Tokyo 2020 Already Secures Record Olympic Sponsorship Revenue In 2018   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Olympic records are being set, but not just in Pyeongchang. It may be difficult to think about the 2020 Summer Olympics while the 2018 Winter Olympics are still happening, but the International Olympic Committee (IOC)  stated  that 2020 host city Tokyo has already secured almost $3 billion in sponsorship deals.   To put these record revenues in context, both London and Rio de Janeiro generated approximately $1.1 billion in sponsorship revenue for the Olympics in their cities. This also comes shortly after companies such as McDonald’s, AT&T, and Citigroup recently ended high profile agreements with the IOC  because of  “rising Olympics sponsorship costs and declining TV ratings.”  How could Tokyo be generating record revenues when trends at both the host city level and the international level seem to be against this outcome? We can start by looking at the host city analysis.  According to John Coates , the head of the IOC’s Tokyo Games Coordination Commission, “Forty-three domestic sponsorship deals signed by Tokyo organizers so far had exceeded expectations” in 2017. That number has now increased to  47 partnerships in 2018 .  What is behind Tokyo’s success? Japan is the home to many multi-national companies that are Worldwide Olympic Partners including Toyota and Panasonic. However, Tokyo also appears to be working with companies to maximize their fit with an Olympic partnership. In particular, the Olympics are typically an extremely strong platform for companies to increase brand awareness to an international audience for new product offerings.  That is what Tokyo appears to be stressing in its deals. For example,  Nippon Oil & Energy  Corporation will “provide oil, gas and electricity supply services for the Tokyo 2020 Games.” However, the company is also focused on using the Olympics as a platform to showcase a new product.  More specifically , “Through the supply of  hydrogen energy, for which demand is expected to increase for the Tokyo 2020 Games , we are contributing to the expanded use of new types of energy (emphasis added).” Nippon, through its ENEOS products, is using the Olympics as a platform to showcase the value of its new product to a global audience.  Mizuho Financial Group is an example of a Gold Partner that can use the Olympics to generate global brand awareness for its company. Mizuho is a company headquartered in Japan but has a listing on the New York Stock Exchange (NYSE). Mizuho is using the Olympics  in part to  “[stimulate] the Japanese economy to enable it to meet the demands of people from around the world who will be gathering in Tokyo and Japan for the Games.” Mizuho is using a local mission to create a worldwide platform to engage with a worldwide customer base important for its future growth as a company.    Mizuho’s partnership also demonstrates another interesting element of Tokyo’s approach. One of Mizuho’s competitors, Sumitomo Mitsui Financial Group Inc., has also signed on to be an Olympic partner. Tokyo is one of the first Olympics (with the IOC’s support).  to allow multiple companies to become in the same category.  Having the ability to maximize partnership revenue through non-exclusivity is important to the Japanese organizing committee. Yoshiro Mori, a former Japanese prime minister and president of the committee,  stated  “We’re making an effort to reduce the burden on the city of Tokyo as much as possible.” The country had a clear goal to limit cost overruns and make the Olympics as fiscally sound as possible. Taking part in the  opportunity to  “build the world’s most efficient financial infrastructure” is a good fit for Mizuho’s brand goals and makes lack of exclusivity less important to the company.  It is not just Tokyo, however, that has seen a boost in sponsorship deals. For companies like McDonald’s with near-global ubiquity for many of its core products, renewing an Olympic sponsorship does not make as much sense.  Intel, however, is using the 2018 Olympics to  feature its Virtual Reality  (VR) product offerings in its “biggest production experience that we have delivered to date”. This includes distributing 30 events in 360-degree videos to 10 broadcast partners around the world. Virtual Reality is a new product offering for which the Olympics provides a great platform to showcase new capabilities. The ability to broadcast and consume games in VR in 2018 and 2020 (and beyond) makes the Olympics a good sponsorship fit for Intel and shows the importance of fit in increasing sponsorship revenue.
BY ADAM GROSSMAN

Tokyo 2020 Already Secures Record Olympic Sponsorship Revenue In 2018

Olympic records are being set, but not just in Pyeongchang. It may be difficult to think about the 2020 Summer Olympics while the 2018 Winter Olympics are still happening, but the International Olympic Committee (IOC) stated that 2020 host city Tokyo has already secured almost $3 billion in sponsorship deals. 

       Brady, Smith Generate Highest Percentage Of Wins For Their NFL Teams   BY ADAM GROSSMAN & ROSS CHUMSKY     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Update – The trade reported yesterday of Kansas City Chiefs QB Alex Smith to the Washington Redskins is a good example of how to use our new winning metric. As part of this deal, it is likely that the Redskins will not re-sign QB Kirk Cousins, trade cornerback Kendall Fuller to the Chiefs, and trade a third round draft pick in the 2018 draft to the Chiefs. The Redskins will receive a 2019 third round compensatory pick when another team signs Cousins. Our analysis of the 2017 season shows that Smith generated a 1.67 wins while Cousins and Fuller combined generated 1.14 wins. This demonstrates that Redskins will receive more wins from these transactions if Alex Smith performs at or near his 2017 performance while Cousins remains at or near his 2017 performance. For the Chiefs, providing the opportunity for Patrick Mahomes to become its starting QB at a lower salary may mean fewer wins in the short-term but at a lower cost than keeping Smith  -----------------------------------------------------------------------------------------------------------------------------  What NFL player has the most impact on his team? It is common question, and unlike baseball and basketball, football does not have a consensus, baseline advanced analytic metric to rate players. More specifically, Wins Above Replacement (WAR) is typically used in baseball and Player Efficiency Rating (PER) is typically used in basketball. The idea behind both metrics is to quantify an individual player’s impact on winning above a minimal level of performance. With WAR, for example, a player’s overall contribution is essentially based on how many runs he created and how many runs he prevented as compared to a Triple-A player in the same position.  The reason that it has been more difficult to create this type of metric for football is that the sport relies on the performance of multiple team members on every play. This complexity makes it difficult to identify an individual player’s contribution. For example, the typical successful passing play requires an offensive line to protect a quarterback from a sack, a quarterback to throw the ball, and a wide receiver to catch the pass. Who deserves the most credit if the play is executed successfully?  In the process of creating the  Revenue Above Replacement (RAR ) for the NFL for this season, our team at Block Six Analytics (B6A) identified the need to create a version of this on-field metric. RAR examines how an individual player generates revenue for a team based on his on field, off field, and personal performance. In the past, we relied on WAR or PER to create our models for the MLB and NBA, respectively.  To solve this problem for football, we used multiple data sets and multi-factor regression analysis. We were able to determine two main attributes that accounted for wins. In essence, these factors are how well (Grade) and how much (Snaps) did a player perform during the 2017 season.     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     We found that these two factors identified how many yards over a replacement level player each player contributed to his team (the equivalent of how many runs were created in baseball). We found that summing each individual player’s yards contribution for a team in conjunction with regressing a team’s total wins provided a strong and statistically significant descriptive “winning” statistic.      

