DC Should Tackle Batman Beyond Next And Cast Donald Glover

DC Should Tackle Batman Beyond Next And Cast Donald Glover

Nerds of the world will recall Batman Beyond as a refreshing animated take on the Batman mythology. We were gifted with an incredible Batman animated series in the 90s that stands the test of time. (Don’t believe me? Go back and check it out.)

Here are some reasons why DC should tackle a Batman Beyond movie next:


They can stop rehashing the same story. Even Nolan’s trilogy was beholden to the rogue’s gallery of villains and characters that had previously existed in the Batman movie universe. Whether it is the Joker or Catwoman, they have to be reinvented in a new decade, but much of the story remains the same. In setting a movie in the future, you can create a new set of villains and characters in this  version of Gotham. For too long has the detective aspect of the character been absent on the big screen, but a reboot in the future could allow for new methodology. An aging Bruce Wayne would be playing mentor and watch tower for a younger Batman: this means new challenges and methods for overcoming problems.



Recast Bruce Wayne. Since this version of Bruce Wayne would be an older man, you have an entirely different set of actors from which to choose. There would be no need to hire the en vogue  actor who is going to provide an appeal to key demographics. You could cast based on the needs of the character, which would no longer be purely (or predominantly) physical; the detective aspect of the character would be more important, as well as being a mentor. I think it would be interesting to bring back Michael Keaton as an older, grumpy version of Wayne.


Add diversity to the story. Not to be a prisoner of the moment, but Donald Glover is fantastic. I’m one of the many fans who would have loved to see him play Spider-Man on the big screen. Sure, he got a cameo, but that’s not the same thing. Can you imagine a movie where Michael Keaton plays mentor to a snarky Donald Glover in the ultra-tech version of the bat suit? If you don’t want to go with Donald Glover (or perhaps he wouldn’t be interested in it), then utilize some diversity by casting a person of color.



Perd Hapley is a Dimension-Traveling Alien

will-the-real-perd-hapley-please-stand-up-2-26474-1428472335-14_dblbigIf you’re like me, then you have a strong and abiding love for Parks & Recreation. I can admit that it is one of my favorite comedies in recent memory, due in large part to the brilliant casting and writing. And Ron Swanson, always Ron Swanson.


However, I came to an interesting realization the other day: Perd Hapley is a dimension-travelling alien observer who is clearly the most powerful being in the shared TV universe. For eagle-eyed fans, you will notice that Jay Jackson, the actor who plays Perd, has a penchant for reading the news on your favorite TV show.


What you never noticed though, his how closely he likes to stay to danger and intrigue. Much like the Observers from Fringe, I think that Jay Jackson has created a kind of shape-shifting constant character that is not unlike Stan Lee in every MCU movie.

Starting in 2007, Jackson has played a report or news anchor no less than 14 times, including:

Bones (TV Series)
Scandal (TV Series)
The Catch (TV Series)
Supergirl (TV Series)
Pretty Little Liars (TV Series)
Parks and Recreation (TV Series)
Revenge (TV Series)
Battleship (Movie)
Fred: The Show (TV Series)
Body of Proof (TV Series)
The Mentalist (TV Series)
Fast Five (Movie)
The Closer (TV Series)
Dexter (TV Series)

Now, perhaps you can say that he has been typecast as a reporter/news anchor or even that Jay Jackson likes playing these parts. However, I like to think that a casting agent, writer, producer, director, or even Jackson himself is purposely creating one of the greatest easter eggs in all of TV history.

So, the next time you are watching a TV show and they cut to a reporter in the field or an anchor delivering the news, don’t be surprised if it is Perd Hapley looking back at you.

Why I’m A Coward

04fWe all have something that we really want in our lives. Perhaps it is a dream that we actively and purposefully ignore; maybe it is something just on the periphery of our awareness. In my opinion, either we are honest about what we really want or we allow ignorance of it to guide our actions.

For me, it is wanting to be a screenwriter.

I’ve found a way to work on my own terms and make money; I’ve created a universe where I can write novels and short stories, while working freelance, without having to show up to a traditional 9-5 job. Even so, if I’m being honest, I have failed to really pursue what I want to do.

It is right there, in the forefront of my mind, when I wake up every morning.

It is right there, within reach, in everything I do.

But I don’t go after it.

Why? Fear.

I’m afraid of what it will cost me to pursue it.

