NBA All Stars are retiring older

With Vince Carter’s NBA career seemingly now over at 42 years young, I started to wonder whether NBA players are playing longer. There’ve been a fair few long-toothed retirees recently – Kobe Bryant was 37 and Dirk Nowitzki was 40 in their final seasons, for instance.

A couple more come to mind from earlier decades – Bob Cousy retiring (for a second time) at 41 in 1970 or Kareem Abdul-Jabbar at 41 in 1989. A quick scrape of suggests these legendary players are, in fact, outliers. But even so, All Stars, at least, have been retiring older.

Here’s a chart of all 503 All Stars listed on basketball reference. Their age in their final season against the season:

There’s limitation here. I didn’t feel like getting IP blocked for scraping every NBA player ever, so I decided to focus on a small subset – NBA All Stars. This is problematic for a number of reasons, noteably that All Stars are (probably?) more skilled and athletic than the average player, therefore more likely to play longer even if they do drop off. They probably also have longer, guaranteed contracts – meaning they stick around a few extra years regardless.

But as you can see from the following chart, the mean age of an All Star in their final season has increased in the past ~70 years.

So, the next obvious question is what’s going on? Some part of this is that athletes and the league are richer – NBA players aren’t likely to be working other jobs or have better offers elsewhere. Also medicine has advanced greatly and so some of what used to be career ending or degrading just isn’t anymore.

I wondered whether it might also be about better “load management”. That stars just play fewer, more strategic minutes per game nowadays, meaning they have fewer injuries and put less of a toll on their bodies. But a quick scrape suggests that isn’t So. Career minutes per game seems about the same for guys retiring recently as it did six decades ago.

If anything it seems that All Stars are playing far more. There’re definitely playing more total games. Some of this is due to more games in seasons and playoffs as the league has expanded. Private charters have also become a thing etc.

But better gear, medicine and sports science must come into it somewhere. Players and teams have access to so many more resources than they ever did, such as conditioning coaches, dieticians, chefs, masseuses and other therapists. This reminds me of a brilliant article from a few years ago, delving into all that Lebron James and his team go through to help him recover between games. The techniques are full on and constantly evolving.

But while they show their careers are lengthening, none of this really captures the impact over a career, which players and positions survive longest, and how a big a drop off there is. Maybe that’s what I’ll stick my nose in next.

Hide and seek during a lockdown

Foot traffic has fallen dramatically because of the coronavirus. Obvious things have stopped, like air travel and professional sports. But what about less high profile activities? One’s that aren’t explicitly banned and could even count as exercise.

Geocaching is kind of like a global game of hide and seek. Someone hides a container somewhere, publishes coordinates or clues and others try to find it. When you find a geocache you “log” it through an app or a website, maybe with tips and photos.

Geocaching should be the perfect social distancing activity. They’re usually off the beaten track. It can be done solo or just with your household. Geocache logs are also a pretty clean indicator of non-essential movement – nobody has to go geocaching or logs for work.

I’ve hidden a few geocaches. One under a bridge on the Gold Coast in Australia and another in a Sri Lankan park. Even now I get occasional alerts that they’ve been found. But I scraped the logs of 300 geocaches around the Gold Coast and there has been a 50% drop in geocaches found from March to April. April is down 45% from the previous year.

Gold Coast geocache logs

I wanted to make sure this isn’t an anomaly, or that there isn’t some state bias here. So I also scraped 300 geocaches from Adelaide, Sydney and Melbourne. The numbers in Adelaide aren’t as dramatic but the effect still appears. There were 38% fewer finds in April than April last year.

Adelaide geocache logs

The effect is even clearer in Sydney and Melbourne. Finds in April are about a quarter of the previous month. There were only a couple hundred finds in April, down from almost 1500 last year.

Melbourne geocache logs

My dataset goes back almost a decade. April normally is a solid month, with a couple of public holidays and the weather starting to turn. There’s also a general upward trend over the decade, probably due to an accumulating number of geocaches but maybe also smartphone uptake. Apart from those succeeding a massive outlier, this kind of drop off seems anomalous.

Sydney geocache logs

This is a pretty clear sign of how hunkered down everyone is. It hasn’t fallen off completely because some people probably use it as exercise – I often plan my walks around where geocaches are present. But the marginal users have completely fallen away.

Does it pay to win the toss?

Something that has always bugged me about cricket is that the coin toss seems to have a huge impact. That’s the framing, anyway. The entire first morning of a test match is usually taken with what the winner should do – bat or bowl first.

Innumerable factors play into this decision, including weather, recent games, psychology and schedule. It sometimes seems more art than science.

But does it matter? Between 2000 and 2018 the toss winner won about 40% of games and the loser about 35%, according to noted cricket statistician Ric Finlay. Considering the sheer number of games, this seems pretty significant. I decided to scrape Cricinfo’s stat page to see if there’s anything else to tease out.

