There is a new reign of sports analytics revolutionizing how organizations and businesses practice information technology. However, we are not dealing with a new idea. What we see is organizations taking advantage of the first-movers toward sports analytics, and now those renovating the processes is what is being deemed “revolutionary”. Although most sports that are emerging are not the first to utilize it, they are successful because they observe the first-mover’s mistakes and reconstruct the model, allowing them to enter into their own market with something that is both effective and efficient. Limited by resources and desperate for a solution, these first movers’ boldness was vital in orchestrating a paradigm for other sports organizations to follow.
A first-runner in sports analytics started in baseball during the late 1990’s and early 2000’s. Billy Beane’s Oakland A’s struggled to maintain a competitive balance due to their lack of financial support. Deviating from traditional strategy, Beane turned his attention away from the popular home run hitters and focused on players that got on base, creating an “on base percentage”. Beane’s Oakland A’s went on to make the playoff four consecutive years from 2000-2003, allowing them to maximize payroll. (A third of the New York Yankees during this time). Since the Moneyball Era, the focus shifted to stolen bases, intentional walks and the sacrifice bunt. From 1993 to 2013, the number of stolen base attempts and sacrifice bunts-per-team-per-game both fell by 30 percent, while the number of intentional walks fell by 36 percent making its deepest plummet since the statistic was first recorded in 1894.
Basketball was the second sport to get involved in data analytics. According to 82games.com, three-point shots from the corners produced an average of 118.8 points per 100 possessions. Comparably, we can observe Michael Jordan’s career statistics which shows an offensive rating of 118.0 points per 100 possessions. NBA organizations picked up on this inefficiency which forced the league to push back the three-point line from 22 feet to 23 feet, 9 inches when Dennis Scott set a then-record for most three-pointers made in arc in the 1997/98 season (267 according t 82games.com). Nevertheless, NBA teams’ strategy still incorporates their corner-three point shots, as the number of attempts has increased from 2.34 per team per game to 5.48, showing a 134 percent increase.
Next we look at the NFL, where analytics have rendered themselves ineffective. In Bill Barnwell’s “Thank You for Not Coaching” column to the New York Times, he demonstrated that teams kick and punt the ball too often, making the argument that teams “ought to go for it on fourth and short situations” at any time or location on the field. In 1991, teams followed this on an average of 14.6 times per season, or slightly less than once per game. In 2013, that number jumped to 14.8 per team. According to “4th Down Bot” NFL teams kicked or punted when they should have “gone for it” 693 times in the past season. This caused 21.7 mishandled situations per team. “4th Down Bot” also explains that the average NFL team sacrificed about half a win over the course of a 16-game season. The problem here is that “4th Down Bot’s” conclusions are based on long-term averages of NFL games since 2000 and do not factor in intangibles such as strength of a team’s short-yardage offense or weather conditions. Coaches are not ignorant to numbers, however there is a stigma attached to risk taking as a strategy. This stigma has been foot printed into the NFL culture by its decision makers; the owners. The average NFL team has been owned by the same family or organization since 1980. There lacks justification to alter anything when the 32 NFL teams have a combined value of 45 billion dollars.
Launching after their predecessors, the NHL has been the slowest mover to the sports analytics movement. Its timing is explained first by recognizing hockey is a game involving rapid transition of the puck from defense to offense. Sports such as football and baseball are often known for their lack of transition, either being on defense or offense per play. Secondly, hockey does not require a stoppage of play to switch players on the ice. Sports such as basketball, soccer and football do require this limitation. Understanding this is crucial because analysts want to determine what value each individual player offers to their respective teams. However, this lacks value to the everyday fan wanting to understand what the information means and how it is applicable. For analytics to be effective in hockey, we cannot expect the same questions to be asked in baseball, soccer, football or basketball. Instead, the focal point needs to be on teams’ characteristics. We can do this by comparing their success on Man Advantage vs. Even Strength Play to determine strengths and weaknesses. Looking at the individual evaluation, a player’s chemistry with his linemates and the appropriate role on the team will garner his effectiveness on the ice. While the role of analytics have yet to cultivate fully, it’s important to recognize failures by other sports throughout this analytical movement. That’s the advantage of the second-mover: to capitalize and advance the process before the first-mover can pivot.
The most active NHL teams on this front are the Tampa Bay Lightning. Since hiring statistician Michael Petersen for the 2008-2009 season, the team has also been approaching advanced scouting from a holistic standpoint. Petersen’s responsibilities vary between assisting management and coaching staff by providing analytical and statistical evaluation for hockey operations and business operations. This includes contract valuation, draft, trades, and free agency recommendations as well as ticket sales, pricing and building revenue models. As General Manager of the Tampa Bay Lightning, Steve Yzerman, expressed:
“I’m somewhat old school when it comes to the evaluating process. You ultimately watch players on tape to form your decisions. But with Michael, he takes it one step further from a statistical point of view, creates analysis and uses it to reaffirm each and every decision we make on a player. The more information we have, the better decisions we can make, and it is also useful in that it makes us go back and look at something different that we might have missed the first time around.”
Petersen’s role is to help the organization improve decision-making in all facets of the game and business. Yzerman makes a key inference; the use of analytics cannot be the sole factor in any decision. Instead, it is an element and tool that when used correctly expands the potential of the team as a sport and as a business.
Hockey was not the first-mover in sports analytics, however there is undeniable commitment shown by the Tampa Bay Lightning to be active in establishing a culture in sports where mistakes won’t be repeated. This vantage point to decision making is why Jeff Vinik hired Steve Yzerman, and another reason why Lightning fans should be excited for the future of the organization.
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