Data vs Instinct

Introduction

If you were a football fan growing up, you would remember the 2014 advertisement: “The Last Game: Risk Everything”. The advertisement depicts how the top players played taking risks and a scientist from ‘Perfect Inc.’ wanted to perfect the game by creating clones of these players. These clones who made calculated decisions and ‘Risk Nothing’, took over football pushing the originals out and making the game dull for fans. However, the original players rallied up against this ‘Perfect’ team to have one final match to decide who stays, underscoring the message that "there is no greater danger than playing it safe."

Since 2014, data has entered the realm of sports and transformed its landscape; from meticulously analysing play patterns to predicting player performance. Its impact has transcended the field and sparked heated debates: Was sports better when it relied solely on instincts? Has the influx of data diminished the essence of the games we love? Could data-driven methodologies eventually dominate?

This article explores the transformative impact of data on sports. Ultimately, I will argue that a balanced approach, where data informs but doesn't replace instinct, is the key to unlocking the full potential of both. This creates a more exciting, effective, and ultimately, human experience for athletes and fans alike. Yet, it is imperative to dissect the changing landscape of data in sports, weighing its positive and negative implications meticulously.

How has Data changed Sports?

Data has become a key player in the world of sports, transforming both on-field and off-field experiences. Let's dive into how Major League Baseball (MLB) and the National Basketball Association (NBA) have embraced data analytics, and how it's impacted the games they play.

Major League Baseball (MLB)

The integration of analytics into sports can be traced back to Oakland Athletics' historic 2002 season, chronicled in Michael Lewis's "Moneyball: The Art of Winning an Unfair Game" and later adapted into the movie "Moneyball" (2011). Billy Beane, the A's general manager, challenged traditional scouting methods. Instead of relying solely on subjective scouting reports and overvalued metrics like Runs Batted In (RBI), they embraced Sabermetrics—a statistical approach to analysing player performance. By identifying undervalued skills overlooked by other teams, the A's assembled a playoff-caliber roster despite operating with a limited budget. Their unprecedented success, including a record-breaking 20-game winning streak, revolutionised how teams assessed player value and reshaped the MLB landscape.

For example, the OPS was believed to be a very important stat by Sabermetrics. In 2018, two low-budget teams, Cleveland Indians (.766 and 106) and Oakland A’s (.764 and 110) qualified for playoffs and were in the top 5 for OPS and OPS+.?

National Basketball Association (NBA)

Data has also profoundly affected the NBA. The league actively collects a wealth of information, ranging from player movements to positional data, enabling nuanced evaluations and strategic optimisations thanks to the organisation installing six cameras in every arena. This data-driven paradigm has redefined player evaluation beyond traditional and overvalued statistics like points scored, to metrics such as efficiency, spacing, and defensive impact and assesses their overall impact to be scouted or bought for the team's playing style.?

Notably, the rise of the 3-point revolution exemplifies how analytics has reshaped gameplay strategies, with analysts revealing the efficiency of three-pointers compared to mid-range shots, leading to teams prioritising high-value shot opportunities over mid-range attempts.??

Teams like the Golden State Warriors, spearheaded by Stephen Curry's sharpshooting, capitalised on this trend winning the NBA championship in 2015, propelled by a record-breaking volume of 3-pointers, epitomising the transformative power of data-driven insights in shaping on-court outcomes. In the 1990s long-range 2-pointers were common but statistically, their output was the worst. “Over the last 10 years, the percentage of total shots that have come in the paint has remained pretty steady. But as the percentage from the 3-point range has jumped from 22% to 39%, the percentage from mid-range has dropped from 31% to just 13%.”?

Beyond the Scoreboard

Data's impact extends beyond on-court play with the on-field impact of wearable technology tracking player performance and health, informing training regimens and injury prevention. Analytics also has an off-field impact on fan engagement, tailoring marketing strategies and offering data-driven insights during broadcasts. Scouting and team management leverage data to identify talent and make informed decisions. But has data overruled instinct, damaging the sporting culture with possible fan fatigue from data overload, potential bias in algorithms with immeasurable intangibles, and more possible downsides? (Like the Perfect Clones from Perfect Inc. in the ad?)

