Author: RTNM

 

This analysis examines the relationship between a team’s On-base Plus Slugging (OPS) and their total wins in Major League Baseball (MLB) over a five-year period from 2004 to 2008. OPS is a key statistic in baseball that combines on-base percentage and slugging percentage, providing a comprehensive measure of a player’s (or team’s) ability to get on base and hit for power. The scatterplot visualizes this relationship, with each point representing a team’s OPS and corresponding number of wins for a particular season. The data points are colored by year, allowing us to observe any patterns or trends across the seasons. That factor proved not to be very useful.

A linear regression model was applied to determine if there is a significant correlation between OPS and team wins. The analysis revealed an R-squared value of 0.196. The R-squared value indicates that approximately 19.6% of the variance in team wins can be explained by their OPS, suggesting a moderate correlation. While OPS is a useful statistic, the relatively low R-squared value implies that other factors, such as pitching, defense, and managerial decisions, also play a significant role in determining a team’s success over a season.

The analysis covers data from five consecutive MLB seasons, providing a broad overview of the relationship between OPS and wins over multiple years. The consistency of the trend line and equation across the years indicates that the OPS-wins relationship is relatively stable during this time period.  However, given the moderate R-squared value, this analysis suggests that while OPS is an important metric for assessing team performance, it should be considered alongside other variables for a more comprehensive understanding of what drives a team’s success.

In a recent post, I demonstrated that WHIP is much more predictive of a team’s record than OPS, at least in the mid-2000s. I don’t think anyone will be surprised to learn that pitching is more important than hitting if you want to win baseball games. There will be more on that and related topics coming soon.

 

 

This scatterplot visualizes the relationship between a baseball team’s WHIP (Walks plus Hits per Inning Pitched) and the number of wins they achieved during the seasons from 2004 to 2008. I included both the AL and NL in this analysis. Each point on the graph represents a team in a specific year, with the color indicating the corresponding season. The WHIP is plotted on the x-axis, while the number of wins is plotted on the y-axis. This visualization allows us to observe if there is a pattern or trend between these two variables across different years.

A trendline, represented by a solid red line, has been added to the scatterplot, which provides a general indication of the relationship between WHIP and wins. The slope of the line suggests that as WHIP increases, the number of wins tends to decrease. The strength of this relationship is indicated by the R-squared value of 0.49, meaning that WHIP accounts for approximately 49% of the variability in the number of wins. This moderate R-squared value suggests a fairly significant correlation between the two variables.

In summary, the scatterplot illustrates a moderate negative correlation between WHIP and team wins, indicating that WHIP is a meaningful factor in a team’s success, though not the sole determinant. Including both leagues from 2004 to 2008 allows for an interesting, if limited, analysis over multiple seasons, with the trendline and R-squared value providing insights into the overall pattern between these two metrics. This plot highlights the importance of WHIP in predicting team performance while suggesting that other factors certainly contribute to a team’s total wins.

Here is a scatterplot illustrating wins in terms of team ERA (earned run average). When I was a kid, I didn’t think ERA was very valuable, and the following plot shows that it has less explanatory value than WHIP.

As we saw in a previous post, payroll differences explained approximately 19 percent of the variability in win totals. Team ERA explains about 44 percent of the variability, while the WHIP metric has more explanatory value (49 percent) when determining what leads to wins in major league baseball. I will keep posting more information as my research progresses.

 

 

The scatterplot titled “2018 AL WAR vs OPS (Colored by Position)” visually explores the relationship between Wins Above Replacement (WAR) and On-base Plus Slugging (OPS) for players in the American League during the 2018 season. Each point on the plot represents a player, with OPS on the x-axis and WAR on the y-axis, and the points are colored according to the player’s position. This allows us to observe how players across different positions performed in terms of their offensive output and overall contribution to their teams.

Notably, the plot highlights standout players such as Mookie Betts and Mike Trout, who are positioned in the upper right corner, indicating their exceptional performance. Betts, then an outfielder for the Boston Red Sox, and Trout, still a center fielder for the Los Angeles Angels, both had extremely high OPS and WAR values. Their positions in the plot underscore their status as two of the most valuable players in the league during the 2018 season.

