Fall Training: Getting Ready for the Hot Stove League

If your team didn’t make it to the playoffs, this is a tough time of year. You can’t watch your guys play, and your club can’t start making major moves to improve until after the World Series. But fret not, while your buddies rooting for teams still in the championship hunt are concentrating on the playoffs, you can bone up on the upcoming action in the hot stove league with my new book Hot Stove Economics: Understanding Baseball’s Second Season.

Book-jacket description:

The final out of the World Series marks the beginning of baseball’s second season, when teams court free agents and orchestrate trades with the hope of building a championship contender. The real and anticipated transactions generate excitement among fans who discuss the merit of moves in the arena informally known as the “hot stove league.” In Hot Stove Economics, economist J.C. Bradbury answers the hot stove league’s most important question: what are baseball players worth? With in-depth analysis, Bradbury identifies the game’s best and worst contracts—revealing the bargains, duds, and players who are worth every penny they receive. From minor-league prospects to major-league MVPs, Bradbury examines how factors such as revenue growth, labor rules, and aging— even down to the month in which players are born—shape players’ worth and evaluates how well franchises manage their rosters. He broadly applies the principles of economics to baseball in a way that is both interesting and understandable to sports fanatics, team managers, armchair economists and students alike.

Table of Contents (The Preface and Chapter 2 are available online as free previews.):

Preface

PART I. GETTING STARTED
1 Why Johnny Estrada Is Worth Kevin Millwood: Valuing Players As Assets
Hot Stove Myth: Every Trade has a Winner and a Loser

2 Down With The Triple-Crown: Evaluating On-Field Performance
Hot Stove Myth: Some Players are Clutch

3 A Career Guide From Little League To Retirement: Age And Success In Baseball
Hot Stove Myth: Players Peak at 27

PART II. TRANSLATING PERFORMANCE INTO DOLLARS
4 Putting A Dollar Sign On The Muscle: Valuing Players
Hot Stove Myth: Replacement Players are Cheap and Abundant

5 Deals, Duds, And Caveats: What Do The Estimates Reveal?
Hot Stove Myth: The Size of the Free-Agent Pool Affects Player Salaries

6 Winning On A Dime: The Best- And Worst-Managed Franchises Of The Decade
Hot Stove Myth: General Managers can Buy Low and Sell High

PART III. PROJECTING PERFORMANCE
7 Is C.C. Sabathia Worth $161 Million? Valuing Long-Run Contracts
Hot Stove Myth: Player Salaries Raise Prices at the Gate

8 You Don’t Need A Name To Be Traded: Valuing Minor-League Prospects
Hot Stove Myth: College Players are Better Draft Bets than High School Players

9 Epiliogue

The book will be released tomorrow, and the e-book version is already available for purchase. There are links to the book’s pages of several online sellers on the right sidebar. You can get more news about the book on it’s Facebook page and by following me on Twitter.

During the offseason, I’ll be writing quite a bit on the topics discussed in the book as they apply to current events. If you have any topics relating to the book that you’d like me to write on, please let me know.

Why It’s OK for Players to Call Out Fans

Earlier this week, Evan Longoria and David Price stated that they were embarrassed by the weak attendance to their potential playoff-clinching game in Tampa Bay on Monday night. Their comments brought immediate backlash from the baseball media. How could guys making millions of dollars criticize fans for not supporting them, especially in the climate of a recession?! Pundits also cited the ugly facility, the difficulty of getting to the stadium, and the possibility that puppies might be run over by fans driving to the game. Oh, the horror.

What this was, was a rallying of the troops, and it’s exactly what the Rays need. Sporting events benefit from bandwagon effects. People want to go where other people are. If the Rays game is the place to be, then citizens need to know that. The way to make it so is to get someone who is well-liked to say it’s the place to be. I can’t think of better spokesmen than Longoria and Price.