Or at least what I think it will cost me.

I make excuses about not wanting to move to LA or put my wife in the position of us not making enough money. I talk about wanting to be able to provide a standard of living as a means to not jump in with both feet. The reality is that it is achievable if I wanted it bad enough; if I wanted more than I wanted comfort, more than I wanted to succumb to fear and let it guide my behavior.

I’m a chicken-shit when it comes to the thing I will regret as I lay dying.

Sure, I’ve published a lot of books and I’ve manged a modicum of success. However, I talk about becoming successful enough with my books that Hollywood will take notice. If I were being truly fearless, I would doggedly pursue that dream, hustling and working toward it without regard for failure. I wouldn’t wait for my success and my dream to overlap. I would go out and get, leaving nothing on the table when I do.

I wanted to end this with something powerful like no more or  I will pursue it now that I have laid it bare. But really, I remain afraid of upsetting what I have. I will continue down the path of least resistance, holding the idea with me each day that my dream will remain beyond my reach as long as I don’t pursue it. I will continue to be honest about not pursuing it, but will likely remain afraid to go after it in some misguided notion of homeostasis.

I will live a great life with the woman I love, but I will always know that I was too scared to pursue the personal goal that, objectively, would not have upset my life, but more than likely given a rich texture to it I would have cherished.

I will continue to be a coward hiding behind a veil of simpler personal success accented by easy-to-attain personal goals that are easier to recover from if I fail.

I can do better.

You, dear reader, don’t have to be a coward like me.

Take chances. Chase your dreams.

Procrastinate Procrastination (Or How I Learned to Love Setting Goals)

Procrastination_(No_Wall_Uncovered_VII)There are countless articles spread across the vast universe that is the internet on how to eliminate procrastination; to put a finer point on it, all that has been said on the subject has been studied, collated, optioned, and opined about. We all know that procrastination is kryptonite for successful business practices (and not to mention writing goals). But what can we do about it?

I love talking about time management; no, seriously, that was not meant as joke. (Stop laughing.) Being productive means growth, and I am all about growing early and often. Here are some of my favorite methodologies:

Chunking. This method is often used to memorize numbers and names. If you wanted to remember a phone number, remember it as two numbers: 434 and 7133 (instead of 434-7133). For tasks in a given day, put a few different tasks together as a block and complete all of them together before taking a break or rewarding yourself with something salient or moving on. (More on behavior modification in a bit.)

Momentum and motivation. Motivating yourself can be difficult; often, people hide behind a lack of motivation when explaining away why they didn’t complete a project r finish that novel. The easiest way to overcome this is to give yourself some motivation: do something you really want after completing the task. Even better, once you get some momentum, knock out some more goals!

Location. Some places lend themselves to procrastinating more so than other places. Sitting in front of your TV binge-watching a show is not the best place to get some work done (or meet your writing goals). Relocate to a distraction-free zone (as best you can) and set yourself up for success.


Establish rewards and consequences. Behavior modification remains one of the few tried and true methods for creating behavior change (like procrastinating less). For the purposes of simplicity, let’s say that it is building a contingent relationship with clear rewards and consequences. For instance, if you wanted to write a certain number of words a day, say 2000, then you would want to reward the action of writing 2000 words with something you can only get from completing the task; you don’t write the words, you don’t get your reward. Pretty simple, right? Building your day out of a series of contingent relationships like this can pay real dividends in terms of getting things done.

Create and adhere to deadlines. Setting deadlines has been proven to help people reach their goals. Knowing that there is a finish line helps you to think about your time in a meaningful way. Adhering to those deadlines, over time, makes you averse to procrastinating in the future.

Share your goals for increased accountability. Sometimes, letting other people know about what you need to do can create a network of accountability: people asking you throughout your day whether or not you finished what you intended can keep the task on the top of your mind. Fair warning: this can be very exhausting, especially if you are have difficulty adhering to your plan (or if you are easily upset).

Adapt your goals accordingly. In many ways, this might be the most important tool. Things change, and it is important to change with them. Too often, we just keep doing things the same way to reach the same goals with little real success. We become accustomed to doing something because we have always done it this way. If you want different results, think about doing things a different way.

