The Hardball Times -- Vince Gennaro

Ticket pricing: Let’s get creative, people

Vince Gennaro — 2007-08-28T04:05:15+00:00

Major league baseball teams are constantly seeking to grow revenues: devoting energy to landing that next corporate sponsor, getting a large group to come to a mid-week game, or unveiling new merchandise to sell in their team shops. However, one of the biggest revenue growth opportunities may be to capture a larger portion of an existing revenue stream—the price fans are willing to pay to attend games.

While baseball has evolved from the "one size fits all" general admission prices during the deadball era, it has stopped far short of reaping the financial benefits of pricing segmentation. My analysis indicates there may be a $300 to $500 million revenue opportunity, collectively for all MLB teams, if they were to price their tickets more efficiently. This is not about padding the pockets of owners, but about generating resources to invest in international scouting, player development and payroll to be more competitive on the field.

The secondary ticket market gives us a window into the revenue opportunity. For instance, a quick survey of transactions on eBay and Stub Hub provide ample evidence that there is a large gap between a ticket’s face value and the price some fans are willing to pay. Recently, the Indians hosted the Yankees to three sold-out Jacobs Field crowds. Based on Team Marketing Reports ticket price info, the team grossed about $2.9 million in ticket revenue for the three games. However, by sampling ticket transactions from various secondary market sources over the last several months, I estimate the 125,000 fans attending those same games paid a collective $4 to $5 million for their seats.

This example is far from the exception, as similar scenarios can be found throughout MLB all season long. What drives the prices people are willing to pay to attend a game? How do MLB teams get more of the gate revenue from the games they produce? Let's take a closer look at these secondary markets.

The secondary ticket market provides two functions—convenience and pricing arbitrage. It reallocates tickets from people who can’t use the tickets, to those who can. For example, a full season ticket holder who is too busy (or chooses not) to attend all 81 home games can resell tickets in the secondary market. In this example, convenience, rather than price, may be the driver of the decision to sell.

The second function is to profit from the simplistic pricing practices of MLB teams, by finding buyers willing to pay higher than face value for tickets. This arbitrage opportunity is enabled by MLB teams’ inefficient pricing policies, which fall short of segmenting pricing to capitalize on the various types and levels of demand for individual games.

The primary ways in which teams segment pricing today are generally limited to:

The quality of the seat/view of the game
Volume discounts

Season ticket pricing—discounts for buying 81 games or some partial-season package
Group pricing for large groups to attend one game

Individual game discounts—student pricing, proof of purchase of sponsors’ products, military discounts, etc.
Day-of-Game premium, which is presumably intended to be an inducement to purchase tickets in advance, at a lower price.

Some teams have ventured into other forms of price differentiation. The two most common are:

Ticket and all the food you can eat
Premium pricing for selected opponents. In the AL, it’s often a team’s game against the Yankees or Red Sox, or an intra-division rival.

A New Way to Think About Ticket Pricing

While that may seem like significant pricing segmentation, pricing in the secondary market for tickets tells us that teams are too conservative with the premium prices they’ve set, and there may be opportunities to differentiate ticket pricing along some additional criteria. Some other reasons fans will pay more to attend a ballgame are:

Potential Milestone Games Bonds setting the home run record, A-Rod’s 500th and Glavine’s 300th wins are recent examples of “hot tickets.” At the extreme, an outfield seat at AT&T Park on August 6 was a lottery ticket for a $500,000+ jackpot—the price tag mentioned for the ball from Bonds’ record-setting home run.

Marquee Pitchers Pitchers such as Johan Santana, CC Sabathia, Daisuke Matsuzka, Carlos Zambrano, etc. have a substantial following and fans gravitate to games in which they pitch. When pressed, fans would likely pay a bit more to see their stud hurler take the mound, in part because they like the player and appreciate an expected well-pitched game, but also because they know he gives their team a greater chance to win, making it an overall better ballpark experience.

Weekend vs. Weekday games Weekend games typically are in much greater demand. The demands of the workweek make weekday night games inconvenient for a portion of fans. Also, the drawing radius expands on the weekend to include fans from several hundred miles away that have the leisure time over a weekend to make the trek to attend a ballgame or two. This group often includes visiting teams’ fans, which can dramatically expand the market for weekend tickets.