Baseball is a business, and if fans don’t want to pay to see the games, that’s their right. But they have to understand that when you don’t patronize a business, it goes away. Do fans want that? If fans aren’t going to come out, then the owners may decide it’s in their best interest to trade their valuable commodities elsewhere instead of actively seeking improvements on the free-agent market. The owners may even decide it’s not worth staying in town, find a prospective new location where fans will go to the game, buy out the lease, and hit the road. Why stick around if fans won’t even come when the team is doing exactly what fans in many other cities wish their front offices would do?

Rays owner Stuart Sternberg has already announced that the Rays will be slashing payroll. The reason for this is that all the investments intended to improve the team were done, not out of kindness, but to make money. As I have found, in most cases winning begets high returns. But this hasn’t been true for the Rays.

If Tampa Bay residents want good baseball to remain, they are going to have to support it. Good fans sometimes need a push, just as good soldiers sometimes need a reminder from a general. That’s all Price and Longoria were offering, and I don’t think there is anything inappropriate about their comments.

Identifying Prospects

Request:

1) What traits or skills do MLB teams scout for, and what do they expect players to develop over time?
2) What traits/skills do teams avoid? How do they estimate injury risk, and do they do this well? Can they?
3) What pitches generate more injuries? It seems that pitchers who throw a curveball more often get injured more (think Ben Sheets, Chris Carpenter, Stephen Strasburg). Is this really the case, and if so, is it worth the risk?

I don’t know exactly what teams look for in players beyond the five tools, tall pitchers, and possibly the “good face.” Kevin Kerrane wrote a marvelous book on scouting in the 1980s Dollar Sign on the Muscle, which follows the lives of several scouts and discusses the characteristics they look for. While there is some agreement over what makes a baseball player good, different scouts and organizations have their own philosophies as to what characteristics mark future success.

I can’t comment on how to predict success based on personal observations (a technique one of my professors referred to as “ocular least squares”), but I have looked for makers for success in minor-league performance statistics. I report my results and explain my methods for predicting success in the Chapter 8 of Hot Stove Economics. I even put a dollar value on prospects using these characteristics.

The difficulty with picking out major-leaguers before they’re ripe is that while most future big-league players excel in the minors, many bad players do as well. Looking beyond the slash stats reveals some common characteristics of big-league players, and some of the stats I found useful for predicting major-league success aren’t necessarily stats that I find to be the most useful stats for evaluating players once they make it. For example, I rarely look at the batting averages and strikeout rates of major-league hitters, but I find that high batting averages and low strikeout rates are important predictors of major-league success. You can succeed in the big leagues with a low average and striking out a lot, but even players who struggle in these areas typically handled the bat much better in the minors. Also important are a player’s walk rate and isolated power. If you have patience and can hit the ball hard, you’re more likely to succeed in the majors that players who lack these skills. And you can’t look at minor-league performances without also accounting for age. A twenty-year-old who’s treading water in Triple-A may have more promise than some of the older guys having success at the level.

Another interesting finding was that the stats below High-A ball have no predictive power. At this level, predicting success requires personal observations of trained scouts.

As for how players skills develop, I’ve done some work looking at how major-league players improve and decline over their careers. For hitters, batting average and power peak in the mid-to-late-20s, but these skills see minimal improvement and decline. The ability to walk improves into the early-30s, but the age-range of peak performance is less than it is for batting average. Pitcher strikeout ability is at its greatest almost as soon as pitchers enter the league, but this ability doesn’t diminish as fast as other skills. Like hitters, pitchers improve in walking into their early-30s. This is likely the result of acquired knowledge that allows older players to succeed, even as their physical athletic skills are deteriorating.

As for identifying injuries, that’s something that is not well-understood outside of baseball. I would hope that teams are conducting their own internal analyses of injuries, but most of that knowledge is kept private. Baseball injury data is just starting to become available where we can look at factors that influence injuries. The research being done in sports medicine journals is good and is still developing. What I have found interesting is that the medical community seems to have a better grasp on youth injuries than it does on adult injuries. For example, I’ve got a study on my desk that looks at factors that impact elbow injuries among youth pitchers—arm fatigue and mechanics seem to matter, but curve balls don’t. Play tracking systems like Pitchf/x, and motion analysis technology like Dartfish should help us better predict and prevent injuries for all players.

