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This paper outlines the key issues adjacent to big data management in Soccer World Cup 2014 and its consequent effects on FIFA as an organizing body. It also analyzes the effect of big data on the quality of the game played, the health of the players, the revenue earned, the predictions by fans and the overall performance of the county’s teams. With increase in the influence of the data analysis globally, the paper analyses the role played by proper big data management in one of the most awaited for events globally; Soccer World Cup that takes places after every four years. It analyses planning, analysis, influence of big data and the appropriate human resource policies and data management tools that are employed. Additionally the paper seeks to find out the challenges experienced and solutions used to mitigate the event and country’s national teams from experiencing losses. Finally a conclusive report is given about the structure and composition of the human capital factor and how big data management in soccer aids in proper soccer team management.
Significance of big data management in Soccer World Cup 2014 is a vast topic and this paper does not claim to cover all the issues involved in the one event aforementioned. Conversely it shares and analyses the key issues that affected and are currently affecting FIFA by using research from scholars, current facts and figures and mostly data analysis from the actual event in 2014 including fan base reaction and the significance of fan emotion. More research does require to be conducted on the same topic and in different occasions including the next Soccer World Cups that came after 2014 that have utilized big data in order to fully grasp the role in proper management of the organization resources and improvement of the performance of the different teams.
Soccer world cup 2014, also known as the FIFA world cup 2014, held in Brazil ended with the Germany team as the winner after 64 matches were played. Occurring between June 12th and July 13th, the event was marked an increased global audience as country’s citizens tuned in to cheer for their teams of choice (2014 FIFA World Cup Brazil). It was also marked with an increase in contribution, opinions and critique by the public via the social media platforms like Face Book, Twitter and LinkedIn.
Big data is an occurrence that is realized when there is a lot of large and complex data that requires being analyzed yet the conventional data analysis and management cannot be able to handle (Raheem et.al 2013). Big data if not properly analyzed, it can be a nuisance but when properly analyzed it has value. Data is very important during the soccer world cup not only for the management team and the players but also for the fans (Almeida et.al 2016).
According to Yang, 2015, during the soccer world cup 2014, there was a clear correlation between use of social media to release emotions and intentions for the teams. Social media was used to criticize the teams that were not performing the way their supporters wanted, communicate progress and for posting apologies from players and teams.
Rein &Memmert, 2016, affirm that big data proper analysis is a game changer in the Soccer world. This is because tactical analysis of teams was made through analyzing observational data during game and practice time. Conversely, this is not enough to come up with a comprehensive report on elite soccer. Even though there is data on team function, physiological ability and technical skill, there was just no way to incorporate all these data to come up with one result (Ohmann et.al 2015). Big data indeed it was but during Soccer World Cup 2014, this complex data was analyzed and better results were observed. There are several ways in which big data makes the collection, analysis and interpretation of the large amount of data easier as discussed below;
According to Yang, 2015, during the period of one month when the world cup was taking place, each day there would be a flow of roughly 1.5 Petabytes of data every day from the fans in terms of purchase of merchandise to tweets and Facebook analysis. This data was too much to handle and manage. There is need to measure this data so as to manage and interpret it for better outcomes (Balagué & Torrents 2005). There was also an increased need to improve on the tactical skills of the different teams meaning that all factors that affect the players had to be analysis and their correlation established. Big data managers came in handy as all this data was linked, analyzed and properly managed for the best outcome.
For instance, on June 22 when the United States team played versus the Portugal team (2014 FIFA World Cup Brazil), an analysis of the tweets and internet purchases showed joy and anticipation from the American fan base. There was an increase in the number of tweets that were congratulating the United States team and a consequent increase in the online purchase of the team’s merchandise. After the game, the American team was defeated by the Portuguese team; there was expression of anger and frustration from the tweets and a steep decrease in the merchandise purchased. According to the disposition theory, (Provost & Fawcett, 2013) the tweets can be used to analyze the fan emotions and whether it will translate into profits for the teams that were participating and for the FIFA world cup 2014.
When it comes to data and information speed is more significant than the volume. This is the reason as to why people will tune in to watch live games. During the 2014 World Cup, TV stations that had the rights to air the matches live as they were happening in Brazil had a competitive advantage in comparison to their other counterparts (Fujimura & Sugihara, 2005). This would in turn lead to an increase in the revenue earned from increase in advertisements. There is even a benefit to being one minute faster in the live streaming; every fan wants to see everything as it happens without any delays.
