The study took a considerable sampling of data and integrated a comprehensive review of previous research. It focussed on whether or not replacement managers were able to generate increased output from individual players.

I’d read some reviews of the study prior to actually reading the study itself (waiting for adobe to update), and most of the secondary commentary seemed to take the study’s abstract conclusions and run with them without bothering to look into the text, which appeared, (at least to this reader) to be speckled with disclaimers and reservations. To be fair, numerous readers took issue with the fact that by looking strictly at individual performance the study neglected the ultimate benchmark — winning — which is potentially problematic because winning and individual performance don’t always correlate. By looking strictly at performance, however, you eliminate strength of schedule as a factor, not to mention the impact of injuries, trades, changes in batting order and so on.

The author estimated the impact of managers on player performance using a sample of major-league baseball players from 1980 to 2009, available from Baseball-Databank.org. He estimated Equation 1 using the Baltagi and Wu (1999) random-effects method, which corrects for detected first-order serial correlation.

(1) Performanceiy = γ Manageriy+ β1 League Performancey + β2 Career Performancei + β3 Ageiy + β4 Ageiy2 + θ Parkiy + νi+ εiy

Performance is the individual performance of player i in year y. Manager is a vector of individual manager dummy variables. For hitters, performance is measured using on-base-plus-slugging (OPS), which is a simple metric for measuring how effective a hitter is at producing runs. For pitchers, performance is measured using earned run average (ERA). The coefficients for the dummy variables in vector γ should reflect the impact that individual managers have on player output. League Performance is the league average OPS for hitters and league average ERA for pitchers. The league average controls for fluctuations in run scoring in the leagues may cause deviations in performance across leagues and over time. Career Performance measures the quality of the observed players by averaging the performance of each player over his entire career, which normally spans several managers.

Of the 134 managers in the sample, the estimates for 25 managers are statistically significant at the ten-percent level for hitters. 21 managers are associated with improvement and four managers are associated with a decline. For pitchers, the estimates for 24 managers are statistically significant at the ten-percent level. 15 managers are associated with player improvement and nine managers are associated with a performance decline. Five managers are associated with improvement and decline for both groups; however, in all cases, the managers are associated with the opposite effect for the two groups of players. Thus, no manager is associated with improving performance for both offense and defense.
The results indicate that if managers have some influence on player performance, the impact is small and difficult to identify.

Baseball Between the Numbers, analyst James Click also tried to tease some signs of managerial impact out of statistical record but came up empty. After examining the measurable effect of in-game strategies (bunting, stolen bases, intentional walks), wins and losses relative to run differential, playing time distribution, in-game substitutions (pinch-hitters, relief pitchers, and defensive replacements), and direct impact on player performance (coaching), Click was unable to find evidence of a repeatable skill in any one of those five areas for any of the 456 managers he studied. That is to say that, much like clutch hitting, individual performances varied so much from season to season that the results appeared to be as much the result of chance as anything else.

There was also the 2006 study by James Cliff in which he stated:

“Only six times in thirty-three years has any manager used sacrifice attempts, stolen base attempts, and intentional walks to increase his team’s win expectation over an entire season. Even the best managers cost their team more than a game per season by employing these tactics. At worst they can cost a team three games per season.”

Finally, Chris Jaffe wrote a definitive and comprehensive analysis of Manager competency and effect in his book, Evaluating Baseball’s Managers. A History and Analysis of Performance in the Major Leagues, 1876-2008. (highly recommended). In it he more or less shows that good managers don’t have much of an effect, and even bad managers don’t do as much harm as you might think.

Independent of whatever considerable support the argument that managers don’t matter may have, I still have my reservations. In the first study above, OPS doesn’t correlate with winning as much as several other statistics, namely runs scored, but even more troublesome is one very problematic variable – the unearned run.

