Moneyball — A Digital Transformation Story

Sam Lin
5 min readAug 15, 2021


What can the Moneyball story inspire a new digital transformation venture? What can a startup adapt the inspiration to take the industry to the next level?

There are crazy ones. When a crisis calls, they dare to challenge common wisdom to take a path forward no one ever tries. Often, they fail. But when a few get really “lucky”, they pave the way & change the industry forever. Moneyball is such a story, when Oakland Athletics (A’s) lost 3 star players, Billy Beane did not play the same old game. He adapted new statistical models & turned the team around to win a record-breaking 20 consecutive games with the lowest budget in the League. You may surprise it’s a “Digital Transformation” story & still much wisdom to be learned today.

Think differently

“What’s the problem?It’s an unfair game. Now, we’ve been gutted. We’re like organ donors for the rich. Boston’s taken our kidneys. Yankees have taken our heart. And you guys are sitting around, talking the same old “good body” nonsense, like we’re selling jeans. Like we’re looking for Fabio. We got to think differently.” — Moneyball

I’m lucky in the past decade I got to do new things almost every year. Partly because of the hockey stick growth of smartphones, and an unconventional company I work for. Of cause, this can be very scary & stressful. But I lean much more from each failure or small win. And when those small wins start to pay dividends after I move on, I still get a bit of satisfaction by seeing the bigger teams benefit from them. One simple trick that helps me to predict the end-game ahead is how much I & the team are doing “business-as-usual”. And to avoid the trap, you have to think differently.

For example with the EV transition & 5G adopting are accelerating, this is a tipping point for smarter cars. All carmakers are trying to adapt, but my worry is they are not quick enough. From my observation, most decision-makers are still talking about the same old “good body” nonsense. Like they’re selling “the dump drill” when the market rewards Tesla handsomely by playing an unfair game. Furthermore, more and more consumers just want “the hole” 😉.

To be fair, stability is a natural human tendency. For example, many are still talking the same “good-old-way” nonsense for returning to the office. To ride a big new wave better, you got to think differently 🙏. But how?

We got to think differently, Moneyball


Before Moneyball, the common wisdom is you need good seasonal scouts to “see the future” for new players. So you can build a winning team, assuming you can afford them. Sure, they do use some metrics, such as 5-tools: hit, power, run, fielding & throwing. But the decisions were still mostly based on instincts. There are surely skillful scouts who can make a difference with skills only, right? But Billy proved “counting cards” can win big even with a small budget. He modernized the whole process to leverage more on the power of statistics by digitalizing the player capability evaluation & team construction.

In 1990, Ikujiro Nonaka introduced the SECI model of knowledge dimensions. It provides a general framework for such “magic”. According to the model, Billy externalized the scout’s dark magic, tacit knowledge to quantitative stats, explicit knowledge. They could be computed by old computers quicker & cheaper. More importantly, it can find undervalued players because all scouts despise them. When Billy proved it worked for A’s, other teams rushed to adopted that to survive. SW’s been eating that world since.

The SECI model. g, Group; i, individual; o, organization. Source: Figure 1 entitled ‘Spiral evolution of knowledge conversion and self-transcending process’.


“The goal should be to buy wins. In order to buy wins, you need to buy runs.” — Moneyball

As soon as the activities are digitalized, you can scale by machines 🦾. Billy asked Peter to evaluate 3 players, and Peter did 51 instead. No doubt Peter is a Yale-talented & passionate-hardworking newcomer, trying to impress his new boss. But more importantly, Peter did not evaluate players by the old way manually. Instead, Peter used a quantitative approach & a computer. The data analysis not only allowed them to evaluate in days instead of “scout years”. But also, it revealed the blind spots of all scouts. So they could buy a winning team A’s could afford quickly by offering dream opportunities to a few extremely undervalued players. For example, their focus on On-base percentage instead of traditional “looking good” guts feeling paid off huge.

Even at different paces, such mass digitalization & automation have happened or is happening in every industry, such as the retailer, education, financial, medical, etc. For example, 5G & smarter cars are similarly transforming transportation as 3G & smartphones did in the last decade. With 5G connects the real-time data feed & controls wired in cars (the client devices) to the edges & backends. Such mass distribution will unleash the power of next-generation distributed computing sooner rather than later. When it does, it’ll change the way we move better, not by one killer app, but by many different killer apps.

The DIKIW pyramid is an easy way to think about this. As you get activity in concern digitized & harvesting the data automatically, you can compute for Information, Knowledge & Insight to transform your industry because they can be Software-Defined. As we don’t need to do those computational “hard work”, we can focus on understanding & extrapolating Wisdom.

DIKIW pyramid, adapted from DIKW pyramid

How to get started

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Full Disclosure

The opinions stated here are my own, not those of my company. They are mostly extrapolations from public information. I don’t have insider knowledge of those companies, nor a whatever expert.



Sam Lin

A Taiwanese lives in Silicon Valley since 2014 with my own random opinions to share. And, they are my own, not those of companies I work for.