Big data managers aid in increasing the speed at which the data reaches the recipient and the speed at which it is analyzed and translated (Olthof et.al 2015). According to Yang 215, before the first game was played, there was a reduction in the price of tickets to watch the launch, this led to an increase in the speed of purchase and they were bought. This was later interpreted that most people would like to watch the games live but due to the high costs they are unable; the price was reduced slightly and more tickets were bought increasing the profits made by FOFA and Brazil.
According to Junque de Fortuny, et al 2013, sometimes bigger is better, simply because some events cannot be fully and properly analyzed without having massive data. Massive data is always available especially for global and significant sporting events like the World Cup, big data managers analyzes the data and leads to conclusive evaluations. Conclusive evaluations lead to improvement in team performance and it explains why the team we least expect to win the Cup ends up winning it.
Let’s analyze the current World Cup Champions; the German Team started off a bit weak during their initial games especially when it drew with Ghana on 21st June (2014 FIFA World Cup Brazil). Before the games started, the manager and coach, Joachim Low, were intent on creating a proper team dynamics off and onset. They did the fine grain data collection and analysis which involved historical data analysis, external factors affecting the team and finally the team member’s tactics (Junque, et.al 2016). The team member’s data was analyzed in terms of technique, physiology, psychology and differences in individual parameters. This data was in terabytes and its analysis was still being conducted even as the team went into the world cup.
Variety allows for choice and understanding the different choices allows for one to pick the most favorable and one with best and optimum results. Improvement in technology and ease of access of information via internet offers more variety than one could possibly imagine (Shafizadehkenari et.al 2014). The data is usually in either of two ways; structured or unstructured. Structured data is one that is stored in specifics and can easily be researched. This is because data about specific information is under one file therefore finding it is easier. Unstructured data makes up 90% of human data because information is randomly put in any folder and serious analysis has to be embarked on in order to draw inferences from the same (Valter& Barry, 2006). Data from social media platforms is unstructured and big data mangers come in to aid in structuring unstructured data, then analyzing it.
During the World Cup in 2014, there was numerous volumes of unstructured data send in via social media and internet. If left unchecked, it would have been a burden trying to understand the fans and the players as well given that the event was global and entire world could plug into online conversation. The data first of all had to be structured and then understood and managed. According to Yang, 2015, the IP address from where the tweets were sent from was checked and they were labeled under files named after the countries. This made it easy to understand the emotional stand of a population in a particular country.
In his analysis, Yang asserts that after determining the emotional reaction of the United States fans before and after certain games, he used the disillusion theory to come up with the reaction. This helped because it created a predictive pattern that in case the country wins, losses or draws, the fan’s emotion was predetermined and the resulting financial consequent was well understood and the officials would brace themselves properly without being caught unaware.
eracity in big data analyzes the unpredictable and uncertain data collected. Even with volume, sometimes, there is a lack of trend that can lead to proper predictions of outcomes (Araújo et.al 2006). With increase in betting platforms globally, 2014 world cup required to have predictions that would allow for the fans to make money while being entertained (Bialkowski et al 2014). Predictions are also good for tea managers as they will be able to strategically place and arm their teams with the necessary tactics to defeat the others.
Veracity is not dependent on volume or velocity but origin. The source has to be trustable and known (Shull et.al 2014). Data that affects global events like world cup, team line ups and the championship cannot be based on hearsay. The information therefore has to be verified before it is analyzed. Although verification may at times lead to discard of valuable information due to lack of knowledge on its source, more irrelevant data that would not be of use to management is eliminated (Toga et.al 2015). Consequently, there is need to have clear and concise methods of analyzing data and information to ensure that vital details are not dismissed due to lack of authenticity.
Decision making for soccer 2014 started before even the games began; first there was need for FIFA to determine the host countries. It had been preordained that 2014 Soccer World Cup had to take place in South America; the countries were urged to apply. FIFA had to collect data on all the applicants and determine the one that had the best infrastructure, up to standard stadiums, a good political environment and resorts for the players to relax in (2014 FIFA World Cup Brazil).
The data analysis took more than one year to be analyzed and for FIFA to come up with a host country. Even after the decision was made, Brazil was given plenty of time to make several corrections in regards to the set standards. There was also the decision on the teams to participate that year (Noor et.al 2015). After the world cup qualifications had been played, FIFA standards had to be met in terms of the team. The team must and should have all members as citizens of the country six months before the qualification, the players must have adhered to the anti-doping policies and the country paid all its required contributions to FIFA. This is another set of voluminous data to be analyzed during the prequalification stage and requires precision and accuracy (Gama et.al 2014).
