Scouts or Scorers?

Editorial Comment

For decades, in the world of professionalbaseball in America, the selection process to identify players most likely tobe successful in the “major leagues” began with a small group of colorfulcharacters known as “scouts.” Usually older, perhaps former players at somepoint in their lives, they are portrayed in movies as tobacco-spitting,sharp-eyed judges of prospective talent. They would roam the dusty roads of smalltowns across the USA in the quest to discover the unknown phenom who couldthrow the ball 100 miles per hour or hit it 500 feet. It was a pure form ofhuman factors analysis.

Today, as a profit-driven entertainmentbusiness, baseball clubs (and many other sports) are turning more to “scorers”who rely on “data capture” of a player’s myriad and often esoteric statistics.Past performance is used to, hopefully, predict future behaviour. (And how biga signing bonus to offer.)

Airline training is undergoing a similartransition. Data analytics are moving to the fore, from the selection processfor cadet candidates to type-rating and recurrent training for line pilots.

In the transition, the role of instructors(the scouts) is changing. Instead of monitoring how far off the glide slope thecrew has strayed in a flight simulation session, the data is captured anddissected by software (the scorers). Objective parameters such as excessivecontrol inputs, point of touchdown, and so forth can be assessed against abenchmark; airlines can identify which measurements are priority to theirstandards. Trends across the pilot community can be identified, and thetraining curricula adjusted to address frequent out-of-range issues.

Some airline training organisations are alsoemploying eye-tracking technology to determine where a pilot is looking (andfor how long) at critical moments during emergency scenarios.

At the moment, most of the attention on dataanalysis is on simulator statistics. But the technology exists to capturesimilar information from operational flights and compare it with trainingsession data. Assuming, of course, the pilot unions assent to the proposedprivacy provisions.

Researchers are also exploring applications ofmachine learning/artificial intelligence to predictwhat will happen, based on analysis of the simulator/aircraft attitude, etc.,and the pilot’s data-identified tendencies.

So what of the instructor now? Has she or he beensupplanted, their years of experience abandoned, retired to the bleachers likethe old baseball scout?

Not at all. The instructor can now focus moreattention on so-called “soft skills” such as communication between the Captainand the FO. Moreover, the data capture will provide information which is noteven visible to the instructor in the jump seat. They will no longer betethered to the IOS monitors and mental math gymnastics, but can instead engagewith the crew as needed and truly draw on their depth of flying experiences.

In effect, instructors, who have historicallybeen both scouts and scorers, can now concentrate on their strengths of observingand assessing pilot behaviour and crew coordination.

Whether scout- or scorer-driven, baseball’s track record is not stellar. Of the thousand or so players drafted each year, fewer than 10% ever play in the majors. Airline training is somewhat better – airline heads of training estimate 50% of training school graduates are sufficiently qualified to begin flying passengers – but that ratio needs to get much better if the training industry is to keep up with the predicted demand for pilots over the next couple of decades. Perhaps the emerging approaches to data analysis, as they are refined, together with the retained expertise of instructors, will improve the quality and quantity of pilots.

Rick Adams, CAT Editor

Published in CAT issue 4/2019