Deflategate and the National Football League

Marco   Iansiti; Christine  Snively

Case | HBS Case Collection | September 2015

Deflategate and the National Football League

by Marco Iansiti and Christine Snively

Abstract

On January 18, 2015, the New England Patriots faced the Indianapolis Colts in the AFC Championship game. In the second quarter, a Colts player intercepted a pass from Patriots quarterback Tom Brady. Colts equipment personnel alerted NFL officials that the ball's air pressure was below the required 12.5 PSI (pounds per square inch). Some argued that lower PSI provided a competitive advantage as it made the ball easier to grip and harder to fumble. At halftime, game officials found the air pressure in 11 of the 12 Patriots game balls to be under 12.5 PSI. The NFL launched an investigation into what became known in the media as "Deflategate," and commissioned attorney Ted Wells to investigate whether or not the balls had been intentionally deflated. Wells' team, with expert consultants, examined air pressure data recorded by referees, the temperature on game day, the behavior of Patriots players, and other evidence. Did the Deflategate investigation reveal any actual evidence of cheating? Were there flaws in Wells' investigation?

Keywords: ＂deflategate＂; analytics; National Football League; NFLPA; Roger Goodell; Tom Brady; Operations; United States;

Language: English

Format: Print

21 pages

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Citation:

Iansiti, Marco, and Christine Snively. "Deflategate and the National Football League." Harvard Business School Case 616-008, September 2015.

In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing. Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna’s pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as “a COVID-19 company?”

In 2019, Fitbit lost its leadership in the wearable sensor market to Apple and to cheaper alternatives.
Why did it lose its market position?
How will the proposed acquisition affect it and Google?

Working Paper | HBS Working Paper Series | 2020

Time and the Value of Data

Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and, thereby, a decline in created business value. We model time-dependency as a shift in the probability distribution and derive several counter-intuitive results.
We, theoretically, prove that even an infinite amount of data collected over time may have limited substance for predicting the future, and an algorithm that is trained on a current dataset of bounded size can attain a similar performance. Moreover, we prove that increasing data volume by including older datasets may put a company in a disadvantageous position.
Having these results, we answer questions on how data volume creates a competitive advantage. We argue that time-dependency weakens the barrier to entry that data volume creates for a business. So much that competing firms equipped with a limited, but sufficient, amount of current data can attain better performance. This result, together with the fact that older datasets may deteriorate algorithms' performance, casts doubt on the significance of first-mover advantage in AI-based markets.
We complement our theoretical results with an experiment. In the experiment, we empirically measure the value loss in text data for the next word prediction task. The empirical measurements confirm the significance of time-dependency and value depreciation in AI-based businesses. For example, after seven years, 100MB of text data becomes as useful as 50MB of current data for the next word prediction task.

Keywords: economics of AI; Machine learning; non-stationarity; perishability; value depreciation;

Citation:

Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. View Details

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Deflategate and the National Football League

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