Thursday, September 15, 2016

How Big Data is Affecting Sports

            In Today’s world, immerging businesses across all different industries have found it extremely beneficial to collect massive amounts of data. In doing so, businesses are able to use Big Data and analytics to develop strategic plans that evolve their companies and maximize their profits. One industry where this is specifically relevant is sports.
Today, in most professional sports, you can find a camera almost anywhere on the field. In the stands, you can find scouts recording every move a player makes. Even a player’s free time is recorded; what they’re eating, how they’re sleeping, and their exercise routines are all evaluated. All of this data is collected by organizations, critically assessed, and then molded into a strategic plan to improve a players speed, skill, strength and value.
With sports organizations collecting Big Data and improving their analytic strategies, I believe they will find themselves saving millions of dollars on contracts and trades, as these methods basically eliminate the risk that a player they are looking at will not preform up to the standard they were valued at. Collecting Big data not only limits the amount of risk an organization faces when signing a player, it also helps organizations improve a player’s overall skill and value. Players and coaches can closely evaluate every move a potential prospect makes at practice and in games, find problems in their performance, and plan how they can improve on these weaknesses. As coaches use this data to improve their player, I believe that in the future, organizations will begin producing athletes that can preform at a much level than athletes today. Ultimately, improving the overall quality and value of the organization.
            Though Big Data in Sports is very beneficial for organizations, many players argue that these analytics do not give the whole story and are not always correct. For example, When the LA Lakers were considering signing Kobe Bryant, 35, who was coming back from an Achilles tear, if they had just gone by the data of past injuries and players his age, the organization would have come to the simple conclusion that he was not worth it. However, Bryant disagreed. He insisted the data was wrong and was eventually signed. This signing proved to be a huge fail for the Lakers and ended up being a $48 million mistake.
            In conclusion, I agree that “Big Data” and analytics are very beneficial to any organization within any industry, especially the sports industry. In the sports industry, Analytics play a special role because without these statistics no player, team, or origination could be given a value or be evaluated based on skill. Sports organizations are reliant on this “Big Data” and I believe that in the future, as innovative technology continues to progress, collecting “Big Data” will ultimately become a necessary tool to improve, grow, and expand any organization, within the sports industry and all industries.


Marr, Bernard. "Big Data: The Winning Formula in Sports." Forbes. Forbes Magazine, 25 Mar. 2015. Web. 15 Sept. 2016.

5 comments:

  1. This article is somewhat controversial in my opinion. Big Data has the potential to benefit sports franchises and teams in regards to guiding them in the right direction of which players they should be looking at in terms of their overall statistics and averages. However, data cannot determine the qualities and characteristics that players need to make them a fantastic teammate or overall great player. Yes, it may show that they are great at what they do, but it doesn't show how the player will mesh with the current players or coaches on that team or franchise. I believe that big data is a great starting point in narrowing down potential players but it is not quite precise. Hopefully in the future big data will take into account the qualities, traits, and characteristics of a player in order to create an overall rating that doesn't just rely on their athletic ability alone. If big data could do this, and it has the potential to, it would be an extremely effective tool for franchises and could potentially save them a lot of money that can be utilized for other things besides player contracts or potential issues that may arise from a players actions.

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  2. I thought this topic by Nick was extremely interesting. I love sports and to see how analytics can contribute to sports is fascinating to me. There is absolutely no doubt that analytics and other forms of data have made a huge impact in the sports in the last 15 years. It basically all started with the 2002 Oakland Athletics. The GM, Billy Beane, and assistant GM, Paul DePodesta, created a make shift roster with the cheap budget that they had. They used analytics to get cheap players that no one wanted but had a strength that they needed. Ever since then analytics in sports took off. Theo Epstein even used it to break the Red Sox curse in 2004. There is without a doubt some validity to the sabermetrics. But in my personal opinion it shouldn’t be the only thing sports franchises care about.
    I do agree with Nick that although Big Data is beneficial to sports teams it’s not the only thing to take into consideration. There are certain intangibles that Big Data cannot tell management. Being a good locker room presence, ability to bounce back from injury, work ethic, and character. Those kinds of things are only going to be determined by observing the player and talking to other people that have dealt with the player closely. But besides those few issues I think data in sports is absolutely huge. You may see somebody who can put the puck in the net 30 times in an NHL season but if you look closer at his stat line he may be a complete defensive liability. Just by looking at the player at the surface you can see one thing, but not the whole picture.
    One sport that data is essential now a days is baseball. It’s almost insane how much data plays into the day to day operations for a baseball club. Everything from how a hitter hits a lefty or righty, to how a hitter can hit an off speed pitch from a righty. The impact it has on baseball is just crazy. It actually has caused some controversy with some teams. On the surface an old school coach may like one player against a pitcher, but the analytics team may call up and say no go with the other guy. Ned Yost, the manager for the Kansas City Royals, has completely loved the analytics team. He used the club’s analytics team to help the team win a world series last year. It’s not always about signing that big flashy player, sometimes you just need players that will the job done.
    In conclusion, I believe analytics in sports is huge and will only get bigger and more important. I also believe it’s not the only aspect, but it can definitely create a clearer picture for teams in terms of signing players. It’s a tool that has proven that it can be effective so why not utilize it? It could be a championship difference maker.