Mid-season vs. Early- and Late-season A couple of factors are at play that may place the Memorial Day-to-Labor Day games in higher demand. During "tourist season" there is increased ticket demand from the influx of travelers to MLB cities. Also, school is out giving kids and parents more time and flexibility to attend games, opening up the family market to teams. In northern cities the weather can be appreciably better in the summer months, further expanding the potential pool of attendees. Finally, strong attendance in September is usually reserved for the contending teams, as the .400 winning percentage ballclubs’ fans are already thinking about "next year." All of these factors combine to present an opportunity to price mid-season summer games at a higher level than April or September games. Today, only the Phillies currently practice this form of price segmentation.

Promotional Days A high value promotional giveaway night can be worth a premium to some fans. Some promotions have become overdone in today’s MLB environment, and items such as wristbands with a local bank’s logo to beach blankets compliments of your favorite wireless carrier hardly leave fans with a sense that they’ve received extra value for their ticket price. However, promos like bobbleheads of your team’s star players can be big hits among fans. In analyzing the effectiveness of the Orioles’ 2006 promo calendar (in my book, Diamond Dollars: The Economics of Winning in Baseball) I concluded that the Brian Roberts bobblehead giveaway was responsible for about 16,500 additional fans, after adjusting for day of the week, opponent, the weather at game time, how the Orioles were playing leading up to the giveaway date and other factors that impact attendance. A high impact promo is an opportunity to at least consider charging a premium price for a ticket to the giveaway day.

These are just a sampling of situations in which fans will pay more to attend a game, but just because fans may be willing to pay a premium doesn’t necessarily mean there is a pricing opportunity for the team.

One way to determine which segments offer true pricing opportunities is to evaluate them against several criteria—the degree to which fans will pay a premium, the ease (or difficulty) of executing a different price and the size of the overall opportunity. In addition to the untapped segments, we’ll also consider the differential pricing based on the opponent, as well as the day-of-game premium that some teams currently implement.

By analyzing several months of pricing history for tickets sold on Stub Hub, I was able to create some estimates of the amount of price premium and the volume of tickets that might be sold at a premium. Despite the lack of rigor in my sampling methodology, it should provide a perspective on the relative size of the opportunity. The following graph displays the results of the analysis, with the y-axis representing the degree to which the segment can command a premium (% above face value), while the x-axis is an assessment of a team’s ability to execute a unique pricing strategy for a given segment. An estimate of the size of the opportunity—the sheer volume of tickets and dollars transacted for each segment—is represented by the size of the bubble.

Two opportunities on the left side of the grid—milestone games and games started by marquee pitchers—may be impractical to capture through a team’s pricing practices, no matter how innovative they’re inclined to be. Often a milestone game becomes one (or loses its status) with one swing of the bat and marquee pitcher starts may not provide enough predictability, regularity or lead time to be the basis of a pricing plan.

This is where the recent MLB-Stub Hub relationship comes in, as it allows for teams to participate in the revenue opportunity by sharing in the transaction fees generated in the secondary market. Even prior to the Stub Hub arrangement, many teams created a ticket exchange on their own website as a service for season ticket subscribers to resell tickets, charging a fee for the transaction. The MLB-Stub Hub alliance formalizes and standardizes this arrangement for all teams, allowing them to share in the transaction fees charged by Stub Hub.

The Opportunity

The biggest pricing opportunity is likely to be the premium price teams can charge for certain opponents. Today about 60% of MLB teams have a differential price for selected opponents—their geographic or division rivals or marquee teams like the Yankees and Red Sox. However, most teams charge a modest premium, when compared to prices in the secondary market.

Having the Yankees in town for a three-game series has the potential to be the revenue equivalent of an extra home game. That’s tantamount to 20% of the seats reselling at an average premium above face value of 150%. An estimate of the annual size of the premium-price-based-on-opponent segment is $100-$150 million for all MLB teams. Current premium pricing practices capture only a small percentage of this opportunity, while the remainder is lost to the secondary market.

Fans would likely bear more aggressive premium prices, particularly for the best seats. Another mechanism to capture the price premium is for teams to hold back inventory of some of their highest price tickets and release them into the secondary ticket market at periodic intervals in the months and weeks prior to the big game or series. A team can net an additional $300,000 by releasing 3,000 (1,000 per game) of its best seats for an otherwise sold-out Yankee series, at an average $100 premium per ticket—a modest premium given the transactions I’ve recently observed on Stub Hub.

