). Seeking a new direction, football clubs are using big data analytics to avoid these failed investments.
Big Data’s Impact on Fitness and Successful Team Scouting
“Once you have the talent it’s time to let technology play an objective part in helping you achieve that extra bit,” said Real Madrid’s fitness coaches (Murphy, 2017). Big Data analytics is making a huge impact in the lives of professional players as to how they should train. Statistical technology is being shown to the coaches and players before and after trainings, and games to better understand where they are statically. This is making inroads for youth academy teams as the game continues to improve through big data analysis. Real Madrid is an example of how this is being used today. They are currently personalizing each fitness plan to each player to better understand their needs. MiCoach is the system being used see which players need more cardio work and which players do not. Each player wears sensors which tracks their movements on the pitch and during matches as requested by the fitness coaches (Murphy, 2017).
Big Data analytics produced by Micoach tracks players heart rate, top speed and distance ran by biometrics and GPS. Each year before the MLS draft, graduating college players who are eligible to participate, compete in the MLS combine tryouts which gives MLS teams a chance to scout their pick (Turbow, 2012). According to an article from Wired, MLS scouts were armed with information from adidas reps on the sideline displaying valuable intelligence about each players capabilities as the games played. Micoach is designed to show a baseline amongst players and if they are not reaching this base it could indicate fatigue or injury. Philadelphia Union’s Interim manager John Hackworth in 2012 described Micoach as “That becomes an invaluable tool. Rather than coaches estimating that a guy looks fatigued, this gives you a metric to actually measure (Turbow, 2012).
In addition to the various physical data shown in tools like Micoach, Soccer has utilized data to compare and contrast upcoming opponents. This has been beneficial to coaches as they prepare for their upcoming matches. Instead of an observatory approach such as film and scouting, big data analytics has been able to produce hard numbers has to where an opponent is strong or weak. This research can have a huge competitive advantage for teams looking to gain a competitive edge in head to head matches. For example, if a club was looking to gain information as to how a player strikes a ball, the Micoach ball technology can be used to determine the opposing players preference. Below depicts how this data would be shown (Smith, Phillips, 2014).
Big data analytics helps with game preparation analysis. According to the German Journal sports medicine, the author argues that Key Performance Indicators (KPI) are the key to fully understanding the data behind traditional performance measures like heat maps and pass frequency. Furthermore, the author explains how positional data is more predictive than the qualitative video approach typically used in the past. A total of 50 matches from the 2014/2015 season were analyzed in the broadest big data field study known. A software model named SOCCER was created to evaluate performance for individual players, tactical groups and for the entire team. Three Key Performance Indicators (KPI) were discovered from this study. KPI – 1 Space Control, this study found that the dominate teams control the most space especially in the critical zones of the playing field. KPI – 2 Out Played Opponents, the successful teams on average face fewer opponents when attacking even for passes that are vertical. KPI – 3 Pressing index, with an independence of playing strength, the losing teams significantly presses more often during games won with differences of two goals. In comparison to winning teams, they successfully press across the board more frequently than the bottom third of the division (Rein, 2018).
During the 2006 World Cup final, positional analysis was conducted, and the figure above explains where the critical areas on the field were. Italy penetrated the yellow areas and pressed at a more alarming rate than France. This could explain their eventual victory to take the World Cup.
From fitness standards, to positional analysis that looks past traditional analysis, big data is making a significant impact on the way the game of soccer is executed. Implanted sensor technology is tracking physical standards rather than old fashion systems that leave some team members behind. Statistical analysis of where players are currently in their physical fitness standards allows coaches to better understand whether someone should play on the weekend, or if they are ready to be drafted into the MLS. The most significant research found was the big data analysis through key performance areas. This study was able to predict the likely hood of performing a certain task on the field increased the likelihood for a win. It also provides useful data for coaches that need to know what distinguishes good teams from poor teams.
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Big Data’s Impact on Soccer Betting
A typical sports betting app like Draft Kings allows betters to place bets upon who they believe is the best team to win them money. Big data analytics in soccer is changing the way teams and players are viewed due to intelligence gathered. According to NorthWestern Engineering Professor Luis Amaral, using sophisticated coding techniques and analytical tools, Mr. Amaral created an Average Footballer Rating (AFR) for each player during the FIFA World Cup. He based this ranking off how influential they are in soccer matches. The figure blow is algorithms put together by Mr. Amaral that indicate which players have a greater impact amongst their team. The thick lines indicate a strong connection amongst players. The larger the circle the more impact they have amongst the group. Below analyzes Italy and Spain’s team (Milliken, 2018).
The network explains who passed the ball to whom, the accuracy of those passes, and how likely the passes were to end up being a goal. Mr. Amaral further stated that “a player with an AFR greater than 70 is pretty much superhuman,” ( Milliken, 2018).
