Archive for the ‘ Statistical Matters ’ Category

The Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge (Part 5: Conclusions)

And now it’s all over.  Who wins?  Is there a true statistical difference (in terms of statistical science, not just “football stats”) between Hall of Fame quarterbacks and non-Hall of Fame quarterbacks?  Click the link below and read the final segment of this five-week investigation of the differences (and even similarities) between quarterbacks enshrined in Canton, Ohio, and those who hope to one day be immortalized in bronze.

Hall of Fame QBs (part 5: Conclusions)

With our 2010-2011 NFL Season countdown complete, we can watch the season’s opening kickoff and wonder which quarterback will rise to the occasion and lead his team to glory and who will fail to reach – and win – the grand prix of football: the Vince Lombardi Trophy.

Cam Suarez-Bitar.

Thank you all for participating through comments and emails.  This has been one of the most entertaining and fun projects I have worked on throughout the opening years of my career in sports administration/marketing.  Successful completion of this project would not have been possible without assistance and guidance from Dr. Carolyn Nordstrom, Associate Provost for Campuses of Kaplan College and Professor of Statistics at the Northwestern University Master of Sports Administration Program.  This project stands as a tribute to all that I learned from her a couple of semesters ago when I enrolled in her stats class.  Even though this project could mark the beginning of a master’s thesis or dissertation due to the intricacies of statistical analysis, it allows us to examine how the application of statistical principles affects the decision-making process.  Here, the decision is “to induct or not to induct.”  That is the question.


Oh Archie Manning... so many hard years in New Orleans when the Saints were the "Ain'ts." His skill was not enough to move a sub-par team to glory; nevertheless, it was his skill, talent, and grit that earned his peers' and opponents' respect. Jack Youngblood, Hall of Fame defensive end of the Los Angeles Rams, routinely pounded Manning as the quarterback's protection broke down all around him. Still, Youngblood's admiration for Manning defines true sportsmanship, for he was reputed to always help the embattled Manning get up after dealing him a solid, mind-scrambling tackle. In spite of his efforts, Archie Manning is not in the Hall of Fame and remains somewhat of a long shot for induction because of his teams' unsuccessful campaigns year after year.


The Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge (Part 4)

Week four.  The heavy stats are here.  Now we are in deep and things begin to look a bit hazy at this point; admittedly, as I worked on this project, I was often confused by what I saw!  Just click on the link below and see how statistical models help determine what factors contribute to an NFL quarterback’s Hall of Fame status.  Apparently, some factors are redundant and should not be included in a specific set of metrics used to determine induction.  In other words, a strong and valid argument for – or against – an NFL quarterback’s induction to the Pro Football Hall of Fame should depend on truly independent variables and not those that conveniently fit one side of an argument (i.e. bias should be avoided at all costs).

Hall of Fame QBs (part 4)

So, some of these graphs and tables look even more cryptic than last week’s set.  This is the point of my research, though, that helps us isolate which factors included in my project are worth considering and whether or not they should be used in conjunction with one another in arguments on Hall of Fame induction.


Cam Suarez-Bitar

Thank you all for your participation.  Remember that if you have any questions or comments, you need only post them or send me an email.  Keep them coming.


Phil Simms - here about to get rocked by Hall of Fame Chicago Bears linebacker Mike Singletary - led the 1986 New York Giants to a Super Bowl title. Even though the 1986 Giants are one of the greatest teams in NFL history, their defense receives the lion's share of the credit. Simms' performance in Super Bowl XXI earned him MVP status as he completed 22 of 25 passes and set a Super Bowl record for best completion percentage. Consistent - when not hurt - and efficient, Simms' career numbers and Super Bowl champion status make one wonder why he is not in the Pro Football Hall of Fame. Perhaps the fact that he did not necessarily help redefine the image or role of the NFL quarterback to some degree has kept Canton, Ohio on his "to do" list.

The Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge (Part 3)

The 2010 pre-season is becoming all the more dramatic with Brett Favre’s decision to stay one more year (very good) and the New York Giants and Jets counting down the days to their home openers at the new Giants Stadium.  Still, we all know that this is the year that Dallas takes the title (fingers crossed).

So, we have arrived at week three of our Pro Football Hall of Fame challenge.  So far, the Hall of Fame quarterbacks have not demonstrated a “significant statistical difference” between their numbers and those posted by 33 of the greatest quarterbacks who enter the Hall as guests only.  In the previous section, we discovered that league championship winners comprise around 75% of the Pro Football Hall of Fame QB list and that both samples are 1) large enough to conduct a good statistical analysis and 2) normally distributed.

This week, we begin to see more sophisticated statistical models in the analysis.  With descriptive statistics, a couple of hypothesis tests, and the beginnings of a statistical regression, we derive a fairly good body of evidence that will help us arrive at our final conclusions.  This part gets heavy, so if you have any questions, please feel free to ask at any time.  Statistics can get cryptic, especially when you begin to deal with confidence intervals and tests, p-values, and residuals, to name but a few more statistical concepts beyond basic averages and means.

We have good samples and plenty more to cover, so let us press on to part 3 of the Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge!

Hall of Fame QBs (part 3)

Cam Suarez-Bitar.

One final note.  On Tuesday 29 June 2010, Randall Cunningham’s 2-year-old son, Christian, drowned in a hot tub and passed away shortly after arriving at the hospital.  Our thoughts and prayers are with the Cunningham family and may the Creator guide them with a loving hand through this tragic and terrible ordeal.


Randall Cunningham, one of the greatest quarterbacks of all-time, revolutionized the position by passing, punting, and running the ball like no other QB of the modern era. His elegance and grace along with a steely determination to succeed and keen improvisational skills made him a sheer joy to watch on Sunday (and Monday). Though his career stats underscore the intangibles he brought to the game, somehow he is not in the Pro Football Hall of Fame.


The Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge (Part 2)

We have arrived at part two of our statistical analysis comparing Hall of Fame quarterbacks with 33 of the best who played the game and have yet to be enshrined in Canton, Ohio.  Here, we will look at a few graphs that reveal trends in both categories addressed in this investigation.

Please click on the link below to open part two of my project.  This segment, along with all others, will be posted as a .pdf for your convenience.

Hall of Fame QBs (part 2)

So, sit back, relax, and enjoy this week’s installment.  Remember, ask questions and add your point of view any time.

Cam Suarez-Bitar.


Unsung hero of the Miami Dolphins' undefeated season in 1972, Earl "The Pearl" Morrall, is not in the Hall of Fame.

The Pro Football Hall of Fame Quarterback vs. Non-Hall of Fame Quarterback Challenge (Part 1)

It’s late Tuesday evening and we are that much closer to Kickoff 2010.  To celebrate the beginning of what will prove to be yet another great football season, we will count down the remaining five weeks to the season’s opening kickoff with a five-part series of weekly posts that will explore the reasons why some NFL quarterbacks are enshrined in Canton, Ohio, and others patiently (or impatiently) await induction.  This is not your standard barroom discussion or argument over who is the best quarterback of all-time (<cough> Johnny Unitas! <cough>).  Rather, this five-part series represents weeks of extensive statistical analysis I performed a few months ago that offers explanations as to why, for instance, Joe Namath is in the Hall and Earl Morrall is not.

Furthermore, we will look at exactly how relevant player stats like career passing yards, career touchdowns, career completion percentage, and career Super Bowl victories are to the Hall’s answer to Shakespeare’s great question: to induct, or not to induct.  I am sure you also know by now that “The Bard” himself, yeah old Billy Shakes, was a rabid Dallas Cowboys fan, too.