Unearned runs are not like other runs, they are the neglected stepchildren of baseball, they are the runt of the litter that nobody wants, they are the ugly babies that the Spartans would throw off a cliff. The problem with unearned runs is that outside of an error here or there, no one is truly accountable for them. Defensive metrics being what they are, a study that looks at changes in offensive output and ERA doesn’t control for runs that cross the plate that are unearned. The unearned run can be a death knell in the late innings. It can also be an indication that defensive alignment isn’t what it should be, fundamental defensive practices may not be in place, and players may simply not be well coached.

Defense appeared to be lumped together as one of many variables affecting ERA. Compounding this problem is the fact that the unearned run may be a direct result of poor defense. This omission renders  an entire competitive dimension (team defense) inconsequential, when any fan can tell you it is not.

You can make a strong argument that while a player can rise to the major leagues on a given set of abilities such as hitting and throwing, defense, perhaps more so than any other aspect of the game, may be a reflection of focus, preparation, and most importantly (for purposes of this argument), effort … These traits are inexorably linked to good coaching. Bear in mind it was defense (among other things) that let the Mets down in the late innings of the series vs. the Marlins. Defense may in fact be the greatest litmus for a manager’s overall effectiveness, but in the end how do you measure a leader’s ability to get his players to “run through walls” for him?

There are countless anecdotal narratives that run contrary to the claim that managers don’t matter — there have been numerous cases where a change has resulted in a dramatic turn-around. Buck Schowalter of the Orioles as recently as 2011 changed his team’s fortunes almost upon his arrival. Could it be that given a manager’s already marginal impact on the field of play (as shown in multiple studies) it takes a really tremendous manager to actually effect a turn-around? Well, if this is true, and it may be, it would undermine the broader argument that manager’s don’t make much of a difference no matter how good (or bad) they may be because it would imply that while most managers are mediocre (which is why broad based studies show a negligible effect) the exceptional ones do make a difference.

In the end we are left with the possibility that a manager’s effect may not be measurable by any conventional analysis. How do you measure inspiration? How do you quantify cohesion? How do you control for that little bit of effort that could be the difference between a run saved and another loss?

As much as Bill James and Nate Silver and others have tried to quantify managerial performance, it is an elusive and ethereal component of the game that is far too complex to pin down with metrics.

I’ve always gotten the sense that Sandy Alderson ascribes to the premise that managers don’t really make much of a difference. Even great managers after all make about as much as a middle reliever. In a baseball landscape where value in wins has become a catchphrase, the numbers people will tell you that the difference between a truly extraordinary manager like a Herzog or a Cox or a Hodges, and an Art Howe is maybe 3 to 5 wins over the course of a season, if that …

My contention nevertheless resides primarily in all those intangible inspirational and interpersonal aspects of the game that numbers can’t speak to, warm fuzzies such as determination, camaraderie, and above all, hope. There is nothing like giving a band of dejected players immersed in patterns of learned helplessness the gift of believing in themselves and in each other. How do you measure that? And yes, that sounds like something only a select few exceptional leaders would be able to pull off.

It’s always been a glaring paradox to me how an organization that in practice may marginalize the role of the manager, will, with the same breath trumpet the importunate significance of a good development program. Your manager, and your development program, are in practice part of the same operational system. In the end, having seen exceptional coaches take rag tag assemblages of high school kids and transform them into champions, I can’t in good conscience ascribe to the notion that coaches, managers, and leaders, don’t play a role in that effort. They matter, but they matter in ways that relate more to the spirit than they do to numbers on the field.

As Vince Lombardi once said:

“The spirit, the will to win and the will to excel – these are the things that endure and these are the qualities that are so much more important than any of the events that occasion them.”

Bill James

The popularity of the statistic among fans soared after Bill James and numerous others lobbied for it in the 1980s and 1990s. THere reasoning is this: a player making an out drastically reduces a team’s odds of scoring a run. They also argued that not making an out increases your team’s chances of scoring. In other words, they said getting on base is the most sure-fire way to score runs.