    ReplyDelete
  3. I thought this topic by Nick was extremely interesting. I love sports and to see how analytics can contribute to sports is fascinating to me. There is absolutely no doubt that analytics and other forms of data have made a huge impact in the sports in the last 15 years. It basically all started with the 2002 Oakland Athletics. The GM, Billy Beane, and assistant GM, Paul DePodesta, created a make shift roster with the cheap budget that they had. They used analytics to get cheap players that no one wanted but had a strength that they needed. Ever since then analytics in sports took off. Theo Epstein even used it to break the Red Sox curse in 2004. There is without a doubt some validity to the sabermetrics. But in my personal opinion it shouldn’t be the only thing sports franchises care about.
    I do agree with Nick that although Big Data is beneficial to sports teams it’s not the only thing to take into consideration. There are certain intangibles that Big Data cannot tell management. Being a good locker room presence, ability to bounce back from injury, work ethic, and character. Those kinds of things are only going to be determined by observing the player and talking to other people that have dealt with the player closely. But besides those few issues I think data in sports is absolutely huge. You may see somebody who can put the puck in the net 30 times in an NHL season but if you look closer at his stat line he may be a complete defensive liability. Just by looking at the player at the surface you can see one thing, but not the whole picture.
    One sport that data is essential now a days is baseball. It’s almost insane how much data plays into the day to day operations for a baseball club. Everything from how a hitter hits a lefty or righty, to how a hitter can hit an off speed pitch from a righty. The impact it has on baseball is just crazy. It actually has caused some controversy with some teams. On the surface an old school coach may like one player against a pitcher, but the analytics team may call up and say no go with the other guy. Ned Yost, the manager for the Kansas City Royals, has completely loved the analytics team. He used the club’s analytics team to help the team win a world series last year. It’s not always about signing that big flashy player, sometimes you just need players that will the job done.
    In conclusion, I believe analytics in sports is huge and will only get bigger and more important. I also believe it’s not the only aspect, but it can definitely create a clearer picture for teams in terms of signing players. It’s a tool that has proven that it can be effective so why not utilize it? It could be a championship difference maker.

    ReplyDelete
  4. I thought this topic was extremely interesting, especially being an athlete. I was not aware of the implementation of technology into sports in this manner, I only thought they were using data to collect things such as breathing patterns or heart rate. I think this idea of using data to prevent injuries is great, but I think it could also be flawed. My trainer always says form is most important when lifting because it prevents you from injuring your body and when you strengthen the correct muscles you also help yourself avoid injury. Since, I have started lacrosse at Loyola many people have been injured, but four of my teammates have torn their ACL’s, and I believe these cases are what would benefit from this technology. Instead of using this technology as a means of debating who to recruit, I think if would be more beneficial to use to improve the players currently on the team. This technology would not only help the trainers understand what they should have the individual players focus on, but also would allow the players to have some kind of reassurance that they are less likely to get injured.

    I think there is a flawed side to this technology too. Many times when you scout or analyze a player with this technology they show their value or potential, but if they are them recruited by a team the team is not guaranteed to have this player maintain this performance level. There are many details to consider like how the player’s performance changes as they age, or how they fit in with the team. What I am saying is there are many uncontrollable variables that come into play even though this technology is implemented. There are players that do get old, but that does not mean they lose their potential. Even though this articles discusses the lacking performance Kobe Bryant gave when the Lakers decided to keep him, I believe he proved them wrong in some cases. His last game he played he scored a career high of 60 points. I believe that the athlete can be the best they are on that day and even though this data gives teams an idea of the reliability of the athlete, I do not think it would guarantee anything. Also, there are many injuries that cannot be predicted, so if the technology is showing its capability to predict the reliability of the athlete it cannot take details like this into account. Sometimes injuries are unlucky and they just happen – the data cannot predict when someone will be injured it can only tell how likely they are and what the athlete needs to do in order to try to prevent injury. Overall, I do believe that technology in sports will be very helpful, but I do also think it will take time for them to figure out the most useful data they will need in order to truly build their teams and programs to what they consider as perfection.

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  5. I found this blog post to be quite intriguing. It is always cool to here about all the ways that Big Data has an effect on different industries and sports is no less fascinating. The way that athletes are tracked and monitored is outrageous and I do not know how I would feel about being so closely monitored especially knowing how this data could be used against the player. As Nick mentioned specifically, Kobe Bryant would not have been resigned to the Lakers if the organization had gone strictly by the data, and if he had been any other player and not such a big name, he definitely would not have been given the benefit of the doubt in this case. It is crazy to me about the lengths at which athletes have to go through to be in the industry these days, as well as how much privacy they have to give up, as everything they do it transformed into data that can either work for or against them.
    Although Big Data is bad for the athletes, it is important to remember that the sports industry is a money-making industry and having the knowledge that they do is essential to profitability. In order to be the most profitable, teams have to get the best or most popular players and also win championships. By using Big Data to find these players, the various organizations can see what their teams’ are lacking and where they are lacking to target the right players for the right positions and potentially at the right price. Once again, this relates back to the Lakers situation with Kobe Bryant and how they did not listen to the data, which was a mistake that cost them $48 million dollars.
    Finally, another potential negative side of Big Data is that is that, in theory, by having access to all of this data, organizations can stack their teams the way that they would like and the way that would guarantee wins. Obviously this can only be theoretical because there would be no perfect situation in which a team was able to obtain every single player that they would need, but if it was to happen there would be an unfair advantage among the leagues. These are just some things to think about.

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