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Using Machine Learning to Evolve Sports Entertainment

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Consistent across time, sports organizations strive for success and to be the best. However, winning on the field is not sufficient to succeed as an organization as sports entertainment evolves.

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Using Machine Learning to Evolve Sports Entertainment

  1. 1. Using machine learning to evolve sports entertainment David Cunningham, CEO, Insipher Young Bang, Chief Growth Officer, Atlas Research
  2. 2. Agenda Young Bang Our approach to sports analytics David Cunningham Our implementation
  3. 3. About us 4 David Cunningham has more than 20 years of Information Technology experience supporting advanced technology programs and major transformational programs within the Department of Defense, Intelligence Community, Civilian Agencies, and Commercial clients. He is consistently at the forefront of technology evolutions to drive customer success and meet critical missions and business objectives. From architecting complex geographically dispersed systems to evolving and strategizing the transition into data-driven organizations, David has provided exceptional support to meet his clients’ demands for industry-leading cloud and data solutions. DAVID CUNNINGHAM CEO, Insipher As the Chief Growth Officer at Atlas Research, Mr. Bang uses his broad knowledge of the Federal market (Health, Defense, Intelligence, and Civil) to guide the firm’s growth in existing, adjacent, and new markets. He is a recognized expert and speaker in health IT, artificial intelligence, data science and big data, DevSecOps, orchestration, containerization, systems engineering, the software development lifecycle, cloud computing, product development, portfolio management, biometrics, acquisition process, telecommunications, logistics, supply and maintenance, and manufacturing. YOUNG BANG Chief Growth Officer, Atlas Research
  4. 4. Integrated Sports Analytics Fan Engagement Sponsorship Game and Player Analysis Business Operations In today’s environment, all aspects of a sports organization are highly connected and dependent on each other to drive success on and off the field
  5. 5. Fan engagement ▪ By integrating multiple data sources, organizations can build an enhanced fan profile that highlights ▪ Who they are, ▪ Where they come from, ▪ What they bought, ▪ And their interests ▪ Leading to increased fan base and higher stadium attendance Analytic Opportunities ▪ Live engagement - earning rewards for concessions / merchandise ▪ Request songs for in-game playlist and be recognized ▪ Track history of attendance- longest consecutive streak / awards / discounts ▪ Enhanced targeting of fans that attend and those who do not ▪ Alumni nights through fan profiling - if certain alumni are coming, drive higher participation through alumni nights
  6. 6. Sponsorship ▪ By using an enhanced fan profile, organizations can maximize their sponsorship opportunities through ▪ Targeted marketing, ▪ Cross-promotions, ▪ And in-stadium advertising ▪ Leading to increased customer reach and sales opportunities Analytic Opportunities ▪ Profile ticket purchases to determine a “relationship” that would drive sponsor in game engagement ▪ Post-game outreach based on attendance and concessions ▪ Targeted advertising based on profiles – “target east side differently than west side based on attendance” ▪ Track activation with non-sports related events - how many fans are also going to the concert at the stadium?
  7. 7. Game and player analysis ▪ By capturing and analyzing game footage, player metrics, and conducting skill assessments organizations gain a competitive edge ▪ Leading to success on the field Analytic Opportunities ▪ Engage fans with the players - beat your favorite player’s training data for this day and receive a discount ▪ Pre, during, and post-game analysis using image recognition and video analysis ▪ Identify “tendencies” of players to find opportunities to exploit a potential weakness ▪ Use biometrics integrated with game analysis to identify if a player show tendencies as the game goes on and their fitness level decreases
  8. 8. Business operations ▪ By integrating business systems (e.g. financial, ticketing, CRM, etc) organizations can ▪ Immediately identify how one part of the organization impacts the other ▪ Leading to increased revenue and overall success Analytic Opportunities ▪ Most teams share stadiums with other teams, drive data integration across teams to increase overall picture of a stadium’s activity ▪ Integrate concession revenue, with merchandise, with fan attendance, with in-game statistics to identify opportunities to increase revenue ▪ Integrate ticketing with sponsors with revenue to determine return-on-investment
  9. 9. Insipher is a modern, open insights platform © 2 0 2 0 D A T A F A C T O R Y . A L L R I G H T S R E S E R V E D . 10 O N P R E M I S E / C L O U D / H Y B R I D / E D G E Data Sources support both developers and analysts Data Model Pipeline Insights Decisions Rapidly prepare datasets with simplified data engineering for full exploration. Build scalable machine learning models without worrying about security or infrastructure Build powerful data science pipelines with no programming experience required Run reporting from your latest data sources and visualize it for a single source of truth Simple, transparent, & flexible infrastructure
  10. 10. With an integrated approach to secure operations
  11. 11. Examples to date ▪ Leveraging Spark and Insipher, we have conducted analysis within minutes to ▪ Identify sentiment analysis on social media before, during, and after the game to address fan feedback and potential concerns ▪ Run geographic analysis against ticketing to determine reach in and around a stadium and more importantly “where is the message not going” ▪ Map 2 – 3 degrees of connectivity between fans and companies to identify potential sponsor targets or campaigns
  12. 12. Thank you! david.cunningham@insipher.com ybang@atlasresearch.us
  13. 13. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

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