Since sports started to commercialise and wealthy individuals suddenly had the urgent feeling of buying professional sport clubs for doubtful reasons, many sports have become an uneven game. Over the past two decades, this has lead to increasingly polarized sports leagues.
The unfair Game
Let’s have a look at European club football. France is probably the most absurd example, where Paris St. Germain spent 222 million Euros for a single player in 2017, while also holding the transfer record for the second- (145 million Euros), third- (64.5 million Euros), fourth- (63), fifth- (50), and sixth-highest transfer fee (49.5) in the league. In Germany, Bayern Munich won its eighth consecutive league title. Although not known as a big spender, transfer fees as the 80 million Euros for French defender Lucas Hernandez cannot be matched by any other club in Germany. In professional sports, money actually makes the world go round. So how is it possible that a team like Leicester City won the Premier League in 2016? And why are the New England Patriots the most successful American football team since the turn of the millennium, despite comparingly cheap players? The answer is data.
The Role of Data Science in Professional Sports
Statistics have always been part of sports, however, its usage was and still is somewhat limited. Statistics such as goals scored or assisted have minor informative value. Especially, when a manager is financially constrained it is probably not affordable to buy the player who scored the most goals since market values are adjusted on a daily basis. Therefore, low-budget teams need to be creative when it comes to scouting athletes. The blueprint for a creative transfer policy showcased the Oakland Athletics. An American baseball team that used Statistics in combination with Data Science, so-called Sabermetrics, to compare players and predict performances. Despite the financial inferiority compared to the top teams in the MLB (Major League Baseball), the Oakland Athletics went to win 20 games in a row. The reason for this: the general manager Billy Beane picked players based on Sabermetrics, which were undervalued and only average according to conventional statistics. He formed a cheap team that was expected to lose every game of the season but made a playoff run for the next three years. The era of Moneyball was born.
A Future Outlook
Since then most professional sports use data analytics to quantitatively analyse players, games, and to base their transfer decisions on it. Particularly, in a world of professional sports with today’s financial disparities this component becomes more and more important. A humble assumption for the future? Data analytics in combination with the age of Big Data will continue to revolutionize decision-making in professional sports.
Sources:
https://www.transfermarkt.de/ligue-1/transferrekorde/wettbewerb/FR1/plus//galerie/0?saison_id=alle&land_id=alle&ausrichtung=&spielerposition_id=alle&altersklasse=&leihe=&w_s=&zuab=zu
https://www.transfermarkt.de/1-bundesliga/transferrekorde/wettbewerb/L1/plus//galerie/0?saison_id=alle&land_id=alle&ausrichtung=&spielerposition_id=alle&altersklasse=&leihe=&w_s=&zuab=zu
https://www.ibm.com/blogs/business-analytics/how-data-science-conquered-baseball-and-why-fantasy-baseball-is-next/
https://de.wikipedia.org/wiki/Oakland_Athletics#Moneyball_Years
https://en.wikipedia.org/wiki/Sabermetrics
