Authors: Dr. Rocco P. Porreca

Corresponding Author:
Rocco P. Porreca, Ed. D.
380 SE Mizner Blvd. Apt. 1718
Boca Raton, FL 33432

Dr. Porreca is an adjunct professor in the College of Business and Management at Lynn University.

General Managers and the Importance of Using Analytics

Albert Einstein defined insanity as “doing the same thing over and over again and expecting different results.” Sport is changing. Athletes are becoming faster and stronger. The rate and pace of play is steadily increasing. Therefore sport, as a result, adapts and evolves. Recently, the way in which franchises draft players and build rosters is beginning to change. In order to remain competitive, sport franchises are beginning to shy away from the conventional norm and are thinking outside of the box. Specifically, franchises are exploring analytics and how this type of statistical analysis can be beneficial.

Keywords: analytics, moneyball, moneypuck, statistics

Sports in general are overwhelmed and consumed by statistical analysis. Statistics have always been a part of sports, but in today’s modern age of technology, statistics are even more prevalent. From in-game announcers, to sportscasters, and sport journalists, statistics are everywhere. Fans are constantly being overwhelmed with the statistical analysis of their favorite player and/or team. Statistical analysis is occurring more and more, which has led to a more progressive model of roster building among organizations.

Throughout history, statistics have been more of a talking point among sport participants and fans. In the past, statistical analysis had not been used to build team rosters. Scouts continued to look for the tangible and intangible qualities they felt made up a professional ball player, more so than their statistics. In a sense, the game of baseball had been missing thought provoking conversations as they relate to sport statistics and how they can be used to benefit a roster. Recently this has changed, and general managers are focusing more on the positive affects statistical analysis can have on building rosters. The purpose of this paper is to explain and showcase the importance of statistical analysis and how the use of analytics can help general managers formulate successful rosters.

The structure of this paper is intended to provide the reader with the history of statistics in sport and how statistical analysis can benefit general managers in sport. This paper will focus on what the Moneyball Theory is, followed by how this theory can be applied to general managers in their respected sport. Furthermore the areas of why analytics are necessary to the evolution of sport, how Moneyball is used in other sports, and how analytics will be used in the future will be explored.

History of Statistics in Sport
The analysis of statistics for the purpose of sport is relatively new. Statistics with regards to sporting events traditionally consisted of box scores, summaries of players and teams, written scouting reports, and unedited game film. Mathematical equations used to analyze data collected throughout sports have been traced back just over 50 years. As recent as 2005, the premier issue of the Journal of Quantitative Analysis in Sports was published. During the 2005 year only a couple of National Basketball Association (NBA) teams used an advanced level of statistical analysis with regards to players and tactics. In 2003, Michael Lewis published a book, “Moneyball: The Art of Winning an Unfair Game,” which chronicled the Oakland Athletics’ use of statistical analysis. Despite the success story in this book, no other teams thought about using technology and/or other methods of capturing data to analyze players with (Alamar & Mehrotra, 2011).

Since 2009, slightly more than half of the teams in the NBA, and the majority of teams in the MLB use a form of analytics to aid in their operations process. Outside of professional sport franchises, companies including STATS LLC are setting up cameras throughout stadiums in the NBA and NFL with the hopes of acquiring more useful data to analyze. In addition to this, a group of Massachusetts Institute of Technology (MIT) students created the Sloan Analytics Conference to invite individuals to come together and discuss the importance of analytics. Fewer than 300 attendees made up the initial 2006 conference, and since then the conference has grown exponentially. In 2011 over 2,000 attendees were present at the Sloan Analytics Conference (Alamar & Mehrotra, 2011). Analytics in general have become a widespread conversation topic over the years. More and more individuals are experimenting with acquiring data, and using that data to provoke thought and change towards the recruitment of players.

What is the Moneyball Theory?
The Society for America Baseball Research (SABR) has collected various amounts of research on using the analysis of statistics to evaluate the performance of a team and its individual players. This process became known as sabermetrics. Essentially sabermetrics involves the study of the sport of baseball through analysis of baseball related questions. For example, how can a low payroll team compete against a team with a significantly higher payroll (Mason, 2006, p. 54)?