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


      We were also able to attribute yards to every single football player in the NFL this season.  The top-15 players in our analysis are in Table Two. The first area of focus is PlayerWin%. This describes how much that player contributes to a team’s win over a replacement level player (in football think an undrafted free agent) with a 100% meaning the player accounts for all of a team’s wins. Unsurprisingly, Tom Brady leads the NFL in PlayerWin% with the Patriots quarterback accounting for 18.04% of team’s wins. What else is likely not surprising is that quarterbacks makeup all of the top-10 and 11 of the top-15 players in this rating.     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     What is likely more surprising is that Kansas City Chiefs quarterback Alex Smith had the second highest PlayerWin%. How valuable is he? Let’s examine the fbWAR, ExpectedWin% and WinDelta columns. fbWAR calculates the wins a player adds over a sixteen game schedule (fb stands for football in our metric). ExpectedWin% looks at what percent of a team’s wins a player should contribute on an average team. WinDelta looks as the differences between the PlayerWin% and the ExpectedWin%. A positive number means the player is more valuable to that team than to the average team. A negative value means a player is less valuable to the team than the average team.  Not only did Smith have a Pro Bowl year, he is particularly valuable to the Chiefs because he had the highest WinDelta of any player in the analysis. His PlayerWin% is so high because the Chiefs as a whole have fewer players that contribute wins above a replacement level player. In our model, the Chiefs were expected to win 7 games (as seen in Table One) given the performance of the players on its roster (meaning the team won more game that would be expected given the talent on its roster). As a contrast, Brady has a  negative  WinDelta meaning he would be even  more valuable  on a different team than he was to the Patriots.  The three Panthers players followed the same pattern. These players are more valuable to the Panthers than to other teams because the Panthers had an even lower win contribution from its players than the Chiefs. The Panthers were also expected to win 7 games given the performance of its players (and actually won 11).     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     What if you wanted to see a player’s contribution overall (not measured to a specific team)? Table Three sorts players by fbWar instead of PlayerWin%. Once again, Brady leads the way in this metric meaning he generated the most wins and the highest contribution of his teams win total. However, Smith drops to 9th in this analysis. Two other players of note are Jimmy Garopplo and Alvin Kamara. Garoppolo ranked 13th overall even though he only played in five games. Had he performed in this same way over a 16 game season then he would have surpassed Brady on this list. Kamara also had relatively few plays this year as compared to other skilled position players (running backs, wide receivers, and tight ends) yet still finished as the highest non-QB on this list.  Why have we not called Brady the Most Valuable Player? We mentioned that we originally completed this analysis as part of our RAR model. Our approach looks at players’ contributions on and off field. Our next blog post will have the completed RAR analysis where we can determine the overall value of each player. In terms of wins generated, however, Brady is the highest performing player. 
BY ADAM GROSSMAN & ROSS CHUMSKY

Brady, Smith Generate Highest Percentage Of Wins For Their NFL Teams

In the process of creating the Revenue Above Replacement (RAR) for the NFL for this season, our team at Block Six Analytics (B6A) identified the need to create a version of this on-field metric. RAR examines how an individual player generates revenue for a team based on his on field, off field, and personal performance. In the past, we relied on WAR or PER to create our models for the MLB and NBA, respectively.

To solve this problem for football, we used multiple data sets and multi-factor regression analysis. We were able to determine two main attributes that accounted for wins. In essence, these factors are how well (Grade) and how much (Snaps) did a player perform during the 2017 season.

       How Super Success Occurs In Super Bowl Advertising   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Do Super Bowl television commercials work? It is the five-million dollar question facing companies looking to purchase advertising inventory during America’s most watched television event coming up on Feb. 4th. More specifically, do Super Bowl advertisements lead to incremental revenue growth?   There have been several attempts to analyze the impact of Super Bowl advertising. However, confounding variables have been the primary issues in determining a clear ROI. In particular, it is difficult to isolate the Super Bowl ad itself as the catalyst for new sales when factors such as national advertising, local advertising, new product introductions, and spikes in sales overall could impact revenue.  The authors of the journal article  "Super Bowl Ads"  in  Marketing Science  found a compelling approach to address these issues. These professors from Stanford and Humboldt University found there was significant variance in television ratings in different markets depending on which teams played. In particular, there were increases in viewership based on if local teams to a geography were playing and/or if 3-5+% of Facebook likes came from certain geography. For example, if 3-5% of fans in the Los Angeles DMA like the New England Patriots on Facebook then this becomes a “local” Patriots market. If Super Bowl ads were effective then there should be increases in revenue when there is an increase in viewership.  This enabled the authors to isolate the impact of the television ads while controlling for the cofounding variables that usually impact this type of analysis (for more information click on the “Super Bowl Ads” link at the top of the previous paragraph). They completed a five-year analysis of the impact of television advertising on the increase sales volume and increase in revenues for beer and beverage brands before and after the Super Bowl.  The study focused on the impacts of Budweiser, PepsiCo, and Coca-Cola advertising during the Super Bowl, and the findings were:   Budweiser and Pepsi saw a statistically significant increase in sales in the week prior to the Super Bowl occurring where Coke saw a decrease in sales. The authors hypothesize that Pepsi generates the increase because customers identify Pepsi as the official sponsor and therefore associated the company's products with the Super Bowl. Coke is not seen in this light and therefore is not associated with the event in the same way.    Budweiser and Pepsi saw a statistically significant increase in sales in the 8-weeks following the Super Bowl but only when it was the sole or primary advertiser. When both Pepsi & Coke advertise in the Super Bowl in the same year, the impact of advertising on sales disappears.   Budweiser and Pepsi saw a statistically significant increase in sales around the NCAA Tournament but only when it was the sole or primary advertiser during the Super Bowl. When both Pepsi & Coke advertise in the Super Bowl in the same year, the impact of advertising disappears on sales in a similar way as it does post Super Bowl.    The main takeaways, however, are that Budweiser and Pepsi do see a significant return on investment with the Super Bowl and it should do everything possible to "own" the event. The effects seem to resonate many weeks after the Super Bowl but mostly around other sporting events. More specifically, customers seem to associate Pepsi with sports after the Super Bowl occurs with sales increases post Super Bowl around sporting events like the NCAA Tournament.   In fact, the researchers may have underestimated the impact because they did not have access to all purchasing data in all markets including access to sales from Walmart. Even without Walmart data, the authors determined that Budweiser generated $45 million in direct revenue from its Super Bowl advertising with PepsiCo achieving similar results.    The study is definitely not perfect in large part because the analysis was only from 2006-2011. In addition, it also only focused on beverage brands that have a primarily business to consumer / retail strategy. It is unclear that companies in other industries or companies that focus on enterprise or business clients rather than individual customers would see similar results. The authors do identify these as areas for future analysis.      Block Six Analytics (B6A) does conduct this type of analysis when looking at ROI of sponsorship spend. Our  Corporate Asset Valuation Model  examines partnership spend to determine the expected and actual incremental revenue growth. Rather than only looking at television advertising, we look at in-venue, digital, social, IP, event, and hospitality to determine a clear ROI for spend in different channels across different major sporting events.  There is no question that Super Bowl commercials are expensive. However, there is clear evidence that answers the question of whether these commercials can generate significant ROI for companies when used in the right way.   
BY ADAM GROSSMAN

How Super Success Occurs In Super Bowl Advertising

Do Super Bowl television commercials work? It is the five-million dollar question facing companies looking to purchase advertising inventory during America’s most watched television event coming up on Feb. 4th. More specifically, do Super Bowl advertisements lead to incremental revenue growth? 

