Big Data analytics has been changing different industries of the world. We can see that it has already had an impact on the entertainment, energy, healthcare, and higher education industry. If we take a closer look to the sport industry, we can see that industry is also not an exception. The sport industry is an enormous business for all of its primary stakeholders (e.g. associations, teams, athletes, media, and sponsors) and it is still rapidly growing yearly. That is also another reason why technological development is necessary to help sustain its growth in the future.
Classical statistics has always been a major part of sport analytics since its beginning. However, the Big Data revolution started with the so called Moneyball effect in the sport industry parallel with the technological revolution in the 2000s. The Moneyball method means that a player performance should be only measured and rated by using a data-centric approach rather than a subjective opinion and intuition. For today, profound data analytics is used by all organizations in all major sport leagues in the USA (e.g. NFL, MLB) in order to improve the teams’ performance and results. Additionally, Big Data analytics has also recently become more important and used in Europe (e.g. English Premier League, Formula 1) as well.
By having access to vast amount of data, sport scientists can utilize this information to create competitive advantage for their teams and athletes. The data is mainly gathered and used by wearable sensors, state of the art cameras, and motion analysis software. Then this information is transformed into knowledge. This competitive advantage can be increased efficiency, more precise performance forecast, and the best decision for team formation and line up for a match. For instance, the German national team also used the help of SAP’s Big Data analytics software during the 2014 World Cup. According to Joachim Löw (head coach of the German national team), the software provided them a genuine competitive advantage during the whole tournament and helped them to win the World Cup.
Moreover, Big Data analysis is not only important for primary stakeholders in the sport industry. It also improves customer experience and entertainment. The sport supporters can get more insight about the game by interactive applications and real time statistics. Fans can have the so called ‘second screen’ with relevant information parallel with the live game that makes the game even more interesting and understandable for them.
In the future, there will certainly be even more significant improvements in Big Data analytics in the sport industry. We can expect that the importance of Big Data analytics is going to increase and the information provided to coaches is going to be even more helpful (e.g. omnipresent real-time analysis) for the teams’ performance. On the other hand, since we are all humans we will be never be able to reduce the luck factor to zero in any sports, especially in team sports. Thus, sports will never get boring just more transparent and maybe even more complex for everyone.
References:
https://www.technologyreview.com/s/600957/big-data-analysis-is-changing-the-nature-of-sports-science/
https://www.targit.com/en/blog/2014/11/9-ways-big-data-analytics-changing-the-world
http://www.datapine.com/blog/big-data-in-sports-revolution/
http://www.techweekeurope.co.uk/data-storage/how-big-data-is-changing-the-sports-industry-182365
http://bigdata-madesimple.com/big-data-an-ultimate-weapon-in-the-field-of-sports/
It’s an interesting development in the industry. Some sports (e.g. field hockey) are already using advanced analytics for quite some years, while others (soccer) are just getting started. It’s nice to see it’s becoming more widely used in sports in general.
Believe it’s also extremely interesting for the ‘consumers’. It gives a new, extra dimension to watching sports.
What is nice to check out is fivethirtyeight.com, its a website purely focused on research and analysis of (sports) statistics and how they relate to a prediction in performance. They started in 2008 with the MLB and became so successful in the sports analytics that they started analysing politics as well. In 2008 they predicted the outcome of 49 out of 50 states in the presidential election!
Indeed Ruben, it is really interesting topic. I hope sport big data analytics will evolve even more in the near future. Thank you for your constructive comment and suggestion about the website! I will definitely check it out.
Interesting article Zoltan. However, I was wondering whether you believe if the big data analysis will overcome the nature (nature/nurture e.g.) effect? Do you believe that aspect can be completely overruled by the analysis? And if big data analysis leads to such significant improvements, shouldn’t it be considered the same as other illegal performance improvements such as doping?
Hi Elisa!
I personally do not believe that big data analytics will ever be able to completely overrule the nature effect since data can only be gathered from past events. So, we cannot predict the future completely. Moreover, the teams will be just forced to be more innovative and creative in the future by coming up with unprecedented strategies and tactics. I think it will just make sports even more spectacular and renewed. In my opinion, the big data analysis only helps teams to be more prepared from their opponents. So, it just makes the teams even more equal mentally which is also better for the spectators. Since the games will be more intense.
I definitely do not believe that it should be considered as doping since the athletes does not use performance-enhancing substances they just become ‘smarter’ by the analysis. I think the only remaining question is about the application of the new technology that can analyse the opponent in real-time. In my opinion, this should be taken care by the sport associations to lay down the fundamentals of these rules. For instance, some teams broke these rules in the past (e.g. N E Patriots spy gate-http://yourteamcheats.com/NE#Spygate-2007).
I hope I have completely answered your questions.
Hi Zoltán, very interesting article about big data in sports. I personally think that data analytics should be used for analysing an opponent for an optimal preparation before the game. However, I don’t think players should only be evaluated based on data. I play icehockey and for example we have a plus/minus stat, which shows the amount of goals that my team scores, or gets against when I’m on the ice. This only applies for an even strenght situation (5vs5), which means that I can score 5 goals on the powerplay (5vs4), but get 3 goals against with even strenght. This would results in a minus 3 stat for me that game, while the benefit of me scoring 5 goals might be bigger for the team. What do you think about this?
Hi Tim!
Thank you for your thought provoking comment!
It is really interesting that you come up with ice hockey as an example. I am also a really big fan of ice hockey since it is one of the fastest and most intense team sports. For example, all players need to move together during the whole game. However, statistics is just a part of Big Data analysis. Big Data can measure way more things for instance the positioning, fitness of the players during the game etc. This can help the coach to put the best line up together for the power plays and for the normal game. However, in every sports there are statistics that never reflects the complete truth. For instance, the whole line gets points when they score and also there are two players who get points for assists next to the goal scorer. Even if the goal was scored by an individual action. Furthermore, I also believe that in every sports there are statistics that can be seen as negligible. Another example, in American football nobody cares about the passer rating of the quarterback. What matters is only to lead the offense to the end zone and win the game.
On the other hand, Big Data is getting more and more popular in ice hockey. I suggest you to read these articles if you are interested about the topic.
http://data-informed.com/data-pucks-and-money-analytics-applied-to-the-national-hockey-league/
http://dataconomy.com/next-up-for-big-data-ice-hockey-and-game-play-analytics/