With modern society being so technologically driven, people are constantly searching for newer and more convenient ways of doing things. This is the same when looking at sports analytics. We have began to see multiple teams across various platforms who are beginning to incorporate different forms of statistical analysis in order to gain knowledge of their competition as well as improve their own player performance. “Sports analytics is the art and science of gathering data about athletes and teams for analyses to create insight that improve sports decisions, like deciding which players to recruit, how much to pay them, who to play, how to train them, how to keep them healthy and when they should be traded or retired” (Tichy, 2016). Sports analytics applies to the players, scouts, coaches and other team personnel and is used for many different purposes. Michael Shuckers, a professor of statistics at St. Lawrence University in Canton, N.Y would go on to say that “teams are using analytics in the same way that a business would try to gain an advantage over their competitors” (Buckstein, 2017), and ultimately, the sports world is a business.
Funding for Analytics
Investments in analytics will go from $125M per year in 2014 to $4.7B by 2021 (Tichy, 2017)
Interest in sports analytics is reaching a global platform and more and more clubs across various sports are beginning to implement some form of statistical analysis into their programs.
“Yes, interest is steadily growing. You can see the growing interest by observing which teams are sending attendee to the MIT Sloan Sport Analytics Conference. It has grown to 3800 attendees, and I counted from 450 professional teams. There was a big range of pro athletes team by team. Since it was in Boston, the New England Patriots had more attendees than anyone else, but 100 percent of the NBA basketball teams were in attendance, 80 percent of NFL football teams, and 70 percent of MLS soccer teams sent people to this event. And not just Americans; I met people from Australia, Germany, Italy and Russia” (Tichy, 2017)
- Many professional sports teams now have statisticians on their payroll, there are regularly scheduled conferences devoted to sports statistics, and the Moneyball movie has recently demonstrated the value of applying statistical methods to the drafting and utilization of players in sports. (Albert, 2012)
The goal of this analysis is not only to prove the growing recognition of sports analytics but how they are used to accurately or inaccurately predict future superstar athletes. Analytics are a useful tool when it comes to determining physical attributes, but it is important to note that not all teams have access to the same data, financial capital and other resources, which may impact their ability of benefiting from sports analytics. Peter Chow-White is a communications professor and big data researcher at SFU, he states multiple underlying factors which could prevent different clubs from gaining access to these resources “teams may not have technical capabilities, they may not be interested, coaches may be old school” (Clipperton, 2016) but it is undeniable that when these analytics are being used by more and more clubs and it is transitioning into success, it is only a matter of time before everyone begins to implement it into their organizations. We are able to relate this back to the course theme of ‘a new digital divide’ which explains how some teams have access to resources that others do not, as we saw in the week four lecture “sport institutions with the greatest financial sway have the capacity to deploy Big Data in ways of which much smaller institutions can only dream of” (Szto, 2018) . Although financial restrictions impact many smaller clubs, professor Chow-White would confirm that more and more teams are finding ways to incorporate analytics into their programs by saying “there is a generational shift happening right now and it seems fairly inevitable that most sports will turn to it as part of what they do” (Clipperton, 2016).
As explained previously, analytics have multiple different usages within a sports franchise. Owners are using analytics to maximize fan experience at the stadium and build their teams from a marketing perspective. General Managers are using ‘big data’ and other linear models to manage salary cap and bring in different personnel. The head coach is using analytics to scout the opposition as well as put his own players in the right positions/situations in order to have success. Players are constantly watching film and studying both themselves and their opponents. All of these different individuals are benefiting from analytics, but do they have negative implications too? Can analytics be false? or a misrepresentation of true talent? This falls back to the initial stage and the point of this analysis. The scouting process in order to find future superstar athletes is a daunting task and one that is crucial to building a successful franchise. As we saw in class, Billy Beane was famous for implementing an analytic system to draft and recruit players based on their statistical performance (specifically their on base %, OBP) rather than some of the more ‘flashy’ or appealing statistical categories such as home runs , this proved to be a successful model as the Oakland Athletics transitioned from one of the worst teams to making the playoffs in 2002 and 2003 when they won back to back division titles.
Scouting Future Talent for the NFL and the NHL:
Evaluating athletes before the draft process follows a series of steps and tests to prove that individuals can handle themselves not only physically but mentally as well. Focusing on Hockey and Football, this is why scouting combines for the NFL and the NHL are so important, depending on how you score and the way teams evaluate you, it can have a tremendous impact on your draft stock.
Beginning with the NHL, much like the rest of the society, “hockey is one of many industries that are going through dynamic changes due to the increasingly availability of data and data analysis tools” (Buckstein, 2017).
Hockey was actually one of the last professional sports organizations to begin using analytics and analytic software “analytics have been prevalent for some time in other sports like basketball and baseball, but hockey has lagged behind” (Clipperton, 2016). It may seem fairly easy to assume that talent at a young age will transition to talent at an older age, however, unfortunately with hockey that is not the case. Hockey is interesting in the fact that you are often drafted before you begin post secondary school, which is much different than the NFL where the significant majority of their athletes come from Post-secondary institutions. This presents another big challenge for NHL scouts, “you’re trying to select players at 18 that are going to be really good at 23, 25, 27 years old. Nobody has figured out the secret to doing that” (Buckstein, 2017). Eighteen is a very difficult age to take the data collected and make an accurate assumption about the player’s potential. This is such an important time for males of that age, the body is just beginning to develop physically and some athletes hit their physical ‘peak’ before others. This is why there is such a large amount of risk with NHL draft picks and why player development is so crucial for a player to have success. Peter Tingling focused his research off the ice and looked into management, more specifically “drafting, trades and how the Detroit Red Wings have excelled in player development” (Clipperton, 2016).