Has Sports Analytics Ruined Sports?

Former Red Sox/Cubs’ executive Theo Epstein, considered by many the High Priest of modern baseball analytics, stated:

“It is the greatest game in the world but there are some threats to it because of the way the game is evolving. And I take some responsibility for that because the executives like me who have spent a lot of time using analytics and other measures to try to optimise individual and team performance have unwittingly had a negative impact on the aesthetic value of the game and the entertainment value of the game. I mean, clearly, you know the strikeout rates are a bit out of control and we need to find a way to get more action in the game, get the ball in play more often, allow players to show their athleticism some more and give the fans more of what they want.”

Adding in the ‘Human’ factor

At its core, sports are played by athletes who at the end of the day are just human beings whose performance transcends mere statistical analysis. While metrics offer valuable insights, they inevitably fall short of capturing the full spectrum of human performance because there are many intangibles to consider. Not everyone can consistently perform and give the same numbers every single game, especially with the nerves of an all-or-nothing game. Take, for instance, the words of Billy Beane who famously said: ?“My sh*t doesn't work in the playoffs. My job is to get us to the playoffs. What happens after that is fucking luck.” This sentiment was starkly evident during the A's remarkable 2002 season, where despite a record-breaking run, they faltered in the postseason against the Minnesota Twins.

The unpredictable nature of sports extends beyond baseball, permeating across various disciplines. Consider the 2016 NBA Finals, where statistical projections favoured the Golden State Warriors over the Cleveland Cavaliers. With a 6% chance of coming back from a 3-1 deficit and with history advocating against them, no team ever came back from a 3-1 deficit in the Finals. The Cavaliers, led by LeBron James, defied expectations and staged a historic comeback. They became the first team in the 21st century to beat the home team in game 7 assisted by a legendary ‘rejection’ by LeBron and Irving’s beautiful offensive play giving the Cavs their first NBA Championship.?

In football, memorable comebacks, known as "Remontadas" in Spanish, epitomise the sport's unpredictability and the undefeated human spirit. Take, for example, FC Barcelona's legendary turnaround against PSG in the Champions League, overturning a 4-0 first-leg deficit to win 6-1. An improbable feat with odds of 200/1 with the winning goal causing a micro-earthquake in the Catalan city.?

Similarly, Real Madrid's journey in the 2022 Champions League featured a series of remarkable comebacks. Facing PSG with just 50 minutes left in the second leg and trailing 2-0, Real Madrid rallied, inspired by Benzema's hat trick to secure a 3-2 victory. Against Chelsea in the quarters, trailing 3-4 on aggregate with only 15 minutes remaining, they summoned extraordinary courage to force extra time and clinch the win. Their semifinal clash against title favourites Manchester City seemed lost until the final minute of regular time, where they needed two goals to draw and three to win. Against all odds, the then 13-time champions demonstrated their resilience, scoring twice in two minutes and ultimately securing victory in both the semi-final and final, cementing their status as the "Kings of Europe."

Can analytics be wrong?

In the realm of sports analytics, fallibility lurks beneath the veneer of data-driven precision. As Harman Dayal aptly noted in his article, “While analytics offer valuable insights, they possess inherent limitations and can often prove misleading.” A poignant example arises from the NHL, with Calgary defensemen Chris Tanev. The 30-year-old injury-prone veteran was let go as a free agent by the Canucks in 2019-20 with all data pointing towards his decline, according to a model (Game Score Value Added (GSVA)), Tanev only had a couple of years left in him.?

Despite all the data analysis, Tanev flourished in his new environment, emerging as one of the league's top defensive defensemen. This anomaly emphasises the intrinsic complexities that elude statistical modelling, pointing out how a change in environment affects the player. “Even the stats that supposedly isolate or adjust for those teammates and environment-related factors don’t do a sufficient job of accounting for those differences” stated Dayal.?

