Securing high quality coverage in the US

The brief

Betting.com wanted to earn links and coverage from high authority publications in the US with a focus on NFL team-centric publications. Distinctly were briefed to create content that can be used throughout the NFL season.

The objective was to create a campaign that would include all 32 NFL team as well as encompassing 22 states. As such we examined a story that would allow us to analyse data relating to each of those teams, giving us a wide range of local and sports journalists to target, as well as stories that would appeal to as many states as possible. 

Our strategy involved searching and creating data-sets that can play into the supposed bias, or unfairness, in the NFL among fanbases. This was particularly pertinent because there had been several viral trending tongue-in-cheek stories about the ‘script’ being leaked for the NFL. It’s also regular for all fans to believe there is a referee bias against their team, and there is also regular discussion on social media about announcers, with people having their favourites and least favourites. 

This led to us brainstorming around how we could capitalise on this combination of emotional factors, but prove the subjective nature of that in an objective way, whilst providing us a story we can serve to press for high authority, relevant US links.

What we delivered

Our campaign: Bias in the Booth utilised a unique approach to data that proved to be the most comprehensive data-set we have worked on for Betting.com. We found the “Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts” data-set, a set of hundreds of thousands of announcer transcripts from 50 years of NFL history. It was initially created to examine racial biases by academics contributing to a journal, as such providing a high amount of authority we were able to piggyback on by adding our own methodology. 

We wanted to discover which teams were talked about with bias. In order to get the context of each team, we utilised ProFootballReference’s dataset of tens of thousands of players, and matched up each name to a year and team. From there we were able to isolate each string of commentary and attach it to a team, and then run a sentiment analysis using Python’s natural language toolkit in order to discover whether it was a positive or negative comment. 

This gave us the teams that are spoken about more positively in NFL history, and the team’s that are spoken about more negatively. We then turned this into a blog post, and created multiple graphics for the blog, too. 

We then created a set of national NFL journalists, local NFL journalists, and finally team blogs and newsletters to which to target the individual and overall stories that we had pulled out of the piece.

The data

Our experience working with larger datasets allowed us to get the most from it as well as utilising other sources available. Using a custom script to crawl the data gave us a unique and informed view of the landscape and helped us to establish different angles and approaches to the story.

The outreach

When it came to outreach, we initially focused on a nationwide angle for our objective of high authority US publications but adapted to a strategic and detailed approach to be locally focused, pulling out key examples and highlighting specific issues that local teams had suffered.