{"id":31656,"date":"2018-11-13T15:01:51","date_gmt":"2018-11-13T20:01:51","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-nfl-and-machine-learning-a-touchdown-for-technology\/"},"modified":"2018-11-13T15:01:51","modified_gmt":"2018-11-13T20:01:51","slug":"the-nfl-and-machine-learning-a-touchdown-for-technology","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-nfl-and-machine-learning-a-touchdown-for-technology\/","title":{"rendered":"The NFL and Machine Learning: A Touchdown for Technology?"},"content":{"rendered":"

\u201cPerfection is not attainable. But if we chase perfection, we can catch excellence,\u201d pronounced National Football League (NFL) legend Vince Lombardi. Each game day, NFL athletes play through sweat and tears to give millions of fans unparalleled game experiences. However, NFL ratings and fan satisfaction are at an all-time low.[1] Moreover, fans now view games through new mediums. In 2017, viewers watched more than 10 billion minutes of video across the NFL\u2019s digital and social platforms.[2] At a crossroads, the NFL must \u201cchase perfection\u201d to satisfy its evolving customer base and return to ratings growth.<\/p>\n

Data scientists have recognized the predictive value of machine learning in the sport.[3] For example, machine learning models have been able to predict football turnovers with surprising accuracy.[4] The NFL has noticed the value in these methods as well. In 2015, the NFL began Next Gen Stats (NGS) to revolutionize the sport with machine learning.[5] The visual below shows how the NFL\u2019s NGS technology was built over time.<\/p>\n

\"\"<\/a>
The NFL’s Next Gen Stats technology wasn’t build overnight. Source: https:\/\/operations.nfl.com\/the-game\/technology\/<\/figcaption><\/figure>\n

Every week of NFL football generates 3TB, equivalent to 1500 hours, of data.[6] In the short term, the NFL wants to turn this data into value for teams and fans.[7] The NFL has embraced radio-frequency identification (RFID) tags as one strategy for achieving this goal.[8] RFID-implanted equipment captures live data, and NGS puts this data to work.[9] NGS creates adaptive models based on historical data (e.g. past routes run, field data, weather conditions, etc.) that provide key decision-makers with useful in-game strategies.[10] Once implemented, this machine-learning technology will instantly analyze a play\u2019s formation, route, and key identifiers in real time.[11] For example, machine learning can help a coach determine if a quarterback made a good decision on a pass right after the play. Real time analysis replaces \u201cMonday morning quarterbacking,\u201d said NFL Senior VP and CIO Michelle McKenna.[12] After the game, coaches also receive insights that include fitness summaries, heat maps of player locations, and relative speed and distance play diagrams for each player to help them adjust future game strategies.[13]<\/p>\n

Beyond the game itself, the NFL leverages data to create value for the increasing number of digitally-savvy fans. Each week, the NFL publishes the \u201cNext Gen Stats: Hidden numbers\u201d that presents data-informed predictions for teams and individual players across a variety of performance metrics.[14]<\/p>\n

Plans also include innovating the fan experience with machine learning. The NFL has developed educational tools for commentators and television broadcasters to better use data to drive more rewarding programming for viewers.[15] Matt Swensson, NFL VP of Emerging Products and Technology said, \u201cMachine Learning and other computations that could take months to refine now take weeks or days, allowing us to engage, inform and excite fans in new and unique ways.\u201d[16]<\/p>\n

However, achieving the promise of machine learning poses challenges. To add value with data, the NFL needs to move swiftly to assess skills gaps, invest in training, and make smart hires. Coaching staff education must rank high on the priority list. In order for data deployment to achieve success, coaches need to buy into the idea. Right now, many coaches remain skeptical. \u201cAll that stuff is good to have. But it\u2019s on film, too, and the film don\u2019t lie,\u201d said former Indianapolis Colts Coach Chuck Pagano.[17] Others echo Pagano\u2019s concerns, including Seattle Seahawks Coach Pete Carroll, \u201cWe don\u2019t have enough background yet to kind of make sense of it, how it\u2019s helping us at all.\u201d[18] Earlier this year, NFL union executive Ahmad Nassar estimated that \u201c30 of the 32 franchises ignored the data altogether.\u201d[19] Data can help coaches win games, but coaches need to believe that first to make it happen. As of March 2018, NFL teams have access to data of the opponents. [20] As a result, coaches will be incentivized to understand how this data can be used in game strategy as a source of competitive advantage.<\/p>\n

The NFL has only recently adopted machine-learning technologies, and there are many unanswered questions. For example, the use of data may overwhelm and confuse fans. What are the most important NFL data analyses from machine learning that can not only interest, but increase the loyalty of viewers? Assessing an additional key stakeholder, how can the NFL accelerate adoption of NGS metrics among coaches? As the NFL progresses their understanding of the role machine learning can play in the organization, are there comparable organizations to take lessons from? In this early stage of machine learning integration, the NFL has every opportunity to thoughtfully engage machine learning in the most value-adding way for all. (word count: 749)<\/p>\n

