# NBA Draft: Why Age Should Be a Big Factor When Evaluating Prospects

How many times have you read about a college basketball player and seen his pro prospects diminished just because of his age?

It seems as though the birthdays of NBA draft candidates are sometimes evaluated as much as their actual skills on the basketball court. But should they be?

My immediate reaction to that question has always been to think that scouts and general managers overvalue the importance of age. I thought they got caught up in the tantalizing allure of the teenage years and the potential that came with that relative youth. I thought they overlooked the number of busts that result from the immaturity of those young players.

More than anything else, I thought they overlooked the 23 and 24-year-olds a little bit too often.

As many of you probably know by now, I like to challenge sports conventions when I don't feel that there's sufficient evidence to back up the claims or norms. This was one of those cases.

I like to think that this doesn't happen very often, but I was completely wrong. So let me prove to you that age actually should be a big factor when evaluation basketball prospects. As a caveat, the rest of this article is steeped in statistics and graphs. It may not be the easiest thing to read through, but the information is quite important.

Starting with the year 1990, the year in which the NBA draft lottery system developed into its current manifestation, and ending in the year 2007, for reasons that will become clear in a few sentences, I analyzed data for each and every player drafted into The Association, a total of 1,028 players.

I tracked their draft positions, the team that drafted them, their age and their Win Shares over the first four years of their career (Four-Year Win Shares).

For those of you not into advanced basketball stats as much as you should be, Win Shares are a metric, calculated so that one Win Share is exactly equal to one win provided by that player to his team's cause. It's the combination of Offensive Win Shares and Defensive Win Shares, a full breakdown of which can be found on this page, called "Calculating Win Shares."

The reason that I chose the first four years of a player's career for the time frame in the final category was that unless a team releases or trades a player, they are under the team's control after the draft for up to four years, the maximum length of a rookie contract.

This is also why I stopped analyzing players after the year 2007. Players drafted in 2008 have only had an opportunity to play for three seasons in the NBA and thus have not finished compiling their Four-Year Win Shares.

Once I had collected all of that data, I created the following graph, the first of 10 that you'll see in this article.

On the above graph, each draft position is represented along the x-axis and the average age at each draft position is represented on the y-axis. As you can see, somewhat unsurprisingly, there is a strong upward trend. That indicates that as the draft carries further and further along, players typically get older and older.

If you're curious, the one huge outlier is due to Cenk Akyol, who was drafted at No. 59 by the Atlanta Hawks in 2005 at the tender young age of 18.

But how do these players produce? That's where the Four-Year Win Shares come in.

As you can see in the above graph, in which age is on the x-axis and Four-Year Win Shares is on the y-axis, a prospect's production clearly goes down as that prospect gets older. The correlation is obviously nowhere near perfect, but once a prospect is over 21 years of age, the production does seem to drop off significantly.

That's not where the analysis can stop though. Because the first graph showed that older players are drafted later, it makes sense that their production is lower on average. You may have noticed that I've carefully been avoiding the use of the word "value" up until this point. But no longer.

In my eyes, a draft pick's value is solely comprised of how much that player either exceeds or fails to exceed the expectations that come along with the spot in which he was drafted.

The above graph shows draft position on the x-axis and Four-Year Win Shares along the y-axis for all 1,028 players analyzed. You can see that there is clearly a correlation.

Using a best-fit logistical regression, I found the following formula (which I have used in a few previous articles): Four-Year Win Shares = -5.836* ln (draft position) +24.537.

For the statistically inclined out there, that equation has a coefficient of determination (r^2) of 0.91024. For the non-statistically inclined, the equation fits extremely well.

By applying this formula, I can plug in a number for draft position and let the formula spit out how many Four-Year Win Shares a player drafted there should be expected to produce. For example, the first overall pick of a draft should produce 24.537 Win Shares while the 30th overall pick should produce 4.688.