Why Are Modern Pitchers So Fragile?

Another request.

Why are modern pitchers so fragile? They pitch fewer innings per game, start fewer games, and have more days rest between starts. In addition they have much better training and medicine to cope with the stresses of pitching.
I have heard it repeated that there will no longer be 300 game winning pitchers. What happened to the Nolan Ryans and the Bob Gibsons?

Unfortunately, I don’t have very good data to examine exactly how total pitches thrown have changed going too far back in time. But, given the pattern going back to the late-1980s, I think it’s safe to assume that the extreme loads of pitchers are declining, even though the average pitching load has remained constant at about 99 pitches per game.
Maximum pitches thrown per game

Using some simpler measure of workloads, innings pitched, the pattern is interesting. The figure below shows the change in total innings pitched in a season over time for the maximum number of innings pitched, and by 95th and 75th percentiles, and the median (minimum 10 games started).

IP

Though there has been a general decline in pitcher workloads over time, there was a bump in the late-1960s and early-1970s, when pitching loads increased over what they where in the 1960s. Since 1962, when the leagues both started playing 162 games a season, there have been 65 pitcher-seasons with 300 or more innings pitched. The last one occurred in 1980 when Steve Carlton threw 304 innings. Below are a table of the number of 300-inning performances by seasons and a list of those performances by pitchers.

Year	Count
1962	1
1963	3
1964	1
1965	2
1966	4
1967	1
1968	4
1969	9
1970	4
1971	4
1972	4
1973	7
1974	8
1975	4
1976	2
1977	4
1978	1
1979	1
1980	1
Total	65
Pitcher		Year	IP
Vida Blue	1971	312
Bert Blyleven	1973	325
Jim Bunning	1967	302.33
Jim Bunning	1966	314
Steve Carlton	1980	304
Steve Carlton	1972	346.33
Jim Colborn	1973	314.33
Larry Dierker	1969	305.33
Don Drysdale	1965	308.33
Don Drysdale	1962	314.33
Don Drysdale	1963	315.33
Don Drysdale	1964	321.33
Bob Gibson	1968	304.67
Bob Gibson	1969	314
Dave Goltz	1977	303
Bill Hands	1969	300
Catfish Hunter	1974	318.33
Catfish Hunter	1975	328
Fergie Jenkins	1968	308
Fergie Jenkins	1969	311.33
Fergie Jenkins	1970	313
Fergie Jenkins	1971	325
Fergie Jenkins	1974	328.33
Randy Jones	1976	315.33
Jim Kaat	1975	303.67
Jim Kaat	1966	304.67
Sandy Koufax	1963	311
Sandy Koufax	1966	323
Sandy Koufax	1965	335.67
Mickey Lolich	1974	308
Mickey Lolich	1973	308.67
Mickey Lolich	1972	327.33
Mickey Lolich	1971	376
Juan Marichal	1966	307.33
Juan Marichal	1963	321.33
Juan Marichal	1968	326
Sam McDowell	1970	305
Denny McLain	1969	325
Denny McLain	1968	336
Andy Messersmith1975	321.67
Phil Niekro	1974	302.33
Phil Niekro	1977	330.33
Phil Niekro	1978	334.33
Phil Niekro	1979	342
Claude Osteen	1969	321
Jim Palmer	1970	305
Jim Palmer	1976	315
Jim Palmer	1977	319
Jim Palmer	1975	323
Gaylord Perry	1974	322.33
Gaylord Perry	1969	325.33
Gaylord Perry	1970	328.67
Gaylord Perry	1972	342.67
Gaylord Perry	1973	344
Steve Rogers	1977	301.67
Nolan Ryan	1973	326
Nolan Ryan	1974	332.67
Bill Singer	1969	315.67
Bill Singer	1973	315.67
Mel Stottlemyre	303	1969
Luis Tiant	311.33	1974
Wilbur Wood	320.33	1974
Wilbur Wood	334	1971
Wilbur Wood	359.33	1973
Wilbur Wood	376.67	1972

Thus, it seems that teams tried to ramp up pitcher workloads just prior to the modern decline. What happened in the 1960s and 1970s that caused an increase in pitcher workloads? Did teams realize the ramp-up was a mistake, which caused the trend to reverse? This was an era of low offense, and the mound was lowered and the designated hitter added as a response. Was there a shift in pitching philosophy or did something structural cause this shift? I’m open to suggestions. The bump may offer a clue.

