Golf and GameForge: Innovative Analytics for Recommender Systems; Predictive Analytics in Sports: Offsetting Human Bias

Background
Winning in sports stems from the success of the great leadership, a robust front office, and a superior coaching staff. In the past, decisions about which players to draft, trade, and play were a product of the gut feeling of managing staff, as opposed to an objective, quantifiable method. This practice was forever changed in all of sports when Billy Beane, General Manager of the Oakland Athletics baseball team, used statistical analysis to discover the secrets of success in the imperfect science of evaluating baseball players in 1997 (Steinberg, 2015). This was the first known use of prioritizing data and statistics to drive decision making in all of professional sports. This story was the inspiration of Michael Lewis’ famous book Moneyball. Since the “moneyball” approach was first documented, sport analytics has grown into the large focus that it is today. As a result, the success of professional athletes is rarely reported without relevant numbers and statistics.

General Research Problem
In the U.S., how have sports organizations used analysis to promote fairness? Ostensibly, the rules of athletic competition give all competitors an equal chance at success. In reality, most sports are far from a level playing field. Analytics, such as evaluating a player’s batting average or on base percentage, offer quantitative evaluations that may supplement or substitute for qualitative evaluations, like a player’s physical size, strength, and appearance. “Fairness is part of the promise of sports analytics. By judging an athlete’s performance through good data — as opposed to reputation, image, or outworn clichés — analytics creates the possibility that people can be judged more consistently on merit than often occurs elsewhere in life” (Dizikes, 2021). The extensive use of analytics is no shock for the younger generation. Analysis of sports data has proliferated as access to analytics capacity has spread in the form of large data sets with decades of player statistics and game outcomes. Proponents of sports analytics are enthusiastic about its use in the present and future. “Embrace data,” said superstar of U.S. women’s hockey Hilary Knight. “It’s here, and it’s the future.” Analytics has diminished some elements of chance in sports regarding the predicted success of up and coming athletes. Andrew Friedman, president of operations for the Los Angeles Dodgers, stated: “Fifteen years ago you saw a lot more bad bets happening a lot more frequently” (Dizikes, 2021). The optimization of sports continues to proliferate, but this calls into question the ethics of implementing such practices.