CTA_writer's desk

Full Court Press: Chasing the Gold Medal Since 1992

(An important note: standard deviations and effect size were purposely left out of the reporting of the statistics for ease of reading)

An Overview

When the conversation began to build and people started to take sides, I decided that I wanted to investigate the 1992 Olympic Men’s Basketball team, affectionately known as the Dream Team, and see if I could come to some kind of statistical conclusion about whether or not the 2012 team stacked-up in a head-to-head contest.

The conclusions that I will draw will say nothing of what would happen, since we do not have a time machine (at least I do not) and as such we cannot see what would actually happen if the squad in 2012 met the 1992 version of the Hall of Fame members of the Dream Team.

Instead, my goal is to look at the statistics of the game, the numbers that are used to generate fantasy leagues and talk about the hierarchy of all-time greats with a standardized precision. I will utilize the statistics from Basketball-Reference.com, which is a database of extensive statistics that are wonderfully robust for the time periods I am investigating.

I will use statistical tests to compare the two teams as a whole, and as well a position-by-position analysis. For the sake of an objective analysis, I will not be taking into account the career statistics or career accolades of the much-lauded 1992 Olympic Team. Also, I will be omitting the statistics of rookie Anthony Davis from the 2012 team (because he does not have professional basketball numbers) in addition to Christian Laettner of the 1992 team, for the same reason. I will be using statistics from the year immediately preceding the Olympics games for all of the players included in the analysis, with the notable exception of Magic Johnson. I will use his career numbers, as 1991-1992 averages were not available. I had, briefly, considered using his 1990-1991 numbers, but they would be many months removed and felt that the career numbers were a more conservative estimate of his numbers (as his career numbers reflect comparatively) and a better representation of his contribution at the time.

The teams will be evaluated on a variety of variables. I will not take into account point differentials during Olympics wins because of the difficulty presented in comparing the other national teams. The PER rating (the per minute efficiency rating of a player) as well as true shooting percentage will be used in conjunction with a variety of statistics (averages over the year leading up to the Olympic games) taken from Basketball-Reference.com: points per game, assists per game, rebounds per game, steals per game, blocks per game, free throw percentage, field goal percentage, offensive rebounds per game, defensive rebounds per game, three-point field goal percentage, and defensive rating. I will break down the comparisons in the following three ways: team, front court, and back court.

What will follow will be a statistical analysis of the Olympic squads from the Dream Team forward. While statistics do not reveal everything, there is something to be said about an objective, comparative analysis in the only way that is possible: statistics. The intangibles of the game are what make watching it so great. This is an exercise in combining two great loves: statistics and basketball.

What Does It All Mean

For many of the readers, I imagine statistics are simply that awful word-based math you had to sit through during school. Perhaps mentioning statistics conjures the famous Mark Twain quote: “There are three kinds of lies: lies, damned lies, and statistics.” For the sake of this analysis (and subsequent analysis), I will be using group comparisons. I had contemplated the variety of ways with which I could talk about the data and the most pertinent was using each Olympic year as its own group and drawing conclusions from there.

What we are interested in when considering group comparisons is whether or not a group is distinguishable from another group in a statistically significant way. This is to say that if there were no labels that we would be able to say one group was larger or smaller than another group in a meaningful way and not just due to chance. So when I talk about the mean values (the averages) of any particular statistic, I will be sure to differentiate purely numerical differences and statistical differences. Since I know that statistical analysis is about as interesting as pulling teeth for most fans, I will try and rephrase everything in more palatable terms. Much of what we love about fantasy sports is due to statistics. I love using statistics to examine basketball because it allows me a unique perspective into the game that is often only afforded to basketball-operations types.

The first step when considering so many variables and groups is to perform a multivariate analysis of the data. There are multiple dependent variables and we want to guard against statistical errors, so performing due diligence toward that end did indeed yield a significant multivariate effect at the p < .001 level. This means something and nothing all at once.

Take a breath.

What this means is very simple: there are differences between the Olympic teams and that difference is statistically significant. The reason why it doesn’t tell us much is that teams are different and how they are different is not revealed in a multivariate analysis; instead, we know to look further and we shall. In addition to a multivariate analysis, I was curious whether or not there were factors that hung together when considering teams that won a gold medal since the 1992 Dream Team.

A factor analysis, in the simplest terms possible, is an attempt to understand how variables hang together. So when I say that the top three factors account for 67.37 of the variance, I am certain that means very little. The number goes over 80 percent when including two more, much smaller, factors.