Aside from the bump, what has caused the declining trend in workloads? Most obviously, the rise of the five-man rotation gave pitchers fewer games to cover. On top of this, teams began to rely more on relievers within games pitched than in the past, going with fresh pitchers in late innings rather than asking starters to pace themselves. The number of complete games has declined continuously since the late-1970s.
CG

I think the decline in pitching loads is less a response to a toughness of pitchers than it is a change in pitching philosophy. Every year, someone is supposedly going to go to have four-man rotation, but then we never hear any more about it. Whether that is because no manager has the guts to stick to a plan that is easy to criticize when injuries that were bound to happen anyway happen, or because the five-man rotation actually leads to better pitching, I don’t know. I have found that days of rest appear to have little impact on performance, so I don’t believe there is any performance benefit from giving pitchers more rest days in a five-man rotation. I’d love to see a team go with a four-man rotation from start to finish.

As for the increased use of relievers, I believe managers have discovered that 100% mediocre arms can be more effective than paced good arms. Teams can increase their chances of winning by going to the bullpen, exploiting match-ups, and pinch-hitting for pitchers. Furthermore, I’ve found some evidence that fewer pitches per game can improve future performance among starters.

In summary, my best guess is that the decline in pitching loads is part fad (four-man rotations) and part innovation (relievers can be better than paced starters).

UPDATE: In my initial cut and paste of the 300-inning pitchers I accidentally left off six seasons at the bottom of the table. I have added the missing seasons.

Determining Awards: Results Versus Performance

I’m starting on blogging requests. This one actually came in before I opened the queue.

How much do you think the Cy Young voters should take into account things like BABiP? On the one hand, it’s not [Tim] Hudson’s fault he’s getting lucky and getting outs, and that is his job. On the other hand, it’s not all Hudson’s performance that has led to all those outs. My tendency is to want the BBWAA to hand out individual awards based on performance and not based on results. How much should results matter over performance, if at all, in your opinion?

Sports awards are kind of silly when you think about it. We use awards for the arts when there are no winners (e.g., Oscars, Emmys, etc.), but sports competitions have winners. Why bother? And the ESPYs? Who watches that? But, for whatever reason, awards have been around for a long time, and it’s fun to argue over who is the best. So, it’s a worthy topic of discussion.

The results versus performance debate is an interesting one. If I was trying to predict future outcomes, then distinguishing true performance from luck in outcomes is of utmost importance. Awards are backwards looking, and in sports competitions all we care about is outcomes. At the end of the regular season, we don’t pick post-season participants based on Pythagorean records or some other luck-sanitizing measure. For individual awards, should the standard be different? On the hitting side, I developed PrOPS to identify when players may be over- and under-performing based on the way they hit the ball. Fortuitous bounces and wind gusts may push a good hitter into the elite category, but I wouldn’t pick a player with a higher PrOPS over a player with a higher OPS to win an offensive award. I think what actually happens on the field matters more.

Here is another example. I don’t think Jose Bautista will ever hit 50 home runs in a season ever again, but should the flukyness of his season take away from the luster? Maybe he’s not in the MVP discussions, but if he was, I don’t think any favorable match-ups, wind-conditions, or other factors beyond his control that may have helped him outperform other players should put him out of contention for the award. The reason I’m reluctant not to single the performance out as an aberration is that an alternate explanation for Bautista’s improvement is that the took active steps to play better. Even if I can specifically identify good luck he benefited from, there is also the chance that there is unidentified bad luck that I am missing.