This is a picture of two of the best late round draft picks to ever play the game. Never considered to be first round talents initially, they both finished their careers top’s of their team and a almost certainly first ballot Hall of Famers. On the left, Henrik Zetterberg was selected in the 7th round of the 1999 NHL draft with the 210th pick, by the Detroit Red Wings, he would go on to captain the team from 2013-present date and tallied 960 points in 1,082 career games. On the right was perhaps one of the most talented players to ever play the game, Pavel Datsyuk was taken in the 6th round of the 1998 NHL draft with the 171st pick by the Detroit Red Wings, he would go on to score 918 points in 953 career games. These two would go on to be the faces of the franchise for years to come and Detroit had built a successful franchise through key draft choices. Although it could be argued that in 1998 and 1999 analytics were not as big as they are now, scouts have been evaluating prospects for years. They have always had a way of collecting data, it is now just much easier with all of the different technological platforms. Easier to gather data and share it with others.
Football on the other hand may be the sport in which analytics had the greatest impact on future athletes. We’ve seen instances where an athlete has either ‘shot up’ or ‘shot down’ draft boards because of some of their combine scores. American football is one of the most physically demanding sports out there and analytics are used to test the athlete’s physical traits at the present time, but also by using linear data and big data in order to gain knowledge about a prospect’s past tendencies that could possibly translate into the future. “Technically, the analyses are carried out using a variety of approaches such as deep learning, Bayesian networks or archetypal analysis” (Brefeld & Zimmerman, 2017) and we can follow this up by saying “the primary tool for every analytics professional (sports or otherwise) should be linear regression” (Lewis, 2017). These are some of the many analytics used when testing athletes, and they all have a purpose, often analytics reveal things that could have only been revealed through ‘Big Data’ by making connections between different variables. Although it seems easy to gather and analyze data, “the problem is that mastering the technical side of statistical analysis usually takes years of education. And, more critically, developing the wisdom and intuition to use statistical tools effectively and creatively take years of practice” (Lewis, 2017). Much like what was discussed before, the similarities between business analytics and sports analytics is increasingly similar, being able to predict and properly plan for the future based on analytics will give the corporations/team the best chance at success.
Will Saquon Barkley’s combine scores transition to on-field success?
Many sports writers have said that Saquon Barkley in not only a physical force on the field but is a very confident and hard-working individual. His combine scores are unlike anything we’ve ever seen.
At 233 lbs he can bench press as much as a 300+lbs All-Pro offensive lineman in Joe Thomas, run as fast as Desean Jackson and Devin Hester and Jump Higher than one of the most physically gifted athletes in the game, and that is 6’3″ All-Pro Julio Jones. All of this looks great on paper, but will this accurately transition into the NFL? Where the competition increases to whole new heights. Because of these analytics, experts have talked about him being the first running back taken first overall since 1995, when Ki-Jana Carter was taken by The Cincinnati Bengals. Barkley’s test results were the best that scouts have ever seen, some going as far as calling “Barkley’s workout a complete freak show.” (The NFL.com broadcast team). We visited these issues in both the race section of the course and again in the disability section when we talked about describing athletes. By using the term ‘freak’ although they are implying something positive, the actual terminology is intended to be negative. There are plenty of other ways to describe him as an amazing athlete without putting those labels on him.
Lets wrap it up:
While technology is constantly advancing, so will analytics. There is no denying the benefit that analytics has on the sports world. It has transformed the game, how we play it, how we prepare for it and how we market it. A statement made by professor Chow-White of SFU summed up my feeling perfectly, he said that “I think that some of the misunderstanding of analytics is that somehow it’s a magic bullet- somehow going to solves things, somehow going to win games he said. Numbers don’t win games, people win games” (Clipperton, 2017). As Billy Beane proved, if used in the right way, analytics can be a useful tool to ensure success. “Analytics are about how we get the most juice out of the lemon? said Tingling. How do we get the right pieces of the jigsaw puzzle in place?” (Clipperton, 2017). We could be provided with all the data we wanted, but we must have a system in place to use it properly and see how this data will translate into future player performance. By giving the readers insight on how analytics is gaining more attention, the importance of analytics and their value to sports, and then by giving examples of how the NFL and NHL use analytics to draft prospects, we hope to gain an understanding of the importance that analytics has on sports. Ultimately, by providing you with this information, it is up to you to decide the importance and accuracy of predicting future athletes using analytics. Ideally, we want to incorporate as much statistical analysis in as many ways as possible into sports, while still being able to maintain the integrity of the game.
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Buckstein, J. (2017). NHL teams finding analytics can give edge on the ice. ProQuest, 1-2.
Clipperton, J. (2016). Hockey analytics conference set to ‘expand the conversation’ on advanced stats. ProQuest.
Costello, B. (2018, March 2). Saquon Barkely’s Combine numbers are utterly jaw dropping. Retrieved from New York Post: https;//nypost.com/2018/03/02/saquon-barkley-combine-numbers-are-utterly-jaw-dropping/
Lewis, M. (2017, November 12). Part 4 – Statistical Models. Retrieved from https://scholarbugs.emory.edu/esma/tag/draft-analytics/
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