This week, we begin with an introduction to the problem at hand.  Next week, though, we will view our first descriptive statistics.  Please open the .pdf file by clicking the link below.  For your convenience, I included an appendix with my data set and samples at the end of each weekly installment.

If you have any questions, concerns, or anything else to add, please feel free to post your comments here or send me an email.  If you use any material I present herein for academic purposes, just be mindful to write proper citations and/or contact me directly through this blog or email.

The next few weeks will feature heavy statistical theory and relatively complex models and I would be more than happy to explain anything you may not recognize – it can get a bit cryptic.  Lastly, I paid close attention to detail and did my utmost to provide a quality product; nevertheless, should you catch a mistake, a correction would be much appreciated.


Hall of Fame QBs (Part 1)

Cam Suarez-Bitar.

Thank you all for reading my blog and for your continued support (by the way, Mr. Bryant, an article on fantasy football will follow our five-week trip through the wonderful world of statistics and the great Pro Football Hall of Fame).


Surprisingly, Ken Stabler has yet to be inducted to the Pro Football Hall of Fame.



If You Know Statistics, the Truth Will Follow.


Marketers, journalists, and a great many people in general throw statistics around like eggs on Halloween.  Those commercials on how coal is “the future” and how it is clean come to mind.  In fact, the conservatively dressed woman hired to convince the public – sound bite after witless sound bite – by stating that “most Americans” agree that coal is good for the environment uses an empty and unsubstantiated statistic in her harangue.   The coal lobby then throws an unlabeled pie chart in the viewer’s face that features magnified, smiling, “average-looking” people standing in the largest slice to drive home the point that you should support them because a majority of Americans believe in the alleged benefits of coal as an energy.

Statistics serve no other purpose than to draw inferences about the truth – a truth that depends both on the statistician’s ability to study a population and the size of the population itself.  When parameters (values drawn from studies of a population) are much too difficult to attain, statisticians redirect their efforts to derive a statistic instead.  She is left with the option of taking a “good” sample and drawing inferences on the population it represents.   Tips on how not to get bamboozled by vague statistics we find in the media and at work is the focus of this week’s article.

What the… I Thought This Was a Website on Sports Business?

It is, though success in all types of business depends on a thorough understanding of statistics and what they mean.  If you are not familiar with the fine art of inference that is Statistics, it would certainly help to have someone on your team who does.  Imagine for a second that you manage a tennis league and are looking for the right shoe sponsor.  The world’s largest shoe company tells you “hey, we have a great shoe that boasts a median life expectancy of 80 matches according to our latest tests.  We’ll sell you each pair at cost if you put our logo on all of your promotional items.”  Ah, fantastic!  The largest shoe company in the world wants to sponsor your league!  Life just couldn’t get any better, could it?

It’s not that simple.  That’s when you ask how they arrived at the 80-match number.  “Well, we tallied the number of matches each tested pair lasted before the sole completely wore out and listed them in order from least to greatest.  We found the number in the middle and determined that we expect our shoes to last 80 matches,” says the shoe rep.  Though the figure is impressive, one must always ask for the range and size of the sample when dealing with medians.  After all, they could have tested only 11 pairs and had the following results (measured in matches): 40, 45, 45, 47, 73, 80, 81, 84, 84, 85, 85.  Now, the median is not so impressive since we have discovered in this hypothetical example that the measurements are skewed.  Conveniently, you were told just a median and had you not asked for at least the range of measurements (40-85) and the sample size (11), you would not have known that the statistic you were given was terribly inaccurate but served the shoe rep’s purpose!

Averages are another story altogether.  Let’s say that you work for your favorite local roller derby team in their marketing department and you are averaging the amount of revenue the team generated in merchandise sales in the 2009 season.  You calculate the mean (another word for average) and determine that the team raked in an average of $1950 per bout in merchandise sales.  Your numbers could be thrown completely off if you included outliers (quantities way out of the norm… there is a way to determine which figures, if any, are outliers in your sample space, by the way) that brought your numbers way up or way down.  In this example, your team actually generated $1500 per bout, but due to the fact that you included the one day that your staff sold $2900 in your calculation of the mean, your average now looks more like $1950 per game.  This leads to faulty measurements of your team’s performance and may cause you to form an inaccurate assessment of your sales initiatives (among other possible errors.)