When it come to pitchers, that analysis gets a little more complicated. Batters do most everything they do by themselves. Pitchers need the help of fielders to get outs. Batting average on balls in play is something that pitchers have little control over, and BABIP is heavily influenced by randomness. I’m a strong-DIPS proponent. I think the evidence is clear that pitchers have very little control over hits on balls in play, and even on extra-base hits on balls in play. When I see a pitcher like Tim Hudson near the top of the league in ERA with a strikeout-to-walk ratio of less than two, I have a hard time treating Hudson as pitching as one of the league’s elite pitchers. Tim Hudson is a good pitcher, and at times this season he has been one of the best, but the award should go to the best pitcher for the entire season. Even if Hudson led the league in ERA, I couldn’t support him for the Cy Young.

It may seem that I am judging Bautista and Hudson by different standards by ignoring luck for the former but not for the latter, but I’m really focusing on different types of randomness. The role that randomness plays for pitchers is different than it is for hitters, because the main metrics that we use to judge pitchers are heavily polluted by factors beyond their control. If a pitcher gets lucky in striking out batters or preventing walks and homers, I’d have no problem supporting a Cy Young campaign, even if I thought there was little chance that he could continue to perform with the same level of outcome success.

So, when its outcomes versus performance, I prefer to focus on outcomes, but I there has to be some accounting for luck. And due to the nature of the luck they experience, I think we have to treat pitchers differently than we treat hitters when accounting for luck.

Taking Requests

The hectic summer took me away from the blog more than I had anticipated, but things are beginning to lighten up to where I can settle back into a routine in October. If you have any topics that you would like me to write on, let me know via the comments, e-mail, Twitter, or Facebook.

On the JQAS Study of Attendance and Winning

The other day, the Wall Street Journal posted a note about an interesting research finding regarding baseball attendance and winning. According to research published in the Journal of Quantitative Analysis in Sports increasing attendance increases the home team’s chance of winning. The finding makes some intuitive sense. If players are motivated and umpires are influenced by a large boisterous crowd, then teams might want to make more of an effort to get fans to the ballpark. I’ve had a few people ask for my opinion of this study, and since I am quite familiar with the paper I will offer a very blunt negative assessment.

When I first saw a draft of this paper as a referee for another journal, I was intrigued by the finding. I reviewed the paper positively, but I still had a few questions that I thought the authors needed to address before the results could be accepted and published according to the quality standards of the journal. A few months later a revised paper was submitted to me, but I was not pleased by the revisions.

In my initial referee report, I suggested using a method that I had used for investigating how umpires are influenced by outside pressure using QuesTec. The authors bizarrely interpreted my suggestion and did something that made little sense. But more importantly, they credited a source that they did not use, and that source happened to be me. Using the citation that I had provided, they stated that in my work I had found that QuesTec monitoring limited racial bias by umpires. I was shocked to read this, because I have done no such research. The authors simply fabricated this. I can only guess their motives: I suspect laziness in an attempt to placate an annoying referee.

If the authors had lied about something so simple and easy to verify, what had they done behind the scene, where numbers can easily be manipulated? I looked at the results more closely, things looked fishy, and the explanations in the text didn’t make much sense. I wrote up my report, in which I stressed the severity of the academic integrity violation and expressed my other concerns about the research. I recommended that the editor reject the paper, and he agreed with my recommendation. In his letter to the authors, he also noted the false citation.

Jump ahead to earlier this year. I stumbled across the paper at JQAS. To my surprise, the paper had been published with the offending text that I had identified in my report. I contacted JQAS editor Ben Alamar to tell him the saga of the paper. I was most upset by the academic dishonesty, but I was also concerned that my work was being cited as finding results that I didn’t find. Dr. Alamar responded promptly and stated that he would discuss the matter with the editorial board.