In contrast, Chris Davis, a first baseman for the Baltimore Orioles, is positioned in the lower-left corner of the plot. Davis had one of the lowest OPS and WAR values in 2018, indicating his struggles. The spread of points across the plot also reveals how different positions cluster in certain areas, with players like Davis standing out as outliers in underperformance. At the same time, Betts and Trout exemplify top-tier performance. This is a pretty cool visualization of this type of data. I find scatterplots useful.

 

 

How is this for a different perspective? The 3D Cluster Analysis of 2023 National League (NL) shortstops visually represents player performance using an extra dimension, highlighting their key differences and similarities. Using a sophisticated technique called Principal Component Analysis (PCA), the high-dimensional performance metrics of the shortstops were reduced to three principal components, which encapsulate most of the variance in the data. This dimensionality reduction (or expansion, if you prefer) allows for a clear visualization in three-dimensional space, where each player’s metrics reflect their overall performance. The players are grouped into three distinct clusters, each represented by a different color, providing insights into how these athletes compare to one another based on their statistics.

The clusters were determined using the K-means clustering algorithm (much more of that down the line), which groups players with similar performance metrics into the same cluster. As earlier, the plot reveals three main clusters: Cluster 1 in blue, Cluster 2 in green, and Cluster 3 in red. Each cluster represents a subset of players with comparable performance profiles. For instance, the player in Cluster 3 (Mookie Betts), shown in red, exhibits stronger or more consistent performance in certain areas, distinguishing him from those in the other clusters.

Unsurprisingly, Betts is once again highlighted in the analysis. Notice that he is off by himself in red, focusing our attention. This emphasis allows for a closer examination of where Betts stands relative to his peers in the 2003 NL shortstop group. While I do believe that the two-dimensional plot from the last post is more diagnostic, no one can deny how cool the 3D plot looks. And that is why I published this post.

 

To the surprise of no one, Mookie Betts was, by far, the most unusual offensive performer last year among NL shortstops. If you study the plot, you can follow the line connecting Betts to the other players.

 

Betts is a cluster of one. His offensive production was so far above all the other shortstops that no one could cluster with him. And that, I must say, is highly unusual.

 

The title of this post is unusual (you see what I did). Why? Historically, I have chosen titles inspired by the band Arctic Monkeys. They had a propensity for overly long song titles that may or may not have anything to do with the actual song. For the longest time, they were my favorite band. The last two CDs, though, have given me pause. I listened to both of them hundreds of times, and I must admit that I just don’t get it. Maybe on some deep (nearly subconscious) level, I have given this post a most unlikely title as a mild form of protest. I am dubious of my potential impact.

This short essay is about the offensive production of American League second basemen in 2009. I will view each player’s statistics through an Explanatory Data Analysis lens, specifically by creating different Scales of Unusualness.

Here is a table of some of the data used for the study. Of course, the variables would be very different if we used players from last year. In 2009, no exit velocity or launch angle data were available. Even with this partial data set, many advanced metrics were eliminated from the table to make it more legible. Variations of these variables (and the others) were used to create the plot, which will appear shortly.

Table 1. AL Second Basemen, 2009

If we were just considering batting average (BA), it would be easy to rank the players. In fact, the players happen to be ranked in descending order based on that column, with Cano leading the way at .320. What happens if we want to consider all the variables together? Human brains aren’t very good at that task, but computers have no problem.

Next comes the scale that was referenced earlier. We can take each column and standardize the data by giving each value a z-score. A z-score measures the distance of a number in standard deviations from the mean of the data sample, in this case, all the columns with a “Z” prefix. Cano had the highest batting average, and you can see that his z-score for “Z_BA” is 1.68, which is more than double the next highest number in the column. That means his batting average for that year was highly unusual compared to other AL second basemen.

Table 2. AL Second Basemen Z-Scores, 2009

One interesting note. Look at the table and see if you can determine the two most unusual players when all the data is considered. I don’t think it can be done. There are eight variables, and that is six or seven too many. Fortunately, a technique called Cluster Analysis quickly solves the problem. Below is a Cluster Tree, or Dendrogram, of the computer’s analysis.