       Determining the Value of Brandwatch’s Football Sponsorship Analysis      

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      In a post titled “Which Football Sponsors Get The Best ROI?,”  Brandwatch  examined the value created by kit (or jersey) partners over the past year on Twitter and Instagram. Not only did it look at the number of images produced, Brandwatch also created an ROI index that looked at the cost per image for soccer teams for Nike, Adidas, Puma, and Under Armour. More specifically, this analysis “divided the total images each sponsor received by the amount paid for their sponsorship.”  The analysis had two findings that may be surprising to most soccer fans. First, Leicester and Paris Saint Germain (PSG) returned the highest value on a cost per image basis. In  addition , “Puma performs incredibly in this analysis.” Puma’s £13.62 per image is far lower than Nike’s £43.53 per image. Given that Adidas pays only £8.45 per image, “Nike struggles to generate the same ROI as its rivals.”  Unfortunately for Leicester, PSG and Puma, there are several reasons why Manchester United and Nike are likely not too worried about this analysis. To start with, the cost per image metric needs further examination because of the way in which Brandwatch calculates cost. In particular, it is not clear why Brandwatch examines the entire cost of the partnership while only looking at one channel for its ROI analysis (social media focused solely on Twitter and Instagram). There is an argument to be made that companies should be focused on social media for activation of jersey deals. However, many deals now are focused on television viewable signage in video and not static images in social media. Maximizing time on screen for broadcast video is typically the goal of partners with these deals.  In addition, the analysis seems to lack standard image metrics when considering value. In particular, it omits prevalence (how much of the screen does an image take up), centricity (how close the image is to the center of the screen), and confidence (how clear is the image on screen). If an image is small or not in the center of the screen, then it is less likely that a person will be able to identify the logo.  The cost per image has another methodological flaw – it is only looking at the quantity of the images posted rather than the quality of posts with those images. Nike, Adidas, Under Armour, and Puma (the brands featured in this analysis) each have different goals for their sponsorships. For example, having a static image of a company’s logo can be a good way to increase brand awareness. The more images there are, the higher the likelihood that someone will see the brand. Nike arguably has near universal brand awareness while Puma is not at Nike’s level. While Puma is generating value from its jersey partnerships for this reason, Nike should not be looking at this metric in the same way given its business goals.   In addition, Adidas has taken market share from Nike, in large part, by becoming the “ coolest brand in sports ”. Does the quantity of images help Nike address this issue or is the context in which those images appear more important? Looking at the sentiment of posts would be a critical part of Nike’s analysis to determine if people are saying positive or negative things about the brand.  Finally, all of these brands would be looking at engagement metrics to determine whether people are interacting with these visual posts. More specifically, if no one is liking, sharing, retweeting, or commenting on posts then it is likely that the audience is not engaged with the content. The number of images is often less important than having engaging content consumed by a company’s targeted demographics.  The Block Six Analytics  Social Sentiment Analysis Platform (SAP ) conducts jersey analysis by looking at the quantity, quality, and engagement of social media content. Our technology can identify which posts have company logos and determine the engagement of those posts. We also examine the value of those posts by looking at the specific revenue and marketing goals of a company to determine how value is being created. We then use our Media Analysis Platform (MAP) to examine the ROI in live television, highlights, and digital channels to examine how video content featuring a company’s logo creates value.     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     To its credit, Brandwatch did uncover valuable insights. In particular, Puma’s unique placement (as compared to other jersey partners) on the sleeve of a jersey (instead of on the chest) did enable the company to maximize the number of photos containing its logo. However, the other issues make Brandwatch's conclusions difficult to substantiate. B6A’s cross-channel, company-specific approach directly addresses the challenges with this Brandwatch analysis.  
BY ADAM GROSSMAN

Determining the Value of Brandwatch’s Football Sponsorship Analysis

In a post titled “Which Football Sponsors Get The Best ROI?,” Brandwatch examined the value created by kit (or jersey) partners over the past year on Twitter and Instagram. Its primary methodological flaw is that is only looking at the quantity of the images posted rather than the quality of posts with those images

       David Falk Joins Block Six Analytics Advisory Board      

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Block Six Analytics (B6A) is excited to announce that David Falk has joined its Advisory Board after making an investment in the company. Falk will now take a leadership role in growing the company’s sports corporate partnership and player valuation offerings.  Falk joins B6A after having spent more than 40 years in the sports industry, primarily focused on being the leading National Basketball Association (NBA) player agent. Falk has represented over 100 players including Michael Jordan, Patrick Ewing, Dominque Wilkens, John Stockton, Danny Ferry, Elton Brand, and Otto Porter. He has negotiated hundreds of millions of dollars in player contracts and most famously secured Michael Jordan’s endorsement deals with Nike, Gatorade, McDonald's, Sara Lee, Wilson Sporting Goods, Rayovac, and Wheaties. Falk was also an Executive Producer for the movie  Space Jam  starring Jordan .  He sold FAME, the agency he founded, in 1998 for $100 million to SFX and re-launched FAME in 2007. He also has experience with investing in and helping grow multiple startup ventures.   “I am very happy to be joining the B6A team,” Falk said. “This partnership provides me with the unique opportunity to leverage my experience and industry relationships in direct ways to have an immediate and substantial impact on the growth of the company.”  David Falk will take a leadership role in developing and monetizing multiple products within B6A’s Partnership Scoreboard software as a service platform. This includes enhancing its Revenue Above Replacement (RAR) model that examines how an individual player generates revenue for a team based on his or her on court, off court, and personal performance. In addition, Falk will further help develop the company’s sales, marketing, and pricing strategies for its Media Analysis Platform (MAP) and Social Sentiment Analysis Platform (SAP). These artificial intelligence products measure the value of corporate partnerships in near real-time for both the buyers and sellers of sports sponsorships.  “David taking a significant role in B6A is a great example of how much industry leaders believe in the company’s future success,” CEO & Founder Adam Grossman said. “His experience, relationships, and insights will be a catalyst for the company to become the industry leader in partnership and player valuation.”  Falk also endowed the David B. Falk College of Sport and Human Dynamics and its Sport Management degree program at Syracuse University. It is the first college in the U.S. to award a degree in analytics. Students from the program will be able to obtain real world experience by completing internships and class projects with B6A.  For more information please contact B6A at  info@blocksixanalytics.com .   About Block Six Analytics    B6A's analytics-fueled technology enables companies to maximize ROI on their corporate partnership and player investments across all on field an off field channels. Sports properties, agencies and brands are using B6A's platform to create a truly interactive experience focused on delivering sponsorship value. Our machine learning technology helps our clients generate incremental revenue growth and reduce reporting costs. We use our technology and analytics to determine the value of television viewable billboards, signage, and calls-to-action, and social media conversations.   By having a fully transparent valuation model that is built for specific companies for specific partnership opportunities, B6A ensures that buyers and sellers of sports sponsorship have the information they need to make the best data-driven decisions for their organizations.
BY BLOCK SIX ANALYTICS

David Falk Joins Block Six Analytics Advisory Board

Block Six Analytics (B6A) is excited to announce that David Falk has joined its Advisory Board after making an investment in the company. Falk will now take a leadership role in growing the company’s sports corporate partnership and player valuation offerings.

