NHL

In order to improve revenue for the NHL, we analyzed hockey datasets to better improve team performance and drive a more engaging fan experience.

Opportunity

How might we potentially encourage the NHL to surpass other major league organizations in terms of revenue?

To focus on driving more impactful business operations and customer engagement, we determined that there was the opportunity for the NHL to gain more revenue by increasing a team’s on-ice performance as well as fan engagement by using qualitative and quantitative analysis methods.

Descriptive Analytics & Predictive Analytics

Two of the key methods used for quantitative analysis in order to better capture the understanding of how on-rink performance affected NHL revenue included descriptive analytics to understand how faceoff wins affect the overall team’s on-ice performance as well as logistic regression uncovered that playing games at home, power plays/faceoff wins, and goalie save percentage were key indicators as well.

Sentiment Analysis, Text Mining & Cluster Analysis

Using the Buffalo Sabres and Chicago Blackhawks teams as comparison use cases, sentiment and text mining was conducted to understand what key words and associated emotive feelings were associated with a poor performing team against a high performing team. The competitor was used as a comparison benchmark to draw detailed learning opportunities and actionable recommendations that were done differently in a team’s Twitter activity.

Fan Personas

To visualize the information, I led the creation of key personas from my design background to personalize the data and add a more empathetic approach to the team’s business decision-making. These personas were created based off of two key groupings of fans from the extracted Twitter data. One group was one that was depressed over the team’s performance and overtly critical of what they see on-ice while the other group is analytic, personable and upbeat when it comes to hockey games.

Social Network Analysis

To uncover the biggest contributors to the Buffalo Sabres as a use case example, three key fan accounts arised from the analysis that had the largest connections to their nodes that could potentially be key targeting opportunities for the NHL.

Recommendations

Using the quantitative and qualitative data analysis findings, our team constructed two main actionable recommendations that the NHL can leverage to gain more revenue. By tailoring on-rink performance and increasing fan engagement & experience for each NHL team, our predictions are that the NHL as an organization will gain from an increase in revenues.

Reflection

By working on this project, I got the experience to determine a self-directed data analysis approach for a generic dataset in order to derive meaning from it and provide actionable recommendations that the NHL can take. This project experience increased my confidence in being ability to fuse together my design and analytics background by being able to see how data can be synthesized in design formats and then communicate the findings to the audience in a clear and articulate manner.