Soon after I initiated my complaint, I received an e-mail from one of the study’s authors Erin Smith. She apologized to me and stated that the incorrect text should not have referred to my book but to some commentary that I provided on another study of racial bias among umpires in The New York Times. I replied to Ms. Smith that I appreciated her apology; however, this did not explain why the error made it to the JQAS. Ms. Smith was unaware that I was an anonymous referee on her paper, that I was someone who had previously pointed out the error to her, and that I was aware that another journal editor had also pointed out this error to her. Yet, the offending text remained in the paper. Ms. Smith was lying again, and I never received another response from her.

A few weeks later, I received an e-mail from JQAS editor Ben Alamar in which he stated, “I just wanted to let you know that we have finished our review and have rejected the paper based on the incorrect citation of your work.” So, you can imagine my surprise when I read an article on the study in the mainstream media. When I went to the JQAS’s website, I found the paper still published with the following appended.

Erratum
Please note that the following statement has been retracted from Page 4:

“Bradbury (2007) shows racial discrimination is less likely to occur when the umpires are monitored by an electronic pitch tracking system called QuesTec.”

There is no argument in the paper by Bradbury that such discrimination is likely to occur.

The offending passage was removed with explanation; however, this is not what I had been told would be done to rectify the situation. I contacted Dr. Alamar to request an explanation, but he has not yet replied to me.

As you might imagine, I am not particularly happy about this affair. I don’t like academic dishonesty, I don’t like being lied to, and I’d rather spend my time doing other things. The thing that annoys me the most is that none of this should have happened. Ms. Smith could have removed the offending passage, or Dr. Alamar could have just told me that the journal would publish the paper with an erratum. I still wouldn’t think much of the paper or the decision to publish it, but that would be the end of it.

I would also like to note that this is the second time that I have identified serious errors published in JQAS articles (I identified coding irregularities in a paper that claimed to find performance spikes among players included in the Mitchell Report) and nothing was done about it.

Interesting Facts about My Dad

Please excuse my personal post. My father died yesterday after a long battle with Progressive Supranuclear Palsy (PSP).

Tom Bradbury, journalist and advocate, dies

Paul Thomas “Tom” Bradbury, 67: Journalist and ham-radio whiz

»» Interesting Facts about My Dad

Revenue Sharing, Incentives, and Competitive Balance

Rob Neyer takes issue with the conclusions of my NY Times column on revenue sharing and competitive balance, in which I suggested MLB abandon revenue sharing for the purpose of aiding competitive balance.

I can’t say that I’m convinced, but then again I can’t say I’m objective, either. Because it makes me happy to see the rich giving to the poor. It makes me happy to see the Yankees and the Red Sox writing checks to the Rays and the Royals.

Also, Bradbury’s argument isn’t terribly convincing. Maybe competitive balance hasn’t improved with more revenue-sharing … but that doesn’t mean it wouldn’t be worse without revenue sharing. Bradbury points out that the balance has hovered around 1.8 — as measured by the Noll-Scully ratio — since the early ’90s … but can anyone prove that it wouldn’t be lower than 1.8 without revenue sharing?

Maybe someone can. Economists love to play around with models. But I haven’t yet seen a model that gets the Rays into the playoffs twice in three years without a little help. And I suspect they’re happy, this year at least, with Commissioner Robin Hood and his Merry Men.

Let me clarify a few things and extend my argument to possibly convince Rob and other skeptics that revenue sharing isn’t a useful policy instrument for manipulating competitive balance. 800 words isn’t a lot of space to make an argument, and the book chapter which contains much of my argument is too long to include here. Although, as the son of a newspaper editor, I have to admit that I have a soft spot for short policy pieces.

First, I am not opposed to revenue sharing, per se. As a collective profit-maximizing entity, MLB may find guaranteeing payments to all franchises, regardless the level of locally-generated revenue, is the optimal business strategy. By having teams in Pittsburgh, Miami, etc., MLB receives media attention and retains interest in baseball among potential fans in the area. Even if local receipts aren’t sufficient to keep the franchise in the black, the net benefit to the league is positive. Therefore, in order to encourage an owner to own and operate a franchise in the area, a subsidy may be required. I have no problem with such an arrangement, nor do I have a problem with owners pocketing such transfers.