[1] Joe Harpaz, 3<\/em> Ways Artificial Intelligence Can Save the National Football League<\/em>, Forbes (Jan. 11, 2018), https:\/\/www.forbes.com\/sites\/joeharpaz\/2018\/01\/11\/3-ways-artificial-intelligence-can-save-the-national-football-league\/#563994f7da74.<\/p>\n

[2] NFL Digital Media Unveils New Product Features and Content Offering Fans a Deeper Connection to the Game<\/em>, NFL Communications, https:\/\/nflcommunications.com\/Pages\/NFL-Digital-Media-Unveils-New-Product-Features-and-Content-.aspx.<\/p>\n

[3] K. Pelechrinis & E. Papalexakis, Footballonomics: The Anatomy of American Football: Evidence from 7 Years of NFL Game Data,<\/em>\u00a011(12) PLoS (2016).<\/p>\n

[4] J.R. Bock, Empirical Prediction of Turnovers in NFL Football<\/em>,\u00a05(1) MDPI (2016).<\/p>\n

[5] NFL Next Gen Stats<\/em>, NFL Ops, https:\/\/operations.nfl.com\/the-game\/technology\/nfl-next-gen-stats\/.<\/p>\n

[6] Jason Hiner, How the NFL and Amazon unleased \u2018Next Gen Stats\u2019 to grok football games, <\/em>Tech Republic (Feb. 2, 2018), https:\/\/www.techrepublic.com\/article\/how-the-nfl-and-amazon-unleashed-next-gen-stats-to-grok-football-games\/.<\/p>\n

[7] Next Gen Stats: Powered by AWS, <\/em>Amazon Web Services, https:\/\/aws.amazon.com\/nextgenstats\/.<\/p>\n

[8] See<\/em> J. Pletz, A Victory Lap for Zebra?\u00a0<\/em>41(38) Crain’s Chicago Business (2018).<\/p>\n

[9] Taylor Soper, Microsoft Will Show \u2018Next-Gen Stats\u2019 on NFL App Thanks to RFID Chips Worn by Players, <\/em>GeekWire (Aug. 7, 2015), https:\/\/www.geekwire.com\/2015\/microsoft-will-show-next-gen-stats-on-nfl-app-thanks-to-rfid-chips-worn-by-players\/.<\/p>\n

[10] Michelle-Doyle McKenna Shares How the NFL Can Take \u2018Next Gen Stats\u2019 to the Next Level, <\/em>YouTube (Nov. 30, 2017), https:\/\/youtu.be\/gjDLN3qJudA.<\/p>\n

[11] Id.<\/em><\/p>\n

[12] Id.<\/em><\/p>\n

[13] Id.<\/em><\/p>\n

[14] Nick Shook, Next Gen Stats: Hidden Numbers That Could Define Week 9, <\/em>NFL (Nov. 1, 2018), http<\/em>:\/\/www.nfl.com\/photoessays\/0ap3000000983256.<\/p>\n

[15] Michelle-Doyle McKenna Shares How the NFL Can Take \u2018Next Gen Stats\u2019 to the Next Level, <\/em>YouTube (Nov. 30, 2017), https:\/\/youtu.be\/gjDLN3qJudA; see also <\/em>D. Kudenko & M. Zheng Automated Event Recognition for Football Commentary Generation<\/em>, 2(4) International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 67-84 (2010); see also\u00a0<\/em>Paolo Del Nibletto, NFL Adopting Machine Learning, <\/em>itbusiness.ca (Dec. 18, 2017), https:\/\/www.itbusiness.ca\/news\/nfl-adopting-machine-learning\/97487.<\/p>\n

[16]\u00a0Next Gen Stats: Powered by AWS<\/em>, AWS, https:\/\/aws.amazon.com\/nextgenstats\/.<\/p>\n

[17] Kevin Seifert, NFL Coaches Skeptical on Benefits of Chip-Generated Game-Day Data, <\/em>ESPN (Jul. 24, 2017), http:\/\/www.espn.com\/nfl\/story\/_\/id\/20116303\/nfl-head-coaches-skeptical-chip-generated-game-day-player-data-pete-carroll-sean-payton-dan-quinn.<\/p>\n

[18] Id.<\/em><\/p>\n

[19] Joe Lemire, NFL To Distribute Full League-Wide Zebra Tracking Data<\/em>, ESPN (Mar. 5, 2018), https:\/\/www.sporttechie.com\/zebra-nfl-full-league-wide-tracking-data\/.<\/p>\n

[20] NFL Next Gen Stats, <\/em>NFL Ops, https:\/\/operations.nfl.com\/the-game\/technology\/nfl-next-gen-stats\/.<\/p>\n","protected":false},"excerpt":{"rendered":"

In an increasingly competitive industry, professional sports teams are seeking innovative ways to create unparalleled fan experiences. 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