Another high opportunity segment is weekend games, particularly in the summer months, when visiting fans and families tend to schedule road trips to see their favorite team at an out-of-town ballpark. The Giants seem to be a best practice in this category by pricing Friday, Saturday and Sunday games at a 10% to 60% premium over weekday games. (Some teams attempt to address this opportunity in the reverse by offering a variety of discounts for weekday games.) An estimate of the weekend market places the premium pricing opportunity at more than $75 million, collectively, for all MLB teams.

Promotional giveaway nights may be an untapped opportunity for teams to secure higher revenue per ticket. If a bobblehead of your star player can sell on eBay for $10 or $20, is it unreasonable to charge a $5 premium price to a bobblehead giveaway game? As mentioned earlier, many of today’s giveaway promotions are not worthy of a premium priced ticket, making this opportunity considerably smaller than some others.

Two other ticket pricing opportunities are charging a premium for tickets that are bought on the day of the game and the all-you-can-eat (AYCE) ticket offers that some teams have recently instituted.

Game day premium pricing is a tricky strategy. For teams like the Marlins and Pirates—two of the teams that employ this approach—that play to small crowds, incentives should be geared to promoting a strong walk-up crowd on game day. Raising the game day price seems to fight that goal. The assertion that it motivates advance ticket purchases for these teams is dubious at best.

I believe game day premium pricing makes the most sense in a high capacity utilization scenario, i.e., 90%+ of the stadium is occupied, as is the case with two other teams that employ this approach: the Yankees and Dodgers.

The AYCE strategy that has gained popularity among teams this season is a bit more complicated to evaluate. While most of the other pricing opportunities we’ve discussed have virtually no cost component, there are food costs (and potentially food waste) associated with an AYCE ticket offer. As a result, this is more of a value price, discount strategy than a premium pricing opportunity.

Each of these demand differentiators creates a potential pricing arbitrage opportunity. Teams can either re-price to capture the difference in value, or rely on Stub Hub and its counterparts to reach the open marketplace and re-price. Teams can argue that they are addressing the higher fan demand for these premium games, by selling additional tickets—the other variable in the demand equation—instead of getting more revenue per ticket. But even in non-sellout situations, substituting higher attendance for a premium price is an inefficient solution that leaves money on the table.

The opportunity is not to move to a different point along the demand curve, but rather to shift to a higher demand curve brought about by one of the higher demand segments we identified earlier. Figure 2 shows a hypothetical demand curve for Field Box seats at Camden Yards. The solid line is a demand curve for a Tuesday in April against the Devil Rays, while the dotted line represents a Saturday in July against the Yankees. Baseball’s pricing opportunity is all about tailoring pricing to various segments to get closer to the price each fan is willing to pay. The result can be both higher revenue per ticket and higher attendance (Price B vs. Price A).

Pricing segmentation has its risks and some industries have arguably gone overboard, such as the airlines, where it’s common for one passenger to pay five times as much for the same flight as the passenger in the next seat. Airline pricing may be a poor model to emulate as their lack of pricing transparency and the arbitrary appearance of their pricing practices erodes customer trust and chips away at the airline’s brand equity.

The Solution

The key is to develop a manageable pricing strategy that capitalizes on the revenue opportunity. There are a few “musts” that are critical for any successful pricing segmentation plan.

The plan needs to have an appropriate level of transparency that builds trust with fans, accounts for the capacity of a team’s stadium, and is capable of being flawlessly executed. Another important consideration is to balance a team’s need for revenue certainty and stability with the upside of slicing and dicing pricing to capture pockets of demand. It would start with analyzing the local market to determine which of these opportunities are likely to generate the biggest impact.

If teams believe the MLB-Stub Hub agreement "takes care of the secondary market opportunity” and they don’t need to give it a second thought, they’re missing the point. The only ticket transactions teams should concede to the secondary market are the impractical to execute (e.g., milestone game premium prices) and tickets that are reallocated to meet the "convenience" need of the seller (i.e., a seller wanting to put the tickets in the hands of someone that can use them, rather than a profit-motivated seller). Beyond those types of transactions, teams should strive to set prices that make the need for the marketplace to re-price tickets obsolete.

