Features

The Milwaukee Brewers are described by Major League Baseball (MLB) as “the model” for the sport’s use of offfield analytics, despite being one of its smaller-market and, in playing terms, less-successful franchises.

Major League Soccer (MLS) runs a CRM programme it calls its ‘fan funnel’; a four-phase process beginning with data acquisition, which it pursues through internal and external sources.

The quality versus quantity issue at the heart of all datadriven strategising applies not just to the sourcing of information, but the analysis of it too.

The first phase of the data analysis process is about preparing not just the data, but the systems that will be used to interrogate it.

With a cleansed and consistent set of data to work with, the data-driven sports organisation now moves on to analysing this information to identify the trends, anomalies and correlations that can best inform its commercial strategy.

The identification, sourcing, cleansing, integrating, mining and analysing phases of the data analytics process are all positioned as successive rungs on a ladder that lead the datadriven organisation to new insights about its customers, its business practices and its future ambitions. It is, however, easy to lose sight of that destination along the way.

Insights are the tangible results of the analysis process, but ultimately they mean little if they are not translated into immediate or longer-term actions that will have a demonstrable impact on commercial performance.

Diny Hurwitz, Data Analyst at the Milwaukee Brewers, identifies three priorities in their approach to database analysis.

The Oakland Raiders are the only National Football League team to share a stadium with another professional franchise in a sport other than football (soccer): the Oakland Athletics of MLB – coincidentally, the team at the centre of Moneyball.

There is another powerful but simple reason why advice to the budding data-driven sports organisation centres on prioritising quality over quantity – not all the numbers will add up.

The risks related to the use of big data as a determinant of business strategy can typically be categorised as stemming from two primary sources: issues with the data itself, and issues with the people analysing it.

The principal means of avoiding many of the pitfalls associated with reliability of big data sit with the preparation element of the analysis process described in Chapter 4 – ensuring the information to be interrogated is clear, consistent and cleansed of obvious errors, blanks and duplications.

In the world of digital marketing and communication, content, they say, is king. In the world of big data, however, it is context that wears the crown.

Unstructured and semi-structured data has been made fashionable by its association with social media, and the new possibilities these channels offer of collecting first-hand opinion and sentiment in real-time. However, this data has long been a part of the sports research mix – on the brand side at least – through quantitative and qualitative surveying.

Survey-based research typically falls into one of two categories

Social media has emerged as a platform for sports consumption with a global reach and significance arguably beyond that of any other