A common belief is that the top scorer is the best offensive player. Basketball analytics has spent years explaining why that is only partly true.
Volume versus value
Scoring totals depend on usage, the share of possessions a player finishes. A player who takes many shots can score a lot at an average efficiency, while a more selective player may add more value with fewer points. Total points reward opportunity; efficiency rewards judgement.
When looking at leaderboards on any service, the 22Casino website is an example of a page where several columns can be sorted. Sorting by efficiency instead of volume often changes the order a good deal.
Beyond the points
A player who creates shots for teammates, sets screens, rebounds and defends contributes in ways that points do not capture. Some of these are measurable, such as assists and rebounds, and some are hard to quantify. Good analysis acknowledges both groups and does not pretend the second one does not exist.
Team context matters as well. A scorer on a team with little shooting around him faces tougher defences than one surrounded by shooters. Identical skill can produce different numbers in different settings.
A second misleading ranking is based on points per game across seasons. Eras differ in pace and in the share of three-pointers, so a total that was ordinary in one decade can be remarkable in another. Measuring a player against the league average of the same season is a fairer test than comparing raw numbers across decades.
Some readers also follow betting markets while watching the game. If so, settle a budget first and watch the clock as well as the numbers. A leaderboard tells you who did something often, not necessarily who did it well.
Myths survive because they contain a grain of truth, and analysis separates the grain from the rest. The most useful habit is a simple one: read the data as a description, not a prediction. The next moment is still open.