Data Pulls “Yellow Card” on EPL Player Valuation
The world of football (“soccer” for us Americans) is about to change forever. In a recent article titled “Fantasy football manager”, the Economist used Ayasdi’s platform to provide a completely new way to look at player value, playing styles and positions, and uncovering how, in the billionaire’s game of the English Premier League (EPL), teams could assemble a contender for a fraction of the cost.
The world of football (“soccer” for us Americans) is about to change forever. In a recent article titled “Fantasy football manager”, the Economist used Ayasdi’s platform to provide a completely new way to look at player value, playing styles and positions, and uncovering how, in the billionaire’s game of the English Premier League (EPL), teams could assemble a contender for a fraction of the cost. The Economist is the first media outlet to use the Ayasdi platform and this application of Topological Data Analysis could change the way that media provides data to readers.
For this story, the Economist gathered playing stats from Opta Sports and player transfer data from TransferMarkt to analyze the 500+ EPL players across 191 different statistical categories ranging from time played, goals scored, tackles, blocks, and more. This analysis was done in under two weeks.
The Economist was able to automatically discover some extremely interesting insights around player values. For example, Danny Welbeck of Manchester United, Gastón Ramírez of Southampton, and Robert Snodgrass of Norwich FC have all exhibited similar qualities to Gareth Bale. The only difference, these players are a fraction of the cost in relationship to Gareth Bale’s £85 million (US$130M or €100M) price tag.
Deep sports analytics is front page material and the popularity of “Moneyball” has driven teams to invest millions in data analysis. But the potential of professional teams applying Ayasdi technology is far more than “Moneyball for X”. Like any business, change is constant. Different competitors dictate different alignments. Players suffer injuries and require substitutes. Many of the decisions that teams make require a rapid response. Speed of analysis is critical for maximum performance, particularly for the in-game adjustments. The speed of the game offers no time to build new models, configure new algorithms or contract a team of data scientists to pour over reams of stats.
Much like how baseball was portrayed in “Moneyball”, the “Beautiful Game” has been slow to adopt deep statistical analysis. While some teams have hired data analysts, football purists have resisted this kind of change for years. In their wonderful new book, “The Numbers Game: Why Everything You Know about Football Is Wrong”, authors Chris Anderson and David Sally summarize this resistance to change in seven words: “That’s the way it’s always been done.” To us at Ayasdi, this brings a wry smile to our face as this same perception has held fast in many industries, only to wash away once customers make their first discovery with our software.
We will be watching closely to see how teams react to what the Economist uncovered. And we encourage you to play armchair EPL data scientist and interact with the data yourself. Please visit the Economist for the interactive viewer; here is a quick demo video of how to use it.
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