By Justin Rogers, Senior Vice President, BrandMatch Score
On Friday March 1st and Saturday March 2nd, Boston’s Convention and Event Center hosted the renowned gathering of the sports industry’s top owners, executives, and next crop of sporting experts at the 7th Annual MIT’s Sloan Sports Analytics Conference. Being our first time attending, we had heard how infinitely informative, educating, and productive the conference was. Yet, now a full week removed, it still overwhelms the mind contemplating the sheer impressiveness of it and what the overall content theme means to the ever-changing landscape of the business of sports.
It’s no secret analytics are abundant everywhere. Almost to the point it’s incomprehensible the sheer volume of not only the data, but also the number of industries and capacities that use them. Social media of course, has exploded a whole new industry of culling data – raw numbers on reach, sentiment, demographic splits, marketing uses and influence.
There are many reasons why analytics have constantly grown along new and different data point lines and industries themselves. Numbers are easier and more streamlined than language, or content. Spreadsheets are more efficient and hard numbers easier to process than a lengthy worded report. Not to mention, there are language barriers, be it actual dialects or industry terms and jargon. Figures help circumvent obstacles.
Analytics are versatile, or universal. They illustrate instant measurements and benchmarks, and most importantly, are based in the same metric as dollars. As we all know, those are the ultimate “analytics”. Fair, good, or not.
Incredibly there are still areas and angles that have not been measured using algorithmic means. Further, there are faculties, organizations and information across “aisles” that can be combined and compared using mathematical methods.
Which is why SSAC is such a great forum and showcase. There is always going to be a “next” something, but there are also current accepted practices and those that are testing, for lack of a better word. SSAC provides space for both well-known industry executives and those looking to break into it to deliberate on implemented products, test potential improvements, and discuss areas where these techniques are not used but need to be.
As an example, our company, Empirical Synergies, was there to present BrandMatch Score, a market research tool rating the compatibility between brands and potential endorsers. This product is enhancing and increasing the use of empirical data and proprietary techniques in researching and hiring spokespersons for companies.
This brings me to a panel I was able to attend, and where these expert professionals thought more and more information and data can be used – Beyond Reason: Sports Labor Negotiations(1).
Beyond Reason: Sports Labor Negotiations
The idea behind this particular panel was pretty simple; how can analytics be used in labor negotiations between teams and players associations to make the collective bargaining process more efficient, better leagues themselves, and of course avoid lengthy and contentious lockouts (or strikes).
There were overall intentions discussed; commissioners and owners creating trust amongst their constituent players by engaging them far more and earlier, separating leagues’ playing points (competition committees, eligibility, morality clauses etc) from financial considerations, changing people in the room to freshen and change (seeing opponents side) perspectives, “looking at the abyss” much earlier, and make negotiating difficult by, for instance, taking away anti-trust exemptions or rights (Sherman Act, TV Rights) leagues possess.
Throughout these discussions, there were two main bullets with respect to use of analytics – balancing market competition and rookie eligibility rules. Both of these, according to the panel, had the main principle of forecasting and using analytics to do so.
Righting Revenue Sharing
Market size competition and cultivation is arguably the most difficult task facing “federations” and franchises. With respect to collective bargaining, it creates strife not just among owners and players, but owners and owners and owners and leagues. There are currently many sets of data used to predict things like ratings, revenues, player contract costs and others. However, no process brings these points together to project full models of future market structure. This lends to the idea of both sides seeing the growth of the game, and not just their respective organizations’. After all, growth of the game means growth of your franchise.
To this point, Kevin Murphy, a renowned economist professor from the University of Chicago Booth School of Business talked about “working backwards”. Having goals of where the league wants to be with revenues, team shares, ratings, attendance et al, and using a full market model to predict how they can achieve this through each team. Not seeing teams as unequal individuals. Currently, they are.
Revenue sharing now is (mostly) determined through television deal money, attendance, and revenues and how each team performs(2). There are floors and ceilings of those eligible to “give or receive” based on market size. However they deal only with respect to gate receipts and revenues and whether qualifying teams get a “full share”. The NFL is different simply because of national and not local television deals.
However, with other leagues, can you calculate how to reach a revenue sharing figure by first calculating a weight to each market (other than simply qualifying), business effected, and not just how a team’s attendance and revenue fair? Shouldn’t there be different levels depending on the uncontrollable means available? And what about including ratings into calculations? Heck, with how the business world has turned the past 6-8 years, factor social media reach and contributions into this. Incentivize not just by getting people to games, but efforts in marketing and branding your franchise as well.
Tom Penn, an NBA analyst for ESPN brought up the case of the San Antonio Spurs, arguably the most successful on the court franchise of the past 10-15 years, alongside the Lakers. How then, with near perfect attendance and Top 5 television ratings, do the Spurs often struggle to turn a profit(3)? Should there not be some credit or subsidy for market size (again, other than simply qualifying). The financial impact of local business is a huge influencer is measuring market effect. Factor this into a revenue sharing model.
All of these points in getting as much hard data as possible for negotiations highlighted another objective; using all this data for both the current negotiation and preparing for the next, something most argue is completely ignored during current bargaining.
The second focus discussed regarded eligibility for rookies. There has been much debate on NBA and NFL league laws that prohibit legal adults from taking part in their profession. In the NBA, you must be 19 during the calendar year of the draft and be a year removed from your high school graduating class. In the NFL, you must be three years removed from your high school graduating class.
There were two arguments for the issues – How can a legal adult not be allowed to pursue their careers in their trade? And, why should teams in a league with ant-trust laws and protection have to be strapped with costs for younger, normally less prepared and talented employees? Of which, can and do take away from their ability to best improve their product. The answer of course was, new analytical data supporting cases for both.
Andrew Zimbalist had a great quote; “There are many anecdotes why younger kids should not join professional leagues. I want the empirical data on it.” With limitless information available to build support for cases, for and against, the question becomes, “How has this not been done yet?”
Quantify and compare how well 18 year-old players do with their financial gains. Calculate the difference in injury proneness of younger athletes versus seasoned. Measure and project what will happen to team’s salary caps, max contracts, midlevel exceptions, veteran minimums and the like if an influx of young athletes are introduced.
While nothing very technical (as in, the math itself) was discussed, clearly individuals who have been involved in every labor negotiation of the past twenty years know data is available to create new models of measurement over multiple and very important levels of professional sport. It is forever said, and often used in many an ad campaign, that change and improvement are always positive constants. Professional sports leagues have opportunities now, especially in a period of relative labor peace, to truly prepare and support their positions for proficient future negotiations and more importantly, the mutual growth of their respective games.
1This panel consisted of; Mike McCann, an award winning scholar and journalist of Sports Law, founder of the Sports and Entertainment Law Institute at the University of New Hampshire School of Law; Kevin Murphy, a University of Chicago (Booth School of Business) professor of economics focusing on labor rights and wage inequality; Tom Penn, an NBA expert of advanced analytics, salary structures, and collective bargaining issues; Andrew Zimbalist, the Robert A. Woods professor of economics at Smith College while being a visiting professor all around the world including Kyoto, Geneva, and Hamburg. The panel was moderated by Deepak Malhotra, Professor in Negotiations, Organizations and Markets Unit at Harvard Business School.
Editor’s note: The views expressed in each post are those of the author(s) only and not those of the conference organizing team or blog sponsor.
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