 

Figure 1. Dendrogram

The plot shows two large categories, those with high and those with relatively low offensive productivity. Among the top performers, the software identified Ben Zobrist as the most unusual. That means that Zobrist had the best offensive season of any AL second baseman that year. If you study the plot, you will see that Alberto Callaspo finishes a close second.

I would like to point out a couple more things. The plot shows that Maicer Izturis and Howie Kendrick had the most similar seasons. Their statistics were highly correlated in their unusualness with respect to the other players. Who knew?

So, as you might have guessed, there is a payoff to this post. A Scale of Unusualness doesn’t just identify the best or most productive offensive player; it works equally well on both ends of the scale. The most unusual offensive second baseman in the AL in 2009 wasn’t Zobrist; it was the unfortunate Nick Punto (with Chris Getz closing fast). Punto was much more unusually bad than Zobrist was unusually good. My guess is that when I include defensive metrics, Punto will more than redeem himself. You can’t play from 2001 – 2014 (and win a World Series) without being a big-time player. This one-year snapshot does not do him justice. Maybe I will post the defensive analysis next. Perhaps I will include offense and defense together in a more comprehensive study. Now that I think of it, I should take a break and give those Arctic Monkeys’ CDs another 300 listens.

 

19 Percent…huh?

I spent a lot of time putting together the lone figure in this post. My forthcoming baseball book will be filled with plots like the one that follows. I have known many people whose eyes glaze over when presented with figures or graphs (including professors who should know better). Pay a little attention to this one; you will be rewarded.

Between 2004 and 2008, there was a growing disparity in the payrolls of clubs in Major League Baseball. Lots was written about the unfairness of this. I agree with those who thought it outrageous that one team could spend 8 or 9 times what others could afford to pay their players. Consequently, every season began with plenty of fan bases lamenting the stone-cold truth that their teams had no chance to compete for a title or make the playoffs.

Growing up as a fan of the team then known as the Cleveland Indians, I knew that as soon as a young player started to excel, he was on his way out of town. It was a simple fact that other larger market clubs could easily outbid us for a young star’s services. Such was life in the big city.

Every year, big-money teams seemed to crush the less fortunate, and no one seemed to care. The fact that always got me going was that if a team (think Yankees), signed a player to a big contract and that guy floundered, all they did was treat the signing as a sunken cost and go about their business. Clubs like Cleveland, on the other hand, could be crippled by one bad signing. That is a statement of fact.

So, let’s see if we can gain some insight. One of the great things about a scientific mindset is that we can cut through the narratives and what people think is true, and get at the mathematical heart of the issue at hand. The following figure does just that.

I plotted payroll data from 2004 through 2008 against the winning percentage of all MLB teams. I colored the data points using a playoffs variable to simplify the plot. I think it makes it more interesting and easier to read.

Figure 1. 2004 – 2008 MLB Payroll versus Win Percentage Data.

The scatterplot is basically a blob (yes, the Yankees are in the upper right corner). That means a minimal relationship exists between a team’s payroll and that team’s record, at least for these 5 years. Note the equation in the lower right of the plot. That means a team’s payroll explains only 19 percent of all the MLB team’s record. In other words, there was very little explanatory value in predicting the number of games a team would win if you knew their payroll. The relationship between payroll and record is minimal.

Surprised? Well…if payroll is not a predictor of a team’s success, what is? If payroll accounts for about 19 percent of the outcome; what explains the other 81 percent? I will be looking into that in my book. Perhaps I will find that left-handed middle relievers are the key to success (I doubt it), or maybe if you are putting together a team, you need batters with high exit velocities, pitchers with exceptional strikeout rates, and outfielders who can run like the wind. I am going to try my best to find out.

 

 

A Crush, a Data Viz, and a Book Long Postponed

I have a crush on a YouTuber. There, I said it. I hesitate because there is no chance I would ever approach her and “shoot my shot.”  She is probably half my age…maybe. She might be much younger. I am not delusional; even at my advanced age, I tend to still have my wits about me, so I will choose to keep my powder dry. So, why the crush, and, even more importantly, why would I choose to write about it? Let’s get into that.