A large portion of the Met fanbase has been enraged by Sandy Alderson (a well-documented supporter of OBP) and his use of advanced statistics, especially when it comes to patience at the plate, walk rate, and on-base percentage. Through his comments in the media, he has lobbied numerous times for the stat, despite the displeasure of so many fans. That led me to conduct a little research on the effectiveness of the stat.

For my sample, I used every team’s stats over the last ten seasons (2003-2012). It’s a sample size of 300 different teams, which I felt would be enough to give an accurate measure of how OBP correlates to runs scored. Every little dot on the graph is one team, such as the 2007 Diamondbacks.

First, I wanted to look at something simple, like home runs. Teams with more power tend to score more runs, or so it is thought. Over the past ten years, the results were rather surprising.

There clearly is some type of correlation between home runs and runs scored, but it is much weaker than I thought it would be. Obviously there is a correlation because frankly, if you’re hitting 240 home runs in a season, you are going to score a ton of runs. However there are many cases in which teams have not followed this trend. So home runs do not directly correlate to runs scored, although they influence it.

Next, I wanted to see how this compares to On-Base Percentage, and whether it has a stronger effect on the number of runs a team scores.

Here, of course, you can see a very clear correlation. Teams that get on base more, teams who have batters that make outs less are more successful, and score more runs. However, you can obviously see that it is no perfect indicator of runs scored. Why is that so? Looking at last year’s rankings for both OBP and team runs scored, I saw that they were similar, but not in the same order.

The key thing to remember about On-Base Percentage is that it is a very rudimentary statistic. It has a very basic formula, and doesn’t take into account the values of the ways a player can get on base. Slugging percentage attempts to, but fails to put an accurate vale on each way someone can reach base. Is a double really worth twice as much to a team as a single? Is a walk worth three times less to a team than a triple? Over 100 years of baseball tells us that is not the case. That’s where wOBA, or weighted On-Base Average, comes in. It assigns a value to each way a hitter reaches base. How can they just choose those values? Doesn’t that make the stat arbitrary? The coefficients of walks, singles, doubles, etc. are based off the added probability of a run being scored. It’s essentially a bulked-up version of On-Base Percentage, and is even more effective at predicting runs scored.

I took the same date from earlier, the 2003 to 2012 seasons, and applied the same method I used for home runs and On-Base Percentage, only replacing it with wOBA.

Even a metric as well-developed as wOBA has its flaws, but it may be the most accurate in determining how many runs a team will score. There are still imperfections, but it’s about as close as we can get right now. Opponent defense, the offense’s ability to steal bases, managerial moves, and more all tweak the results a bit. This is no super-stat. There are none. But they are very effective.

There are always going to be exceptions to the rule, especially in a sport as complicated as baseball, but On-Base Percentage and more complex versions of it (wOBA) clearly correlate to runs scored, and are not by any means, meaningless statistics.

Once upon a time, baseball was a simple game. The goal is to score more runs than your opponent. Each team is given 27 outs to score as many runs as they can. In order to score runs, a team’s players have to get on base. Once a player gets on base it was the other players’ jobs to drive them home to score runs. On the other hand, the defense’s job is to get 27 outs allowing the opposition to score the fewest amount of runs. Whoever scores more runs in 9 innings of play wins – simple.

Now let’s fast forward to the 1980s. The 1980s were famous for Nintendo, big hair bands, Reaganomics, and the invention of rotisserie baseball.

Fantasy baseball exploded onto the scene in the 80s, and the men that played this game were looking for ways to build better teams. They wanted to build better teams in order to take home the lucrative prize money that came along with winning their rotisserie league. They used different combinations of stats to form equations, which in return would spew out which players they should select on their team.

Yes, the advanced stats that the game uses today were ultimately developed by men that maybe never even played the game. They were simply looking to build better fantasy teams. It leads the people with advanced knowledge of how the game is played on the field to butt heads with those that sat at their desk and computer doing all the math.

