How Athletic Intelligence Relates To Partnership Valuation
BY ADAM GROSSMAN
The National Football League (NFL) season kicks off this week and serves as the first on-field test for players during the 2020 season. One of the biggest questions heading into this season is how players and coaches will be impacted by not having any preseason games prior to the start of the regular season.
COVID-19 has put a spotlight on non-traditional sources of player evaluation given this context. A The New York Times Magazine article recently featured the increasing importance of “athletic intelligence” in NFL player evaluation. The most interesting finding that we derived from this research is that a leading approach to on-field athletic intelligence has a direct application to off-field partnership valuation.
To start with, it is important to define “athletic intelligence” before we apply it to different use cases. The NY Times article focuses on the founders and the application of a test called the “Athletic Intelligence Quotient” or A.I.Q. The test “measures sport-specific cognitive abilities like how you see the playing field, reaction time. If you think of sports as an unsolvable puzzle, this is just how you go about solving it.”
The similarities to the more familiar Intelligence Quotient (I.Q.) tests are no accident. The founders of the A.I.Q., Scott Goldman and Jim Bowman, have had a long fascination with the intersection of sports and intelligence. Bowman specifically is a cognitive psychologist who specializes in test construction.
Bowman specifically designed the A.I.Q. to have a “solid theoretical” base rooted in intelligence research. In particular, the A.I.Q. leverages the “Cattell-Horn-Carroll Theory of Cognitive Abilities, a composite of multiple peer-reviewed advances in the field of brain science that represents the most current understanding of how intelligence actually works.”
One of the most important primary insights that came from Bowman’s and Goldman’s work is that it is critical to look at intelligence using a player and position specific lens. The A.I.Q. is comprised of ten sections that test for specific elements of intelligence such as navigational / spatial awareness, pattern recognition, rapid decision-making and long-term information retrieval.
While the A.I.Q. does provide a “full scale” score across all sections, the more valuable component of the A.I.Q. is the score for each section. More specifically, “The promise of the A.I.Q., meanwhile, is the radical notion that once you stop looking at a player’s brain as a single test score and instead as a multilayered instrument, it changes your view of the person too. So many talented athletes have been tossed aside because they can’t memorize a playbook, no matter how hard they try, or they got lost in a scheme that was all wrong for their talents.”
These ideas become clearer for many when comparable ideas are applied in a different sports context. Quarterbacks, skill position players, and lineman usually have very different body types. A defensive tackle does not typically look like a wide receiver given the different demands of their positions. Similarly, different NFL positions require different types of intelligence to be successful. Players can learn in different ways and have different intellectual strengths.
Teams that understand this can now take a Moneyball approach to intelligence in sports. For example, the A.I.Q. has shown that players “with higher A.I.Q. scores tend to get on the field sooner…They tend to start more in their rookie years, and then they also tend to have longer careers.” Team executives can use the A.I.Q. (or similar tests) to identify “undervalued” players that help a team win but typically would have been overlooked. Coaches can then build game plans and teaching methods around athletes’ individual intellectual strengths to maximize the probability of on-field success.
So, what does this approach to athlete intelligence have to do with corporate partnership valuation as stated earlier in this post? Our Corporate Asset Valuation Model (CAV) is built on the idea that different businesses should obtain different values even from the same corporate partnership. More “traditional” strategies have focused on the “full scale” approach to partnership valuation. Many models rely on one metric, the reach of a partnership, and apply value in this context. The bigger the actual or potential reach the more value that is generated.
Yet, each business has different revenue and brand goals. Reach can be important but it is not the only important metric in the same way that one form of intelligence is not applicable to all football players. Different companies, like different players, are “multilayered” and excel in different ways. They each have different revenue and brand goals and partnerships have game plans that optimize these goals.
A frequent example we use is based on the goals of a business-to-business (B2B) company when it comes to partnership. These companies have relatively few customers that spend millions or thousands of dollars for products and services. Therefore, partnerships that optimize reaching the right audience rather than a large audience will tend to have the most value for B2B organizations.
One of the promises of big data is the promise of more intelligent decision-making. However, it can sometimes be difficult to see how big data can solve specific challenges. The A.I.Q. and CAV demonstrate how similar data-driven approaches can deliver on this promise.