Many months ago, I was doing my thing, surfing around the internet in an attempt to find a mathematical basis for the meaning of life (cough, cough), when I came upon an astonishing young woman. Indeed, I wasn’t looking for her, but that is how these things work, right? Most of the interesting things in my life have happened to me while I was standing in a corner, minding my own business, and breathing my own air.

This mysterious YouTuber is a brilliant Ph.D.  in theoretical physics who left academia because…well, that is one of the reasons why she is a content creator. She has many videos detailing why she left the academy to join the corporate world. I was instantly smitten. I was enchanted; I didn’t have a chance to surf away. The deed was done.

Was I instantly attracted to her obvious intelligence? Absolutely.  Was I impressed with her charm and personality? No doubt. And I must say, it didn’t hurt that I found her very attractive.

Immediately after I discovered her, I quit watching her videos. I didn’t need to be reminded of what I was missing while living here in Hillbilly Land. I say from experience and with all confidence that there is no woman like her anywhere near where I live. If such a bright light flickered near me, I imagine we would have crossed paths at some point. As it stands, I have no recollection of such a person. In fact, I just stepped outside and looked up and down my street…nothing. There was a chance she was driving through my town and got a flat tire in front of my house, right? Hold on, I’ll calculate the odds…ah, forget it.

As many of you know, it is much too early for me to reveal the lede (or thesis statement, if you like) as it has not yet been sufficiently buried. Trust me, the payoff is not a bad one. I felt this topic deserved its own essay mainly because I find the whole story unusual and fascinating.

Now, we can leave the present (where I sit overly impressed by a woman I will never meet) and travel back to the mid to late 1980s. The setting is Cambridge, Massachusetts, on the campus of Harvard University. I then was a dude learning graduate-level statistics. Believe it or not, Stem and Leaf Plots and Box and Whisker Plots were on the agenda. Now, kids learn about these things very early. I know a young man presented with these techniques in 6^th grade. There are lots of reasons for this. John Tukey, the great statistician, published the seminal book Exploratory Data Analysis in January of 1977. Things take time to filter down to the mathematical masses. The lack of personal computers had something to do with the lag, as did the fact that high school teachers don’t spend much time looking through statistics textbooks. Also, who paid attention to mathematicians back then?   Who read their books? You get the idea. It was about as many people who pay attention to them now, at least on a percentage basis.

Of course, the bigger problem is how long it takes ideas, even great ones, to trickle down to society at large. An idea must go through levels of bureaucracy before it can be included in a public school textbook. No such stipulations apply to university settings. A professor can read a paper and talk about it in class the next day if they are inclined. I was known to do this a time of two. Not that it mattered; I don’t think my students even cared that they were learning something “hot off the press.”  They just yawned and asked if the material would be on the test.

Back then, and to this day, I spent a lot of time studying Tukey’s previously mentioned Exploratory Data Analysis (EDA). His book greatly impacted the study of statistics in general and proved to be a revelation in my little corner of the mathematical universe. I instantly understood the value of visualizing data in the way Tukey described. I wasn’t the only one, as Box Plots are as common today as bar graphs and pie charts.

Inspired by Tukey, I went on numerous statistical  “deep thinks” back in the day. I derived all the equations, both as an exercise and as a way to convince myself of the validity of the methods. It’s not that I didn’t trust the people who set the foundations of statistical thought; I simply thought it was required of me to do so. Many of my professors and I saw it as a way of paying my intellectual dues. Today, there are applied statistics programs that focus on the applications of the methods; they leave the mathematical nuts and bolts to those studying pure statistics. The applied statistics folks are experts at using the techniques; they don’t necessarily care what is under the mathematical hood. Nothing wrong with that. I think there might be an appropriate analogy with those who opt for English degrees instead of the more popular English Literature track.