So the first factor is comprised of the following: RPG, BPG, ORPG, DRPG, HGT, WGT (38.940%). This looks a lot like a defensive statistic, which would work well to describe a power forward or center. The second factor is comprised of these variables: PER, OFFRAT, PPG (16.384%). I would venture to say this factor is very much a scoring statistic, which might fit very well with a shooting guard or small forward. The final factor was comprised of these: TSPER, FGPER, USAGEPER (12.046%). This looks suspiciously like a leadership statistic that would fit well with a point guard (or the newly minted point forward).

What the deuce does that mean? Nothing, really. For a statistician such as myself, I might be inclined to take my analysis further utilizing these new categories, but I won’t. For the casual basketball fan, it means that there are definitely three factors to a great team: defense, offense, and leadership.

Alright, enough with the explanations. Let’s get to the statistics.


Stat1The mean ages (in years) for the Olympic teams were as follows: 1992 (29.64), 1996 (29.58), 2000 (30.83), 2004 (23.82), 2008 (25.92), and 2012 (26.36). There was a significant difference in mean age, F(5,63) = 7.48, p < .001. There were also linear (p < .001) and cubic (p = .07) trends, which would suggest a gradual decrease in age over time punctuated by a stark decrease in 2004 (the only year since 1992 that the US did not win a gold medal).

How were they different?

The 1992, 2004, 2008, and 2012 teams all had significantly lower ages than the 2000 team. What about the question of whether or not the 2012 team was younger than the 1992 team. Numerically, this is true. However, there was no statistical difference in age between these two teams. This, in plain terms, means that we cannot be certain that the difference in the ages is not due to chance.



Steals might seem like an insignificant statistic when considering the whole of a team, but there is something to be said about how defensive pressure impacts the game. The means for each Olympic year were as follows: 1992 (1.86), 1996 (1.58), 2000 (1.14), 2004 (1.42), 2008 (1.38), and 2012 (1.36). The only significant difference was between the 1992 team (which had the highest average steals) and the 2000 team (which had the lowest average steals). There was also a significant linear trend (p = .040), which would seem to imply a steady decrease in average steals since 1992.

Defensive Rebounds


Unfortunately, there was no real difference in defensive rebounds between the different Olympic teams. Based on the scale of the graph you might be inclined to think otherwise, but there was nothing significant about the different years. For numerical sake (because we know I love numbers), here are the average means for each year: 1992 (6.23), 1996 (5.47), 2000 (4.96), 2004 (5.33), 2008 (5.27), and 2012 (5.11).


BLKAnother defensive statistic that certainly looks like it decreased over time. While there were no significant differences between groups, there was indeed a significant linear trend (p = .037) that would certainly suggest a decrease in average shots blocked from 1992 through 2012. To better visualize the data, the means were as follows: 1992 (1.26), 1996 (1.03), 2000 (.96), 2004 (.86), 2008 (.64), and 2012 (.54).

Free Throw Percentage

Free Throw

The graph would seem to suggest that there is a polynomial trend at work here, but that is not the case. There were not significant differences between Olympic teams. However, because I know how much you love numbers, here are the averages by year: 1992 (79.5%), 1996 (74.8%), 2000 (77.9%), 2004 (75.2%), 2008 (77.7%), and 2012 (79.8%)

Defensive Rating


The Defensive Rating statistic is based on how many points were allowed per 100 possessions (The lower the number, the better the defense.) There were not significant differences between any particular teams, but there was a 4th order trend (p = .023) that would seem to suggest that at least teams were different enough from the predicted trend that they stood out (1992, 2004, and 2012). However, this does not tell us anything about how the teams are different from each other, only how they are different from an overall trend moving from 1992 until 2012. Moving from oldest to most recent, the average defensive rating by year was: 1992 (102.55), 1996 (103.92), 2000 (103.50), 2004 (101.18), 2008 (106.00), and 2012 (102.91).

Field Goal Percentage

Field Goal

Field Goal percentage is how many shots were made relative to how many shots were taken. (This number excludes free throw percentage.) While there were no differences between the different Olympic teams, a quadratic trend was observed (p = .023) that shows a clear drop-off during the 2004 Olympics. It should be noted (again) that the 2004 team was the only team since 1992 not to win a gold medal. The averages by year were: 1992 (51.3%), 1996 (50.0%), 2000 (46.6%), 2004 (45.4%), 2008 (48.5%), and 2012 (48.2%).

