If you have ever read my work elsewhere (shout-out the late FakeTeams) or are familiar with me at all, then you already know that I’ve been working the fantasy sports beat for a while. If you have never done so, well, now you know. That explanation is only needed because I will be using something as simple as “fantasy points” in this article instead of your casual PER/WS/BPM metrics, as fantasy points are a much simpler (and not necessarily off from what does other fancy metrics think really matters, mind you) and easy to understand.
If you’re not into this thing, here’s a very simple explainer. Simply put, each of the “classic” statistic (points, rebounds, assists,...) are given a value. Each time a player gets one of those statistics, he gets points for it. At the end of the game, the player gets awarded a total score that comes from adding all of those individual tallies, and that’s what we call Fantasy Points (FP or DKFP). Over the season, a per-game average can be easily calculated (total FP/total G played, aka FPPG).
Just for context, Giannis finished 2022 with 3,817 DKFP and 57 FPPG. Jokic with 4,422 and 59.8. Ja Morant with 2,670 and 46.8. Wilt Chamberlain’s best season topped at 6,890 and 86.1. Rule of thumb: Elite (50+ FPPG), Great (42+), Good (35+), Average (30+), Meh (25+), Bad (<25). That’s pretty much it to understand all of what is ahead.
Let’s start with a little bit of an introduction and some context, and try to come up with some answers later in Part II.
You probably didn’t realize, and if you did realize then you probably weren’t aware of the uniqueness of the feat. The 2022 season saw four rookies—none of them past 20 years of age—score 1800+ total FP while averaging 32+ FPPG for the first time since the class of 1996 did it in the 1997 season (with five! players reaching those numbers). No need to introduce that bunch, is it? The kids that accomplished this thing: Allen Iverson (21 years old as a rookie), Antoine Walker (20), Kerry Kittles (22), Shareef Abdur-Rahim (20), and Stephon Marbury (19).
Throughout the history of the Association, there have been a few phenoms capable of pulling off such rookie campaigns, but it’s not often we get to see that happen on an individual basis—let alone four freshmen racking up those gaudy scores in the same season. In a league in which youth and value are tightly linked, and in which age is tied to max-value deals (those into the first few years are lowly paid as part of the rookie salary scale), it makes sense for franchises to go young and reap the rewards as soon as possible and for the maximum number of seasons, assuming contract extensions are reached down the road.
While there is a dichotomy regarding youth and experience, it is often said that young players are capable of performing better than their older counterparts. How much of that is true? Which of those positions, based on historic data, holds a larger truth and edges the other one? Are younger players truly in an advantageous position when it comes to scoring higher FP? And if that is the case, why is it?
Age & Experience In Context
For the purposes of this analysis, I’m using a dataset comprised of all player seasons to take place between the 1990 and 2021 NBA seasons, both included. There are a total of 14,485 entries (player-seasons) in the dataset. Of all those, I have the age each player-season was played at and the experience of the players involved in each of those entries.
The first question to explore would be, then, whether an NBA player’s “current” age is important compared to his draft age. Are younger players at an advantage over older players? It makes sense to start by finding out the distribution of performances by age and experience in order to set a foundation and a context within which we can work going forward.
I have taken all player seasons in the database and plotted them in a bar chart by both Age and Experience. There are, as was expected, wide variations in terms of how many players debuted at which ages, which could, in turn, skew the representation. That’s why I have included the number of players included in that age/experience group. All entries are present in the Age-related chart, while those without a years-pro number attached to them were skipped in the Experience-related chart.
There are a few takeaways to note from the first, Age-related chart, so let’s go one by one:
- Just a few players played NBA basketball at age-18, and none of them did so after the 2006 season. That is because of the approval by the NBA of a minimum draft age of 19 and the prevention of players jumping straight out of High School.
- Those aged 19 are either prep-to-pros players aging a year from their debut seasons, prep-to-pros players making their debut with that age (ex. LeBron James), one-and-done players who spend just one season in college/overseas, or international prospects arriving from their countries.
- Most of the player-seasons fall in the 22-to-28 years old span, with a larger representation toward the left (younger) side. There is a simple reason for that and comes down to players debuting in the NBA only to fall off a cliff, underperform their supposed talent and level of play, and thus leave the league early in their careers. Basketball’s interpretation of the Darwinian Survival of the Fittest theory.
- The older the player, the fewer the chances he can still perform at a reasonable level to keep playing in the NBA. Thus, the fewest and fewest cases as we advance to the right side of the age axis. (Shout-out to Kevin Willis for keeping up until age-44, if only for five games!)
It’s been historically accepted that players peak at or around 27 of age. This dataset proves that true, while it has the prospects with the highest FP averages performing to such top-level in their age-28 seasons. There is a regression in both the age-27 and age-29, the former due to a still ongoing improvement and the latter due to a decrease of talent and age-related decline.
Of more interest is the fact that player-seasons coming in the 20 and 21 age gap are clearly above those surrounding them to the left (either those from players younger, mostly prep-to-pros and freshmen) and to the right (abundance of older players—Juniors and Seniors—declaring for the draft once they complete their college graduations or close to it). Even while older groups of players include debutants from past seasons (ex. Kobe Bryant debuting at age 18 but re-appearing in every other age cohort going forward as he aged), the fact that a lot of rookies enter the league in the 19-to-24 age span lowers the average score of those groups.
This last point calls for an approximation of the player’s age from an Experience-based POV on top of one based on mere age. The next chart represents the same information, now grouped by the player’s experience at the time of playing such player-season (ex. Vince Carter was the lone player to reach 22 years-pro; there are 2,434 rookie-player-seasons in the dataset).
Same as we did with the Age-related chart, let’s address some of the main takeaways we can extract from this representation:
- Rookies go through some growing pains more often than not, thus the low FP average players on the first bucket (first season as pros) present. That, though, is clearly affected by the fact that first-year players come in all types of sizes, shapes,... and levels of talent. The Exp-1 group doesn’t differentiate between no. 1 picks and international players coming to the NBA at age 32 to feature on a deep-bench role, which means there is a wide variety of players in that cohort.
- The more a player grows into their career as the years go on and the experience piles up, the fewer players are in each year-to-year cohort and thus the more representative the data is.
- There is a clear improvement on a yearly basis from years 1-to-5, with a final step up in the level of play leading up to year 6. After that, on average, decline hits all players and they start putting up fewer FP per season. Once more, this is affected by the Survival of the Fittest theory. Players that prove worth staying in the league in Yr N are going to stay in the league in Yr N+1 and most probably improve. Then some will move on from Yr N+1 to Yr N+2 while others (the worst) will get removed from the franchises’ rosters, and so on.
The chart (production by years of experience) can be easily broken down into three categories: growth (Yrs 1-to-6), decline (Yrs 6-to-12), and steady-washed-up-level (Yr 13 onwards).
That's all for now. Keep an eye on P&T as Part II of this series!