Analytics
News, events, thought leadership and more.
We’re pleased to present a guest blog from Ari Kaplan, a pioneer in sports analytics who has worked with over half of all major league baseball organizations and many global sports media organizations. Ari spoke at our annual Strategic Analytics conference, Inspire, this past March and you can view his presentation here. In this blog Ari will share some of the lessons he’s learned over 23 seasons and how they can be applied off the field to improve your organization’s use of analytics.
Transforming business
The movieand book Moneyball showed the transformation of a business by making data-driven decisions. What made it interesting is that the business happened to be a Major League baseball team - the Oakland A’s. What makes it compelling is that similar principals in collecting, analyzing, and leveraging data can be applied to pretty much any field out there – retail, finance, government, telecommunications, restaurants, technology, entertainment, and beyond.
I have had the privilege to work with over half of all MLB organizations to help them transform and use information to make decisions - not guesses. What I have found are universal lessons learned that can be applied outside of baseball – another world I have been part of through my work at several database and technology firms.
Success typically comes from a culture that is willing and able to act upon information, combined with team members who can find meanings in proper context that can lead to real actions. Like all businesses, baseball has a spectrum of cultures from the top down. Baseball also has a variety of people who can collect and interpret data into meaningful recommendations – or merely find patterns that are more interesting facts than actionable recommendations.
Finding meaning vs. calculating
There is a difference between calculating answers and finding meaning. A large part of that is putting results into proper context, finding ways to improve analytical models, collecting new and varied data, a natural curiosity of discovery - and common sense.
For example, a forecast can say that a baseball player has a career trajectory to produce three additional wins per season. You then make a recommendation to the GM that this player is a great one to sign to your team. The GM gives you funny looks – because two days ago the player’s arm was injured and placed on the disabled list. The player has much reduced value and greater future risk. In hindsight, you would want to add an injury data source into your forecast models, and anything else you can think of that could limit playing time such as a 50-day drug suspension. You need to use common sense and ask as many reality-check questions as possible before presenting your recommendations.
Another business example: a retailer releases a new product in Chicago, but the first week sales are weak. Is the conclusion that consumers just don’t want the product? Using common sense, it was really due to a blizzard and shoppers simply stayed home. You can use common sense to figure out what new and creative sources of data can help put insights into the proper context. In this case the retailer would improve their model with a new weather data source.
The human element
It is helpful to have an understanding of how human decisions are made, and understanding the underlying components that drive actual human decisions. People’s behaviors are elastic, and models should be aware of and accommodate that elasticity. This has implications across all industries - anywhere humans are involved.
Umpires are human, and when an umpire calls a strike on a pitch six inches outside the strike zone, it’s a strike in real life regardless if it is technically a ball. Whether the data says customers will buy a product, the market will dictate reality. Your data insights can be enhanced by taking into account subjective information, quantifying it wherever you can, and incorporating it into your models and processes. Your success can be increased by understanding how the human mind works and what triggers an event such as buying a product, signing a deal, or hesitating to throw a pitch.
I often listen to scouts and sit with them during games to learn what they are observing, and whenever possible I quantify and automate their observations. This way you are leveraging human elements for advantage. For example, a scout notices that a pitcher is hesitant to throw a slider with a runner on third because he is subconsciously worried he’ll throw a wild pitch and the runner will score. I can make sure to collect that data, and automatically check for that condition to alert the manager and player of that pitcher tendency. Then, when there is a runner on third base, analytics can provide the batter with a huge advantage because he knows that a slider is probably not coming, allowing him to better time his swing against a fastball.
Culture for change
Once your organization starts producing information that is in-context and actionable, there still needs to be a culture in place to act upon the data. Is the business open to information for transformation? Are they actively seeking it? If they are, can the business act quickly to enable those changes and thus enable success?
Was this driven from the top, from leadership who has the vision and ability to make actual business changes and corporate transformations based on evidence? Or, are the insights being pushed up the chain in a possibly reluctant, defensive, or even hostile corporate culture?
Businesses themselves need to embrace change to realize the value of insights. They must also make sure that changes are based on recommendations not simply from end-numbers, but rather a combination of informational analysis with business acumen.
I find that baseball has three types of players: players who want information so they can actually improve, players who want information but are unable to adjust, and players who do not want information because they feel no need to change. What kind of player will you be?
Ari Kaplan
President, AriBall
www.ariball.com
Twitter: arikaplan1
Bio:
Ari Kaplan is a leading figure in sports analytics, having worked with over half of all major league baseball organizations and many global sports media organizations over 23 seasons. In 2011, Sports Illustrated named Ari “Top Ten General Manager Candidates”. He received Caltech’s “Alumni of the Decade” distinction for pioneering groundbreaking sabermetrics used to evaluate pitcher talent. Crain's Chicago Business also recognized Kaplan's work in business, baseball and humanitarian endeavors by including him in their annual "40 Under 40" cover story. In addition to his scouting background, Ari is one of the few long-term baseball leaders who has a proven track record at Fortune 500 companies, as well as successfully running several high-profile organizations as CEO.