Similar instances abound in cricket, as evidenced by England's humbling defeat in the 2013-14 Ashes series against Australia. Tim Wigmore harshly wrote about England’s then-coach Andy Flowers, “Flower's reign, for the most part, showed the virtues of using it [analytics] smartly. But cricket data is affected by the unpredictability of human beings and so constantly fluctuates. Data is emphatically not a substitute for intuition and flair - either in the office or on the cricket field.” He further mentioned, “By the last embers of Flower’s rule, England seemed not empowered by data but inhibited by it, as instinct, spontaneity and joy seeped from their cricket.” Indeed, cricket, like all sports, defies reduction to numerical abstractions, necessitating a balanced approach that acknowledges the intangible elements that define sporting excellence. Flowers' ignorance of those cost him his job as well as England, their pride in the Ashes series.??

If One Has It, Others Can Follow

In the relentless pursuit of victory, sports teams are quick to embrace the latest advancements in analytics. However, this pursuit of statistical perfection risks homogenising gameplay, eroding the spontaneity and creativity that define the essence of sports. The disappearance of the No.10 role and long shots in football serves as a poignant example of this phenomenon. AC Milan and Real Madrid legend Kaka lamented “It’s not that it is being lost, it’s that it has already been lost completely, amongst other things because the defences push so high up there is practically no space left in the middle of the pitch. So it is very difficult to find holes in which to play your football. For that reason all of the teams try to open the pitch and play more on the wings.”

The No.10 role was dedicated to the team’s most offensively creative players and was donned by some of the game's greats such as Messi, Ozil, Zidane, and Ronaldinho whose elegance and creativity mesmerised all fans alike. Victor Olorunfemi states “I miss the days when it felt like, at any given moment, a player might do something unpredictable. Now even when that moment comes, it feels scientific and manufactured, not creative and ingenious. For example, Kevin De Bruyne's signature out-swinging cross to the back post is enjoyable to see but loses its magic when you realise coaches instruct their players to play that ball because analytics say it's a great way to create a goal-scoring opportunity. And if you watch any game this weekend, count how many times you see teams try that same cross; it'll be plenty.”

Similarly, the advent of expected goals (xG) has heralded the demise of long-range strikes, as teams prioritise high-probability scoring opportunities over audacious attempts from a distance.?

Looking back at the goals scored in the early 2000s, the entertainment value provided by them is quite high as compared to goals scored in 2023-24. With the rare spectacular shots attempted, let alone goals scored. But long shots should not be disregarded and could have some merit.

Finally coming back to the low-budgeted MLB teams, Billy Beane and the Oakland A’s. Even after the low-budgeted teams applied Moneyball Sabermetrics, they were not able to achieve the end goal of winning the World Series since 2002 since “Three of the most prominent teams that have adopted the “Moneyball” approach are the Cubs, the Astros, and the Red Sox. Since these teams have much bigger budgets, can buy the statistics that they want. They can sign the big-name players who have all of the assets that Sabermetricians love.” The difference between Beane and Epstein (Cubs analyst and believer in Sabermetrics) is that Epstein can pick and choose whichever player he wants according to the statistics he likes whereas Beane is restricted and has to develop a player in some way.?

Is Analytics really all bad?

While many discussions surrounding sports analytics focus on its perceived negative impacts,? it's important to acknowledge the positive contributions analytics has made in the sporting landscape. At its core, sports is about competitiveness, and the pursuit of excellence and victory, and analytics serve as a powerful tool in achieving these objectives. Let's delve deeper and explore some concrete examples of how analytics benefit both players and the sports industry as a whole.

On-Field Impact

Player Performance Optimisation

Analytics go beyond traditional player development methods by collecting data and analysing a player's movement patterns, training style, diet, and sleep habits. They do this through wearable technology to collect various data points and match and training video analysis.

This data helps coaches and trainers design personalised programs that maximise individual strengths and minimise weaknesses, pushing athletes toward their peak performance more effectively than ever before.