Photo Credit: bronxbanterblog.com

Bill James, the father of advanced statistics and sabermetrics, didn’t start to gather a serious following until the mid to late 1990s. Until then, he published his yearly baseball reviews and would sell 500 copies per year if he was lucky.

The game wasn’t ready for the story he was trying to tell. James was basically telling everyone in the game that they have been looking at the game improperly for nearly 100 years. Advanced statistics were born. He broke down nearly every single aspect of the game, except defense, which he was never able to develop an accurate statistical rating for.

But did the game really need the advanced statistics?

The game had survived over 100 years just fine without advanced statistics. But now, in every team’s organization, there are mathematicians working in this area. The question is, is it really necessary?

The reason why sabermetrics and advanced statistics took over the game in the 90s is because that is the when player salaries started to get to the point where some sort of projection and analysis was needed. Owners wanted to know if it was really worth it to spend the money on player X.

Baseball had officially become a full-fledged business.

In every MBA program across America, students are often required to take a course dedicated to statistics and spreadsheet analysis. The students are taught how to use Excel spreadsheets and programs like Risk Solver to make business decisions. If you are under the assumption that the CEO of a big company makes decisions based on his/her gut you are mistaken.

More often than not, the decisions are made by a computer than runs simulations based on the data that the decision maker inputs. The program takes all the data and then it gives you the most logical decision after running all the simulations.

It’s actually pretty cool. You could build a model that can tell you the best location to build an ice cream shop, based on three different locations, with three different average yearly temperatures, three different traffic patterns, and three different populations in the towns they’re in. Not only will it tell you the best location to build your ice cream shop, but the expected revenue at each location.

The same thing can be done with baseball players – in theory that is.

Everyone knows that Billy Beane and Paul DePodesta’s use of advanced statistics and sabermetrics officially put them on the map. Their use was chronicled in the book Moneyball by Michael Lewis.

The book that exposed Billy Beane’s strategies.

Beane used the advanced statistics to remain competitive with a team that had the lowest payroll in the league. Once he started winning, people started to question how the heck the Athletics could be winning when they were only spending one-third of the money of the other teams. At that point, every Tom, Dick and Harry fell in love with sabermetrics.

Sabermetrics became the key to unlocking hidden baseball talent.

But here is the fundamental flaw with peoples’ understanding of what Billy Beane actually did – Beane wasn’t intentionally trying to win by spending the least amount of money he could. Beane wanted to spend money. He wasn’t trying to do his owner a favor by spending the least amount of money on building a team. He was simply in a situation where his hands were tied. He had to think outside of the box. He had to get more efficient with spending what little money he had. That’s it.

Somehow Beane’s strategy became an excuse for teams to spend less money, and try to build teams using a philosophy that Beane only developed because he had to and not because he wanted to.

Players are now investments, plain and simple. If a team is going to make an investment, the projections, spreadsheets, models and simulations have to all tell the same story – that the player is worth the investment.

However, there is a problem with advanced statistics – the game is still ultimately played on the field. You cannot remove the human element from the game, and no statistic can factor that in. And while past performance is a good indicator of future performance, there is only so much weight that advanced statistics should carry.

Advanced statistics paint an imperfect picture of the game when used improperly. Here is why:

Advanced statistics use inputs which are plugged into an equation and are determined by the person developing the statistics in order to arrive at a desired outcome. They often have to finagle with different stats until they get an answer that makes sense. What also comes into play is the developer’s bias.

If someone is playing with stats in order to make their equation work, how is that more accurate in telling me which player is better than if I used the old school statistics (OBP, AVG, ERA, etc.) which have been used for the past 100-plus years, and my eyes, used to watch the players play?

Let’s take a look at the Holy Bible. There is a show on TV that comes on one of the learning channels every once in awhile which basically alludes to the fact that the bible has a hidden code in it, which not only predicted things that happened in the past, but also can predict future events. Now on the surface, they did prove that there was a code in the bible. But is there really a code in the bible, or was it manipulation by the developer to come to a desired goal/outcome?

