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.