Usage Percentage


Usage percentage is an estimate of the percentage of team plays used by a player while he was on the floor. The graph would suggest much more is in play than the statistics reveal: there was nether an overall trend nor any differences between the groups. It is interesting to consider the averages from each team: 1992 (25.7%), 1996 (26.8%), 2000 (25.0%), 2004 (26.4%), 2008 (26.7 %), and 2012 (27.0%).

Once again (thanks to the scale) there appears to be an overall trend of a small line-up, but there was neither a trend nor group differences to support that hypothesis. The means would suggest incremental differences (hence the lack of significance): 1992 (79.73), 1996 (79.75), 2000 (78.83), 2004 (78.91), 2008 (78.50), and 2012 (78.46).

Offensive Rating


Offensive rating is the points produced per 100 possessions. There were indeed significant differences between the Olympic teams, F(5,63) = 5.54, p < .001. The 2004 team (109.17) had a significantly lower offensive rating than the 1992 team (117.27), the 1996 team (115.92), and the 2008 team (113.50). The 2000 team (109.17) and the 2012 team (111.09) were not different from any other group. There was as well a linear (p = .014) and quadratic (p = .002) trend, which would suggest a gradual decrease in offensive rating as well a dramatic dip in 2004.



Yet another statistic that was not meaningful in terms of helping us differentiate between teams. The averages (in pounds) are pretty close: 1992 (217.91), 1996 (224.75), 2000 (209.75), 2004 (221.82), 2008 (219.42), and 2012 (216.27).

True Shooting Percentage

True Shoot

True shooting percentage is a measure of shooting efficiency that takes into account field goals, 3-point field goals, and free throws. There was indeed a difference in Olympic teams, F(5, 63) 4.895 , p = .001. The 2004 Olympic team (52.4%) was significantly lower than the 1992 team (58.2%), the 1996 team (58.2%), and the 2012 team (58.1%). The 2000 team (54.4%) and the 2008 (56.6%) were not significantly different from any other team. There was as well a quadratic trend (p < .001), which would seem to suggest a subtle drop-off in shooting efficiency in 2004.

Points Per Game


While the graph of PPG (Points Per Game) looks like there is something going on, I can assure you that there is nothing of statistical significance. The average points per game by Olympic year are as follows: 1992 (23.23), 1996 (22.21), 2000 (19.58), 2004 (19.79), 2008 (21.61), and 2012 (21.50).

PER (Player Efficiency Rating)


John Hollinger created the PER statistic that he described as thus: “The PER sums up all a player’s positive accomplishments, subtracts the negative accomplishments, and returns a per-minute rating of a player’s performance.” There was a statistical difference between Olympic years, F(5,63) = 2.901, p = .020. However, follow-up tests yielded no difference between individual groups. The means are as follows: 1992 (23.71), 1996 (23.28), 2000 (19.96), 2004 (19.76), 2008 (22.06), and 2012 (22.99). There was a quadratic trend as well ( p = .003) that would suggest a lull during the 2000 and 2004 teams in terms of efficiency ratings of the roster.



The Olympic teams were not significantly different from each other in terms of average assists per game. The averages for each year were as follows: 1992 (6.07), 1996 (5.18), 2000 (4.43), 2004 (4.18), 2008 (5.72), and 2012 (4.84)

Total Rebounds


Sadly, total rebounds per game lacked a significant result as well. The averages were: 1992 (8.37), 1996 (7.42), 2000 (6.79), 2004 (7.27), 2008 (6.90), and 2012 (6.56).

Offensive Rebounds


It should come as no surprise that offensive rebounds lacked significant findings as well. The averages were as follows: 1992 (2.16), 1996 (1.96), 2000 (1.83), 2004 (1.96), 2008 (1.62), and 2012 (1.48).


Making sweeping conclusions about statistical data is part of the fun for me. Basketball is a team sport where one individual can dramatically change the course of a game. An Olympic team is, in theory, comprised of the best players at a given time. The only team that was significantly different than any other year was the 2004 team, which was the only team since 1992 to not win a gold medal.

Does that mean we can say that the other teams are better?

Not necessarily.

All we can really say is that the other teams were more similar to one another (teams that won a gold medal) than they were different from the 2004 bronze-medal winning team. I imagine that is slightly anti-climatic. The reality is that looking at averages of all positions created a homogenized sample.