       WHAT CYBER MONDAY RECORD SALES MEANS FOR SPORTS ORGANIZATIONS   BY ADAM GROSSMAN & ROSS CHUMSKY     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Houston Astros second baseman Jose Altuve won the American League MVP Award this year while posting the highest batting average in the sport and leading his team to its first World Series title in franchise history. But from an economic standpoint, baseball’s most valuable player is Aaron Judge, who finished second to Altuve in MVP voting. Judge drives the most revenue for his team according to the Revenue Above Replacement (RAR) model developed by my firm Block Six Analytics (B6A).  Revenue Above Replacement examines how an individual player generates revenue for a team based on his on-field, off-field and personal performance:   On-field performance examines the impact of winning on a team's ability to generate revenue and the player's individual contribution to winning using WAR.  Off-field performance examines how a player’s star power drives ticket sales, television ratings, social media engagement, partnership agreements and merchandise purchases.  Personal performance looks at a player's ability to generate earned media time that increases a team's exposure to its fans in the channels in which they are most likely to consume content.   B6A applied its RAR model to the  NFL  and  NBA  in the past, but MLB results differ because of the way teams generate revenue in baseball. More specifically, the most important revenue stream for MLB teams is usually the local media rights deal with a regional sports network (RSNs) while NFL and NBA teams generally receive more revenue from their league’s national and international media rights deals.  That is one key reason that Judge is so valuable to the Yankees. As part of the Yankees deal to sell YES to  News Corp : “The club got $85 million in fees from YES for [2012], and the sale includes a 5 percent increase  annually  in that rate for 30 years.” Given that the team generates so much money from its television deal, YES Network needs strong ratings to maintain its carriage fees (the price it charges cable and satellite networks to carry the channel) and sell advertising.  Star power plays a big role in driving people to watch games, and there was no bigger star in baseball last year than Judge. However, that is not just because he had one of the most dominant seasons for a rookie in MLB history on the field. Judge generated $145.1 million in RAR for the Yankees in 2017 with $97.5 million of his value based on his off-field and personal performance. Factors driving this number include Judge jerseys as the  best-sellers in MLB  this year (and most ever for a rookie), a  highly engaged  fan base on social media and the highest amount of earned media exposure of any player in baseball.  Even better for the Yankees, Judge generated his $145 million in RAR for the Bronx Bombers for the rookie minimum salary of $544,500. Judge is not the only star that drove significant value for his team. The top-ten players in RAR are detailed in the table below.     

  

  	
       
      
         
          
             
                  
             
          

          
           
              Top-ten RAR players for 2017.  
           
          

         
      
       
    

  


     One notable absence is the Miami Marlin’s Giancarlo Stanton, who hit 59 home runs and won the National League MVP. Stanton has been the featured star in many reports about the annual MLB  winter meetings , which take place next week and mark the “unofficial beginning for teams to make their big moves.” Stanton generated $33.2 million in RAR value for the Marlins.  While this is a significant amount, it is likely Stanton would be more valuable on a team that could maximize his star power in similar ways to what the Yankees have done with Judge. B6A’s RAR model actually quantifies this impact because it is able to demonstrate the value that each player has for a specific team. Understanding the economic impact that each player has on each team should be an important part of the free agent conversation this winter.
BY ADAM GROSSMAN & ROSS CHUMSKY

Aaron Judge, Mike Trout Rank As The Most Valuable Players In Baseball

Houston Astros second baseman Jose Altuve won the American League MVP Award this year while posting the highest batting average in the sport and leading his team to its first World Series title in franchise history. But from an economic standpoint, baseball’s most valuable player is Aaron Judge, who finished second to Altuve in MVP voting. Judge drives the most revenue for his team according to the Revenue Above Replacement (RAR) model developed by my firm Block Six Analytics (B6A).

       AI Makes Humans More Intelligent    BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Another human has lost to the descendants of Deep Blue. First, world champion Garry Kasparov lost to IBM’s Deep Blue supercomputer in chess in 1997. Then Jeopardy’s all-time consecutive show winner Ken Jennings succumbed to IBM’s Watson computer system in 2010. Recently, Go’s “best player” Ke Jie was soundly  defeated  by a computer system called AlphaGo powered by DeepMind.  Ke’s loss to AlphaGo seemed like a particularly tough defeat for humans because Go seemed potentially too complex for a computer to master. As context, the total number of possible moves in Go is about 10170 while there only exist approximately 1080 atoms in the entire observable universe.  Yet, Ke was still soundly defeated in a recent match. As the   Economist     explains (referencing an earlier match with another human Go champion), “Until Mr. Lee’s defeat, Go’s complexity had made it resistant to the march of machinery. AlphaGo’s victory was an eye-catching demonstration of the power of a type of AI called machine learning, which aims to get computers to teach complicated tasks to themselves.”  What may be particularly fear-inducing to humans is that AlphaGo actually taught itself how to beat Ke rather than relying on humans to train the machine. What does this mean in English? This version of AlphaGo was not the first DeepMind machine used for this  task .  The original AlphaGo studied thousands of examples of human games, a process called supervised learning. Since human play reflects human understanding of such concepts, a computer exposed to enough of it can come to understand those concepts as well. Once AlphaGo had arrived at a decent grasp of tactics and strategy with the help of its human teachers, it kicked away its crutches and began playing millions of unsupervised training games against itself, improving its play with every game.  AlphaGo did not train using human teachers. The DeepMind researchers created a “reward function” which told the machine what goal it was trying to achieve. In this case, former New York Jets coach and current ESPN Analyst Herm Edwards stated it best when he said, “You play to win the game!” Then the computer experimented with different moves to determine the best strategy in each game. In only two days, AlphaGo learned how to far outperform earlier versions and vastly outperform the best Go players.  Perhaps the most “devastated” person in this whole experience would be Ke. He was the best player at Go, and it was increasingly clear he would never be better than AlphaGo. Instead Ke took the opposite approach to machine learning. Ke studied what the machine was doing and applied that to his own game. Because the machine did not learn how to play from studying humans, it could “see” the game in an entirely different way than humans could. Ke went on to have 22 match winning streak against the world’s best human competition.  This is not the first time that big data analysis has led to insights that could be applied to human activities that humans were unlikely to develop themselves. The most well-known example involves defensive shifts in baseball. In his book   Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak  , Travis Sawchik perhaps best explains how a team used something completely counter-intuitive to human thinking as a way to gain a strategic advantage. The Pittsburgh Pirates would shift their infielders to all be on one side of the field because their analysis of hitting patterns found that many hitters overwhelmingly hit to that side. This would mean more groundball outs and less runs scored by opposing teams.  The problem is that this strategy left large portions of the field uncovered by players. To humans, that seems absurd. Why not cover all parts of the field with your players? Why would hitters not just adapt their swings and hit where there were no fielders? A manager potentially has the ability to look very stupid by employing this strategy. For a variety reasons, including the speed and spin rotation of pitches at the Major League level, it is very difficult for hitters to change their natural swing. Therefore, Pittsburgh’s seemingly foolish strategy worked surprisingly well.    The difference between what the Pirates discovered and AlphaGo is that humans spent hundreds of hours analyzing big data to determine that field shifting would benefit their team. AlphaGo determined the equivalent of field shifting for Go on its own in two days. This is actually a very good thing for humans. More specifically, companies are often looking for innovation and new insights from big data or technology. The concept of “disruption” is based on the idea that humans will discover the next big innovation that can provide companies (or sports organizations) with that next big competitive advantage.  What AlphaGo shows is that humans can now learn disruptive practices from machines rather than machines “waiting” for disruptive ideas to come from humans. Computers can think “outside of the box” (even though the machines are often contained in boxes) because they do not think like humans. Humans can and should leverage the power of machines to come up with disruptive ideas to gain competitive advantages.  At Block Six Analytics, we use machine learning in this way. In particular, our  Media Analysis Platform  and  Social Sentiment Analysis Platform  use artificial intelligence to help buyers and sellers of corporate partnerships in sports to maximize revenue growth. Our  Partnership Scoreboard  Software As A Service (SaaS) application creates insights that we share with our clients to help them determine the best way to allocate sponsorship dollars.  No one likes to lose and losing to a machine can seem especially difficult and embarrassing. However, humans’ abilities to recognize, understand, and apply disruptive technologies and strategies can be the best result of machine learning in the final analysis.
BY ADAM GROSSMAN

AI Makes Humans More Intelligent

Humans can now learn disruptive practices from machines rather than machines “waiting” for disruptive ideas to come from humans. Computers can think “outside of the box” (even though the machines are often contained in boxes) because they do not think like humans. Humans can and should leverage the power of machines to come up with disruptive ideas to gain competitive advantages.