Finally the decisions made during the live games by the referees, lines men and FIFA analysts are very significant. They have to be as flawless as possible, unbiased and in line with the FIFA rules and regulations. This is the only way that the game can be deemed free and fair (Kostkova et.al 2016). A good example is the introduction of technology that allowed the referees to know when the ball has passed the goal line and a team has scored. There is a magnetic field that is connected to the referees watch for that alert. This was meant to prevent the England Versus Germany incident that happened in 2010 and led to England being denied a goal that they had scored because the referee did not see the ball actually pass the goal line.
This is followed by decisions by the managers and coaches on which player to be on the line-up., who to play which position and who to be excluded. All this requires data and analysis of real time facts and figures (Nevill et.al 2008). Talent management is a key factor in position assignment and talent acquisition is a product of data analysis.
With all these decisions to be made and the deadlines usually set there is need to have a mechanism in which accuracy and accountability is upheld. Countries and FIFA require big data in making these decisions.
Previously data analysis was made via the use of traditional methods that did not put into consideration all the relevant information required (Cintia, et.al 2015). Consequently teams were not tactically armed with the necessary information to improve the game and play better. Also, FIFA would always find out about defiance and bent rules later than it should, these incidences would render such teams with a competitive advantage over all the other teams.
Traditional analysis was based only on video captured, what the naked eye saw and the information presented by the teams and the players. In order to investigate this information should it raise any doubt a committee was formed and it would take time. But with increase in technology, FIFA can even determine the age of a player by the use of a wrist MRI scan (Lago, 2009).
Big data analysis of Soccer World Cup 2014, formed part of the individual player analysis is crucial in team success. This is because more data was collected on the tactics used to evaluate players by coaches and the line-up for different countries. The countries that reached the quarter final had used big data analysis results to their advantage.
A physiological demand on a player is linked to improved tactics while playing. Physiological demand is the ability by the player to implement the necessary action according to their position while playing against opponents. For instance the physiological demands of a midfielder are not the physiological demand of an attacker. A midfielder should have resilience and speed while an attacker should be good at sprinting (Lees & Barton, 2003, Nakanishi et.al 2008). These features of players can only be identified by closely analyzing their performance against the player position requirement. Looking at Soccer World Cup 2014 world champions Germany, the midfielders Draxler and Groetze are very fast and play for big teams in the BundesLiga in the same positions (2014 FIFA World Cup Brazil).
Ball possession is very important as it ultimately leads to goal scoring. But what increases ball possession in any game are the player passes. Long passes have a tendency of being inaccurate as to who will receive the ball but short passes among the players ensures accuracy. A score-box analysis done by Leser et.al 2011, proved that score-box ball possession that ultimately led to actual scores was as a result of ball possession that’s starts from the final third. He also noted that proper recovery was made after an on target ball was blocked. All these analyses are a moment by moment understanding of the game based on close observance of many recoveries and goal scores. With this in mind players can easily be able to know the most opportune time to score.
Younger players whose age ranges between 20-26 years have a tendency to produce better defenders as they can easily tackle elongated ball movements, older players on the other hand made better attackers as they fully understand the goal orientation and can easily coordinate their movements to score (Mesirov, 2010). All these observations made have changed soccer training and it was evident during the Soccer 2014 World Cup. The teams that had more tactical skills than endurance made to the quarterfinals while those that had more endurance than tactical skill did not.
In light of the above details and available data, the national team managers have come up with several approaches to determine the best game tactic to be used and adaptability of players to changes during the match (Beetz et.al 2005)). All the approaches require voluminous data on the other teams, player in their own teams and information about every player. One of the main approaches is the control space approach whereby the distance between all the players is made to form a convex hull and the performance of every player is analyzed within the hull. The defense team covers more surface area than the attacking team while the old and matured players also cover more ground than the young and new ones (Rein &Memmert, 2016). This translates to the older players being in the defense more often.
The other approach is network approach that analyzes ball pass by the players. It allows for coupling where two or more players especially attackers and goal scorers are coupled in order to create a sequence (McAfee &Brynjolfsson, 2012). The couple works as one entity and are brought together by differences in capabilities. One player’s weakness is the other player’s strength. They complement each other and can easily score by working together (Barton et.al 2006). All these approaches used in soccer analysis were evident at Soccer World Cup 2014 and are as a result of proper big data analysis used by the teams to improve performance.