Now, after 10 years in roller derby you are hired by Hendrick Motorsports to make sure the garage always has enough spare parts, but not too many.  Your first duty is to choose and buy tires the team will use during practice and in every race next season.  The manufacturer claims that all but 2% or less of their tires will last an average of 50 laps.  Ah, but you are skeptical.  All those years of working long hours in obscurity have paid off, haven’t they?  Good job.  You tell the manufacturer that you would like more evidence.  The day after, their sales rep sends you information on the number of tires they tested, the conditions they were tested in, how much the measurements vary from one to the other (otherwise known as standard deviation), and they even tell you how they managed to get a “good” sample and the measurements are normally distributed (when graphed, they form a bell-shaped curve… that’s very good for your purposes, by the way).  When you run those numbers through the equation in the Central Limit Theorem, which is used to compare the mean of a sample to the mean of all the means of all the measurements drawn to answer a question, you discover that their “2% or less” guarantee was a gross exaggeration.  In fact, the probability that 7% or more will not last 50 laps is significantly larger than the probability that only 2% will wear out before their time.  The team thanks you and you remember why you chose this career over creating the layout for each year’s swimsuit issue.

The Point

Statistics are used to draw inferences when the truth is inaccessible.  The truth and related inferences are very powerful and, believe it or not, are often used incorrectly on many levels.  The decision-making process depends on one’s intuition and empirical data; exclusive use of one or the other can lead to undesired results in many cases, though.  When you are presented with a statistic, always ask (at the very least) who or what was measured, how many, why they were included in the study, who performed the study.  Often, we are fed very vague and poorly calculated and derived statistics that work only to further someone’s agenda.

Be very careful when basing an important decision on a statistic.  When dealing with a survey, one simple question you can ask is whether or not the sample was self-selected (respondents chose to participate on their own) since such participants tend to hold extreme or biased positions on a subject and will compromise the sample’s ability to represent the entire population (i.e. when polling your fans, you do not want only the opinions of the die-hards if you wish to understand your entire fan base altogether).  It is also good to ask how many people participated in the survey or poll (i.e. asking for the sample size.)  You could also ask if sampling was done with replacement (whoever was randomly chosen from a group to participate in a study is “replaced” in the pool and could be chosen again) as that could affect the variance of the answers you receive.  Lastly, you could ask if participants were part of a systematic random sample in which, for example, every 50th random passerby was chosen to participate in a survey.

So, the next time you hear someone make a claim based on a statistic/probability, ask as many questions as you can.  You could be reading a pie graph that tells you the respective proportions of general managers who who felt that certain specific issues must be addressed by the Chicago Cubs’ new ownership before they return to the World Series, but without knowing which teams or leagues those general managers work for and if they understand the sport and business of Major League Baseball, you probably should not attach too much importance to that graphic until you know more.  Finally, the next time you watch that commercial by the coal lobby or anyone else who misuses a statistic, ask yourself how many people out there would believe such claims without asking for more evidence.  It would not hurt for the source to be credible, either.

Basically, when dealing with a population much too large to study, we use a sample (a statistic) to draw inferences that will bring us closer to the truth.  When interpreting a statistic, critical thinking is key.

Cam Suarez-Bitar.

Thank you for your readership.  By no means was this article a comprehensive analysis of either the complexities of statistics or the decision-making process in sports business.  It was meant to introduce the reader to the important role statistics play in the decision-making process and how to avoid being tricked by either careless or unethical advertisements or claims.  Hopefully, this week’s article will make the study of statistics seem more relevant.

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