Odds are there isn’t a code in the bible, but this just shows how the manipulation of data can get to a desired outcome when played with long enough. One of the major issues with scientists to this day is trying to conduct scientific studies and not have their bias come into play. Bias alters outcomes.

The bottom line is that baseball is still a game where there is still a lot of luck involved. For instance, if a player is half a step to the left or right, a ball drops in that maybe shouldn’t have been a hit. Which stat factors any of these things in? The argument is the law of averages balances everything out. In the end, the math is the constant.

However, there are internal and external factors affecting the game constantly. These factors cannot be built into models. These factors cannot be accounted for statistically.

Where a card counter at the black jack table can turn the odds against the Casino by using probability and a system of advanced mathematical equations to gain an advantage, there is a set number of cards in the deck, and only a certain number of things can occur to account for. You can’t do that in baseball. In baseball, there are an infinite number of things all taking place simultaneously which affect the outcome of every pitch.

The problem at large is that the game has changed significantly since the introduction of advanced statistics. There are too many statistics which are complicating the game. They cause managers to over-manage situations.

For example, is a lefty specialist really necessary in a team’s bullpen? According to advanced statistics they are. But when it’s all said and done a bullpen pitcher is simply a pitcher who could not make it as a starting pitcher. Very few pitchers are groomed to be in the bullpen. In other words, why would I bring a pitcher into a game, and take out my better pitcher, simply because statistics show that one guy is better at getting left-handed batters out?

It doesn’t make sense. The best players should be on the field.

Statistics tell front offices they need lefty specialists. They tell the manager that they better go against their gut which tells them to leave their better pitcher in the game. It sounds crazy when you think about it. I’m going to take out my better pitcher because statistics show that over time, a pitcher of lesser quality has done a better job of getting left-handed hitters out? It doesn’t sound logical.

Now I have decided to take my best pitcher available out of the game to bring in a lefty specialist in order to get one hitter out. After he gets that batter out, I have to take him out of the game to put in an even lesser quality pitcher? Why not just leave my best pitcher in to get the lefty out. Now I have changed the odds of getting the remaining hitters out, all because stats have told me to take my best pitcher out of the game.

The entire landscape of the game changed because of a single stat.

Is there a stat that shows the odds of getting the remaining hitters out in a game after I made that decision? There is a stat that shows me that I should bring a lefty specialist into the game, but not a stat that shows the odds that I will get the remainder of hitters out now that I made that pitching change.

That is just one example of how stats have changed the game, but the question that still remains is – are all of these advanced statistics helping or hurting the game?

Cases can be made for both sides, but the truth of the matter is that all these stats are really good when looked at from the surface. It’s how the people behind the scenes use them that will ultimately determine whether they are good or bad for the game.

My daughter preparing for a front office job someday – you can never start them too early.

Advanced baseball statistics is very similar to the app market for smart phones. App developers are always looking to develop the next Angry Birds, and stat developers are looking to develop the next stat which proves that they have the secret formula to determine who the best player in the league is.

There is no secret formula. Baseball is played on the field, not in a laboratory, and not in a computer program. There isn’t a single stat or mathematical equation that can determine the outcomes on the field.

Nothing will ever change that.

Follow Mitch Petanick on Twitter.

I think it is time to talk about what Moneyball ACTUALLY means and to show some of those who claim to have read the book what the book is all about. Apparently, reading doesn’t always lead to UNDERSTANDING.

I have read the book, it was interesting to learn about the little known “behind the scenes” process of team building and how a front office operates. It is for THAT reason the book sold so well not because it was chock-full of useful information, formulas and strategy. It did have some information which would be useful for ANY team regardless of targeted payroll ceiling, and it did help to show how Statistical Analysis can help you find some hidden value where none is actually perceived.