What impact does this have on the gamming industry? Based upon analytical data presented like the one presented by professor Amaral, more information makes for better decision making. For example, once a team is prepared before a match, the gamer can draw from extensive data driven by how well they match up this particular week. This past weekend, before the match up between Manchester United and Chelsea, gamers are strategizing how to achieve a competitive edge in order to gain the most percentage points. They could have chosen players that staticians at sports organizations like “Sky Sports” rate on a weekly basis. This information is drawn from they way they defend, connected pass, and contributed to the game in their position.
Based upon the research of this paper, big data analytics is changing the way soccer is being watched, performed, and coached. The beginning of this paper discussed the impact on the transfer market for organizations to find the right player for the appropriate amount. The 21st Soccer Club was influential in its analysis of finding a player from the Cyprian league. The data that was big data’s all-around analysis of a soccer player (Kidd, 2018). Typically, a player would be scouted by sheer visibility and know how from the scout. What analytical firms provide is that hidden element that explains why they are choosing a player or not. A lot of the time organizations like to keep their information hidden but it generally covers important elements like how often a player makes important passes to make a difference during the game. Arsenal’s usage of StatDNA was able to help them prevent spending millions of dollars on players that were not successful in the English Premier League. For example, StatDNA and the future of analytical sports data can advise a player that an organization did not think would be of use. Arsenal was told to sign Higuain of Napoli because of his ability to finish as a player. The data supported by StatDNA allowed Arsenal to have the discussion, but it seems like they could not come to a consensus as to whether to sign him or not (Smith, 2017). Their rival Chelsea F.C signed this player instead.
Big data’s impact on fitness is growing at a successful pace. Clubs like Real Madrid as explained in this paper are using Micoach to personalize the fitness requirements for each player (Murphy, 2017). For instance, if a coach wants every starting player to have less than 150 heart beats per minute, he will structure work outs pertaining to certain teammates who are behind the curve. These heart beats are measured by censors attached to a players under garments as he or she wears their practice jersey’s. Most of the time, the big data analytical systems will measure where the player ran the hardest the most during the game. What’s next for big data’s future in fitness? As professional players continue to utilize various systems at an elite level, more and more academies, and independent clubs will emulate the pathway of profession use. I think big data will not make inroads with smaller clubs at young ages that are typically playing soccer for social reasons. There is a significant financial investment that needs to be made into data that will ultimately give players and coaches a better idea of what they are looking at. The questions remain whether coaches will resort to “old fashion” tactics of solid fitness regiments. In my personal experience, the game of soccer has more and more utilized technological data in the past few years. With the addition of VAR Video Assistant Review, technology and data are taking an even bigger role in the game of soccer. My belief is that provisions like Micoach will be used at levels where players are seeking professional status.
What big data has a lot of effect on is the gaming industry. Gamblers want to be backed up by significant analysis before a bet is placed. The chance for people to have a detailed analysis as to how people play and what generally makes a good team is crucially important. Big data analytics that has been used in soccer will continue to help people make wise decisions as to where to put their money.
- Marr, B. (2015, March 25). How Big Data and Analytics are Changing Soccer. Retrieved from https://www.linkedin.com/pulse/how-big-data-analytics-changing-soccer-bernard-marr/
- Milliken, C. (2018, May 8). Using big data to analyze soccer. Retrieved from https://news.northwestern.edu/stories/2018/may/using-big-data-to-analyze-soccer/
- Kidd, R. (2018, November 19). Soccer’s Moneyball Moment: How Enhanced Analytics Are Changing The Game. Retrieved from https://www.forbes.com/sites/robertkidd/2018/11/19/soccers-moneyball-moment-how-enhanced-analytics-are-changing-the-game/#6aafc4e376b2
- Smith, R. (2017, February 03). How Arsenal and Arsène Wenger Bought Into Analytics. Retrieved from https://www.nytimes.com/2017/02/03/sports/soccer/arsenal-arsene-wenger-analytics.html
- Getty. (2013, May 23). £1m per goal! Marouane Chamakh has cost Arsenal £14m.. and scored just 12 times in three seasons. Retrieved from https://www.mirror.co.uk/sport/football/transfer-news/arsenal-transfers-marouane-chamakh-cost-1907339
- Murphy, L. (2017, May 18). Big Data and the Changing Game of Football. Retrieved from https://www.gameplan-a.com/2017/05/big-data-and-changing-game-of-football/
- Turbow, J. (2017, June 03). Soccer Embraces Big Data to Quantify the Beautiful Game. Retrieved from https://www.wired.com/2012/09/major-league-socccer-micoach/
- Smith, J. T., & Smith, J. T. (2014, June 23). Review: Adidas miCoach Smart Ball. Retrieved from https://gearpatrol.com/2014/06/19/tested-adidas-micoach-smart-ball/
- Memmert, D., & Rein, R. (2018). Match analysis, Big Data and tactics: Current trends in elite soccer. Deutsche Zeitschrift Für Sportmedizin, 2018(03), 65-72. doi:10.5960/dzsm.2018.322
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