A central focus of this post relates to an idea I had one day while studying Box Plots, known as Box and Whisker Plots across the pond, and Box and Dot Plots here. Mostly, they are simply called Box Plots, and that is fine. As I was studying a series of plots, not unlike those in the following figure, it started to bother me that the widths of the plots were not diagnostic; they appeared to be totally arbitrary.

Examine the plot illustrating baseball production by position. I created this in R using a dataset I  compiled long ago. The individual plots show the OPS (on-base percentage plus slugging percentage) for different positions in the American League during the 2009 season. The particulars are unnecessary; I just want you to notice the width of the boxes. You will see they all are the same, imparting no valuable information. In fact, the widths reveal no information at all. Shouldn’t the widths of the boxes change to reveal something about the data used to create the plot? Doesn’t that make sense?

 

Figure 1. Box Plots of 2009 AL MLB Hitters by Position.

I considered this issue and decided that the widths could and should reveal some information. I decided to develop a plot with the attributes of a Box Plot but changed widths depending on the number of observations in the data set at each point along the vertical axis of the box. I thought of them as supercharged Box Plots, or Box Plots on steroids, even though I never got to the point where I tried to name them. More on that in a bit.

My task was straightforward and didn’t require much insight to figure out what to do. I put my head down and made some plots, such as the following ones.

Figure 2. Box Plot of OPS for Second Basemen, AL 2009.

As usual, the nature of the data does not matter. This happens to be an OPS Plot of second basemen in the AL from 2009. I used the same data as in Figure 1. Next comes a histogram made from the same data set. Something interesting happened when I fused the two plots together. I say that with hesitation because I was in the extreme minority in my corner of the world.

 

Figure 3. Histogram of same data.

I rotated the histogram 90 degrees and then mirrored it. I then placed those plots on the box plot. It was a very simple process that required no mathematical insight or leap in intuition.

 

Figure 4. Rotated Histogram

 

 

Figure 5. Flipped (Mirrored) Histogram

I came up with the following. It is simply a box plot with varying widths. I wrote up a short paper and started circulating it among my cohorts, professors, passers-by, strangers, and anyone I thought might have an opinion. The results were disappointing.

 

Figure 6. Histogram and Box Plot.

The typical reaction I got was one of confusion. Huh? Why are you doing this? Why are you here? Why would anyone ever need this? This isn’t necessary (the implication being that I wasn’t necessary). I received no positive feedback. I received no neutral feedback. Everyone who saw my plots hated them. I think some people who viewed my plots felt embarrassed for me. It was a disaster.

I believe it goes without saying that I shelved my “box plots on steroids” project and went on with my life. If I had heard one word of encouragement, I would have developed the idea into a publishable paper.

I didn’t think of it again…until…a few weeks ago. I was using R, my computer language of choice, when I came across something curious. That is not unusual in and of itself; it happens constantly. What caught my attention was an image of something called a Violin Plot. I instantly recognized it. The output was very similar to my old project. Sure, the edges were smoothed, but the idea was the same.

I took a deep dive into Violin Plots. I realized that my idea from all those decades ago was now a common choice for those looking to create a statistical plot or data visualization, commonly known in data analysis parlance as a Data Viz.

 

Figure 7. Violin plot of Second Base Data.

 

 

Figure 8. Violin Plot overlayed on my original plot.

 

Figure 9. Violin Plot of Figure 1.

It is now time for the payoff to this essay. No, the point is not that I came up with an idea that was apparently way before its time. While interesting, I am sure that being attributed with the creation of Violin Plots wouldn’t have changed my life in any meaningful way. As mentioned, their existence requires no great insight or intuitive leap of significant consequence. No, the curious thing is what happened when I went on my deep dive of Violin Plots.

As I searched in an attempt to learn all I could about the newly revealed Violin Plots, I stumbled into a rabbit hole. I fell in face first, and as I dusted myself off and began my climb back to reality, I came across a scathing video by a young woman who HATES Violin Plots. She methodically went through her case. Many of her points were ones I had heard nearly 40 years ago, e.g., they aren’t necessary, it is easier to just use a histogram, box plots are fine, etc…

She also had one major criticism that had never occurred to me. In the last few weeks, I had spent a great deal of time looking at different Violin Plots, and I never thought they looked like anything other than violins. Seriously, I didn’t. The young woman’s main criticism of the plot is that immature males take their shape to resemble something other than a beautiful-sounding musical instrument. Unfortunately, she has a lot of anecdotal evidence to support her claim that these plots should never be used by anyone for any purpose.