Where I see the issue as problematic is when we tie revenue sharing to competitive balance. Below is a revenue function that I have estimated for an average MLB team, based solely on winning. The left-side of the function shows “the loss trap” bump that I highlight in the article, which is consistent with revenue sharing creating a disincentive to win. However, the bump is slight, and I don’t think it’s even necessary for explaining why revenue sharing hasn’t improved competitive balance (more on that in a moment). The fact that earnings are relatively flat until wins reach the mid-80s in wins means that there is very little incentive for poor-and-losing teams to invest any money into the club. Whether that money comes from transferred wealth or a pot at the end of the rainbow, investing the funds into a club doesn’t generate sufficient return to justify it. The Pirates and Marlins weren’t being excessively greedy, their behavior reflected a sound business decision. For a team like the Rays, however, putting that money into the club does make sense. The returns to winning are increasing, likely higher than alternative investments. It’s getting to that point that is the difficult part. If you’re in the loss trap, spending many millions of dollars to improve the club doesn’t help much. And revenue sharing doesn’t help you out.

Revenue Function

Teams that have garnered success on small budgets in the recent past (e.g., Rays, Twins, Indians, A’s, and Marlins) haven’t used revenue sharing to get where they are. Instead, another baseball institution has served to give these teams a fighting chance: the reserve system that allow teams to pay players wages below their revenue-generating capability. The amateur draft gives every team in the league rights to valuable player-assets that teams can use to build winners. This mechanism is far more effective at promoting competitive balance and it lacks the disincentives of revenue sharing. Only teams who draft wisely and properly develop their players are rewarded.

Now, to Neyer’s second point. He argues that because competitive balance is no better than it was in the mid-1990s, when revenue-sharing for competitive balance purposes was first instituted, it doesn’t mean that measured imbalance wouldn’t have been worse without revenue sharing. This is certainly a possibility. The graph below shows the Noll-Sully measure of competitive imbalance from 1921-2009, smoothed with a lowess fit to map the trend.
Competitive Balance Over Time

The graph shows that competitive balance improved from the 1930s until leveling off in the late-1980s and early-1990s. Much of this improvement was likely a natural consequence of more high-quality talent becoming available to more clubs, the addition of the amateur draft in 1965 (the mechanism Branch Rickey felt was most important for leveling the financial playing field across teams), and other minor structural tweaks to the league. Why would the improving trend disappear just as revenue sharing came into existence? While I’m not certain that revenue sharing stopped the progress, I doubt it was instituted just in time to counteract a trend reversal.

In my view, if revenue sharing worked, there would be some evidence of it working over the past two decades that it’s been tried under various formats. How much longer are we supposed to give it, especially when what we observe is exactly what theory predicts we should observe? If we think it’s important to correct inherent differences in revenue potential across teams, I think revenue sharing is a poor tool for achieving that goal.

What Do MLB’s Leaked Documents Reveal about Revenue Sharing?

Here is my take in The New York Times.

Baseball’s revenue-sharing system was designed to increase the competitiveness of small-market teams that presumably lack the financial muscle to compete with wealthier franchises. Redistributing wealth would give poor teams more resources to improve their rosters, and richer clubs would have less money to extend their financial advantage. The cumulative effect would be to spread good players around the league to achieve a level of competitive balance where “every well-run club has a regularly recurring reasonable hope of reaching postseason play” — the standard put forth by the Commissioner’s Blue Ribbon Panel on Baseball Economics.

Despite the good intentions behind revenue sharing, doling out money to baseball’s have-nots has the unintended consequence of creating a disincentive to win. Though the correlation is not perfect, winning tends to attract fans, which increases local revenue. But a healthier bottom line means drawing less from the revenue-sharing pool. The quandary faced by poor-and-losing teams is that using the added wealth to improve their clubs increases local earnings, but these gains may be offset by reducing revenue-sharing payments.

A lengthier explanation is available in my upcoming book.