>> We find that the introduction of the new composite racquets temporarily helped younger players at the expense of older players, reduced the rank correlation in player quality over time, and increased exit rates of older players relative to younger players. We find that these effects last for two to four generations of players.
Pretty cool, fun, interesting study. I hope the authors leave it to the reader to make sweeping generalisations/extrapolations from their results. (Still reading.)
Edit: generalisations are pleasantly restrained. I found the supporting statistics much more interesting than the mathematical modelling. Maybe I just don't have the patience for the latter, but I'm not sure it really lends more credibility to the study's conclusions.
No doubt new technology has been important, but there might have been another significant factor influencing generational changes: the number of aspiring pro players. This number has increased dramatically in the last 30 years, but in the paper they only consider the number of successful players (roughly equivalent to the top X ATP rankings, where X is somewhere between 200 and 500). X has also been increasing, but at much lower rate, and as a result there are vastly more people competing for those X spots today than in 1980. It could be that to get into the top 100 today is as hard as it was to get into top 10 thirty years ago.
As the natural selection gets more brutal, new players have to develop new, more effective skills to succeed.
Since upcoming changes in skill requirements for good jobs could be more rapid than in the past, we need to teach many more people how to learn faster and better.
I wonder if there are startups or programs from institution with a focus on this. I am aware of a popular course "Learning How to Learn" on Coursera. But it seems like an app that interactively helps people to apply this sort of lessons on a concrete set of materials (e.g. Finance, Cardiology, Microprocessor Design, Software Design, etc.) would be even more useful.
Since upcoming changes in skill requirements for good jobs could be more rapid than in the past, we need to teach many more people how to learn faster and better.
Why not teach the same number of people to be 10x? ;)
Practically all of the graphs show that the trends they attribute to new racquets were already evident before that introduction. Given the size of the pro tour, it wouldn't take too large a cadre of young hot shots to explain the results independently of racquet technology, and those players' ever advancing age would better explain the U shape of those curves. Indeed this is when McEnroe, Connors, Lendl, and Wilander were all coming to the fore. The evidence for the authors' hypothesis seems extremely weak.
It's an interesting study - but tennis is fundamentally a very bad model for understanding the impact of technology on the employment market. A better tennis player does not produce more 'tennis' than a bad player. So, no matter how the technology changes, the number of tennis players required is unaffected.
Technological change in the workplace almost always refers to technology that makes a worker more productive. Since the demand for most things is constant, this means you need less workers for a given product. So, the problem would not be old workers being worse at their jobs - the problem would be that, absent external factors, there would be less jobs around.
It's not always about "producing more". This tennis model is relevant to scenarios where the job is getting harder with time (e.g. building better computer chips, or performing more complicated brain surgeries).
>> We find that the introduction of the new composite racquets temporarily helped younger players at the expense of older players, reduced the rank correlation in player quality over time, and increased exit rates of older players relative to younger players. We find that these effects last for two to four generations of players.
Pretty cool, fun, interesting study. I hope the authors leave it to the reader to make sweeping generalisations/extrapolations from their results. (Still reading.)
Edit: generalisations are pleasantly restrained. I found the supporting statistics much more interesting than the mathematical modelling. Maybe I just don't have the patience for the latter, but I'm not sure it really lends more credibility to the study's conclusions.