The use of statistics in baseball has been around for years, but it was not until 2001, when the General Manager of the Oakland Athletics, Billy Beane, sought to use sabermetrics to evaluate players. Beane’s thought process was to focus on the statistics of players which were more closely linked to winning games, as opposed to great individual traditional statistics. This type of thinking allowed Beane to put together a very competitive baseball team at a fraction of the price other teams were paying to be competitive (Mason, 2006, p. 54).

Beane used very specific data to identify various inefficiencies in the selective market for professional baseball players. The data collected was then used favorably towards the needs to the team. Most importantly, specialized mathematical systems were deployed to identify the statistics most needed for team success. Once those statistics were identified, players were then selected based on the statistical needs of the team (Stewart, Mitchell & Stavros, 2007, p. 231).

Specifically in baseball, scouts relied heavily on personal scouting and the use of purely the naked eye. Scouts focused on five main areas for position players: hitting power, hitting average, speed, ability to field, and arm strength. Pitchers were evaluated on: variety of pitches thrown, control, and arm strength. General Managers throughout Major League Baseball focused on the strengths of batters which were not conducive to producing offense (Farrar & Bruggink, 2011). The Moneyball theory disbanded traditional scouting advice, and focused on what qualities were most linked to scoring runs and winning baseball games. Traditional scouting methods were becoming defeated by objective statistical analysis. Sabermetrics provided insight into the fact previous predictors of performance were inefficient. For batters, Beane substituted average, home runs, and runs batted in with a player’s slugging percentage and on-base percentage to determine offensive value to the team. For pitchers, Beane focused on walks, strikeouts, and home runs (Wolfe, Babiak, Quinn, Smart, Terborg, & Wright, 2007, p. 250).

Additional statistics showed college baseball players were more prepared to play professional baseball and therefore Beane drafted only college players. Outside of individual statistical analysis, analytics helped to provide tactical measures during the game. Stolen bases and sacrifice bunts, statistically, did not increase the amount of runs a team would score. Also a batter being patient while at the plate proved effective in obtaining walks, getting on base, and allowed for receiving better pitches (Wolfe et al., 2007, p. 251). This entire process became known as Moneyball management.

How can Moneyball Help General Managers?
The main takeaway from Billy Beane and Moneyball is that substantial value can result from the statistical analysis of a player’s performance. The Oakland Athletics were able to have a competitive advantage over other MLB teams since their inception of Moneyball. Statistical analysis was used to help make decisions on the recruitment of players, and on playing tactics during competition. The significance of what Beane was able to do is the fact he took information readily available to all MLB teams, and he used it in a way that became unique to the Athletics (Gerrard, 2007, p. 228-229).

As a small market team, with a significantly low payroll, the Oakland Athletics were able to compete and outperform teams with twice their financial resources. This is not to say all teams should stop acquiring star players and focus solely on the players few teams want. It is well known sport is a business, and businesses need to make money. Franchises want to win games, but they also need to sell tickets and fill seats. Without fans and spectators, professional sports would not be as lucrative and profitable as it currently is.

With that being said, there is no reason only small market, low payroll teams should focus on Moneyball management. Large market teams with huge payrolls can still use statistical analysis and analytics to better their team. Imagine if the New York Yankees took a page out of Beane’s book and viewed building a roster differently. Instead of overpaying for multiple traditional stars, they could rework their roster and pay less for players who could help them win more games. The Yankees could still use a portion of their payroll to entice a big named player to come to New York, which would help fill seats at Yankee Stadium, but they could also save money and become more competitive.

Like all businesses, managers have a set budget for each year. The company expects the business to limit costs and increase revenue. Professional sports are no different. If teams can increase revenue, win games, and limit spending they are doing well. General Managers should be open to any ideas that will help them to achieve this goal. The main issue delaying the use of Moneyball management is the fact in which most individuals involved in the game of professional baseball have been involved for many years. They have been involved when computers and statistical analysis were not commonplace, and most have yet to adapt to this change.

