Data analytics and sports. You didn’t suppose how much they have in common

Beyond the basics, modern sports has nothing in common with what it was, let’s say 50 years ago. One of the many, many reasons for this is the data analytics and forecasting.

We are living in a world full of data and everyone can benefit of it with proper analytics modelling. Speaking of sports it helps decision making in player selection, customer/fan relationships, business management, game and players performance and even injury prevention.

You didn’t think about the last one, right? Let us, the team of A4Everyone, to share few analytics applications in sports.

AC Milan, one of the greatest Italian soccer teams is advanced in injury prevention via its own MilanLab, internal data science group. The soccer team scored 90% reduction in injuries in 2003 — the first full use of MilanLab’s establishment — compared to the five previous years, and they have remained low since then. AC Milan made such an achievement via 60,000 data points which was tracked on each player. Mental, biochemical and muscle-structural data were gathered in every couple of weeks. This way MilanLab made injury prediction, hence prevention, possible.

Sure, this approach is used for fitness tests which players should conduct before signing a contract. If some of the values are far from the analytical set up, this might block the contract signing.

In basketball for instance is known that players which heart rates exceed 160 beats/minute for over two consecutive minutes tend to run much slower in the last quarter of games. This way coaches are aware who and when to rely on.

How about the ticket pricing? Why some games are less expensive than others? The simple answer is because fans interest rate is different. Where is the golden median? Data analytics is able to predict fans interest based on historical data as well to give you a price range which will give balance between interest and ticket pricing. In Major League Baseball, 26 of 30 teams use some sort of ticket pricing analytics for more flexible pricing.

Let’s talk about volleyball! Team statisticians are among the key backstage players in volleyball. They record every attack, pass, movement, strike, etc. This way, coaches will know with proper accuracy the success rate of attack, performed by particular player in particular rally. Go beyond players and move to positions – outside hitters success rate is ranging between 54 – 71%, while the middles are all between 73 – 77%, says Mark Lebedew in his Volleyball Analytics blog.

Data analytics is highly used in motorsports in terms of pit stop strategy. Such approach is also used for attracting players from lower leagues. Their performance data is projected and compared with successful player’s transfers.

And guess what! We`re in front of sports analytics explosion having in mind wearable gadgets are able to collect huge amounts of real time data of any kind.

 

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  1. Pingback: Leo Messi should be the best-paid football player but he is overpaid. Data analytics explains why | A4E Blog

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