IBM Helps Leicester Tigers Predict Their Game

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Predictive analytics will be used to help the team reduce number and severity of injuries

Leicester Tigers, one of the UK’s most successful professional rugby teams, have today announced the partnership with IBM that will see predictive analytics used to improve athletes’ performance.

Tigers will use the SPSS Modeler to analyse game data. Being a contact sport, as many as one in four rugby players are injured during each season. Many teams are turning to analytics to understand and reduce their players’ injury rate.

The SPSS Modeler aggregates and processes thousands of data points that are collected during each game, using Big Data analysis algorithms.

Knowing the future

Professional sport is an increasingly technical and scientific business. There is a huge amount of information that can be collected, from health records and psychological profiles to GPS and accelerometer data. Nothing is safe, even the genetic material. Next year, Leicester Tigers are introducing a DNA test that might shed some light on what it takes to become great at rugby. The biggest challenge professional steams face is to use all the available data all of the time.

Nine times champions of English rugby union’s Premiership and two times European champions, the Tigers will use IBM’s SPSS Modeler for data mining. It should help them assess the risk of injury to players, and even deliver personalised training programmes for vulnerable team members. According to IBM research, organisations that use predictive analytics are 2.2 times more likely to outperform their peers.

“Our team has always been proud of challenging at the top of national and European rugby competitions, but it gets more competitive every year and our focus must be on helping our players stay injury free for longer,” said Andrew Shelton, head of sports science for Leicester Tigers.

“There is a tremendous value to be gained by retaining experienced players within the squad and we are confident that, by adopting IBM predictive analytics, our team will be able to leverage data about the physical condition of players for the first time and considerably enhance our performance.”

Nurturing talent is also an important aspect of team success. The Tigers will start using the IBM predictive analytics solution at their under-19 Academy players to create a more refined selection process, and to ensure a higher percentage of young talent is brought into the first team.

“Sport is no longer just a game, it’s becoming more and more a scientific undertaking which is driven by data and numbers,” commented Jeremy Shaw, IBM’s Business Analytics lead for Media and Entertainment.

Second Screen

IBM also told TechWeekEurpope about its vision of the future of sports broadcasting. Using 3D cameras and movement tracking, it is possible to create a complete model of the game in real-time. This model can be used to supply viewers with a huge amount of interesting information.

“About 70 percent of people are often using a second screen while they are watching sports,” said Shaw. “It can be their phone, laptop or a tablet, which can be used to access social media, for example. And we thought: “why not take advantage of that to enhance the TV experience?” We can do that with analytics, in real-time.”

“The 3D capability allows you to identify players on the pitch, as well as the ball, and track them individually. You can then display a feed about how the team was performing in the previous games, and what do they need to do to win. For example, maybe they need to spend 55 percent of the time on the opponent’s half of the pitch. We can display possession stats, and how much possession a team needs to win. We can predict what impact a substitution will have on the game.”

“The other thing we are looking at is what people are thinking about the game. For example, Twitter analysis. We can measure positive and negative sentiment, what do viewers think about the last goal or referee’s decision, and track it in real-time.”

In the future, predictive analytics could have a variety of uses, from healthcare and retail to tackling youth unemployment, by predicting when someone is on a path to become a NEET.

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