A Mystery Ball, Moneyball and Considered Content Strategies

September, 2019

For my birthday this year, a member of my family gave me a Mystery Ball. A match-used MLB baseball that has a small hologram stuck to it, alongside a code. Enter the code, and the amount of information you get about that one ball is overwhelming: who hit my Mystery Ball, the game, the speed of the delivery, the type of the delivery, who caught the ball, how far it travelled, the pace at which it travelled, the angle at which it left the bat and the result of the play.

An impressive level of information, especially given that 65 balls are used per game, on average. Bear in mind, there are 30 teams playing each other in 162 games each, which amounts to 2,430 games per season. That equals 157,950 baseballs used per regular season, each one with a unique set of detailed data.

When I opened the box, I had hoped to learn that my ball had been pitched by Noah Syndergaard or reigning Cy Young winner Jacob DeGrom. It wasn’t. Even Wilmer Flores taking the at-bat would have satisfied me, but the hitter was instead a Marlin whose name I had to Google, the pitcher? Corey Oswalt. It wasn’t a pitch worth remembering, it was so insignificant it was a pitch worth forgetting, but this made the data provided all the more remarkable. It would never make a highlights reel. But that’s where this era of big data collection is different – Forgettable things are still worthwhile and there is this wealth of information out there about forgettable balls.

Of course, this data isn’t really useful on its own; it’s just one moment, lost in one game, forgotten in the context of a whole season. One pitch and one ball does not tell the entire story of the player who handled it, nor the game it was used in. However, when data is being collected down to this level of detail throughout every moment of every 162 game season, it can be interpreted, and used in ways that have drastically changed the sport.

Many have used this data to their advantage. The film Moneyball portrays the use of data in the modern sports landscape. Highlighting two teams that have a huge disparity in payroll flexibility, the New York Yankees and the Oakland Athletics in 2002. Opening with the two payroll totals that you can see in the image above, it starts by illustrating an uneven playing field. It’s this that encourages the protagonist Billy Beane (played by Brad Pitt) to consider a different approach. He chooses to select a roster purely based on statistics alone, with great (and record-breaking) success as the team went on to win 20 straight games. A record that still stands to this day.

The film is dramatized, but true in its essentials: the Oakland As did indeed turn things around because of their reliance on player performance data. And there are many other examples of this kind of data-driven approach leading to success. All baseball teams, to a greater or lesser degree, are now using these statistics to manage their players. Why wouldn’t you, having witnessed the A’s success in 2002?

There is so much faith in these statistics that it can become the norm for Big League teams to release prospects in their minor league affiliates without even having watched them play. Instead, they rely solely upon a notification that pops up on their computer, having fed the player’s data into the system, to determine that player’s future. Here’s how 5-time all-star and two time World Series champion Keith Hernandez puts in in his book “I’m Keith Hernandez: A Memoir.”

I can see the headline now… NASA LOSES SPACE RACE TO BASEBALL

If a young player struggles in the minor leagues, as I did in 1973, he’s typically sent down to the next lowest rung, or released. In today’s baseball world, that process goes something like this; some computer whiz kid, who either has or hasn’t thrown a baseball in his life but has a job in the office of a Major League Baseball team, gets a notification on his computer that one of the organisation’s prospects is tanking. Said whiz kid, who is perhaps thousands of miles away from said prospect and has not seen the prospect play, plus the statistical drop-off into the all-knowing algorithm developed the previous year at MIT and makes a call upstairs to the GM. Said GM uses more whiz kids with more computers, utilising more algorithms, to arrive at a decision about what to do with said prospect. The decision is made, and GM’s office calls the minor league team manager. Said team manager, a former professional baseball player, who has dedicated his life to understanding the game, and may disagree with the analysis, drops said prospect from the sixth spot to the eighth spot on tonight’s lineup card, keep him there until further instructions, and remember, big brother is watching. Said prospect understands he is under the microscope, goes into a panic and spirals into a 0 / 18 stretch. Said prospect is on the bus to A-ball where he plays sporadically the rest of the season and is placed on waivers thereafter.

For those that might not know, if a player is placed on waivers, all rights to that player have been waived and subject to a few rules, any other club can pick them up. It can often signal the beginning of the end for a career.

Later in the same chapter, Keith Hernandez does admit that he’s been “overly simplistic, facetious and divisive;” however, the heavy dependence upon statistics and algorithms to select rosters and manage the lives of players is clear. Keith is just grateful that this didn’t become the norm until after his career. His future value was clear to those who relied on their own qualitative understanding of the game, and because of them and their recommendations, Keith was able to go on to have the career that he did. In fact, during a spell in which Keith showed the worst form of his career, he was actually promoted. A man called Bob Kennendy Jr, the Director of Player Personnel knew that this young player had the talent, it was his mind that was the problem, a mind that just needed a confidence boost. Long before many of us might have heard his name, long before he would become a two-time World Series champion, as Keith puts it he would have spent “45 years in some business other than baseball”.

Organisations have rushed to adopt these new Moneyball strategies, but how many players like Keith Hernandez have the teams missed out on?

There has been push-back on the strategy of using only performance data to make major decisions. For one, robots don’t win baseball games. There are qualities like leadership, grit, determination, and creativity that can make a difference. Secondly, the past is not the future, and even the most committed data forecasting enthusiast will admit that forecasting is an inexact science for most real-world scenarios. There will always be a place for those who know the game, not just the numbers, to make the decisions that will take a club to the next level.

On and off the field, in sports and away from sports.

For all the development taking place on-field, there is even more off-field data to be reckoned with. A major component of the sports business is sports media, and as you will likely know, the wider media industry is significantly in flux right now. As cable cutting becomes the norm and the relationships between the clubs, the fans, and the media companies are shifting. Clubs are producing a lot more media of their own, traditional media companies are giving way to digital-first businesses, and everyone is gathering as much data as they can to help them figure out what is going to work in this new landscape. There has been a lot of success, and a lot of money made that would otherwise not have been, but for all the success there has been errors and lost money, too.

Businesses have been frantically buying into new concepts, typically land grabbing, chasing followers and views, in fear that they might miss out on a fanbase or a revenue stream. However, as Keith Hernandez pushes back against excessive reliance on easily quantifiable data for on-field decisions, people are also pushing back off the field. They’re demanding that we ask, which numbers are the important ones, and how do you know? What are your goals? Do you really want the biggest audience possible on social platforms, does that translate into better revenue, and using sports as an example, more tickets and merchandise sold? Or do you want the most engaged audience, is that the best way to sell tickets and attract sponsorship revenue? Many are instead now chasing engagement, but what does it mean? and how do you measure it?

Since January 2018, organic reach on Facebook is typically 1%, so why do you want followers? What have you spent the last 10 years chasing followers for? Credibility perhaps, but if you want to reach people, no followers at all and a little social spend would be far more effective. This isn’t even the most uncomfortable thing to hear, what if all the social algorithms change again tomorrow?

Blindly chasing numbers is usually a mistake. Data is just one clue in a bigger picture, and the strategy that knows its real business goals is going to be protected from the distraction of a metric that doesn’t matter.

MLB deserves credit with their Mystery Ball. They repurposed an old baseball, they had already-collected their data, they spent a bit of marketing and distribution money, they created a ton of engagement from me, about a forgettable pitch and an unknown player. Kudos to them, because that’s some creative thinking. It’s no surprise that they’re at the forefront of uniting data, goals, and an understanding of what makes their fans tick to achieve their wider business objectives.

For those of you who have made it this far, a little reward. A beautiful scene from Moneyball, for no other reason, than it’s a beautiful scene.