The mindset of Moneyball combines statistical analysis with expert based decision making (Gerrard, 2007, p. 229). There is an abundance of information in sports, and decision makers must fully understand the information to make a “correct” decision (Rosner & Shropshire, 2011, p. 354). Moneyball is a different way of thinking, one of which is often dismissed by those set in their ways. However Moneyball is an idea, one that is proven to work, and one that can benefit General Managers in professional sports. With that being said General Managers must understand that analyzing player performance through statistical analysis is a process in which options must be weighed and calculated to the needs fitting the team. Winning in any sport cannot be purely based on statistical measures, as risk is also needed to win. Knowing when and how to take risks are just as important as analyzing data. Either way both are equally important. Favoring one over the other will not be beneficial to General Managers. Successfully evaluating performance involves data analysis and expert opinion (Gerrard, 2007, p. 229-230).

Analytics are Necessary for the Evolution of Sport
Analytics and the use of statistical analysis are necessary for the evolution of sport. Athletes will continue to get bigger, faster, and stronger, and coaches will become smarter. Games will for the most part will be played the same, unless, differential thinking occurs within a team’s front office or management. Players will continue to do what they do: play and coaches will coach to the best of their ability the players on the roster. It is up to the General Managers and other members of the front office to best analyze the talent pool and obtain the players needed to win games. The question is: can Moneyball survive outside of baseball?

There are three areas which are potential barriers to the successful implementation of Moneyball management to other sports. The first of which is technology. Teams need technology which is capable to tracking player movements and actions (Gerrard, 2007, p. 229). Currently the era in which sports are played in is an era of technology. Technological advances have taken place in the last few years that present the ability for teams to capture and track all player movements. As technology continues to advance, so will the opportunities for sport to advance.

The second barrier is the conceptual barrier. Once teams can obtain and track player performance data, a team must have the conceptual structure to evaluate player performance with regards to reliant team play where an individual player’s abilities are not distinguishable. Though difficult to overcome, it is conceivable for a structure to be sent in place in which individual player performance attributes are analyzed for the benefit of the team as a whole (Gerrard, 2007, p. 229).

The third and most difficult barrier is the cultural barrier. The art of Moneyball management consists wholly of a different way of thinking. Being able to convince a Moneyball “outsider” on how statistical analysis will benefit a team more than age old traditional scouting methods is very difficult (Gerrard, 2007, p. 229). Persistence and education are necessary to show the way, and help to explain why statistical analysis will help.

The evolution of sport concerning analytics may have a better chance starting in minor league sports. Specifically with minor league baseball, a majority of the major league franchises own their minor league affiliate. There are 160 minor league teams who have player development contracts with a major league franchise. The majority of the costs associated with the operation of a minor league team fall on the responsibility of the major league team, as well as the local community. The twenty most valuable minor league franchises are worth around $20 million, and have average revenues of $11 million per year (Smith, 2012). The minor leagues, despite lower wages for the majority of their players, have created an opportunity for a vast number of major league hopefuls. This allows for more players to train and develop their game with the hopes of reaching the major league level. Most importantly is the major league team can control and experiment with statistical analysis on their minor league affiliate without the fear of losing money.

In professional sports a lot falls on tradition and revenue. Ideas with an outcome of uncertainty cause for a fear of losing money. With minor league sports though, there is not a lot of money to be lost. The worth of the most valuable team in the minor leagues is usually less than the yearly salary of some major league players. The minor leagues are used as a farm system for major league player development, so they should also be used as a farm system for evaluating the process of player performance through statistical analysis. Establishing the use of analytics in the minor leagues creates the opportunity for growth into the major leagues.

Moneyball Used in Sports Outside of Baseball
Billy Beane was extremely effective in instituting statistical analysis with his baseball club. He recognized a flaw in the system used to evaluate talent and he exploited his findings for the betterment of the Oakland Athletics. Therefore if it can work for him in baseball, what is stopping other sports from trying a similar method?

In order for the Moneyball theory to work, player statistics must be tracked and analyzed. Tracking technology can be implemented to help collect necessary data for proper player analysis. This is specifically evident in the sport of hockey. Technological advances in this sport have taken place, mostly though for television and coaching enhancement. Technology for the purpose of tracking specific sport related statistics does not yet exist in the sport of hockey (Mason, 2006, p. 47-48).

Issues concerning the development of statistical tracking technology are: how to attain data for tracking, and how to use the actual data in the process of tracking (Mason, 2006, p. 50). Reasons for tracking data in the first place are to help the coaching staff make decisions. Hockey coaches are looking for specific areas of data from their players. This data includes the distance travelled and the speed at which a player is moving, percentage of successful passes, and how players interact with each other throughout the game (Mason, 2006, p. 50).