       Judging the Success of Pepsi’s Newest Athlete Endorsement    BY ADAM GROSSMAN & JOSH HERZBERG     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      It has been a whirlwind ride for New York Yankees outfielder Aaron Judge. From setting a new rookie homerun record to leading his team to the playoffs to unanimously winning the American League Rookie of the Year award, Judge has had a terrific year. His on-field success has helped translate into off-field partnership opportunities with companies including Rawlings, Under Armour, and Fanatics. It seemed like a no-brainer for PepsiCo (full disclosure: PepsiCo is a Block Six Analytics (B6A) client) to take a swing on an endorsement deal with Judge announced on Monday.  Looking at standard social media valuation metrics, however, would likely say that this is not the case. For example, we used the Block Six Analytics (B6A)  Social Sentiment Analysis Platform (SAP)  to examine the value of a tweet by  Pepsi  and  Judge  to highlight their new relationship. An analysis of the followers would suggest that Pepsi’s reach would dwarf Judge’s based on each of their follower counts. Conventional wisdom states the larger the reach the more value that could be delivered to the brand.   Twitter: Pepsi – 3.1 million followers; Judge – 220,000 followers   However, a deeper analysis shows that Pepsi made a very good choice in partnering with Judge. In particular, Pepsi’s goal in working with Judge is not necessarily to increase exposure. Pepsi has near ubiquitous brand awareness for its current and potential customers. One of Pepsi’s goals is to increase engagement with these customers. Our SAP analysis found that:   Judge (4.44%) had a significantly higher engagement than Pepsi (0.01%) for comparable posts.  Judge (9,789 engagements) had a far greater number of engagements than Pepsi (266 engagements) even though Pepsi has a far larger number of followers.  Judge (368,009 impressions) did also have a significantly higher number of expected impressions than Pepsi (10,000 impressions).  Judge’s ability to increase engagement enables Pepsi to better achieve its initiative (what the company is trying to accomplish), demographic (whom the company is trying to target), and channel (what is the most effective way to reach these demographics) goals as demonstrated by B6A’s  Corporate Asset Valuation Model . This model examines how specific partnership opportunities help a specific company generate lifts in revenue and marketing goals across the initiatives, demographics, and channels of a partnership.   All of these factors helped Judge’s tweet generate  $3,721.63 in value  to Pepsi while its own tweet only generated  $98.67 in value .     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     While SAP has the ability to examine Facebook and Instagram, we did not include these in this analysis. Judge does not have a Facebook account and Pepsi did not have a post directly about Judge on its Instagram account. Judge did post the same video from his Twitter account to his  Instagram account  and that post did generate a similar number of views and engagements as his tweet even though his Instagram account has about a little more than half the followers as Pepsi’s.  This also does not mean that teams, athletes, or brands with large platforms are not valuable to partners. We have previously shown that  Rakuten  is able to achieve its goal of increasing brand awareness in the U.S. by partnering with the Golden State Warriors specifically because of the exposure the team can generate. The takeaway is that different brands will have different goals when it comes to their partnerships in sports. Moving beyond reach and creating a customized valuation to clearly demonstrate how and why value is created is critical to evaluating the success of a partnership. This is exactly what Pepsi has done in developing a relationship with Judge.
BY ADAM GROSSMAN AND JOSH HERZBERG

Judging the Success of Pepsi’s Newest Athlete Endorsement

It has been a whirlwind ride for New York Yankees outfielder Aaron Judge. From setting a new rookie homerun record to leading his team to the playoffs to unanimously winning the American League Rookie of the Year award, Judge has had a terrific year. His on-field success has helped translate into off-field partnership opportunities with companies including Rawlings, Under Armour, and Fanatics. It seemed like a no-brainer for PepsiCo (full disclosure: PepsiCo is a Block Six Analytics (B6A) client) to take a swing on an endorsement deal with Judge announced on Monday.

       How the Astros and Cubs World Series Titles Apply to Evaluating Sponsorship    BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      This year seemed to be the crowning achievement for baseball analytics. The Houston Astros relied heavily on data analysis to construct their 2017 World Series Championship team. This appeared to be a similar formula to what the Chicago Cubs used to end their 108-year World Series victory drought last season. A natural question becomes: why doesn’t every MLB team follow the Astros and Cubs actions to create a winning team?  Despite surface similarities, the Astros and Cubs took divergent analytics paths on their ways to winning championships. The Astros built their 2017 championship team primarily focused on offense. Baseball Reference, one of the most frequently used sites to find advanced analytics, highlights three metrics that demonstrate the team’s emphasis. The first is called BtRuns which  estimates  the number of runs contributed by a team’s players above what a normal player would produce while batting. The Astros as a team produced  228.1 BtRuns  this year. As context, the next highest BtRuns result in the American League (AL) was for the Minnesota Twins with  42.3 BtRuns .  The second metric to examine is called lgBA which looks at what the batting average of a team composed of average (non-pitcher) players would be if it played the same schedule (and more specifically played in the same stadia) as the team in comparison. The Astros had an AL high .281 batting average even though its lgBA was an AL low .246. What this means is that the Astros had the  league’s best betting average even though they played the league’s hardest schedule .  The Astros also were not a good fielding team in 2017. The Rtot metric  estimates  the number of runs prevented by a team’s players above what a normal player would produce while batting. The Astros placed  below league average of AL teams with a -8 Rtot  for the 2017 season. The Astros Rtot has actually  become worse  every year from 2015-2017.  This is not an accident. The Astros typically have a lower than league average lgBA which means the team is playing in places in which it is difficult to get hits. This means that a team that can score runs in difficult conditions will be consistently better than a team that fields better. The Astros demonstrated this perfectly in the 2017 season. The team was by far the most dominant offensive team in the AL while being the only team to make the playoffs with a below average defense.  So why is every team not like the Astros? One only has to look at the Cubs 2016 championship season. The Cubs emphasis on defense can be seen in its  117 Rtot for 2016 , by far the highest in the National League (NL). Jason Hayward had 30 Rtot by himself in 2016 making him extremely valuable to the Cubs even when he struggled on offense. The Cubs’ lgBA was .262 (4th highest in the NL) in 2016 while the team had a batting average of .256 (league average). While the Cubs did have the highest BtRuns in 2016 in the NL (which can show why batting average should not be the primary offensive statistic to examine) that number was only 40.1. In 2017, the Cubs made the playoffs with a BtRuns of -3.1. This analysis demonstrates that the Cubs typically play in hitter friendly stadiums (including most games at Wrigley Field) which puts defense at a higher premium for the team.  The Astros and Cubs demonstrate that there is not a single way for teams to win in baseball. Both teams rely heavily on analytics, and the data shows that each team should take a different approach in building their rosters. What is the lesson learned for buyers and sellers of sports sponsorship with this analysis?  The main takeaway is that there should not be a “one size fits all” valuation approach to sponsorship. More specifically, buyers of sports sponsorship could be the Astros or could be the Cubs. They will have different needs for achieving their sales and marketing goals because of the nature of their businesses and what works for one company will not always work for another company. Looking at the data can help determine what type of sponsorship asset will work better for a company.     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     A good example of looking at this type of approach is comparing a business-to-consumer (B2C) company to a business-to-business (B2B) company. B2C companies sell lower price items to a larger number of people. Good examples of B2C companies are quick-service restaurants, beverage companies, and apparel companies. B2B companies often sell higher price items to a smaller number of customers. Good examples of B2B companies are enterprise software companies, commercial lending companies, or manufacturing equipment companies.  The same suite at a sports venue will usually have different values for B2B companies vs. B2C companies. For B2B companies, it is rare to have the opportunity to spend 2-3 hours with a current or potential client that is spending thousands (and potentially millions) of dollars with your company. Many times a clients’ ability to access suites during a sports season is a primary reason for signing or renewing a relationship.  That is not necessarily as important of a consideration for B2C companies. While suites usually cost thousands of dollars, a B2C customer is spending at most hundreds of dollars with a company (and many times much less). This often makes suites less valuable to these types of companies.  The Block Six Analytics (B6A)  Corporate Asset Valuation Model  specifically examines these types of factors when determining overall sponsorship value. Similar to the Cubs and Astros, we realize that different companies need to prioritize different demographics, initiatives, and channels to achieve their financial and marketing goals. This approach to valuation is then layered into how our  artificial intelligence platforms  calculate value in near-real time. While (B6A) may not be on the cover of  Sports Illustrated , using analytics will help both buyers and sellers of sports sponsorship find assets that help them win at sponsorship spending by finding the best assets based on the specific goals of each sponsor.
BY ADAM GROSSMAN