For teams that believe this is a project for another day because they don’t feel the time is right to capture pricing upside, don’t confuse absolute prices with relative prices. Think of the "baseline" as the price for a Tuesday night game in April against a perennially losing team. At Camden Yards, tickets to a weekend series in July against the Yankees should command a 60% to 100% premium price versus a Tuesday in April against Tampa Bay. If the resulting price premium is too aggressive, I would make the case that the April price is too high, not the other way around.

The goal of pricing segmentation is to leave fewer dollars on the table by setting prices much closer to the amount fans are willing to pay. If all MLB teams implemented a solid, well-thought-out segmented pricing plan, I estimate the revenue opportunity to be $300 to $500 million. In the uneven world of MLB economics that means a team would benefit by $10 to $50 million annually—enough to buy more than a few wins in the free agent market, or invest in the player development system.

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Diamond Dollars: The Economics of Winning in Baseball (Part 3)

Vince Gennaro — 2007-03-29T04:04:15+00:00

Moneyball or Great Player Development?

Much has been written about the success formula for the Billy Beane–led Oakland A’s. In Michael Lewis’ book, Moneyball, he cites a strategy of finding players with high OPS (on-base percentage + slugging percentage) as a key to the A’s success. These OPS standouts were undervalued by the market, meaning other teams valued them less than their fair value and less than the market valuation for batting average and slugging percentage.

Beane took advantage of this market inefficiency after he brought in sabermetric analysts who showed that OPS had a closer link to winning games than did slugging percentage or batting average. Lewis gives credit to Beane for building his team around high OPS players at discounted prices. It’s indisputable that Beane’s A’s enjoyed great success from 2000 to 2004. In 2000 through 2002, Beane delivered first-quartile wins with a fourth-quartile payroll, while in 2003 the A’s remained in the top quartile in wins but slipped to the third quartile in payroll. (2004 saw the A’s in the second quartile in wins and 16th in overall payroll, a more modest accomplishment when compared to their success of the previous years.)

Studies have shown that Lewis was right about the valuation of high OPS players. In a study by economics professors Jahn K. Hakes and Raymond D. Sauer, “An Economic Evaluation of the Moneyball Hypothesis,” the authors validate the lower valuation from 2000 to 2003 but claim the inefficiency was gone by the 2004 season. While the study reassures us that Beane and the A’s exploited inefficiency in the way players were compensated and as result enjoyed a discount in their cost of wins, it did not address an important follow-up question: Was this exploitation the key enabler for the A’s to deliver three straight years of first-quartile wins at a fourth-quartile payroll level?

My analysis indicates it did help, but had far less financial impact than many readers have inferred from Moneyball. I contend that the mix of players on the A’s roster, by pay grade—restricted, arbitration eligible, and free agents—had far more impact on their ability to deliver top wins/lowest payroll performance.
Focusing on the 2001 season as an example, possibly Beane’s crowning accomplishment to date (the second-most wins in MLB, with the second-lowest payroll), we can analyze the composition of the A’s roster and get to the core of our question.

The roster of the 102-win ball club was heavily skewed to young, captive talent. Of the top 14 win contributors (those with marginal wins of 2.9 or greater, according to Baseball Prospectus’ WARP statistics), eight were restricted players not yet eligible for arbitration, four were arbitration eligible, and two were in the free-agent pay grade. The mix of contribution to wins is 62% for the least expensive, restricted, group, 28% for the arbitration-eligible players, and 10% for free agents. In 2000, 90% of the A’s marginal wins came at a discount to the market price, since MLB’s rules prevent players from shopping their services and achieving a market price through an auction process until they are eligible for free agency.

The group of eight restricted players averaged 5.9 wins each, for a total of 51 marginal wins at a payroll cost of about $2.5 million. If we take our 2005 estimate of free-agent costs discussed in Chapter 7 and discount it back to 2001, we can estimate the restricted, arbitration-eligible, and free-agent rates per marginal win. If the A’s had achieved their 102 wins with the league average mix of 35% restricted, 23% arbitration eligible, and 42% free agents, their payroll would have been in the neighborhood of $104 million.