Injury Prevention

Injuries can have a huge negative impact on players, teams, organisations, and fans as well, so much so that prevention is the best cure. Analytics can help, as previously mentioned, wearables and other technologies track many data points along with fatigue levels and predict potential injuries. This allows teams to intervene early with customised exercises, rest periods, and preventative measures, keeping players healthy and contributing for longer durations. Load Management has been used by NBA teams to avoid injuries to their players and make tough decisions to rest key players to avoid injury (even when the fans don’t like it!).

Tactical insights

On-field performance data provides valuable insights into positioning, passing patterns, and shot selection. Coaches can use this information to develop more effective tactics, improve decision-making, and exploit weaknesses in opponents, ultimately leading to better results for both teams and fans.?

Off-Field Impact

Global fanbases

Analytics help teams understand their international audiences, allowing them to tailor marketing campaigns, social media content, and merchandise specifically for these markets. This expands their reach, builds deeper connections with fans worldwide, and generates new revenue streams. Utilising fan data, teams can personalise marketing campaigns and promotions, reaching the right audience with the right message, leading to higher engagement and increased ticket sales and merchandise purchases.

An example of this is Bayern Munich, a football team from the German league Bundesliga. They are the most successful club in Germany and have expanded their fan base into the US by tailoring content based on analysis of data from their fans. One application they have implemented from the gathered data is using English in their social media accounts (which is a recent development in many non-English speaking clubs). They also are in the top 10 in brand value of football clubs in 2023 and the top 5 in social media following across various platforms.?

Enhanced viewing experience

Technology like goal-line technology eliminates controversies and ensures fair outcomes (however, the introduction of VAR has raised many questions), enhancing the viewing experience for fans and increasing their trust in the sport. Additionally, data-driven insights (e.g. heat maps) shared by commentators or provided on online platforms can enrich the broadcast, making the game more understandable and engaging for viewers.

Scouting

Sports analytics has revolutionised scouting since the days of Moneyball, going beyond traditional methods that relied heavily on subjective assessments. Today, scouts leverage a plethora of data points, such as player movements, performance metrics, and even advanced tactics, to gain deeper insights into potential talent and see whether they can fit into their team’s system based on certain metrics. This allows them to identify promising players who might be overlooked by traditional approaches, creating a more efficient and effective scouting process.?

A prime example of this is Leicester City's remarkable Premier League victory in 2016. Despite limited resources compared to the league's giants, their scouts strategically combined traditional methods with data analytics. They identified undervalued players like Riyad Mahrez, Jamie Vardy, and N'Golo Kanté. This data-driven approach allowed them to acquire these players at bargain prices, who then went on to become instrumental in Leicester City's historic win. Their story serves as a testament to the power of integrating data analytics into scouting, enabling teams to find exceptional talent even with limited resources.

Conclusion

Has data analytics ruined sports? The answer, thankfully, is not so simple. While some concerns exist about homogenisation and over-reliance on statistics, we can not ignore the fact that the evolution of sports analytics, machine learning, and AI has undeniably revolutionised the sporting environment, ushering in a new era of performance optimisation and strategic decision-making. From optimising player performance and preventing injuries to boosting revenue and fan engagement, data analytics has undoubtedly transformed the sports landscape. While these advancements offer immense potential, it's essential to recognise that it needs to be used responsibly, acknowledging its limitations and respecting the human element.?

The future of sports doesn't lie in an absolute reliance on data or a complete rejection of it. Instead, what truly holds potential is a harmonious blend of intuition and analytics. The synergy between intuition and data holds the key to unlocking untapped potential across various facets of the sporting ecosystem. Experienced coaches, scouts, and athletes, equipped with data-driven insights, can make more informed decisions, leading to improved performance, captivating strategies, and a deeper connection with fans.?

As we navigate the ongoing debate over the role of data in sports, let us not succumb to binary thinking. It's not a matter of instinct versus data (analytics) or one being superior to the other. Instead, it’s about embracing both sides of the coin: data and instinct, working in unison, will ensure that the sports we love continue to thrive and evolve in the years to come. By leveraging the strengths of instinct and data in unison, we can cultivate a sporting environment that thrives on innovation, integrity, and the relentless pursuit of excellence.