How the Astros and Cubs World Series Titles Apply to Evaluating Sponsorship

The Houston Astros and Chicago Cubs demonstrate that there is not a single way for teams to win in baseball. Both teams rely heavily on analytics, and the data shows that each team should take a different approach in building their rosters. What is the lesson learned for buyers and sellers of sports sponsorship with this analysis?

       B6A Adds Optical Character Recognition To Its Media Analysis Platform    BY ADAM GROSSMAN, ALEX CORDOVER, ALBERTIO RIOS, & JOSH HERZBERG     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Block Six Analytics (B6A) is pleased to announce the addition of optical character recognition (OCR) capability to our Ensemble Training Process (ETP) within our Media Analysis Platform. Adding OCR to our best-in-class logo detection in MAP increases the overall accuracy and speed needed to identify the time, location, prevalence, and value of images in videos and photos for specific brands for specific activations.  “Our proprietary approach to training solves critical challenges that have impacted the accuracy and speed of object identification,” said CEO Adam Grossman. “By adding text-based analysis to our process, B6A's clients receive better results than human or image detection alone.”  Using OCR enables B6A to add detection and character recognition for identifying logos that are primarily text based. In the past, machine learning platforms have had difficulty capturing text logos because they have different features than image-based logos. In particular, letters look very similar which makes them easy to read but hard for a machine to differentiate.  B6A’s proprietary approach enables us to identify and treat these logos as though they were text so we can use natural language processing (NLP). Our system is able to determine unique words by looking at the distance between sequences of letters in an object. For example, the object below shows how our system recognizes “PPG” because it can identify the “P” and determine how far it is from the “P”, and “G”. Each word has different combinations of distances, and the system uses this as the way to identify an object in a video. We then use our proprietary algorithms to both layer in core visual metrics (centricity, prevalence, time, and clarity) and calculate value.         

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     Text analysis does not work with every logo. For image based logos, the problem with training a system is image collection. In particular, it is necessary to collect hundreds, if not thousands, of images in a similar environment to where the object appears in the video. B6A’s proprietary image synthesizer is used to automate this process, requiring only 10-20 original images and then synthesizing the remaining images needed to complete the training set. By streamlining the process to create training data, MAP can be trained on a new logo and deliver results in as little as 24 hours.  Once MAP is trained, it can fully process games / events and produce results in the same day on which the game or event occurred. B6A’s system can track images in situations that were difficult to analyze before. These include:   Having a consistent approach to image occlusion. ETP combined with deep learning enables MAP to automatically determine if a logo / image is clear to a human viewer depending on how much of the image is blocked.  Identifying images with fast camera movement. For example, the TV camera pans up and down the ice during an NHL game. This situation has been difficult for machine learning systems to capture in the past. ETP dramatically reduces this as a problem enabling MAP to identify and value images in this type of video clip.   Combining both text and image analysis can increase the accuracy of detecting an object when it contains both elements. Because B6A’s ETP uses image-based, text-based, or a combination of both approaches in MAP, we are able to process videos quickly and accurately using the latest advances in object identification. For more information about MAP and ETP, contact  Block Six Analytics .
BY ADAM GROSSMAN, ALEX CORDOVER, ALBERTO RIOS & JOSH HERZBERG

B6A Adds Optical Character Recognition To Its Media Analysis Platform

Block Six Analytics (B6A) is pleased to announce the addition of optical character recognition (OCR) capability to our Ensemble Training Process (ETP) within our Media Analysis Platform. Adding OCR to our best-in-class logo detection in MAP increases the overall accuracy and speed needed to identify the time, location, prevalence, and value of images in videos and photos for specific brands for specific activations.

       Interview With NFL Network Analyst and Game Theory and Money Podcast Co-Host Cynthia Frelund   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
       Cynthia Frelund joined NFL Network in 2016 as an analytics expert, providing her unique insight into the game on NFL Fantasy LIVE and GameDay Morning. She is also the co-host of the Game Theory and Money podcast. Prior to joining the NFL, Cynthia worked at ESPN and Disney. She graduated with a Masters of Business Administration and Masters in Predicative Analytics from Northwestern University.      This interview has been edited for clarity and length.     Why do you want to pursue a career at the intersection of sports and analytics?   I am huge sports fan, but my path into the NFL was through Anthony Noto who was the CFO of the NFL (now the COO at Twitter). Before he was at the NFL, I used to read his Goldman Sachs equity research reports and I loved the way he structured his thoughts, and he was smart and it was interesting to listen to what he had to say. Given my background in finance and strategy and my passion for sports, working at the NFL was a great opportunity.    Why the NFL?   The impact on analytics in baseball is pretty widely accepted. Use of analytics in the NFL is far more nascent. We are kind of geeks in a corner in the NFL. I own that. I like that I could ask larger strategic questions and have the opportunity to be impactful in the space. It is exciting to be a pioneer in a new market.  I saw that I really had an opportunity to focus on bridging the sports performance and business sides in the NFL. The NFL has unique performance valuations. Unlike the MLB or the NBA, the NFL has a hard salary cap. Looking at the on-field product and what coaches have to do to win a game is also very intriguing.   How do fans interact with what you are doing?   I am really lucky in that the work I did at ESPN prior to the NFL and at the NFL I have great support from my executives. I have really good producers that are able to take complex concepts and distill them into good stories. No one needs to read or listen to a PhD dissertation on a pregame show.  Yet, people want to know the significant information. What I provide is the opposite of the hot take. It is a logical argument. There is no Cynthia-bias in it. They are getting their own [Cleveland Browns Chief Strategy Officer] Paul DePodesta. I have seen fans go deeper into the math throughout the years. It is awesome, and my favorite part of the job.   What technology do you use?   I code in everything from Python to R to relational databases. I am pretty good at using new tools to solve problems and answer questions. I also use Open-Source video tools (most familiar with tensorflow). Computer vision models are also great.  In particular, it is cool to use deep learning and video to look at shapes and angles of players in video. It allows me to create the “waist bender” metric for offensive line play. Offensive linemen that can keep hips parallel and stay low are better at protecting quarterbacks because they do not lose leverage on their defensive counterparts. I was able to map NFL performance back to the NFL combine 40-yard dash results to find elite, above average, average, below average, and well below average waist benders. The lowest waist benders are the best at protecting quarterback. If you can keep your center of gravity low for the first ten yards during the 40-yard dash that is good (or a low waist bender) and if not it is bad (a high waist bender). You can see it as they run the 40 in the video and map the results using video. It is almost a continuous variable which shows that the lower you can stay, the better you will be at protection.   How important is communication in what you do?   I would not be able to get my job done without my knowledge of analytics, but I think it is like 60/40 communication. You would like to think that you can just be amazing at this, but it’s not enough. You must be able to tell the story of your findings, but you really need to be able to communicate in order to figure out what answers someone (a coach, etc) wants to explore. Choosing the right questions to explore is the key and you have to ask the people who would need to execute these findings what they care about and why.     Do you play your own fantasy team? How does it do?   I have a few fantasy teams. The one on NFL.com is difficult to pay attention to because I have to be on-air right before players lock and games begin. It is more important for other people to get the information than to use the information for my own team. In my one team that is very competitive, I have a bot to sub players in and out because my show is on right before the games start.   What the biggest misconception about what you do?   The biggest misconception is that analytics is like a bunch of listed statistics, and I can read them and they are all equally important or not important. The point is to put situations in context using data and take bias out of it.   Where is analytics in the NFL moving to in the future?   Safety is a big deal. Between the sports science technology with biometrics and training metrics there is a lot going on with each team. There is a lot of cool data showing where we can find situations where safety can be improved. For example, rule changes and information gathered and distributed about when and where injuries occur. It is really cool to see how data has and continues to help.   
BY ADAM GROSSMAN

