Swimming. It used to be that swimmers were supposed to be streamlined and avoid bulky muscles. Then a weightlifter decided he wanted to swim. Swimmers today all lift weights.
Programming. It used to be that people built programs in a very top down, heavily planned way. Think waterfall. We now understand that a highly iterative process is more appropriate in most areas of programming.
Expert systems. It used to be that we would develop expert systems (machine translation, competitive games, etc) through building large sets of explicit rules based on what human experts thought would work. Today we start with simple systems, large data sets, and use a variety of machine learning algorithms to let the program figure out its own rules. (One of the giant turning points there was when Google Translate completely demolished all existing translation software.)
Serve-and-volley is pretty much non-existent in modern professional singles tennis. We were always taught to attack the net, and every action was basically laying the groundwork to move forwards and attack.
Nowadays, top players slug it out baseline-to-baseline.
In terms of stance, we were taught to hit from a rotated position where your shoulder faces the net, and a normal vector from your chest points to either the left or right side of the court.
Nowadays, it's much more common to hit from an "open" position, where your body is facing the net, not turned. This would have been considered "unprepared" or poor footwork in my day, but it actually allows for greater reach. It does make it more difficult to hit a hard shot, but that's made up for by racquet technology and generally stronger players.
If you're in the mood for some long form literary tennis journalism about this subject, check out David Foster Wallace's Federer as Religious Experience from 2006.
Swimming. It used to be that swimmers were supposed to be streamlined and avoid bulky muscles. Then a weightlifter decided he wanted to swim. Swimmers today all lift weights.
Programming. It used to be that people built programs in a very top down, heavily planned way. Think waterfall. We now understand that a highly iterative process is more appropriate in most areas of programming.
Expert systems. It used to be that we would develop expert systems (machine translation, competitive games, etc) through building large sets of explicit rules based on what human experts thought would work. Today we start with simple systems, large data sets, and use a variety of machine learning algorithms to let the program figure out its own rules. (One of the giant turning points there was when Google Translate completely demolished all existing translation software.)