Their payroll of approximately $34 million represented a savings of $70 million versus the average mix of players by pay grade. Of the 12 high-impact restricted and arbitration-eligible players on the 2001 A’s, seven were originally signed by the A’s organization (five drafted in the annual June amateur draft (Jason Giambi, Eric Chavez, Tim Hudson, Mark Mulder, and Barry Zito) and two signed as international free agents (Miguel Tejada and Ramon Hernandez), while Frank Menechino was acquired through the Rule 5 draft.

According to the marginal win statistics from Baseball Prospectus, had the A’s not had the services of Mulder (8.7 WARP), Hudson (7.9), and Zito (7.3), and instead had replacement players in their roster spots, they might have been a below .500 team. So while the A’s clearly had a keen eye for talent, it may have had much more to do with pitching and less to do with OPS, at least for the 2001 club.

In the first several years of this decade, the A’s defined success as winning efficiently—making the play-offs with a low payroll. At the core of the A’s formula for success was their ability to draft and develop superstar players, not their ability to find an occasional bargain hitter who was undervalued on the market because his keen batting eye allowed him to take more walks. They clearly schooled their up-and-coming hitters—Giambi, Tejada, and Chavez—on the art of plate discipline, and it contributed to their success.

In the end, the cost savings derived from internally growing their talent had far greater financial impact than any inefficiency they exploited in batting statistics. The A’s masterfully exploited the ultimate success formula in MLB: draft and develop enough talent to staff your major league roster internally and reap the discount afforded by captive players as they await their free-agent payday.

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Diamond Dollars: The Economics of Winning in Baseball (Part 2)

Vince Gennaro — 2007-03-26T04:03:15+00:00

One strategy that has the potential to shift the marginal revenue curve to a higher level is the team-owned regional sports network (RSN). Of the 12 teams linked to an RSN, four are among the wealthiest teams (Yankees, Mets, Red Sox, and Cubs), and another four are in the top half of the MLB economic food chain (Orioles, Phillies, White Sox, and Indians). The Royals are the only team among the true have-nots to have an RSN. (The Royals have announced their intention to exit the RSN business in favor of a contract with Fox Sports Midwest, beginning in 2008) (See Figure 1)

In a classic case of the rich getting richer, teams endowed with a strong economic base are venturing into the broadcast business to further enhance their economic prowess. I don’t think it’s an overstatement to say that RSNs are re-writing the economics of MLB teams.

Figure 1 - Regional Sports Networks
NETWORK                            TEAM AFFILIATION
New England Sports Network (NESN)  Red Sox    
Mid-Atlantic Sports Network (MASN) Orioles, Nationals
YES Network                        Yankees
Rogers Sports Network              Blue Jays
Turner Broadcast Station (TBS)     Braves
Comcast SportsNet Philadelphia     Phillies
Comcast SportsNet Chicago          White Sox, Cubs
Royals Sports Television Network   Royals
SportsNet NY                       Mets
Sportstime Ohio (STO)              Indians
WGN                                Cubs

How has the RSN altered a team’s link between winning and broadcast revenues? The traditional approach has been for teams to sell their broadcast rights to a regional Fox Sports affiliate (or their competitor) in exchange for a rights fee. These contracts are generally multiyear, for a fixed amount of dollars over the life of the agreement, with some “bonus” provisions for the team reaching the postseason or other on-field success.

The broadcast company—we’ll call it Fox Sports North (FSN)—pays a rights fee to the team and sells the advertising time on team telecasts to consumer marketers. FSN figures that if the team wins (e.g., reaches the postseason), there will be greater fan interest, leading to higher ratings and more viewers. As a result they will be able sell advertising at higher rates and pass some of the increase back to the team in the form of a bonus. FSN is not willing to give up the entire upside resulting from an exciting playoff chase, since they are bearing a financial risk with a fixed rights fee. If the team plunges into the cellar and no one watches their games, FSN is still on the hook to pay the team the fixed fee.

The alternative broadcast arrangement that is gaining in popularity over the last several years is the team-owned regional sports network. RSNs have rewritten the economic rules of the game by giving team ownership a more direct vehicle to monetize fan demand. Understanding how revenues flow through the RSN when a team’s on-field performance varies is more complicated.