Interview With NFL Network Analyst and Game Theory and Money Podcast Co-Host Cynthia Frelund

Cynthia Frelund joined NFL Network in 2016 as an analytics expert, providing her unique insight into the game on NFL Fantasy LIVE and GameDay Morning. She is also the co-host of the Game Theory and Money podcast. Prior to joining the NFL, Cynthia worked at ESPN and Disney. She graduated with a Masters of Business Administration and Masters in Predicative Analytics from Northwestern University.  

       Taking A Bite Out of D.C. Sports’ Apple   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      Laurene Powell Jobs, wife of the late Steve jobs, is  reportedly  “buying a significant stake in Monumental Sports & Entertainment (MSE), a sprawling $2.5 billion complex that includes the NBA Wizards, NHL Capitals and Capital One Arena.” The article by   The Washington Post     business reporter Thomas Heath contains two interesting insights.      The first insight is that Powell Jobs may have decided to invest in Monumental because she eventually can become the majority owner of MSE.  According to Heath , “If [Monumental Sports & Entertainment majority owner Ted] Leonsis, 60, retired, Powell Jobs has the resources to assume his shares. Leonsis has long been the lead shareholder, with around 40 percent. Most contracts with a stake of this size include language that allows the buyer, in this case Powell Jobs, the option of a path to ownership.”  Female majority owners of major professional sports teams are relatively rare. Currently, there are only three woman majority owners in the 30-team NBA. However, women are increasingly making up a large percentage of  the sports fan base . Having more female owners, given the changing demographics of sports customers, should serve to benefit teams and leagues in the future.  The second insight that Heath makes is more controversial. He  states , “Powell Jobs’ investment is part of a trend in which deep-pocketed financiers and Silicon Valley billionaires are buying stakes in professional sports properties, helping drive franchise prices to even greater heights.” The problem with this type of statement is that he is stating that the value of the team is determined by how much the team is purchased for and that the “deep-pocketed financiers” are the ones driving up value.  Yet, this a common way of looking at asset valuation when it comes to sports. As Heath later  asserts , “Forbes earlier this year estimated the Wizards’ value at $1 billion, but after recent sales in the league — including the Houston Rockets for $2.2 billion — the team is worth much more than that.” A natural question is why are the Wizards worth more than a $1 billion because the Rockets were sold for $2.2 billion?  Both teams do participate in NBA revenue sharing which includes the massive media rights deal that contributes $2.67 billion per year to the league. Yet, this deal had already been in effect and accounted for in the Forbes valuation. In addition, the Wizards and Rockets play in different sized arenas, have different local media rights deals, different sponsorship agreements, different population sizes, and different fan demographics. So why would the Wizards automatically be “worth more” because the Rockets sold for $2.2 billion?  A good to way to examine the value of sports teams is to look at the analysis we completed of the Los Angeles Clippers at the time when Steve Ballmer purchased the team in 2014. In asset valuation, there are usually three different approaches that can be used to determine value   Inherent valuation typically uses a discounted cash flow analysis which essentially looks at the operating income (or operating profit) that the company produces and discounts future cash flow so that they are equivalent to present day values.      

  

  	
       
      
         
          
             
                  
             
          

          
           
              A discounted cash flow (DCF) valuation shows the Clippers worth $2.024 billion in 2014 when former Microsoft CEO Steve Ballmer purchased the team.  
           
          

         
      
       
    

  


      Relative valuation uses a ratio analysis to determine the value of a company. The most typical ratio used to evaluate stocks is called the price-to-earnings (P/E) ratio. The market capitalization (or overall listed value on a stock exchange) of a company is compared to the operating profit of that company. In our analysis, we compared the P/E ratio of the Clippers to major stock indexes.      

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


      Comparable valuation is where you compare comparable companies purchase prices to the company being evaluated. In 2014, there were fewer transactions in the sports industry than now.      

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     The average of these three types of valuation places the Clippers value at  $1.92 billion . The reason the Clippers were this valuable is not solely because other sports teams were being purchased at certain prices. In fact, for the Clippers that actually  lowered  the team’s overall value at the time. The Clippers were valuable because the operating income of the team increased. More specifically, all NBA teams received increased revenues from media rights deals (both national and local) that far exceeded their increased costs (primarily the increase in players’ salaries). For the Clippers, the team’s local media rights deal increased from around $20 million per year to the  “low-to-mid $50 million range” .  That is not to say that all NBA teams are profitable. For example,  MSE  “is close to breaking even financially” in part because the Wizards’ deal with NBC Sports Washington pays a reported  $35 million per year  – or at least $15 million less than the Clippers. This difference would potentially make the Wizards less valuable than the Clippers because the team likely generates less cash. The lower annual fees may be in part due to MSE now having a  33 percent equity ownership  interest in NBC Sports Washington. All of these factors, and not just the price of a recent transaction, need to be considered when looking at MSE’s value.  This type of analysis can and should be applied in other areas of the sports industry. For example, Block Six Analytics (B6A) corporate asset valuation and revenue above replacement models use all three valuation approaches to determine the price of a sponsorship or player contract, respectively. To clarify, we do complete a comparable analysis as part of our standard product offering. However, combining all valuation approaches enables us to look at the fundamentals of each asset type and determine how value is created for a business based on the impact a sponsorship has on revenue generation and achieving company goals. 
BY ADAM GROSSMAN

Taking A Bite Out of D.C. Sports’ Apple

Laurene Powell Jobs, wife of the late Steve jobs, is reportedly “buying a significant stake in Monumental Sports & Entertainment (MSE), a sprawling $2.5 billion complex that includes the NBA Wizards, NHL Capitals and Capital One Arena.” The article by The Washington Post business reporter Thomas Heath contains two interesting insights.  

       Amazon’s Attribution Approach To Streaming NFL Games   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      “Content is king” is a familiar refrain in the media and entertainment industry. More specifically, compelling content enables companies to attract large audiences regardless of the distribution channel. Sports is the king of kings in this context. The reason that companies have spent billions of dollars on media rights deals with leagues, teams, and events is that sports consistently attracts large audiences watching live programming.  Traditionally, there have been two ways to monetize sports audiences. Broadcast networks and digital companies with steaming platforms such as Twitter, Facebook, and Yahoo have focused on using sports to sell advertisements to companies looking to target lucrative sports audiences. ESPN and regional sports networks (RSNs) have generated billions of dollars annually in the carriage fees that they charge cable and satellite providers for offering these channels to their subscribers.  The fact that Amazon is potentially breaking this traditional monetization model is what makes its new over-the-top streaming deal for Thursday night football games particularly interesting. Starting this Thursday, a reported  80 million  Amazon Prime members will be able to access the games for free.  Amazon has the capability to sell ads during these NFL games. While 80 million is a large potential audience, it is far lower than the  328 million  daily active global users on Twitter’s platform where NFL Thursday night games were streamed last year. Therefore, Amazon’s potential reach right now is significantly lower, making it arguably less attractive to advertising.  In addition, Amazon will be using NFL content to try to increase the number of Amazon Prime users. By paying $99 per year, Prime customers will receive access to exclusive NFL content while receiving other benefits such as faster shipping of products. However, Prime users make up only a relatively small portion of Amazon’s total customer base. Unlike ESPN and RSNs, a large number of Amazon’s customers are not using the ecommerce platform because it has NFL content.     

