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Changing the Game with Sports Analytics

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Sports clubs have a lot of data from a wide range of sources. For the players themselves, there is physical data, about their condition, there is medical information, and there is performance data — e.g. goals scored or saved, time on the pitch. Clubs also have plenty of data about the other aspects of running a team, such as ticket prices, sales, season ticket patterns and so on.

Utilizing data isn’t new for clubs. Performance analysis has been around for many years now, where teams film matches and analyze footage through live or post-match ‘tagging’; marking a variety of key activities that are occurring in a match. Alongside this, it is common place for teams to use GPS-based systems to analyze players’ movements around the pitch, often termed physical data. However, these two sources are often looked at separately to performance data and by different individuals in the club.

The issue for many clubs is that they are not able to pull together data from all these sources and silos to get a ‘whole club’ picture of the player. This means that they cannot fully take advantage of all the information available to make links between departments and sections, and maximize value – the players’ time on the pitch. This is where analytics comes in, and particularly the use of an analytics platform to unify the data in one place.

However, sports teams and clubs that have started to use analytics platforms have successfully created a ‘whole team’ picture. They are using analytics to improve team performance by reducing injury risk, analyzing and managing training, and predicting performance. They are also finding that it is possible to increase revenues by improved ticket pricing taking into account demand, and to retain season ticket holders by better churn analysis, as well as improving marketing and merchandising. This has a considerable effect on the bottom line.

Taking just one example of physical monitoring, analytics can help reduce injuries by managing training load better. Monitoring the training load for each player means that coaches know how each player or athlete is responding to training, and whether to rest them, increase training, or stay at the same level. This means that each player receives exactly the right amount and type of training to get them into the required condition for competitions. This can be done in real-time, using a laptop on the field working from pre-defined boundaries or by altering the training they receive the following day, using pre-defined ‘norms’ for each player determining how long they would need to train. This allows them to be in optimal physical condition for competition. At the same time, it reduces the risk of injury, meaning that players spend more time on the pitch — essential given the investment that many clubs have made in their top players.

SciSports, a professional sports analytics company, provides solutions to clubs to enable them to analyze player performance one step further than traditional performance analysis. Even today, football in-game data often still is being captured manually making it labour-intensive and by nature prone to inaccuracies. Thanks to the ‘BallJames’ camera solution developed by SciSports with support of SAS Analytics, clubs can now have an accurate and fully automated tracking system giving entirely new insights in 3D on movements and tactics without any sensor on the player or the ball. This for example enables clubs to determine a players’ distance from other team members, the ball and what they do in terms of skill on the ball. This provides insight into a players’ positional play, formational discipline and how good they are at predicting the next move. A massive advantage of this is the ability it gives clubs to more easily get access to more reliable player data, enabling them to scout undervalued players who fit their style of play and may have previously slipped through the net. SciSports’ approach means that clubs & coaches are more likely to find and benefit from these players.

Improving player performance and keeping players injury-free are essential for any sports club, but engaging effectively with fans is also crucial. A key example is getting ticket pricing right to ensure the stadium is full for each match, so fans are not priced out of the market in favor of corporate entertainment. Analytics can also be used to improve variable pricing, and ensure that prices are appropriate for the different types of demand and/or level of the game.

More effective fan engagement is possible with analytics, by sending them statistics and vouchers direct to mobile devices. Examples include image recognition to determine when a fan is becoming emotional with a game, triggering a voucher to their phone, to enhance their experience. It is also possible to use push notifications to direct fans to quieter ticket kiosks, gates or concession stands. Finally, analytics offers a way to identify season ticket holders at risk of not renewing, particularly through their social media activity. This can make it easier to work out how to engage with them again and reduce churn.

Some other practical ways to integrating analysis into sports are possible through:

  1. Getting help from the ecosystem: Pro sports teams are essentially small businesses. They can’t afford to build up big IT or analytical staffs, to gather all their own data, or write their own software. Some teams have created strategic partnerships with vendors of data, analytical software and hardware, and communications. In order to assist them with sports analytics.
  2. Take an enterprise approach to organizing analytics: Most teams don’t nurture much contact between analysts working on team and player performance and analysts working on business analytics, such as ticket pricing and targeted marketing. But given their small size, teams would be better served by combining these groups of analysts and prioritizing the problems they work on. Businesses are more likely than pro teams to have taken an enterprise approach, but there are still many teams with analytical silos that don’t collaborate.
  3. Enlist the external public to help with analysis. Amateur fans have long been some of the most talented analyzers of sports data. Some very capable amateurs have been hired by teams to help them with analytics. Some teams have even gone to the extent of releasing their own player and performance data to any fan who wants to analyze it. In such a scenario, businesses should think about open data initiatives and enlisting their customers in solving key problems.

The bottom line is that analytics can give a strong competitive advantage, and in an area like sports where the smallest margin dictate success and failure, that matters.

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