If you have ever read my work elsewhere (shout late FakeTeams) or have met me at all, then you already know that I have been working on fantasy sports rhythm for some time. If you’ve never done that, now you know. This explanation is only needed because in this article I will use something as simple as “fantasy points” instead of your optional PER / WS / BPM metrics, as fantasy points are much simpler (and not necessarily beyond what other fantastic metrics do) I think it’s really important, keep in mind) and easy to understand.
If you don’t like this thing, here’s a very simple explanation. Simply put, each of the “classic” statistics (points, rebounds, assists, …) is given value. Every time a player gets one of these stats, he gets points for it. At the end of the game, the player gets the total score that comes from adding up all those individual sums, and that’s what we call Fantasy Points (FP or DKFP). During the season, the average per game can be easily calculated (total FP / total G played, called FPPG).
Just for context, Giannis finished 2022 with 3,817 DKFP and 57 FPPG. Jokic with 4,422 and 59.8. I Morant with 2,670 and 46.8. Wilt Chamberlain’s best season at the top is 6,890 and 86.1. General rule: Elite (50+ FPPG), Great (42+), Good (35+), Average (30+), Fur (25+), Bad (<25). It is roughly that we understand everything that lies ahead.
Let’s start with a small introduction and some context, and try to come up with some answers later in II.
You probably didn’t realize it, and if you did, then you probably weren’t aware of the uniqueness of the feat. In the 2022 season four newcomers – none older than 20—achieve 1800+ total FPs while averaging 32+ FPPGs for the first time since the 1996 class did so in the 1997 season (with five! players who reach those numbers). No need to represent that crowd, right? Children who achieved this: Allen Iverson (21 years as a novice), Antoine Walker (20), Kerry Kittles (22), Shareef Abdur-Rahim (20) and Stephon Marbury (19).
Throughout the history of the Association, there have been several phenomena capable of such beginner campaigns, but this does not happen often on an individual basis – let alone four freshmen collect those great results in the same season. In a league where youth and value are closely linked and where age is linked to jobs of maximum value (those in the first few years are low paid as part of the starter pay scale), it makes sense for franchises to be young and reap rewards as soon as possible. maximum number of seasons, assuming contracts are extended.
Although there is a dichotomy in terms of youth and experience, it is often said that young players are able to play better than their older counterparts. How true is that? Which of these views, based on historical data, holds the greater truth and bridges the other? Are younger players really in a good position when it comes to achieving higher FP? And if so, why is that?
Age and experience in context
For the purposes of this analysis, I use a dataset consisting of all player seasons to be held between the 1990 and 2021 NBA seasons, including both. There are a total of 14,485 entries (player seasons) in the data set. Of all of them, I have the age at which each player-season was played and the experience of the players involved in each of those applications.
The first question to be investigated, then, would be whether the “current” age of an NBA player is important compared to his draft age. Are younger players at an advantage over older players? It makes sense to start by discovering the distribution of performance according to age and experience in order to lay the foundation and context within which we can work further.
I took all the seasons of the players in the database and plotted them on a bar chart according to age and experience. There are, as expected, large variations in how many players debuted at what age, which in turn could distort the national team. So I included the number of players involved in that age / experience group. All entries are present in the age-related table, while those without the attached number of years-pro are skipped in the experience-related chart.
There are a few insights from the first age-related chart, so let’s go one by one:
- Only a few players played NBA basketball at the age of 18, and none of them did so after the 2006 season. This is due to the approval by the NBA of the minimum age for the draft of 19 years and preventing players from jumping straight out of high school.
- Those over 19 are either players preparing for professionals who are one year old from their debut season, players preparing for professionals debuting at that age (e.g. LeBron James), ready-made players who spend only one season in college. abroad, or international prospects coming from their countries.
- Most player seasons fall between the ages of 22 and 28, with a higher representation towards the left (younger) side. There is a simple reason for this and it comes down to players making their NBA league debuts just to fall off a cliff, undermine their alleged talent and level of play and thus leave the league early in their career. Basketball interpretation of Darwinism Survival of the fittest theory.
- The older a player is, the less chance he has of still being able to perform at a reasonable level to continue playing in the NBA. So there are fewer and fewer cases as we progress to the right side of the age axis. (Praise Kevin Willis for keeping up to 44, at least five games!)
It has historically been accepted that players reach a peak at the age of or about 27. This data set proves to be true, although it has a chance of having the highest average FP to reach such a high level in its 28-year seasons. There is regression in both age 27 and age 29, the first due to still continuous improvement and the second due to declining talent and declining age-related decline.
More interesting is the fact that the seasons of players coming in the age difference between 20 and 21 are clearly above those surrounding them on the left (either those of younger players, mostly preparatory professionals and freshmen) and on the right (plenty of older players – juniors and seniors – who apply for the draft after completing a university degree or close to it). Even though older groups of players include debutants from past seasons (e.g. Kobe Bryant who made his debut at the age of 18 but reappears in every other age group going forward as he gets older), the fact that a lot of newcomers enter the league in the 19th- In this year, the age range up to 24 reduces the average score of these groups.
This last point requires the approximate age of the POV player based on experience on top of the age-based one. The following chart presents the same information, now grouped according to the player’s experience at the time of playing such a player season (e.g. Vince Carter was the only player to reach 22-pro; there are 2,434 beginner seasons in the dataset).
Just as we did with the age chart, let’s look at some of the main conclusions we can draw from this overview:
- Beginners go through some growth difficulties more often than not, so players with a low FP average in the first bucket are present (first season as professionals). But this is obviously influenced by the fact that first year players come in all sorts of sizes, shapes, … and talent levels. Group Exp-1 does not distinguish between no. 1 selection and international players coming to the NBA at the age of 32 to perform on the deep bench, which means there is a wide range of players in that cohort.
- The more a player progresses in his career as the years go by and the experience accumulates, the fewer players in each cohort from year to year and therefore the more representative the data.
- There is a clear improvement on an annual level from 1st to 5th year, with the last step up in the game level leading to 6th year. After that, on average, the drop affects all players and they start giving less FP per season. Once again, this is influenced by the theory of the survival of the fittest. Players who prove worthy of staying in the league in Yr N will stay in the league in Yr N + 1 and are most likely to improve. Then some will switch from Yr N + 1 to Yr N + 2 while others (worst) will be removed from the list of franchises, and so on.
The chart (production by years of experience) can be easily divided into three categories: growth (1 to 6), decline (6 to 12) and level of continuous leaching (13 years onwards).
That’s all for now. Watch out for P&T as part II of this series!