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In the 1980’s electronic systems to collect data got installed in Formula 1 cars (Wooden, 2019). At the time the storage was limited because they could only store data for one lap. By now, 30 years later, it is the biggest data dependent sport where one mistake can be the difference between victory or defeat (Marr, 2017). This data driven approach has trickled down to other sports as well and the fans are consuming more analytical content than ever (Steinberg, 2015). The data gathered in sports can be categorized in three levels: public data, company data and personal data. In this blog the implications on sports of the different levels data will be discussed.

 

As stated before, sport fans are provided with a high number of statistics during a game. These statistics that seems very common now were first provided on the internet by fans themselves and are generated with public data (Slaton, 2012). Through this, fans got better educated and gained a deeper understanding of their favourite sports. One of the first things being tested was how big a home advantage for clubs could be and with that the effect on their seasonal performance (Kucharski, 2016). This better education of fans created a big change in the betting industry because fans were able to bet more accurately (Hancock, 2019).

These simple predictions with public data also inspired pioneers like Bill James to compose teams based on individual player statistics (Slaton, 2012). The greatest break through of this method happened with Billy Bean and his ‘MoneyBall’ method, which caused the baseball industry to entirely change its scout model and start gathering statistics themselves (Kucharski, 2016).

 

The data gathered by clubs themselves is mainly generated during practices (Steinberg, 2015). During matches players get equipped with GPS trackers to monitor their movements, analyse them and create better strategies (Marr, 2017). In addition to that, new technological advances enable the analytics teams to extract insights from unstructured data like videos (Steinberg, 2015). Because of this, clubs are able to optimize their teams and generate customized strategies for different opponents. However, clubs with the greatest resources tent to benefit the most from these technological advances and with that the gap between top clubs and the less resourceful ones increases even more. Furthermore, it contributes to a less fair competition (Malone, 2019).

Another issue regarding fair play is raised about injuries and their recovery. Not only during practices but also during other activities, data of players gets collected. There is a concern that transfer prices of players will be manipulated because third parties can not access that information (Marr, 2017). This also raises question about the ownership of data. A recent example is the transfer of Gareth Bale from Tottenham Hotspur to Real Madrid. Although it was clear that Bale was the star team, it turned out that Musa Dembélé had the greatest impact on results (Kucharski, 2016).

 

The data generated during practices can be classified as data of the club, but as mentioned before, it does not stop there. With the rise of smartwatches data of players can be collected 24/7. As a result, hours of sleep, movement and other factors. The clubs pay the players to perform at their best, but currently there is a thin line between private data and club owned data (Marr, 2017). On the contraire, there are individual sports, like tennis, where players can completely benefit themselves of all the data but with that are also responsible for equipping themselves with it. With this, the fear for unfair competition is even bigger in individual sports.

 

It is clear that data and analytics have improved decision making in sports and that these decisions will be further optimized in the future. However, the collection of data is dependent on the resources a club or individual player possesses. This discrepancy will probably cause an even bigger gap between the top and the rest. Furthermore, the ownership of data is still in a premature phase and guidelines and rules are needed to protect players. If this happens, the clubs, players and fans will all be winners.

 

 

 

References

Hancock, A. (2019). Sports data groups battle over lucrative rights | Financial Times. Retrieved 4 October 2019, from https://www.ft.com/content/395d3de4-e08a-11e9-9743-db5a370481bc

 

Kucharski, A. (2016). How science and statistics are taking over sport. Retrieved 4 October 2019, from https://www.newstatesman.com/politics/sport/2016/04/how-science-and-statistics-are-taking-over-sport

 

Kumar, S. (2018). 6 Ways Augmented Reality Is Disrupting The Sports Industry : ARP. Retrieved 4 October 2019, from https://www.augrealitypedia.com/augmented-reality-sports/

Malone, E. (2019). Uefa financial report highlights growing gap between rich and poor. Retrieved 4 October 2019, from https://www.irishtimes.com/sport/soccer/uefa-financial-report-highlights-growing-gap-between-rich-and-poor-1.3762998

 

Marr, B. (2017). The Big Risks Of Big Data In Sports. Retrieved 4 October 2019, from https://www.forbes.com/sites/bernardmarr/2017/04/28/the-big-risks-of-big-data-in-sports/

