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A Numbers Game

We look at the statistics behind sports analytics

17Oct

Many sports organizations have had varying levels of success whilst integrating analytics into their decision-making processes. The issue that many are finding is that challenges executing on analytics projects are different and often more difficult than traditional projects. Fortunately, there are some who have learned how to overcome these challenges and maximise the benefits of analytics adoption.

Through collaborating with analytics experts globally, a number of best practices have been created to help teams, franchises and businesses with their implementation. From these best practices and our experiences, the Sports Analytics Program Management (SAPM) framework was created. This program includes:

- Senior management commitment to an analytics culture

- Establishing an effective analytics champion

- Assessing current analytics competency

- Setting realistic analytics goals that provide short term value to the organization while supporting analytics competency growth

- Aligning analytics goals with those of the organization

- Execution and avoiding common pitfalls

We are increasingly seeing organizations that are adopting analytics in an effective way gaining competitive advantages over their rivals, both in a sporting and business sense. It is important for those working within sports to recognize this and see that the initial difficulties in implementation will eventually pay dividends.

A Framework for Sports Analytics Success

Our sports analytics experience comes through working with a number of National Hockey League (NHL) teams. It is well-known that the widespread use of analytics in hockey lags behind other professional sports such as baseball and basketball. While the reason for this adoption lag is beyond the scope of this article, it provides a useful case study to examine the framework that a sports organization can use to achieve success with their analytics program.

1. Senior management commitment to establishing an analytics culture

Researchers have found that having management support for analytics including top-down mandates to be a critical cultural characteristic in organizations that achieve the most success through their use. This commitment from senior management is particularly important in sports organizations due to their highly vertical structures ensuring that all major personnel decisions are made from the general management office. As a result, decision-making methodologies not supported by senior management are difficult to sustain.

This commitment to establishing an analytics culture includes taking on people with significant analytical skill and experience. Those who possess these skills understand the needs of technologies and analytical techniques and can therefore execute efficiently on analytics projects.

2. Establishment of an effective analytics champion

An effective analytics champion most often has a strong belief that increasing their analytical competency is critical for their organizational and individual success. Supported with information provided by analytical tools, the analytics champion is committed to the idea that they will make marginally better decisions over time and in the long run will be rewarded. To achieve success it is important that they have the ability to initiate changes to existing processes and influence the overall strategy. Sports organizations generally have a relatively slow adoption rate when implementing analytics programs and related technologies, so it is important that the analytics champion does not overestimate the short-term impact of their program. They should commit to allowing their program and ideas to grow in alignment with the adoption amongst the people and divisions around them.

3. Assessment of current analytics competency

Organizations typically struggle to assess their analytics competencies and as a result they aren’t evolving quickly or seeing significant ROI. Davenport, Harris and Morrison (2010) describe the DELTA framework for assessing the analytical competency of an organization, where the dimensions of the framework correspond to Data, Enterprise, Leadership, Targets and Analysts. We have created a modified version of this framework that allows sports organizations to quantify their level of competency across these criteria.

4. Setting realistic, valuable, growth-oriented analytics goals

When setting analytics goals, sports organizations often expect results too quickly or in unattainable depth in a small space of time. In their book, Competing on Analytics: the new science of winning, Davenport and Harris (2007) describe a series of analytical techniques each providing increasing competitive advantage . From lowest to highest these are:

- Standard reports

- Ad hoc reports

- Query/drill-down

- Alerts

- Statistical analysis

- Forecasting/extrapolation

- Predictive modeling

- Optimization

Skipping techniques along this series can lead to wasted effort mainly due to an inability to gather actionable data without each process initially. For example, the creation of a broadly used, recurring standard report (less complex) is likely more valuable than an optimization model (more complex) that does not get acted upon.

Each new project is an opportunity for an organization to grow its analytics competency. One way to achieve this growth is by moving to the next level of analytical technique as a new project is initiated.

5. Aligning analytics goals with the organization

It is important that analytics goals are aligned with the broader organizational goals. Laursen and Thorlund (2010) provide their “Business Analytics Model” that can be used to ensure that analytics goals are aligned. It is very easy to concentrate on what may be interesting rather than what makes significant improvements. Analytics projects that are not aligned with organizational goals provide little value and in doing so threaten the viability of future projects.

6. Execution and avoiding common pitfalls

There are a number of common pitfalls that organizations, and more specifically leaders, succumb to that decrease the likelihood of a successful sports analytics program.

One pitfall is attempting to implement an analytics program without hiring personnel with expertise. While many people believe they are capable to analyzing sports data, the production processes of many professional sports are complicated. It is very costly in terms of company resources and the long-term viability of the analytics program to make decisions from inadequately trained personnel that are unable to incorporate the underlying production process of the sport. While analytics has shown to be effective in baseball, the underlying production processes of many other sports are more complicated.

Another pitfall is the belief that more/higher-quality data is required to discern reliable information. In general more data is better than less data. However, the collection and analysis of more/higher-quality data can be costly while organizations often have an abundance of unused and extremely detailed data. Furthermore, more data is often not necessary to create an effective analytics model and/or process. A misconception among sports decision-makers is that information on how an outcome is achieved is necessary to measure a player’s contribution to that outcome. Adequately trained personnel can identify a player’s contribution to an outcome without analytical data that may show unviewable information.

Finally, the data management and program costs are often underestimated. One of the pillars of a successful analytics program is ensuring that data is available in a presentable way to decision-makers and analysts. Partnering with an analytics platform provider is likely to be the most effective solution for sports organizations that are in the early stages of their analytics program. This allows them to build internal competencies while simultaneously benefiting from the information discerned from the analytics.

Conclusion

Combining the best practices developed by practitioners and researchers with their experiences of working with sports organizations on their analytics programs, we have constructed a Sports Analytics Management Program (SAPM) framework. This can be used to design, implement and measure a sports analytics program as it grows. It includes clear support from senior management, the establishment of an analytics champion, setting effective growth-oriented goals that are aligned with the organization and hiring trained analytics practitioners.

Effective use of analytics allows decision-makers to be more informed and consistently make better decisions. By implementing their analytics program through a structured process, sports organizations increase the likelihood that the program will be effective, valuable and sustainable.

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