The National Hockey League (NHL) has experimented in the world of tracking technology in the past. In 1993 tracking technology focused on allowing viewers to see the puck on television more clearly. Inside of the hockey puck was a computer chip that displayed a red glow when watched on television. This use of technology was not very receptive to the fans. In 1996 a similar approach to tracking the puck was implemented. The puck now had multiple sensors implanted inside, and television viewers could see the puck glow on the ice, as well as a glowing trail when the puck was shot, and information on the speed of the puck after it was shot. Despite this adjustment, fans felt this was too distracting (Mason, 2006, p. 51-52).

Due to the success of the Oakland Athletics and their Moneyball adventure, more teams and leagues are becoming intrigued with how statistics can help them. The sport of hockey may be next in adopting a form of sabermetrics. For this to happen, hockey questions, similar to those originally asked in baseball, must present themselves. Questions on how to better evaluate hockey players need to exist. Once the technology is in place, specific areas of tracking must be narrowed down. This will help to better sell the Moneyball theory to hockey coaches and personnel (Mason, 2006, p. 59). It will only take one General Manager to appreciate the benefits of statistical tracking to help implement a more stable way for tracking technology to exist in the National Hockey League.

The idea of “Moneypuck” may soon be on its way to NHL franchises. In May of 2016 the Arizona Coyotes made NHL history in the hiring of 26 year old John Chayka as the team’s General Manager. Chayka, an analytics guru, who in 2009, founded Stathletes, a company which specializes in hockey analytics, will become the first General Manager in NHL history hired in his 20s (Dater, 2016). Chayka may be the youngest General Manager, but he is hardly the first hire with an analytics background.

The Toronto Maple Leafs, in 2014, hired Kyle Dubas, a statistics specialist, as the teams Assistant General Manager. Furthermore, the Edmonton Oilers and the Carolina Hurricanes have both hired individuals with analytics experience to hold front office positions (Dater, 2016). Chayka has the opportunity revolutionize the NHL and become the Paul DePodesta of professional hockey.

Football (Soccer)
Much like baseball in the United States, football (soccer) in Australia is beginning to adopt a system of analytical analysis to help with the recruitment of players. Football itself is a game that relies on possession of the ball. Statistical analysis here focuses on a player’s ability to bounce the ball. A player who can successfully bounce the ball without a need to pass to another teammate decreases the probability of the opposition intercepting the pass (Stewart, Mitchell & Stavros, 2007, p. 242-243).

In addition to bouncing the ball, Australian football coaches are starting to look for a player who successfully executes the knock-on. The knock-on is where the ball is kicked off the ground in an area where most would simply dribble the ball. This maneuver helps to clear the ball away from congested areas on the field in which a turnover is likely to occur. Limiting turnovers greatly increases a team’s opportunity to score (Stewart, Mitchell & Stavros, 2007, p. 243-244).

Statistical analysis is beginning to become more prevalent among football teams in Australia. The statistics are there, and staff members are helping to collect those statistics, however a person who can formulate and use the statistics to the advantage of the team is needed. One of the reasons the Oakland Athletics were successful with Moneyball is the fact Beane hired an economist to help formulate their statistical approach. Teams in the Australian Football League (AFL) who are serious about using analytics to build a roster should consider hiring an expert in the field of statistical analysis to aid in the process (Stewart, Mitchell & Stavros, 2007, p. 244).

Australian Football is a bit more intricate than baseball. Due to the intricacies of football, it becomes exceedingly difficult to measure individual player statistics accurately (Stewart, Mitchell & Stavros, 2007, p. 245). With that being said it appears trial and error will be needed to get the full effect of Moneyball management. Teams have the ability to use statistical analysis to gain an advantage over an opposing team. As analytics are applied to Australian Football more, a complete and accurate model for identify individual statistics will come into fruition.

Analytics for the Future
Moneyball became a way for a professional baseball franchises to view value differently, and to become competitive with a significantly low payroll. Despite being used predominantly in sports, lessons learned from Moneyball can have an effect on various industries moving forward. Similar to that of other managers in business Beane had an idea and he had to convince others as to why this idea would work. It took guts as an individual who believed in something to try and convince others set in their ways to adopt a new philosophy. Moneyball as an idea can be used to help managers provoke thought and change in their own industry (Wolfe et al., 2007, p. 252).