  

  	
       
      
         
          
             
                  
             
          

          
           
              Amazon's first live stream of Thursday NFL games is September 28th.    
           
          

         
      
       
    

  


     Instead, Amazon’s platform enables a company to focus on the quality of its audience rather than the quantity of its audience. More specifically, Amazon users that become Prime customers sign up because they are active buyers of products. Amazon’s NFL stream becomes compelling content for two reasons. Amazon’s wants its Prime buyers on its site as much as possible. In particular, the customers specifically buy Prime accounts because they are active users on the platform. The more frequently Amazon can get the users on the platform the more likely that Amazon can increase its revenue.  Advertisers can also maximize the probability of driving direct revenue by using Amazon through commercials. On other channels, when people see a commercial showcasing something that they may wish to purchase, they have to go to another website to make the purchase. With Amazon, however, a customer is watching a game on the platform where they can also buy the products being advertised directly. Reducing the customer journey will make it easier for companies to sell more products while also enabling companies to much more clearly attribute advertising dollars to a specific promotion or channel.  Examining click-through rates (CTR) is a good example of this new model. In the past, a company would typically measure how often customers clicked on an advertisement to come to its website. It then could measure how many people that clicked on the ad and made a purchase. This required the customer to leave the platform where they were viewing content and also to have a time lag for when they made the purchase. This effort is one reason that CTRs are often lower than companies would like.   For Thursday Night Football, a Prime customer does not have to leave Amazon’s platform to make a purchase. This makes it as easy as possible for customers who are the most likely to make a purchase to complete a transaction. Companies can then also potentially see when spikes in purchasing activity occur from Amazon and likely attribute this success to this channel.  This does not mean that companies do not receive significant value from working in other more traditional channels. Reaching a large audience is a critical objective for many companies. In particular, there are many companies for which revenue from Amazon or ecommerce does not constitute a large proportion of total revenues. Also, there are several other channels that companies need to explore from an advertising and sponsorship perspective that will help companies achieve their revenue and brand goals. In particular, reaching as large of an audience as possible to increase customer acquisition and brand awareness is a critical objective for many companies looking to advertise in or sponsor sports.  However, Amazon’s streaming of NFL games does have the capability to change the content dynamic for sports in a positive way. As some sports have seen a decline in ratings, focusing on the quality of the audience watching games and events is critical. The fact that Amazon has the capability to show significant lifts in purchases and revenue for companies during streams can extend sports’ reign as content king for years to come.
BY ADAM GROSSMAN

Amazon’s Attribution Approach To Streaming NFL Games

Content is king” is a familiar refrain in the media and entertainment industry. More specifically, compelling content enables companies to attract large audiences regardless of the distribution channel. The fact that Amazon is potentially breaking the traditional monetization model is what makes its new over-the-top streaming deal for Thursday night football games particularly interesting

       Rakuten Strikes Gold With Warriors Jersey Patch Deal   BY ADAM GROSSMAN     

 
   
     
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
      
       
         
            
            
         
       
     
   
      The Golden State Warriors have reportedly reached a $20 million per year jersey patch agreement with Rakuten. The Japanese tech company will be featured on the Warriors’ jersey for the next three seasons. The fact that the Warriors would sign the most lucrative jersey patch deal in the NBA is not surprising. The team has won two of the past three NBA Championships, has numerous international stars, and is situated in the Bay Area near Silicon Valley.  The fact that the team received  nearly double  the amount of the second highest team, the Cleveland Cavaliers, and the fact that the deal is with Rakuten, a company few people in the U.S. have likely heard of until now, is surprising. Why would Rakuten spend this amount of money for a jersey patch deal?  Rakuten is a well-known brand in Japan and owns cash-back site Ebates, messaging app Viber and e-book brand Kobo. The company, which generates $7 billion  in revenue  per year, does have a history of targeting expensive apparel rights deals. The company is paying almost $60 million per year to be featured on the jerseys of FC Barcelona.  However, it does appear that Rakuten is following best practices through its new relationship with the Warriors. In particular, Rakuten’s CEO Hiroshi Mikitani understands that his company lacks brand awareness in the U.S. even though its North American headquarters is based in San Francisco.  According to  Mikitani , "We want to be a household name like Google and Facebook. Our partnership in Barcelona has helped us in Spain, and the Warriors will certainly be a pillar of getting us there in United States."  Jersey patch deals are an excellent fit for companies looking to maximize brand awareness because of the amount of exposure they will receive on television, digital, and social media channels. The new badge with the Warriors in particular (what the team is calling the jersey patch) will enable Rakuten to achieve significant brand exposure to a tech-savvy audience in Silicon Valley and throughout the United States (over 84% of NBA fans use online devices for sports-related purposes according to the research firm SBRnet).   We examined the value generated by the Golden State Warriors in Twitter through the NBA playoffs using B6A’s Social Sentiment Analysis Platform.     

  

  	
       
      
         
          
             
                  
             
          

          

         
      
       
    

  


     We found that the team generated $2.49 million dollars in value and over 201 million impressions during the playoffs alone. We also discovered that several of the most valuable posts featured photos with the team’s jersey’s prominently displayed in the image with earned media coming from coverage in  ESPN  and   The New York Times  . We would estimate that the team would generate $5 million in revenue from Twitter for the season and likely more from Facebook and Instagram given that NBA audiences are larger in these social media networks according to research firm SBRnet.    In addition, Rakuten expanded its deal to include terms that can directly help its business generate revenue. According to ESPN’s  Darren Rovell , “Rakuten will be the team's official e-commerce, video-on-demand and affiliate marketing partner. Ebates will become the team's official shopping rewards partner, Viber will be the official instant messaging and calling app, while Kobo will be the official e-reader partner.” As the sports industry pursues technology innovations including e-commerce platforms for its products including tickets and merchandise as well as on-demand content delivered to fans across multiple devices, Rakuten is well positioned to utilize its relationship with the Warriors to monetize its products directly through its relationships.  Our analysis indicates that this deal likely should generate  significantly more  than the “$32 million to $37 million in equivalent advertising” projected by the Apex Marketing Group. Rakuten should generate a substantial positive ROI with the Warriors because the partnership will generate high quantity, quality, and engagement with its target audience in ways that will enable the company to achieve its economic and branding goals. 
BY ADAM GROSSMAN

Rakuten Strikes Gold With Warriors Jersey Patch Deal

The Golden State Warriors have reportedly reached a $20 million per year jersey patch agreement with Rakuten. The Japanese tech company will be featured on the Warriors’ jersey for the next three seasons. The fact that the Warriors would sign the most lucrative jersey patch deal in the NBA is not surprising. The fact that the team received nearly double the amount of the second highest team, the Cleveland Cavaliers, and the fact that the deal is with Rakuten, a company few people in the U.S. have likely heard of until now, is surprising. Why would Rakuten spend this amount of money for a jersey patch deal?