The book discusses quite well the methodology used but mostly because it would be a pretty short book if it just said we used statistical analysis to find cheap players. I suspect this is the main reason why people confuse Sabers with Moneyball. The book expended a great deal of effort explaining what methodology they used to find “cheap” players which was the central goal of Moneyball.

Sabermetrics or advanced statistics were one of the predominant tools used to rank and identify who had the quality that was deemed UNDERVALUED but also used to identify what quality it was that was undervalued.

Sabermetrics are based largely on Bill James’ work and his work is mostly about how important On-Base Percentage is to winning (right or wrong that’s what conclusion he came to in its most simplistic form).

By using that strategy, your initial ranking of ALL players is going to FAVOR players with high a OBP and rank them accordingly. If you use OBP alone to identify what players are undervalued many low cost players will not show up on your list of top players simply because you ignored so many other aspects of the game.

Using the Moneyball method to exploit market inefficiencies in the early 2000’s, doesn’t guarantee that you will come up with all the top low-cost players today. Markets change every year.  It worked great for a little while back in the day, but that was then and this is now.

Now onto the topic at hand, the processes which will no doubt cause much vitriol in the comments?

MONEYBALL no matter what methodology used to arrive at the goal is about NOT SPENDING MONEY or Spending as little as possible. The REASON for implementing it is varied:

• Not having the money to spend due to low fan attendance and revenues.
• Cheap Ownership who are more interested in profit than wins and force the GM to shop at Kmart.
• Blind stupidity that convinces you that minimizing your player options translates to more success. I call this the “penny wise, pound foolish” syndrome!

Sabermetrics was used in the Book as a comparative means to identify players who fit the qualities they were looking for. But it was not the decider in who to get. Sabermetrics does not INCORPORATE player salary in their metrics. Two Players judged by Sabers can be equal while one gets paid 8 Million per year while the other gets the league Minimum!

So while the Sabers may have identified the players and created the initial MASTER LIST of candidates it did not DECIDE which one the team was going to get because there are probably a lot of HIGHLY PAID players on that list as well.

Look at the 2011 Top OBP leaders:

• Joey Votto
• Prince Fielder
• Lance Berkman
• Matt Kemp
• Ryan Braun
• Matt Holliday
• Carlos Beltran
• Troy Tulowitzki

Do you see any hidden gems or $1 million dollar a year players?

So you used OBP to identify all the players that you deemed were GOOD, but then eliminated all the players who were being paid accordingly for their talents, in this case all of them.

You see MONEYBALL is about REMOVING high salary players from the candidate list…

Sabermetrics are not the central driver of the Philosophy in Moneyball. MONEY is!

The Red Sox who are most often used as an example of a Moneyball team use advanced stats, but they SKIP the most important step needed for Moneyball… The removal of any players or options that command a high salary!

The Red Sox never removed those higher priced options from their list of targeted players… Oakland DID!

This is why the Red Sox actually won a WS and have the third highest Payroll in baseball while Oakland has never won a championship since they implemented Moneyball.

The Red Sox never limited their options based on Money.

Yes they both used sabermetrics, in fact most teams do, but the Red Sox did not ignore quality players because of money! They did not discard a better option merely because he made more than a cheaper and more inferior player!

Oakland did!

Even if a player had a superior OBP or SLG,Oakland would ignore those sabermetrics and that better player in favor of the lesser player and $$$$$.

Red Sox did no such thing! THEY ARE NOT A MONEYBALL TEAM! You can say they are a Sabermetric team as many teams are these days in some respect or another.

SABERMETRICS DOES NOT EQUAL MONEYBALL!

SABERMETRICS = A Limited Form of Statistical Analysis!

Statistical Analysis DOES NOT EQUAL SABERMETRICS! There are many ways to analyze stats and they don’t all subscribe to the theories put forth by Bill James and all those profiting in his footsteps.