Moneyball can change the way financial advisors evaluate stocks and other investments. Similar to that of Beane should financial advisors ignore flashy, traditional “money making” investment opportunities and find a new way to evaluate investment opportunities? Here statistical analysis can be used to focus on certain attributes for gaining a return on an investment, and use those attributes when searching for new investment opportunities (Wolfe et al., 2007, p. 252).

With regards to academics, perhaps one’s SAT score or GPA should not weigh as heavily as another trait a person possesses. The same can be said for universities hiring professors. Typically industry experience, education, publications, and teaching experience are used to evaluate the value of a potential professor, but perhaps there is another measure of success that is not being looked at (Wolfe et al., 2007, p. 252).

The use of statistics for the purpose of analysis has evolved over time. As life continues to evolve, so will the ability to obtain data. Improvements in sport science, including new training techniques, nutritional guides, and extensive reporting from training staff, all allow for new sets of data to be obtained and tracked. Technological innovations allow for the ability to better organize, keep track of, and distribute statistics. Statistics of the past can be archived and compared to current statistics for future analysis. Advances in motion detecting capabilities have allowed for the improved ability of tracking all movements during a sporting event. With this technology significantly more information can be tracked and obtained (Alamar & Mehrotra, 2011).

Analytics for the future depend on change. Change is often met with resistance, due to the fear of the unknown. However innovation cannot occur without change and open minded thinking. Billy Beane was open to a new idea on how to be competitive, and this idea provided his team with success. Beane’s existential thinking may eventually change the landscape for not only sports, but for all industries.

For a brief moment in time, Billy Beane changed the course of the sports world. Though many may not admit to it, he changed the way people think about sports, and created the perception that perhaps he was right all along (Lewis, 2003, p. 280). Despite the resistance towards change, analyzing player performance through statistics is the way of the future. A continuous advance in technology combined with a new wave of individuals with fresh ideas creates the opportunity for Moneyball management to occur more frequently in the sports world.

Statistics have been a part of sports for years, but it is only now where sport leagues are starting to use statistics to their full potential. Additional research is occurring on the topic of statistical analysis, and it is only a matter of time before more teams become absorbed in the statistical world. Technology combined with various social media outlets create a non-stop flow of statistic figures for sport fans, coaches, and players to look at. This overwhelming amount of information helps to generate and provoke thought. All it took for the Moneyball theory to come to life was a new idea.


1. Alamar, B., & Mehrotra, V. (2011, August 30). Beyond ‘moneyball’: The rapidly evolving world of sports analytics, Part I. Retrieved August 5, 2014.
2. Dater, A. (2016, May 6). Coyotes’ hire of 26-year-old gm will be referendum on analytics in NHL. Retrieved May 24, 2016, from
3. Farrar, A., & Bruggink, T. H. A new test of the moneyball hypothesis. The Sport Journal. Retrieved August 1, 2014, from
4. Gerrard, B. (2007). Is the moneyball approach transferable to complex invasion team sports? International Journal of Sport Finance, 2(4), 214-230.
5. Lewis, M. M. (2003). Moneyball: the art of winning an unfair game. New York, N.Y.: W.W. Norton.
6. Mason, D. S. (2006). Moneyball as a supervening necessity for the adoption of player tracking technology in professional hockey. International Journal of Sports Marketing & Sponsorship, 8(1), 47-61
7. Rosner, S. R., & Shropshire, K. L. (2011). The business of sports. (2nd ed.). Sudbury, MA: Jones & Bartlett Learning.
8. Smith, C. (2012, June 08). How billionaires like warren buffett profit from minor league baseball ownership. Retrieved from
9. Stewart, M. F., Mitchell, H., & Stavros, C. (2007). Moneyball applied: Econometrics and the identification and recruitment of elite a Australian footballers. International Journal of Sport Finance, 2(4), 231-248.
10. Wolfe, R., Babiak, K., Cameron, K., Quinn, R. E., Smart, D. L., Terborg, J. R., & Wright, P. M. (2007). Moneyball: A business perspective. International Journal Of Sport Finance, 2(4), 249-262.

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