Thinking About The Grizzly Cubs, Part 2: Quincy Pondexter

Yesterday, I introduced Part 1 of my Grizzlies Cubs series, in which I ran some stats to project how good the young Grizzlies players can end up being in the immediate future and at the peak of their career.

Yesterday we did Jerryd Bayless, today we’re going to do Quincy Pondexter.

Before we start, I’m going to do a quick reminder of how this works:

May 25, 2013; Memphis, TN, USA; Memphis Grizzlies small forward Quincy Pondexter (20) brings the ball up court in game three of the Western Conference finals of the 2013 NBA Playoffs against the San Antonio Spurs at FedEx Forum. San Antonio Spurs defeat the Memphis Grizzlies 104-93, and lead the series 3-0. Mandatory Credit: Spruce Derden-USA TODAY Sports

How it Works:

What I did, using the average peak age and experience data (the average player peaks after 5.8 seasons in the league and around the age 27), is I plugged the players’ growth from one year to another, along with the probable peak season, into my calculator to get a basic equation describing the players’ growth. Using that equation, I could then do just a little bit of calculus to get a projected career performance arc.

I did this for PER, Net Efficiency in Points per 100 Posessions (Points scored per 100 possessions minus Points allowed per 100 possessions), and Win Shares per 48 minutes. I used those three because they’re all “comprehensive” metrics to tell us how good a player is, but they all tell us slightly different things.

PER tries to tell us how good a player is, objectively, but is still remarkably bad at accounting for defense. As well, because PER doesn’t really take minutes into account, it’s bad at telling us the impact a player has on a team. Net Efficiency, on the other hand, does tell us about defense and impact, but since it’s basically just an upgraded +/- system, it does a very poor job of telling us just how good the player is, isolated on his own merits. Win Shares is a nice mix of the two, really.

If you go back to the original article, I tried this technique on an already-retired player, Mark Aguirre, and included the results for you to see.

To be able to say that these all of these pairs of graphs are describing the same relationship, the correlation coefficient for each would need to be at least 40%. To be able to say that these two graphs are definite, absolute projections, the correlations would need to be above 90%.

For PER, the correlation was 70.79%. For Efficiency, it was 71.70%. For Win Shares, it was 83.69%.

It’s flawed, but it has some pretty darn good value as a predictor of peak production.

The Up-and-Comer: Quincy Pondexter

Pondexter was actually a seriously problematic case for this particular exercise: his growth has been almost too perfect. Ideally, this exercise imagines that players improve gradually from month to month to month and year to year to year until they peak, and then they get worse from month to month to month and year to year to year.

Of course, in real life, that doesn’t happen. Bayless, for example, had a PER well above 17 in his season in Toronto, but only had one above 13 for Memphis last season. Players don’t necessarily just get better every year. One of the best things about the math I’ve been doing is that it’s built for that. 

When a player gets significantly better in one season, and then worse on the next, the resulting growth equation projects that he is, in fact, growing, but not really significantly. The result is that the final career equation is a little mitigated.

Pondexter is an amazing case because he has grown gradually from month to month to month and year to year to year. Using NBA.com, I broke Pondexter’s stats down into bimonthly intervals since he only has 3 years in the league, and he was, in fact, getting better at every single interval. 

The result of Pondexter’s rapid growth is that I think his final career projections are a little over-blown, I think. On the other hand, maybe not. Maybe this is a guy who will just continue to grow and grow until he’s a league All-Star. You’re the one who can make that decision.

Without further ado, Pondexter’s projected graphs:

For reference: in Pondexter’s first three seasons, he registered a PER of 8.5, 9.4, and 11. In his second and third seasons, he’s been to the playoffs, where he registered a PER of around 12 and 14, respectively. Every time I see that Pondexter projects to have a PER of 19.5 and above for 4 seasons straight starting in season 6, I freak out and start to write off this projection.

Then, I try and think about it just a little more. His numbers for his first 3 seasons are a little high here, but only high if you don’t consider his playoff production, and his production at the end of the year in general. He was certainly playing up to his projection by that time. Then, I remember that his 6th season is 3 years from now, and that we’re hoping to make QPon a starter in this next season. What’s so unreasonable about expecting Pondexter to grow into a 19 PER, at the rate he’s been growing, over the course of 3 years as a starter?

I think I’m honestly just writing off the idea of Pondexter as a bona fide star contributor, but he has been growing really remarkably. Should we really be so quick to write him off as a potential star? This graph tells me…maybe not.

I do think, though, that it’s a little over-exaggerated. Pondexter is not likely to grow this well. I do think, however, that it’s a really optimistic graph nonetheless: it tells us, at the very least, that Pondexter’s production is likely to defy expectations.

As your reference point: in his first three seasons he logged a Net Efficiency of -6, 1, and 10. Notice how close that is to the first three seasons on this graph. It really nails the retrospective part of this.

I have the same hesitant response to this prediction as I did to the last one, but this one is probably even more accurate: it predicts the first 3 seasons better, and QPon’s biggest selling point has never been his individual production (his PER) but his contribution to the team (not-putrid D, floor spacing, smart positioning). In that sense, having an incredible net-efficiency fits with Pondexter’s MO; he has the potential to contribute massively to the team.

Again, I do think this graph is overly optimistic, but I think it’s indicative of how much we can expect from Pondexter. We can expect more from him than we thought, apparently.

Finally, I think the graph model I’ve been using was probably not ideal for Pondexter. I think, given his constant and relatively impressive growth that he’s more likely to follow a “logistic” pattern, where he hits his peak earlier and basically levels out for a while. Unfortunately, Pondexter’s numbers created errors in the logistic regression, so I had no way to project with this graph.

Here’s a vague idea of what that would look like though, with totally made-up numbers from me so that the graph would look about how I imagine it would.

So, again, for that reason, I don’t think that Pondexter’s projections are perfect. They do tell us something really important though: he has a LOT of potential, maybe more than we think.

It might be time to start tapping into that potential, then.