In the NBA, data and analytics are a growing trend that front offices resort to, especially when making future plans. Numbers in general (age, height, weight, etc.) are all contributing points in the evaluation of a player, making them either a commodity or a potential liability going forward.
Yet, even when predicting if a player will become a star or not, time has made it possible to calculate a time period in a player’s career where a “jump” in productivity or efficiency will occur.
Many are the factors that you have to consider when attempting to predict the jump (i.e. experience, muscle maturity, role on the team, etc.), so bear in mind that there are endless cases where a rise in productivity is missing.
The chart below shows the careers of eight players (in age) who have been leaders on their respective teams at some point in time and were also recognized as the star players on those teams. To illustrate their productivity in numbers, the statistic named “PER” (player efficiency rating) is the most suitable one as it calculates every statistical column (points, rebounds, steals, assists, etc.) and narrrows it into one number. Just by looking at this number you can deduct the value of a player.
These were all star players so their PER numbers are fairly high compared to the rest. As you can see, each player’s line begins at a low point then shoots up and remains at a consistent level, then gradually decreases after the 30 mark. Most of the lines experience a radical increase at the beginning, meaning that the player started to show a star potential. When the line falls, it means that opposing teams made the required adjustments to contain that player, and also gave him more attention than they had before when his PER was lower.
From the year 25 on, that is when we see a more consistent pattern. This means that the player started to enter the apex of his career and was also able to counteract the defensive adjustments from his opponents.
Even when a player is not projected to become a superstar, one can still attempt to calculate when he will become more efficient and be able to contribute in a team setting. To do this, the following graph will visulalise the PER career lines of nine players who were never pressurised to lead with their performances.
Compared to the first line graph, the nine values illustrated above belong on a lower shelf; however, some similarities can be identified. The majority of the players see their value stabilise in their mid-twenties and take on the form of a semi-straight line until they reach their late-twenties/early thirties. With this graph, it is also easy to see high level of inconsistency, this can be explained by how they player fits on a system, and whether the system was right for him; a problem that doesn’t apply to a star player.
By looking at the two datasets above, you could argue that one can attempt to construct a respectable team by just looking at the age of the players.
To contend for the championship, a star player is always imperative, but if your goal is to make the playoffs and rebuild, your odds immediately escalate if your personell is mostly formed by players in their mid-late twenties.