With that data firmly established, we can tell exactly what a drafted player's value is in terms of this article: how much players have exceeded or failed to live up to the expectations associated with the slot in which they were drafted.

That can be done by subtracting the expected Win Shares based on the draft position from the actual number of Four-Year Win Shares that players produced. If the difference is positive, the player exceeded expectations by that much and was a bit of a steal. If the difference is negative, the player failed to live up to the expectations and was a bit of a bust.

So now that you have a full understanding of how value is determined, let's take a look at the fourth graph of the article.

Because the expected Win Shares of a player lies on a curve, there was a chance that older players would exceed the average Win Shares based on their draft position more than younger players, so at this point I was still holding out hope that age was overvalued despite the previous graphs. This one shattered that hope.

With age on the x-axis and the difference between expected Win Shares and actual Four-Year Win Shares (which will be referred to from now on as the always-capitalized "Difference") on the y-axis, this graph showed that there was still a decisive negative correlation.

Younger players did indeed tend to provide more value to the teams that drafted them than older players. In fact, players who were drafted at the age of 23 or older actually tended to be busts on average.

I still wasn't satisfied though because I had just been proven wrong so I decided to take this one step further and look at only players drafted in the first round to see if the second-rounders, the majority of whom fail to make an impact in the league, were skewing my data. This lowered my sample size to the 539 first-round picks from 1990 to 2007.

The above graph this time quite simply shows that first-rounders held the same pattern as first and second-rounders together. With the draft position on the x-axis and the average age on the y-axis, there was once more a positive correlation.

Ho hum. No surprises there. Let's move on to the next one.

Set up in the same way as the last Four-Year Win Shares graph, this graph shows that there is a distinct negative correlation, one that would be even steeper if it wasn't for the outlier at age 25.

There were only two 25-year-olds drafted during the analyzed time frame: Mamadou N'Diaye at No. 26 by the Denver Nuggets in 2000 and Dikembe Mutombo at No. 4 in 1991 by the same team. Because Mutombo produced a ridiculous 31.7 Four-Year Win Shares, he is majorly skewing the data.

Regardless though, it is obvious that first-rounder's production drops as the players' age increases. How about their value?

Mutombo throws this one off once again but you can see that there is a negative correlation for ages 18 through 24 when age is plotted on the x-axis and Difference is on the y-axis.

Once more, the data proved my original thought process completely and utterly wrong. But since I don't like being incorrect, even if I'm willing to admit it when I am, I took this one step further still and looked at only the 251 players picked in the lottery during the time frame. To be fair, there were 252 players picked, but I eliminated Mutombo from the data now that he was the only 25-year-old taken and was an even more significant outlier.

There were no surprises held within the first graph with this data set. For the third time, as the draft wore on, older players were taken.

I won't dwell on that for any longer.

This time, with Four-Year Win Shares on the y-axis and draft position on the x-axis, the negative correlation was the steepest of all three data sets.

Despite all of the previous evidence, I was still surprised by that because of the number of well-publicized teenage flameouts who were picked within the lottery portion of the draft.

Now get excited because it's time for the last graph of the article. I know you're probably jumping up and down with joy right now, but hunker down and make it to the end.

With the sharpest negative trend of the bunch, this graph for draft picks' value demonstrated quite clearly that lottery picks were no different from either first-rounders or all players picked. It was now irrefutable that age actually was an important thing to consider when evaluating NBA prospects.

And remember, that is based solely off an objective analysis of the data. It doesn't even consider the fact that franchises like the youthful appeal of young players because their upside helps draw more fans onto the bandwagon.

It likewise doesn't consider that the younger players have a better chance at staying healthy for the franchise if they remain in the same jersey after signing an extension with the team that drafted them.

All in all, next time you look at prospects, don't forget to look at their age. I certainly won't.

*Adam Fromal is a syndicated writer and Featured Columnist at Bleacher Report. Follow him on Twitter.*

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