The first couple decades of professional baseball in the 1800's looks nothing like the modern game that millions enjoy today. For decades teams would trot out their best pitcher game after game with no thought to using a reliever. Pitchers routinely threw over 400 innings, whereas today a high workload is considered anything over 200. Rany Jayazerli, then writing for Baseball Prospectus, published an article in March of 2004 giving a wonderful historical view of how the game of baseball changed centering on the idea of how to get the most value out of a pitcher without diminishing his effectiveness. In October of the same year, Baseball Prospectus released an article written by James Click evaluating relief pitcher performance against that of starting pitchers. The study revealed that many relievers perform better than starters in almost all statistical categories. This revelation of effective relief pitchers is the result of Major League Baseball teams finding market inefficiencies and exploiting them for maximum benefit. That premise is the central theme of Michael Lewis' work in the book Moneyball. There is more statistical research on pitcher utilization, to include radical suggestions about using the best pitchers for shorter outings more often to minimize the negative defensive effects of pitching to a lineup more than twice through. While many of the radical suggestions have not made headway into professional baseball's majority thought process, some changes based on statistical findings have. Relief pitchers, left-handed one out pitchers, platooning players in positions depending on pitching match ups and defensive shifts against left handed batters are only some of the changes that were once radical that are now mainstream. All of the changes share a common set of factors. The changes were data-driven, they were identified as market inefficiencies and they all leverage the skills of the available players economically to maximize their performance benefit to the team.
Like baseball, commercial aviation collects an incredible amount of data - on costs, efficiency, mechanical performance and even human factors. Sports use statistics to gain a competitive advantage. There could be an advantage to be gained from comprehensive analysis of aviation safety data to avoid loss by putting flight crews in the best positions to leverage their skills and wakefulness in the most safety critical times. There has been a documented positive effect for baseball teams based on pitching performance. Could there be a similar advantage in safety performance? Many groups study human performance while fatigued and seek to crack the code on how to measure and maximize performance when tired. Without a quantum leap in monitoring human cognitive performance while fatigued, like a nanotech robot embedded in the brain, we are stuck with the technology at hand to make improvements.
Professional sports are affected and driven by economic factors not existent in commercial aviation. For example, shorter outings for pitchers affects their win totals which affects their market value. Imagine if pilot market value was determined by how many landings one performed? The pay scale would be radically different and the usage rate of those high-value pilots would be radically different from today. Instead, pilots are considered frangible assets and it is taken for granted by the company and customers that the individual at the controls is the most competent individual to safely deliver the airplane to its intended destination. The factors at play in commercial aviation are positive in their effects on safety. Union initiatives and political development of comprehensive duty time regulations work in parallel, and sometimes in complement, to balance the safety and efficiency and effectiveness of the national aviation system. However, the current regulations and company policies are just a start. There are many things we cannot measure well in the realm of human factors. Measuring fatigue during operations in a complex cognitive environment, like the cockpit of an airplane, is one of those elusive metrics. There is a solution in the current technological and political reality.
We don't have to measure wakefulness or do bio-metric monitoring because research already proves that performance degrades in direct proportion to fatigue. The next step in the evolution of safety science is a data problem. Like baseball and advanced analytics we are looking for more complete ways to tell the story or paint the picture. Good data analysis using current technology and monitoring methods could lead to market inefficiencies that positively affect safety, duty time regulations and even profitability. Data and statistics lead us to identify tendencies, which we can use to anticipate issues and avoid problems. This process is already in place with FOQA programs and simulator training systems. The missing link in making the next step in aviation safety science is having flight crews record their actual, daily sleep wake information, or human factors data, to help companies, unions and regulators identify and avoid the biggest problem areas.
Fatigue is one of those issues in commercial aviation that is difficult to quantify and impossible to control. Everyone involved in the industry takes it for granted that tired flight crews perform worse than rested, fresh crews. What if the data could tell a story and what if fatigue can be mathematically proven? Or perhaps even predicted with a probability equation? That solution would change the game and help the industry take a leap forward. The addition of human factors data, like crew sleep-wake information, adds a critical dimension in evaluating usage patterns and duty time regulations that may be revealed as archaic, ineffective and inefficient. The technology and data capture mechanisms are available and in use today to begin a data revolution similar in effect to how data analysis changed, and continues to change, professional baseball. The volume of aviation safety data currently collected is such that we are only scratching the analytical surface to find the market inefficiencies to exploit for industry safety benefit, and quite possibly, profiting the innovative companies who embrace the analytical revolution.