MoneyBall: The Power of Sports Analytics!
That was the moment in May 2016 when a 2-2 draw between Chelsea and Tottenham sealed Leicester's fate as the 2015/2016 Premier League champion! The season prior, Leicester had only just achieved promotion, with chances of winning the league set at 5,000 to 1—the ultimate underdog story.
A fairy-tale run? Sure. But behind the magic was something more—strategy, data, and an approach eerily similar to MoneyBall.
The Oakland A’s and the Birth of MoneyBall
Before Leicester’s miracle, baseball had already experienced its own data-driven revolution. The Oakland A’s had just lost their top three players to free agency, leaving General Manager Billy Beane (played by Brad Pitt in MoneyBall) scrambling for solutions.
Enter Paul DePodesta, a Harvard-educated economist (played by Jonah Hill), who challenged traditional scouting. Instead of chasing expensive, big-name players, he relied on linear regression models to predict a different way of winning—identifying undervalued free agents with high On-Base Percentage (OBP) and Slugging Percentage (SLG) instead of the "outdated" Batting Average (BA) metric.
At first, DePodesta's strategy didn’t work. In the second month of the season, the A’s had a 20-25 wins to loss ratio, and head coach Art Howe refused to play the team according to DePodesta’s statistical recommendations. And can you blame him? If you were an experienced manager and someone suddenly told you, “Everything about your current approach is wrong. Here’s a spreadsheet. Do it this way instead.” Would you oblige? Probably not.
The media trashed Beane’s MoneyBall approach, describing it as an epic failure. But with the season slipping away, Beane doubled down and took a massive gamble—trading away the team’s starting first baseman, Carlos Peña, to force the coach’s hand in playing the way Beane had wanted. Left with no choice, Coach Howe finally adjusted.
The result? A historic 20-game winning streak, the longest in MLB history. The A’s didn’t win the World Series, but the impact of MoneyBall was undeniable. A year later, the Boston Red Sox adopted a similar statistical approach and went on to win their first championship in 86 years!
The MoneyBall revolution had already begun.
Could MoneyBall Work in Football?
Baseball is a numbers-heavy sport, making it ideal for statistical optimization. Football, on the other hand, is more fluid, with complex player interactions. But does that mean a MoneyBall-style approach would not work? Not necessarily.
Consider Sevilla FC, the most successful club in Europa League history (7 titles). Yet, as of March 4, 2025, Sevilla sits 12th in La Liga—far from their former glory. Post-COVID financial struggles, coupled with La Liga’s strict Financial Fair Play (FFP) rules, have severely limited the club's spending power.
So, what if Sevilla applied MoneyBall principles, to identify undervalued players through advanced statistical models, and to rebuild their squad while staying within financial constraints?
Finding Hidden Gems: The Borussia Dortmund Model
A few clubs are already ahead of the curve. Borussia Dortmund, for instance, has built a reputation for scouting world-class talent and flipping these players for massive profits.
| Rank | Player | Bought For (€M) | Sold For (€M) | % Profit |
|---|---|---|---|---|
| 1 | Ousmane Dembélé | 35.0 | 135.0 | 386% |
| 2 | Jude Bellingham | 30.15 | 113.0 | 375% |
| 3 | Jadon Sancho | 20.59 | 85.0 | 413% |
| 4 | Christian Pulisic | 0* | 64.0 | 6,400% |
| 5 | Pierre-Emerick Aubameyang | 13.0 | 63.75 | 490% |
| 6 | Erling Haaland | 20.0 | 60.0 | 300% |
| 7 | Henrikh Mkhitaryan | 27.50 | 42.00 | 153% |
| 8 | Mario Götze | 0* | 37.00 | 3,700% |
| 9 | Mats Hummels | 4.20 | 35.00 | 833% |
| 10 | Abdou Diallo | 28.00 | 35.00 | 25% |
Dortmund’s approach? Identify undervalued talent early, use analytics to predict their peak performance, and sell at the perfect time.
Unlike Dortmund, Leicester wasn’t known for shrewd player sales. But they got it right leading up to the 2016 season, using data to build a title-winning squad on what you could call a shoestring budget.
With the 17th lowest wage bill in the league (£38M), Leicester somehow outperformed big-spending clubs like Chelsea FC (£104M).
How? Smart scouting and injury prevention. They signed overlooked players like N’Golo Kanté, whose elite interception stats made him the perfect fit for the physically demanding Premier League. Purchased for €9M, Kanté became a key part of Leicester’s title run before being sold for €35.8M—a 398% return.
But the club also used a data-driven fitness tracking approach to minimize injuries, ensuring their best XI stayed on the pitch. The result? A historic title win that defied all logic. Except the logic of statistics!
Other Applications of MoneyBall?
Imagine the application of similar principles in basketball. The Los Angeles Lakers, for example, are chasing their 18th NBA championship, led by LeBron James and Luka Dončić.
Could a MoneyBall-style statistical approach identify the missing pieces needed to secure the Los Angeles Laker another ring? Maybe that’s already been done, and, if so, then this might be the year that LeBron "The Goat" James claims his fifth and potentially final NBA championship.
MoneyBall is Really Value-a-Ball!
At its core, MoneyBall is about maximizing value. The same applies in sports, business, or investing—success comes from finding hidden opportunities, making data-driven decisions, and identifying undervalued assets, before the market catches on.
So, who will be the next team to rewrite history with MoneyBall? Time will definitely tell!