Firstly, as you’d expect from a coin toss, the results of a coin toss are about 50/50. Here’s Australia’s record at home:



But let’s go a bit deeper and break it down by country. The results of test matches played in Australia roughly line up with what Finlay says. But, perhaps counter-intuitively, it seems winning the toss is slightly more advantageous in the shorter formats. I would have thought the opposite, as pitches deteriorate and there’s more time for poor weather etc. in a test match.


Some of this is probably noise. There have been significantly fewer T20s than test matches played, for instance. Maybe more to unpack in the ODI’s.

Funnily enough, India is pretty dire for my theory. It’s even worse for test matches in India and even better for T20 matches. But, again, relatively few T20 matches. Also significantly fewer test matches played in India than in Australia, so that’s one to watch.


Let’s look at England. This one is a little closer to what we saw in Australia, which makes me think the quantity of matches played is important. It also makes me question the connection between the toss and weather.



All of this is roughly around with Finlay says, which makes me think there’s something to winning the toss. And the advantage for one dayers is pretty consistent across these countries. I’m not prepared to call it yet. But there could be a marginal effect here. Gonna keep exploring.

Why I’m not a professional sportsperson, maybe?

Following up on yesterday’s deep dive into NBA birthdays I’ve been reading more about the relative age effect. This is the apparent phenomena whereby “older” players are over represented in professional sports. By older I mean that professional athletes are more likely to be born at the beginning of the year.

There’s actually a remarkable amount of research on this phenomena, and it appears to hold for some sports, in some countries, especially in Europe. One paper notes:

During the last three decades, researchers have identified overrepresentations of athletes born in the first quartile of the selection year (i.e. January to March if the cut-off date is 1 January) across cultural contexts in sports such as football, ice hockey, handball, baseball, basketball, rugby, volleyball, tennis, ski sports and swimming, Till et al.. demonstrated the possible extent of such over representations of relatively older players in rugby: 47.0 % of the regional and 55.7 % of the national junior representative players were born in the first 3 months of the selection year

It does not appear to hold for the NBA, as I discovered yesterday. That basketball finding was in France. And many of these studies found the phenomena reduces with age. It’s more prevalent in teams of teenagers than the higher grades, for instance.

But what about cricket? Again, a thought that hit me today as I was planning attending some upcoming test matches. One study of Australian cricket players again found no significant difference in relative age among state level players, but did for lower grades.

Players born in the first quartile of the cricket season were significantly over- represented in both male Under-15, Under-17, Under-19 and female Under-15 and Under-18 levels. However, there was no significant difference at the senior state level for either male or female cricketers.

What about Sri Lanka? Where schools play an outsized role in player development and the club system is a mess. I haven’t been able to find research on this so I decided to scrape the birthdays for all 150 Sri Lankan test players from Wikipedia:

Not much to go on here. Not much of a difference. And not enough data points to say the variation is much more than randomness. I may try to find a larger database of Sri Lankan club players.

But, have I been cursed for my October birth? It seems to depend on a lot. Given I grew up in Australia probably not.

Does it matter when NBA players were born?

I once read something about how professional athletes were more likely to be born at the beginning of the year. I don’t know the provenance of this. The source is probably questionable. But I was thinking about it today while discussing birthdays with a friend.

So, I decided to check it out. For one sport anyway. I scraped birthdays on Basketball Reference, giving me several thousand ABA/NBA players going back to the 1940s.

So, this isn’t too promising. As I remember, the theory says players born in the beginning of the year would have some advantage as children. Someone born in January would have ten months more physical maturity than someone born in October, for instance. This could be significant when you’re eight years old, setting you up for life.

This doesn’t seem to bear out by the time they get to the NBA. There might be some noise in here from international players – seasons and school terms differ, especially between hemispheres. But international players are a small minority, especially over the life of the ABA/NBA.

Some interesting research suggests that peak birth rates are influenced by latitudes. The bulk of the United States lands between July and September. As we can see, NBA players don’t seem to follow this pattern.

So, I decided to slice the data a few other ways. Maybe the theory might hold among elite players? Let’s look at NBA Hall of Famers.

This is getting closer to the distribution we’d expect in the United States. But we’re only playing with about 150 people here, so let’s not read too much into it. Anyway, it doesn’t really support the theory. The peak comes around the beginning of the basketball season in America.

The last notion I have is that older players are throwing us out. Over the past couple of decades there’s been a lot of infrastructure built to target young prospects – camps and tournaments etc. It stands to reason those born in the past thirty years could be more affected by birth month.

So, here are all the players born since 1990. Again, this distribution looks a bit more promising (for the theory) than the entire dataset. But we’re still only dealing with about 600 players. I want to look more into this.