Why Moneyball is creating the age of average

Don't misunderstand me. I adore Moneyball—both the film and the concept itself. I relish delving into data and pondering visualisations. The prospect of analysing data to uncover talent that meets specific criteria at a lower cost for a higher return? Sign me up and take my money.

However, experience has taught me that when the Moneyball concept is applied universally, it often leads to standardised templates and a dilution of essence.

Let's begin with the game where it all started: baseball.

Undoubtedly, Moneyball has led to more efficient team-building and game strategies. Teams can now optimise their resources to maximise wins, much like corporations streamline operations to boost profits.

But at what cost?

The essence of the game—its unpredictability and moments of inexplicable magic—is eroding. We're witnessing longer games with less action, more strikeouts, and fewer balls in play. The average MLB game length has increased from 2 hours and 33 minutes in 1981 to 3 hours and 10 minutes in 2021—a 24% increase. Simultaneously, strikeouts per game have risen dramatically, from 5.8 per team per game in 1981 to 8.9 in 2021—a 53% increase. While games haven't necessarily become more "interesting," they've become more similar, pattern-oriented, and tactical.

Moreover, this data-driven decision-making trend predominantly favours those with the resources to harness big data and sophisticated analytics. In Major League Baseball, the correlation between team payroll and winning percentage has strengthened in the analytics era, from 0.34 in 2000 to 0.47 in 2020.

Small players, indie outfits, and sudden upstarts are becoming irrelevant. The result is a kind of cultural inequality, mirroring the economic inequality prevalent in our society at large.

As ideas percolate, the commoditisation of entertainment spreads. What works for baseball is being tested on other forms of entertainment to boost the bottom line. Let's examine how Moneyball has affected a completely different genre: music.

In music, mixtapes and Sunday RJs have vanished. Algorithms and feeds now shape our playlists and influence production choices. Your listening habits are dissected at a chord structure level, leading to recommendations of compositions that follow the same structure ad nauseam. A study by the Music Machinery blog found that Spotify's recommendation algorithm tends to favour a small subset of already-popular artists, with the top 1% accounting for 90% of all streams. This concentration effect is further evidenced by the decreasing number of songwriters responsible for Billboard Hot 100 top 10 hits—from an average of 4.84 in 2000 to 3.65 in 2020—suggesting a narrowing of creative input.

A 2012 study analysing nearly half a million songs over five decades found decreasing diversity in pitch transitions, a key element of melodic complexity. The average dynamic range of popular music has decreased by about 50% since the 1970s, resulting in a more uniform, less nuanced sonic landscape.

Even more disturbing, 70% of today's "hit" songs share the same 4-chord structure.

The impact of Moneyball on musical diversity has been tangible, and it doesn't stop at music or baseball. It's permeating every aspect of our society. In the film industry, reliance on predictive models for box-office success has led to a notable shift in production strategies.

The 2010-2020 decade was dominated by a laundry list of Marvel and DC Comics sequels and prequels, as if no other ideas existed for big-budget films. The percentage of top-grossing films that were sequels, prequels, or remakes increased from 17% in 2000 to 80% in 2019. This trend towards "safe" choices is further reflected in the declining share of mid-budget films ($20-100 million) produced by major studios—down from 64% in 2000 to just 32% in 2019 (according to a study by Stephen Follows Film Data and Education).

Furthermore, data-driven decision-making has led to increasing homogenisation of film runtimes. A study of over 30,000 films released between 1910 and 2018, published in the Proceedings of the National Academy of Sciences, found that the standard deviation of film lengths has decreased significantly over time, particularly for major studio releases.

This convergence towards a "safe" runtime is indicative of the broader trend towards risk-averse, data-optimised content creation—a sanitised, dry-cleaned version of creativity.

The result is a cultural landscape increasingly resembling a monoculture: efficient and predictable, but ultimately boring and lacking vibrancy. While data-driven approaches are undoubtedly driving commercial successes, they are also raising difficult questions about long-term impacts on cultural diversity, artistic innovation, and the overall richness of our shared cultural experiences.

There's also the temporal aspect. The focus on immediate, measurable outcomes is shifting the discourse from long-term value creation. In economics, short-term thinking can lead to bubbles and crashes. In culture, quick hits and easy wins are sacrificing the development of enduring art and timeless sporting moments.

We're all being TikTok-ed into the challenge of the week.

Even globally popular games like football are becoming tired and jaded in their search for predictability.

In 2008, an unknown football coach named Pep Guardiola took on the coaching job at FC Barcelona and created the greatest symphony of art in world football.