 

Slaton, Z. (2012). Game Changer: MCFC Analytics Releases Full Season of Opta Data for Public Use. Retrieved 4 October 2019, from https://www.forbes.com/sites/zachslaton/2012/08/16/game-changer-mcfc-analytics-releases-full-season-of-opta-data-for-public-use/#72ab255e6c19

 

Steinberg, L. (2015). CHANGING THE GAME: The Rise of Sports Analytics. Retrieved 4 October 2019, from https://www.forbes.com/sites/leighsteinberg/2015/08/18/changing-the-game-the-rise-of-sports-analytics/#34e8204a4c1f

 

Wooden, A. (2019). How Big Data And Analytics Power Formula 1. Retrieved 4 October 2019, from https://www.intel.co.uk/content/www/uk/en/it-management/cloud-analytic-hub/big-data-powers-f1.html

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Satellites might not be visible for the human eye during the day and often not by night, but they have a pivotal role in sharing information. Without even realising it, humans are dependent on satellites with multiple daily activities. From watching television, to checking the weather forecast on an application and sharing locations with your friends on Snapchat, they would not function without the current technology in space. With the commercial exploitation of space, the number of objects orbiting around the world is over 23.000 and still growing exponentially (Liou, 2016). This creates seemingly endless opportunities, but dark times might be lurking around the corner and this will cost the satellite dependent companies a lot of money.

Since the technology that is shot to space is often a one-way trip, it is doomed to fly there in eternity. Of the 23.000 objects only 12.000 are in active use, leaving the rest to what is known as space debris and collectively space junk. This junk contributes nothing to any business or society, and it can be concluded that space is not used cost efficiently. Furthermore, the space debris can collide, with each other or operating objects, which they start to do more frequently (Liou, 2016).

There are over 500 accidents with space debris, which account for 2% of the entire population but is expected to grow since the probability of collisions increases with the number of items in space (Braun et al., 2017). This implicates that companies dependent on the satellites are not able to operate or not with full capacity. To illustrate the consequences with an example: one of DigitalGlobe’s satellites, the WorldView-4, generates 85 million dollars of revenue for the company. The satellite broke down at the beginning of this year and could not operate. Because of that the company was not able to sell their service to their clients, like GoogleMaps, which resulted in a loss of revenue. Furthermore, the insurance costs of the satellites were 183 million, which lead to major loss of almost 100 million dollars, even without the unearned revenue (Grush, 2019). Fortunately for DigitalGlobe, the accident was no collision when even more costs could have incurred.

In the case of a collision, another factor becomes important: responsibility. Since there are legislation around space is still premature, responsibility and with that accountability is hard to determine. The fact that companies from different countries operate in space, makes it hard to determine a place for jurisdiction. With that, the prediction of space lawsuits is not bright. Because of the mentioned factors, the estimated costs for lawsuits in space are higher because of the complexity (Chrystal, 2011).

It can be concluded that the commercial space exploitation created a thriving scene for information sharing technologies. However, the growing crowd in space also has some downsides. The collisions and breaking down of satellites can eat away major sum of a company’s funds. Furthermore, the additional costs of complex lawsuits will attack these funds as well. Humans are getting more and more dependent on satellites but there is probably a price to pay.

References:

Braun, V., Horstmann, A., Reihs, B., Lemmens, S., Merz, K., & Krag, H. (2019). Exploiting Orbital Data and Observation Campaigns to Improve Space Debris Models. The Journal Of The Astronautical Sciences, 66(2), 192-209. doi: 10.1007/s40295-019-00155-6

Chrystal, P. (2011). On collision course for insurers? [PDF]. Retrieved from https://www.swissre.com/dam/jcr:b359fb24-857a-412a-ae5c-72cdff0eaa94/Publ11_Space+debris.pdf

Grush, L. (2019). Fixing broken satellites in space could save companies big money. Retrieved 25 September 2019, from https://www.theverge.com/2019/1/10/18173600/worldview-4-satellite-servicing-repair-gyroscope-space

 Liou, J. (2019). Growth of Orbital Debris [PDF]. Singapore: NASA. Retrieved from https://ntrs.nasa.gov/search.jsp?R=20160012733