I swear to you that what I will now tell you is accurate. If it wasn’t, I never would have written this essay. I almost feel stupid writing this because I am sure most of you have figured out that my YouTube crush and the young woman who hates Violin Plots are the same person. I would never have written such a scenario in a work of fiction because it sounds too contrived, yet here we are. I’ll slowly shake my head in disbelief as I crack open a beer.

What about the book, the one referenced in the title? I am guilty of more than a little foreshadowing. Yes, it is a book on baseball analytics. I started writing it in 2002. It got put off because I was compelled to write another book in its place. That entire book, The Athena Chapters, is posted on this site. My long overdue baseball book will be completed relatively soon, and much to the disappointment of my YouTuber, it will be full of Violin Plots because I find them diagnostic and beautiful. I know she disagrees, but I don’t see us arguing over their utility and functionality at some fancy dinner party. I’ll apologize in advance, place the plots where I want, and take my chances.

 

 

 

I am broken…defeated. I fought the good fight, but I lost. Better people than me have experienced a worse fate.  The future I always had planned for myself is dead.  There is nothing I can do about it.

As some of you know, I spent my best years at Harvard University. I was there for about 6 years. Those are my ‘good old days.’  I still dream about the basement I lived in across from Tufts University.  For a time, I had a lab at Vanserg Hall.  It was miles away from my little apartment, but I used to walk. The entire area is charming.

I didn’t want to leave. I really didn’t want to leave. The Harvard community calls it “Exile from Eden” for a reason. They kick you in the butt, give you a mission, and tell you to go.  For the most part, you have to go.  The first time I graduated, I stayed and got another degree.  They really wanted me to leave after that, and so I did.

I like telling people about how remarkable that place is. I could easily sit down at a table with nine other people and know that there was an excellent chance that I was the tenth most interesting person there. Where I live now…not so much.

I have been thinking about Harvard because I am getting old. My brain has betrayed me. I don’t have trouble learning anything, but retention is a different story. Sometimes, I can not remember what I studied five minutes after getting up. That might be the main reason I study so much. Perhaps I am in a constant loop and have no clue. I do know I still love learning. And that brings me to Cliff Stoll.

I have written about the great Cliff Stoll, an astronomer who makes Klein bottles. He is a national treasure. Seek out his TED talk (The Call to Learn); he is a force of nature. He made one of the most profound statements I have ever heard during that 17 minutes. He said that if you do something once, you are a scientist; if you do it twice, you are an engineer; three times makes you a technician. I would add that the fourth effort makes you a trained monkey.

Stoll was talking about the mindset of a scientist, those true-born intellectual explorers. Once scientists have done something, they aren’t interested in ever doing it again. The appeal is to move on to the next problem. What else is unknown? Confirming someone else’s discovery is uninteresting.

One of the great tragedies is when a scientist, through circumstance or bad luck, is forced to do repetitive, soul-crushing monkey work for their entire working life. If you were not born with the spirit of a scientist, I imagine the monkey work is a little easier to take. For the scientists, even those doing the work of an engineer, it is heartbreaking.

Is there a point to this short post? Sure, as always, I like to bury the lede. I want to plant it deeper, but I am tired, worn out. As I said earlier, I am broken.

I fought the good fight; I really did. Some dreams die hard, and I am still shocked that mine passed away. I am shaking my head at the prospect of a dreamless future. I am disappointed. I need more time to reflect on this.  I will wake up tomorrow knowing that I need to win the lottery if I ever want to pursue my life’s work.  I do not anticipate winning the lottery.

At Harvard, people would often ask what equation would be on your tombstone or what the first line of your obituary would say. Yes, it really is that kind of place. As I have gotten older and my abilities have faded, I find myself thinking about that ‘contribution to humanity’ we were supposed to make. They were serious about it. We were all tasked with making the world a better place. It never occurred to me (until now) that I wouldn’t leave the world a better place than I found it.

