Six Sigma vs. Sports

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Yu Darvish has nearly perfected his pitch delivery by treating each pitch as a process, evident by the same release point for each pitch.

We commonly associate Six Sigma and the DMAIC approach with improving the processes of manufacturing or certain service companies, but what about more unconventional industries? Professional Sports leagues are some of the largest businesses, yet they are hardly mentioned in regards to Six Sigma. Is it possible that athletes and coaches alike can use this quality management approach to improve their performances? Tennis star Steven Falk wrote “Six Sigma Tennis” where he explains how coaches and players can reach their maximum potential.  Falk analyzes a tennis player to find areas that can be improved using DMAIC. After the improvements, the player has minimized his unforced errors; thereby reaching his highest ability. Falk focuses on individual sports like tennis, where it is easy to identify success and failure based on points won or loss. However, the mainstream sports in America like football, basketball, etc. present a harder situation to use Six Sigma.

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The Pittsburgh Penguins have created the best power play in the NHL by treating each play as a process, reaching a high level of efficiency.

Focusing on hockey as an example, new statistical measures are being introduced to the game every season like Corsi and Fenwick now measuring puck possession.  At the core of each new stat introduced is a desire to improve a team’s performance. So why can’t Six Sigma be used along with Corsi or Fenwick for improvement?  Since Six Sigma results in 3.4 defects per million operations, essentially  a forward could expect to miss only 3.4 shots for every million he shoots. A goalie could expect to save all but 3.4 of a million shots he faces. Unfortunately, these two scenarios cannot coexist—exemplifying the issue with Six Sigma in sports.

While reaching the Six Sigma level of efficiency is nearly impossible in sports, DMAIC can still be applied to improve the process. Taking the hockey power play as an example—a time when the offense should be able to capitalize on its advantage—coaches can take each play as an individual process. First, they will define the weakness in the play, perhaps the lack of shots being taken. Then, experts can measure the amount of shots during the power play, and analyze it by comparing league averages and past results. Players improve the process by increasing the amount of shots taken, and control it by maintaining that shot level throughout the season.

Sports certainly can be subject to DMAIC application, but do they need to be? The most entertaining moments in sports and the traits that make them so appealing often center on the anticipation of what will happen next. With Six Sigma and DMAIC, there is less unknown. Every golf shot should be a hole in one, and every batter should hit a home run each pitch.  Gone would be the underdog victories or crazy upsets. Would near-perfect athletes be as entertaining? Even the Sidney Crosby and Tom Brady’s of the sports world make mistakes or bad plays. Perfect athletes throughout the leagues would be too predictable.

Do you think implementing this type of process control would change sports?

Should players and coaches actually take the time to improve their processes or is it dependent on the sport?


5 for Forecasting: How Nick Leddy Lost his Roster Spot

The Chicago Blackhawks and Chicago Bulls faced very different situations this summer.

The Bulls decorated the United Center in hopes of landing free agent Carmelo Anthony
The Bulls decorated the United Center in hopes of landing free agent Carmelo Anthony

The Bulls created an elaborate presentation at the United Center as they tried to lure Carmelo Anthony away from the New York Knicks while the Chicago Blackhawks offered some of their best players to other teams. The core reason for these differences was the accuracy of salary cap forecasting within the specific leagues.

The NHL fell victim to bad forecasting in 2014. Early in the year, the commissioner predicted a $71.1 million salary cap for the upcoming season. General Managers across the league began to plan their roster moves accordingly. It wasn’t until months later, when the actual figures came in, that the league realized it had overestimated the growth of the revenues; the hard salary cap would instead only be $68 million. The $3.1 million difference didn’t affect teams that conservatively reacted to the forecast, but teams like the Blackhawks found themselves needing to eliminate nearly $3 million worth of payroll. So as the team practiced all summer, the atmosphere was not one of anticipation and excitement but rather apprehension, knowing a significant player would need to be traded.   Ultimately, Nick Leddy lost his spot on a championship team because the league falsely predicted the growth of their revenues.

Both Johnny Boychuk and Nick Leddy lost their roster spots on championship teams because of poor forecasting.
Both Johnny Boychuk and Nick Leddy lost their roster spots on championship teams because of poor forecasting.

In contrast, the NBA did not suffer from bad forecasting. They were not perfectly accurate, as is expected, but they conservatively expected a 4.5% increase. By not relaying an unrealistic expectation to the general managers, the league prevented premature dealings by teams. Plus, on three separate occasions throughout the year, the league adjusted their forecast to improve its accuracy. The eventual salary cap was a 7.7% increase from the previous year. This left the Chicago Bulls with extra, unexpected cap space to try to recruit top free agents—a much preferable problem than having to shed payroll.

General Managers have to find the balance between optimistic forecasts and reality. The NHL should’ve considered the effect of the lockout shortened season as well as the decline of the Canadian dollar when they predicted a forecast that followed the record growth trend from previous years.  The NBA did a much better job of using environmental signs such as market values of sold teams to predict their league growth.  In the years to come, I’d expect NHL managers to be more hesitant in relying on initial forecasts, and perhaps adopt NBA strategies in approaching salary cap forecasts. shows the relationship between each NHL team and the salary cap. This specific image shows how restrictive the 2014 salary cap is for certain teams. shows the relationship between each NHL team and the salary cap. This specific image shows how restrictive the 2014 salary cap is for certain teams.

The salary cap in certain leagues is an absolute maximum accompanied by severe penalties for violations. It makes it even more consequential when a forecast is overestimated. Some teams will wait until the cap is released to sign players while other teams sign players based on forecasts. It poses the question: Which is the preferred scenario? New York Jets fans are currently complaining because their team lacks talent despite having nearly $20 million in cap space to be spent while Blackhawks fans were upset with having to trade Leddy in order to be under the cap. Which scenario would you prefer your favorite team be in? Do you rely on the forecast and deal with the consequences of inaccuracy or wait until the actual cap is set to make important decisions and risk missing out on top talent?