Under this scenario, the RSN gets subscription fees from cable distributors and satellite providers to “buy” their channel and carry it on their delivery system. The RSN then sells advertising time on telecasts for a second revenue stream. While this may not sound much different than the way in which a Fox Sports affiliate would operate, the big difference is the team owns all or a large portion of the RSN. As a result, this arrangement places teams in the broadcasting business, with an equity position in any value created in the broadcast entity.

To determine the impact of a team’s on-field performance on broadcast revenues we need to account for the ownership structure of the RSN. To the extent the team (or team owners), has an ownership stake in the RSN, we can “impute” a share of the profits to the team, as if the team and the RSN were two divisions of the same company. We’ll call this “creating transparency” regarding the ownership structure of the RSN.

For example, the Boston Red Sox have an 80% ownership stake in the New England Sports Network (NESN), their RSN. In addition to the rights fee NESN pays the Red Sox for the right to broadcast games, by imputing a portion of NESN’s profitability to the Red Sox, we can better understand their underlying economic payoff to win games. We could not accurately assess the Red Sox true financial gains from improving the team, if artificial partitions blocked the flow of this important revenue stream.

In addition to their rights fees, which show up on the Red Sox financial statements, we may need to add another $10 to $12 million for the Red Sox share of NESN profits. It is critical to include the full value of the broadcast relationship when measuring the value of a win. The Red Sox share of NESN’s profits could mean an extra half-million to million dollars per win over key segments of the Red Sox win-curve.

Another way in which RSNs alter the win-revenue relationship is by creating asset value in a broadcast network. In addition to the revenue impact, the team maintains an equity stake in a network (either directly or indirectly) and bears the risk and reward of the valuation of the network as an asset for potential future resale. Depending on the size of the market and popularity of the team, the Fox Sports-type arrangement would net a team a steady revenue stream of anywhere from $5 million to $50 million in straight rights fees.

With an RSN a team has a variable revenue flow that is more closely linked to their on-field performance and their popularity and an asset that can be worth anywhere from $100 million for the newest upstart RSNs to nearly $2 billion for the mega-RSNs like the YES Network. This asset value rises and falls with broadcast ratings and advertising and distribution fees, which can be greatly affected by a team’s wins and losses.

This broadcast arrangement impacts the business of baseball in at least four ways:

RSNs provide a more direct connection between winning and broadcast revenues. In contrast to the traditional broadcast model, where a team is generally paid a fixed rights fee, regardless of their win-loss record, thereby disconnecting broadcast revenues from the team’s on-field performance, teams with RSNs get immediate financial feedback. Since ratings, and therefore advertising revenue, rise and fall with on-field performance, team-owned networks participate directly in the revenue stream that winning or losing generates. In a sense, RSNs raise the stakes of winning and losing for an MLB team.

RSNs are a team marketer’s dream, as they provide a unique vehicle for marketing the team and building its brand. Instead of selling only telecasts to an independent network, teams with RSNs can inundate their fans with team-related entertainment and brand-building propaganda, all rolled into one programming schedule. Does the Yankeeography of Derek Jeter entertain viewers, or tout his marquee value and build the Yankees brand in the eyes of fans? The answer is “yes” to both questions.

Until MLB changes the way in which it treats “related party transactions,” teams with RSNs may get a break on their revenue-sharing payments. The new Collective Bargaining Agreement (CBA) is thought to contain provisions which may prevent a team from receiving an artificially low rights fee, reducing their revenue sharing contribution while they rake in the cash in their RSN. This issue will likely get solved soon, and even if it does not, it ranks among the least important ways in which RSNs are changing the economic face of baseball.

The most important role the RSN plays is to be an additional vehicle for a team to create financial value, thereby dramatically altering the economics of owning and operating a big league club. RSNs represent an additional separate asset of significant tangible value, while still being intertwined in nearly every way, with the team. In the same way companies create value by entering into businesses that have synergies with their core business, teams have branched out into the broadcasting arena.

A successful RSN can generate more financial value for a team by turning one of the team’s assets—its broadcast rights—into a full-fledged operating business. This strategy creates value because there are true synergies between the team and the RSN and because an MLB teams’ broadcast rights are a substantial enough product to form the basis of an entire network. The RSN also creates an additional option for liquidity. A team can build up the RSN, and then sell it, reaping its value, while still owning the team, or vice versa. In many ways, the RSN is a brilliantly conceived brand extension of an MLB franchise.

