Examining Top NBA Free Agents Using On-Court and Off-Court Analysis


There is little doubt that the Brooklyn Nets have already had one of the most successful free agencies in recent NBA history. This off-season has seen the Nets sign Kevin Durant, Kyrie Irving, and DeAndre Jordan to the team.

Many people define success by a player’s on-court performance. Yet, NBA teams are businesses, focused on maximizing revenue generation. NBA teams, like teams in every other sport, do not rely solely on on-court performance to drive revenue. The NBA in particular has often leveraged the star power of its athletes as the centerpiece of its brand identity.  

Therefore, players’ values should be determined by how their on-court and off-court performance drives team revenue growth. This is the foundation of B6A’s Revenue Above Replacement (RAR) model. We applied the RAR model and our Social Sentiment Analysis Platform (SAP) to examine on-court and off-court factors for five of the top NBA free agents.

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Our RAR and SAP products confirm Durant’s and Irving’s value using both on-court and off-court quantity-based (as opposed to quality-based) metrics as illustrated in the chart above and the chart below. For on-court performance, RAR uses the B6AWins metric. B6AWins examines multiple metrics to determine which factors are most likely to drive team wins during the regular season and how each player performs on each factor. Durant and Irving were determined to be ranked number one and number three in highest B6AWins.

For off-court performance, SAP first examines the total number of Twitter followers and the total number of posts during free agency. The goal is to determine the size of the potential audience and whether the audience is talking about the player. Durant and Irving had the highest and second highest totals for followers and posts, respectively.

 Maximizing revenue, however, should not only focus on quantity-based metrics. In B6A’s Corporate Asset Valuation Model (CAV), fit is one of the most important components in determining the overall value that a company receives from a partnership. We apply similar logic to the top free agents by using the SAP tool to determine the demographic profile of each player’s audience.

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In this example, we specifically focused on the income profiles of each player’s Twitter followers. Despite having the smallest number of followers, Leonard is the best able to target higher income demographics. This is likely one reason that New Balance has decided to sign Leonard to a multi-year shoe deal. More specifically, Leonard and New Balance have significant overlap in their audience demographic from an income perspective, making Leonard a good fit for the New Balance brand.

That is not to say that Irving and Durant do not have significant off-court value. For example, both players are still able to target higher income demographics more effectively than the TWITTER_BASE population, while reaching large audiences overall and generating significant conversation.

 As partners increasingly prioritize fit when evaluating sponsorship relationships, however, it is important to for teams to examine athletes as influencers that can more effectively reach a company’s core demographic. More specifically, star power should not rely solely on follower counts. The ability to engage with specific audiences that are valuable to companies should drive more sponsorship revenue to teams and can be a key factor in determining player values.