Statistical Analysis is a means of calculating stats and placing importance on some stats over others but they do not show you the cheapest player nor compare price per performance in any way shape or form.

Now we COULD debate Sabermetrics in and of itself, but it really isn’t relevant to this conversation. Yes Sabers seem to be good at comparing players but Sabers themselves and the philosophy of Bill James is not required, important, or the be-all and end-all of Statistical Analysis!

Bill didn’t really INVENT statistical analysis we have ALWAYS looked at stats as a comparator. Bill James’ contribution was to create a few metrics that placed importance where he saw fit. I’m not going to debate if he’s right or wrong here, it is not the focus.

You do not need to read MONEYBALL nor any of Bill James books to create or use good metrics. Anyone can do it and if you work hard to ensure you are not biasing the data to show what you want, you will also come up with the right answers.

No single stat will ever give you the complete picture of any player. To say that OBP is a better metric than BA because it takes all PA into consideration doesn’t make it better. An even better metric can be achieved than the ones Bill James came up with.

How about a metric that takes into account moving the runner over or driving in a run regardless of an out being made? It would tell you a lot more about a player than either BA and OBP.

The thing that Moneyball SUCCESSFULLY showed was not that saving money is the way to go, but that DEEP STATISTICAL ANALYSIS is the key to making good decisions because you are making an EDUCATED Guess – an informed decision.

But MONEYBALL discards much of that information and the end result is as old as the game of baseball itself, how much they get paid!

Moneyball uses Sabermetrics to come up with answers, and then IGNORES the answers given based on COST!

Moneyball is not about Sabers or statistical analysis it is about NOT SPENDING MONEY!

The Braves have been used as an example of a team that did it the right way and they did it without the benefits of Moneyball. (They were pretty much done winning championships by the time Moneyball was invented!) They built a good team that was cheap because they developed it from scratch. Fine to do provided you have the patience to wait as long as it took them – decades of losing and a bit of good luck and timing and Greg Maddux. They finally built a team that carried them to five league championships and their one World Series title.

If they had spent some additional payroll to maintain their edge they might have won a few more WS and Titles.

The notion that spending less means winning more does not hold true. Building BETTER (regardless of methodology and COST) leads to better teams.

And by handcuffing and limiting your choices based on money means you make it that much harder to succeed. Because when you place limits on yourself that preclude you from many options,  you helped give the opposition who did not limit their choices an ADVANTAGE OVER YOU!

While Oakland might seem to have done well despite limiting themselves via implementing Moneyball, the bottom-line is Moneyball didn’t get the job done!

And while it might seem wise to a Moneyballer to point out how many playoffs Oakland went to while spending peanuts, you still have that little issue that the Yankees won more titles and World Series spending money.

1998 – 2011

Oakland A’s – AL League Championships 0, World Series Titles 0

NY Yankees – AL League Championships: 6, World Series Titles: 4

The great equalizer, Moneyball was not!

And what COULD/WOULD the story be if they had just spent a little to keep the cheap players they had worked so hard to find from walking away?

Or to compliment the team with players who would cost a little more, but would have increased profits due to WS ticket sales and victories?

How far might they have gone if they simply signed rather than ignored the best players they themselves calculated based on their UNDERVALUED Metrics?

When you LIMIT your choices based on self imposed financial limitations, you will not have the same success as those teams who use the same statistical analysis to pluck all the productive players that are out of YOUR price range!

Cheaper isn’t ALWAYS BETTER… You GET what you pay for!  Not in every circumstance but more often than not.

MONEYBALL is Penny Wise and POUND FOOLISH…

Especially now when all teams use advanced metrics everyday, but are willing to pay for the best available talent.

R.I.P. Moneyball

This Fan Shot was submitted by Mike (Metsie). Have something you want to say about the Mets? Share your opinions with over eleven-thousand Mets fans who read this site daily. Send your Fan Shot to GetMetsmerized@aol.com.