I have yet to make that contribution; I haven’t done anything substantive, at least not in my eyes. That might be one of the reasons I have not set foot on that campus in over 30 years.

Some of you would disagree with my assessment, but I am the only true arbiter of success or failure. Just as you are with your life. No one else’s opinion is of any consequence.

I have been busy, I have written 16 novels and books under various pennames, but none are extraordinary. One was really good, but that contribution the Harvard people told me to make remains elusive.

I always knew I would spend my last years writing that great novel, the work representing my contribution. I worked hard to put together a plan that has been in place for decades. I was going to get a little place in Portugal or in South Africa, and I was going to drink some warm beer and write…a lot. I would leave behind a record of what it was like to be me.  Now, I am hurt.  If I believed in a soul, I would say mine is wounded.

Pushing my attempt down the road was not ideal, but I had little choice. I kept getting up every morning because I knew the day would come when I could sit by the beach with my computer or notepad. I would fight off inferior insights as The Muses battled for my ear.  That is not going to happen.

I have told friends I prepared for every eventuality except what has now befallen me. The universe broke me. Of course, I always knew it was indifferent to me, but it has been known to go way out of its way to make me feel its destructive power. The evolutionary biologists at Harvard used to constantly remind me that the universe is not cruel; it is simply indifferent.  They had to keep telling me because I had difficulty believing it.  I still don’t know what to think.

I won’t be going to Portugal or Africa. I will remain here in Hillbilly Land, a scientist stuck in the monkey clutches of an apathetic world. The hows and whys of my plight are uninteresting and don’t matter.  I must find a new reason to lift my head from the pillow.

The odds of me writing a great novel while stationed in Hillbilly Land are nil. I can’t fake inspiration; unfortunately, this is this continent’s most uninspired piece of land.   Hope does not spring here; this is where hope comes to die. This town is depressing, the people are (predominantly) uninteresting, and the weather is terrible.  I do not understand where I am supposed to draw the inspiration from.

I will let out a sigh as I contemplate my fate. I am sorry for all the people back in Cambridge who believed in me and expected something substantive. It is unlikely that is going to happen. The New York Times will certainly not notice my demise. As for that tombstone, burn me and throw me to the wind.

The Greatest Tweet

I am not a big social media guy.  I do have a Facebook page, but it is under a pen name.  I never visit it and don’t believe I have ever posted on it.  I don’t feel social media is necessary for me; if I have anything to say, I can speak up here.  Of course, this blog is also written under a pen name.  I guess the real version of me has very little to say at all.

So, how did I come across a tweet?  This one made national news, and I instantly realized why.  When the Twitter platform was conceived, the creators could never have imagined something so wonderful and insightful delivered through their code.  And yet, here we are.  I admit I am a novice and have no Twitter experience, but I can say with certain authority that what follows is the greatest tweet in history.  Let me slightly amend that; this is the most fabulous tweet possible.  People who tweet in the future can only hope for second place when the history of great tweets is written.  Behold…

This image was shared the other day.  The left-hand side shows 13 metrics that the team uses to evaluate players.  In the lower right, they tell us that the team assigns each player a score from 0 – 100 based on their analysis.  The revolutionary aspect of this visualization is the “analytics cylinder,” complete with the Bears’ logo.  I have been studying analytics and data visualization for decades, and honestly, this is the greatest thing I have ever seen.

Think about this, did the Bears risk giving away any proprietary information relating to their analytical process?  If anything, the Analytics Cylinder (yes, that deserves to be capitalized) creates more mystery.  What exactly are they doing?  Are they using Python, SQL, and R, or alien technology from Area 51 in concert with quantum computers to pick the most promising players for their team?  I don’t know, but I am interested.  Before I saw the tweet, I didn’t care one bit about how the Bears went about their data analytics.  Now, I assure you, I can’t get enough.  I might set up an account just so I can follow them.  Who knows, maybe a Database Rhombus is next.