Guardiola redefined the game's basics, but it was so good that everyone wanted to emulate it. In the last decade, virtually every club worldwide has created a blueprint built off that frame. Every club now employs inverted wingers, false 9's, and build-up play. Each major club has become a carbon copy of the others in their tactical interpretation of the game.

Guardiola's Manchester City has become the template of success for the top six teams in the Premier League, completely changing the way club football is played in England compared to the 2000s. Aspects like focus on ball retention have been blindly copied by all clubs, with the average possession for the top six Premier League teams increasing from 53.7% in the 2010/11 season to 59.2% in the 2020/21 season. According to Opta Sports, the number of long balls (passes over 35 yards) in the Premier League has decreased by 17% between the 2010/11 and 2020/21 seasons. Simultaneously, the average number of short passes per game has increased by 22% over the same period. This shift towards a more methodical, controlled style of play has come at the expense of the unpredictable, end-to-end action that many fans associate with the excitement of football.

Focus on specific measurable attributes has resulted in a convergence of player profiles, reflecting the prioritisation of ball-playing skills over traditional physical attributes.

The impact of this data-driven approach has also extended to goal-scoring patterns. The expected goals (xG) model, which calculates the probability of a shot resulting in a goal, has become a key metric in modern football. As a result, teams increasingly focus on creating high-probability shooting opportunities at the expense of spectacular long-range efforts. In the Premier League, the average distance of shots has decreased from 18.4 yards in the 2010/11 season to 16.8 yards in the 2020/21 season.

This optimisation has led to increased efficiency but at the cost of the game's unpredictability and excitement. The variance in the number of goals scored per game in the Premier League has decreased by 15% over the past decade, suggesting a trend towards more predictable outcomes. The game is becoming robotic.

Furthermore, the financial implications of this data-driven approach have widened the gap between the top clubs and the rest. Teams with the resources to implement sophisticated data analysis and recruitment have increasingly dominated. In the 2010/11 season, the points gap between the Premier League champions and the 10th placed team was 40 points. By 2020/21, this gap had increased to 52 points, indicating a growing competitive imbalance.

Guardiola's approach has undoubtedly been successful, but it has also contributed to a sanitised, geometrical, and homogenised football landscape. The beautiful game, once celebrated for its diversity of styles and unpredictability, is becoming a more uniform, slightly boring, more algorithmic affair.

And let's not even start on smartphones. The spectacularly uninteresting rectangular slab design, pioneered by the original iPhone in 2007, has become ubiquitous. According to a study by IDC, over 99% of smartphones shipped globally have this basic rectangular design. The homogenisation extends to screen sizes as well. In 2010, the average smartphone screen size was 3.2 inches. By 2020, this had increased to 6.4 inches, with a notable clustering around the 6-6.5 inch range. In terms of user interface, the grid of icons—another innovation popularised by the iPhone—has become the de facto standard across platforms. A study by UXmatters found that 95% of the top 100 mobile apps use a grid-based layout for their main interface. This uniformity, while familiar and efficient, has totally stifled any other UX discourse.

The homogenisation of app design has been equally striking. The rise of design systems and UI kits has led to a convergence in app aesthetics and functionality. According to a survey by UXPin, 70% of designers are using pre-made UI kits in their work, leading to a more uniform look across applications. This trend is further reinforced by platform design guidelines, such as Google's Material Design and Apple's Human Interface Guidelines, which, while ensuring consistency, have made every single app look the same.

It has become the age of the average—a monoculture of rectangles and infinite scrolls. This fixation on quantifiable metrics in cultural spheres is creating a world where everything is optimised but nothing is truly excellent.

Markets, left unchecked, don't always lead to optimal outcomes for society. The same is true for cultural marketplaces. We must be mindful of preserving the diversity, unpredictability, and human element that make sports, arts, and culture vital to our society.

However, it's important to note that resistance to this trend is emerging. Some baseball teams are rediscovering the value of human scouting alongside analytics. Independent musicians are finding new ways to connect directly with fans, bypassing algorithm-driven platforms. Filmmakers are experimenting with new formats and distribution models that prioritise artistic vision over predictable returns.

Rather than wholesale rejection or uncritical embrace of data-driven approaches, we need a more nuanced integration of analytics with human judgment and creativity. We need to develop newer metrics that capture the intangible values that make our culture rich and meaningful.

The challenge before us is to harness the power of data and analytics without being enslaved by them. Our cultural institutions, like our economic ones, have a duty to enrich human experiences, not merely optimise predefined metrics by preserving the irreplaceable elements of intuition, creativity, and serendipity.

Moneyball is great—needed, even. But not everything that counts can be counted, and not everything that can be counted, counts. We need to be mindful of this when applying it.

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I write 'cos words are fun. More about me here. Follow @hackrlife on X