On the other hand, big data analyses are used by fans in live betting and gambling. Using previous player and team history, previous line-up and current line-up, coach previous success and failure and other prevailing external conditions, fans can be able to place their predictions and win. The betting platforms globally have greatly increased the soccer fan base as it has moved from simply being a hobby to a money making endeavor for the fans.
Big data added value and quality to Soccer World Cup 2014 by greatly improving the performance of many teams especially the ones that made it through to the quarter finals. The teams dynamics and coherence were evident from the performances and the seriousness that social media was given throughout the entire event (Barris & Button 2008). Consequently, talent management was improved as physiological capabilities of the players were the ultimate factors to decide the position played.
The other benefit is that big data ensured safety of the players as they were executing their roles (Baro et.al 2015). This is because there was a reduction in player’s injuries during the event. Once the players were assigned to positions that they were physically able to deliver, they had an easier chance on field without straining themselves too much (Mohr et.al 2005, Baca 2008). Big data also played a great part during their training to ensure minimum damage of the players and optimum utilization and management of their talents.
Big data also allowed for proper coverage of the event that ran for over a month. South America has a different time zone with many countries globally especially in Europe, Middle East, Asia and Africa but due to the availability of big data, the fan base was maintained because they were able to follow the games live. During the World Cup, it was easy to monitor the actions of players, coaches, teams and the entire event by FIFA as data could easily be captured and structured in a convenient ways. Big data allowed FIFA to store large amounts of data for scrutiny and historical analysis. This information can be referred to if need be to and will be available and easily accessible to the relevant party.
Due to the extension of big data in other world cup related events and activities, its use increased the revenue earned and helped in proper coordination of the event to make it successful (Bartlett, 2004). The revenue helped in improving the support activities that FIFA oversees and its efficiency has since been improved. Big data also increased the opportunity to improve soccer sporting globally regardless of the leagues played.
One of the biggest challenges when it comes to big data is validation (Yin &,Kaynak, 2015). Due to the high volume and variety of information that is collected, how will the relevant information be secluded from the irrelevant data and put into use? The solution to this dilemma leads to increase in the data management approaches to be used. Conversely this would be the only solution to the challenge. FIFA employs the algorithms that are data based and data driven monitoring processes. These ensure that information is validated before an analysis is done and inferences drawn (Valter& Barry, 2006).
Soccer World Cup 2014, being a globally recognized event that is awaited for every four years, it attracted a high volume of data to be analyzed and properly understood. According to Yang, 2015, he asserts that tweets of terabytes would flow into the twitter handle every one hour and required to be analyzed in order to understand the aforementioned repercussions. This volume was too much and had a small period of time to be analyzed meaning most of the data that flowed in every hour was either not analyzed, dismissed or not used (Bauer & Schöllhorn 1997).
he solution is to have a social media officers and data scientists on standby to analyze the important information by the fan who are the clients in this business (Aguiar et.al 2015). This will ensure that the value of the data and information is not distorted and is fully captured. It will also increase the efficiency of social media management.
Management of big data is an expensive venture, due to the volume of data expected to be analyzed, there is need for purchase of the necessary hardware and software to handle the data. There is need to have specialized labor and onset experts who can easily translate the data and derive a conclusive meaning (Appelboom et.al 2015). Although the initial cost is high the returns are worth it as teams can improve their performances, players will be safer while playing in the fields and FIFA championships will be better in terms of quality. The solution is to have big data management as a long terms plan whose returns are not expected in the short run. This will allow for the process to run its course.
The FIFA World Cups are events that take place every four years and every event has a bigger and more aggressive fan base. Especially with the increase in the number of countries that qualify for the event there is an increase in the data to be received before and during the event. There should be a well-established big data management system that is has velocity ro receive and send data in the shortest and most convenient time.
Big data is an occurrence that is here to stay, especially because organizations have collected more data in the last three years than in the last one hundred years. This is the same occurrence for the FIFA World Cup. In 2014, big data came in handy at the Soccer World Cup as it allowed for more data to be collected and analyzed to understand the fan base and improve on team performances. With this in mind, FIFA should recommend that all national teams to use big data in order to improve on their general performance and their relationship with their fans. The recommendation should come with guidelines on how it can be properly implemented. There are also several departments that require increasing their level of utilizing big data. The social media platform, for instance the tweeter handles and Facebook pages require to be properly analyzed. Additionally the importance of big data management should be made known to all the participating countries. FIFA should in this case, launch a campaign aimed at increasing awareness and utilization of big data.
FIFA World Cup has embraced big data; but requires increasing their data analysis methods in order to fully capture the important and significant role of the data sent and collected.
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