Background

Being a professional rugby player in South Africa is no small responsibility. After soccer, rugby is the most popular sport in the country. The Springboks, South Africa’s national rugby team, have played at the Rugby World Cup 7 times since 1995 and won the championship in three of those years. They are currently the top ranked rugby team in the world according to the World Rugby Ranking, which rates teams based on their performance and successful game results. 

For the Springboks players, keeping up with these achievements is tough. As one would expect,  the team has a very passionate fanbase, especially when it plays against other dominant rugby teams, like the New Zealand All Blacks (ranked no. 2 worldwide) or the national English team (currently at no. 3). Peak performance from the Springboks is expected at every match, as they have a champion title to defend and a legacy to keep building upon. So besides relying on their training or their physical strength to stay at the top, the Springboks exercise another powerful tool: data analysis, partly operated by Mobii Systems. 

Case Evaluation

To keep playing at their peak, the Springboks have to optimize their decision-making processes. The way they choose to position each player in the field is just as important as how they need to be individually trained. That is where data steps in –not just raw and unfiltered, but presented in a way that optimizes the analysis.

The goal was to achieve a sports data analysis strong enough to reveal insights that would benefit all stakeholders in the team: players, coaches, analysts, referees, and even medical practitioners. With the adequate snippets of data, the insight-producing process could be replicated among all stakeholders so the learnings could be used individually or collectively.

Proposed solution

Mobii Systems has been implementing a data analysis platform for the Springboks since 2013, which has become a part of their sports intelligence efforts. The platform is continually evolving, but it operates under three fundamental objectives:

1. To manage the players’ performance
2. To understand team play dynamics 
3. To profile and understand opponents 

Managing the players’ performance means looking after them so they avoid injuries by overtraining. In other words, to analyze their movements, play-by-play execution, and overall   conduct on the field during matches. Understanding in-game team play dynamics is necessary to assure that team members are playing at their peak collectively, not individually. And going through each game provides useful information to profile and understand rivals for upcoming matches.

Perhaps most importantly, the Mobii System platform allows the Springboks to make the most out of their high-performing teammates by finding insights quickly thanks to an easy, collaborative and highly detailed data set. Achieving a consistent peak performance as a team is extremely difficult if the team members come from all over the world and have different training backgrounds, which is what happens with the Springboks. That is why understanding diverse dynamics is key to successfully exploit their assets and achieve peak team strategies and execution. 

To achieve this understanding, Mobii System’s platform leverages on three types of state-of-the-art sports data analysis technology: 

– Video analysis. Mobii created a standardized data model defined by the Springboks team to look for specific types of events on all video footage. It is a time-saving, work-efficient way of analyzing individual or team plays: instead of inputting video files manually, the system tracks the Springboks’ performance KPIs –whether they be plays, scoring, or ball possession, for example– and allows for a more comprehensive way of decision making.

– Platform simplicity. Analysts are not programmers: they don’t need to be inputting data as much as analyzing it. To easen their workload, Mobii developed an easy platform with consistent statistics, standardized metrics and in-depth data that allows analysts to seamlessly compare a set of player profiles or evaluate certain positions from determined viewpoints. This is possible because of two main features: the playlist function, which allows revisiting specific moments of the match from several angles and a more thorough analysis of the footage; and a standard code structure that allows Mobii to pull up and tag complex data.

– Cross-organization unified data. Creating a rich, detailed data set for analysis tends to be a very manual process, often relying on several data sources coming from several providers, historical public data, or in-house information. Mobii’s platform for the Springboks has automated the data flow, making it more structured so analysts don’t have to resync manually and all workflows are easily found, thus reducing the workload. Such a good data set is used for decision making processes that are used cross-organization. This benefits referees, medics, and media teams. As it is very detailed, this data can bring rich insights to everyone involved, depending on what they are each looking to find.