The ultimate payoff for launching an operating a successful RSN is the creation of an asset that can be of equal or greater value than the team itself. Valued at 10 to 12 times profits, or five times revenue, an RSN could achieve a valuation of several hundred million dollars or more for a mid-market team after just several years of operation. Let’s use the St. Louis Cardinals, fresh off their 2006 World Championship, as an example. Cardinals telecasts are currently seen on Fox Sports Net Midwest (FSNM), with distribution into about 4.3 million homes, via satellite and cable, in six states.

If the Cardinals were to start their own RSN, when the current FSNM agreement expires, by charging cable and satellite operators a modest $1.50 per month, they could generate a potential of $75 million or more in annual fees. In addition, a conservative estimate of the ratings of a 90-win Cardinal team could lead to another $15 million in advertising revenue. A $90-million-annual-revenue RSN, owned by the Cardinals has the potential to be valued at about $450 million. As long as the network’s operations could cover the current FSNM rights fee to the Cardinals and turn a normal profit, the Cardinals would have a new asset, which they could factor into their win-curve.

Since the RSN’s asset value would rise and fall with the team’s on-field success, I estimate the RSN could add as much as 50% to the value of a win in the “sweet spot”—the 85- to 95-win range. Over that range a five-win improvement could generate an additional $7 million, allowing a team like the Cardinals to justify competing for top dollar free agents.

So what is the downside of a team choosing to go the RSN route instead of selling the rights to a Fox Sports-type of affiliate? Why doesn’t every team do this? If a team is in a small market, or its team lacks the popularity and ratings to achieve some critical mass level, they may not have enough clout to secure distribution agreements with cable operators for the subscription fees that ultimately make the math work for the team.

If the Pittsburgh Pirates, coming off consecutive 67-win seasons, tried to charge a $1.50 per month subscription fee to carry a Pirates-owned sports network, cable operators might say, “No thanks.” Even if cable operators went with the plan, but offered the channel only as a premium channel to its customers, how many Pirates fans would pay to see their games on television?

If the distribution into households is too low, there would not be much appeal to advertisers, cutting into an important portion of the revenue stream of the RSN. It adds up to a lot of risk to bear for a small market, historically weak-performing team. As a general rule of thumb, if it’s a great challenge to sell tickets to games, it will be an even bigger challenge to sell your own RSN to cable operators and advertisers.

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Diamond Dollars: The Economics of Winning in Baseball (Part 1)

Vince Gennaro — 2007-03-22T04:05:15+00:00

Winning by the Numbers

For an MLB team, it pays to win. But in the local market-driven structure of Major League Baseball the spoils of victory can range from pennies to millions. The rewards from winning vary based on three key variables—an economic system driven by the size and fan loyalty of a team’s local market; the team’s level of competitiveness, defined by its regular season record; and the windfall of revenues that accrue to a team for reaching the postseason. The win-curve is an attempt to quantify the implicit contract each team has with its fans, season ticket holders, and sponsors. At various levels of competitiveness—conveniently measured by annual win totals—fans will allocate varying amounts of time and money to support their favorite ballclub. The more successful the team, the greater the fan and sponsor support, and hence the higher the revenue totals.

In order to sort out these effects for each team, we can turn to the regression analyses to estimate the win-curve for each team (discussed in more detail in an earlier chapter). By analyzing historical data that captures fan behavior, we can ultimately assign an estimate of a dollar value for each win. More specifically, the model estimates the change in a team’s revenues at various levels of team regular season wins. For example, the Houston Astros are expected to generate $1.2 million more revenue as an 81-win team versus an 80-win team. That’s equivalent to saying the value of the Houston Astros' 81st win is $1.2 million. At the extremes, the Pittsburgh Pirates 71st win generates about $300,000 in incremental revenue, versus the $4.2 million that accrues to the Yankees for their 90th win.

Later I will discuss the Yankees in more detail, but first let’s examine the differences across markets and teams. Since the win-curves are not linear—meaning all wins are not valued the same—and the slope of the curve varies by team, an apples-to-apples comparison looks at the change in teams’ revenues at the same point on the win-curve. Figure 1 shows the value of the 81st win—meaning the marginal or incremental value of the last win for a team that plays .500 winning percentage baseball for a season. Alternatively, we can think of the value of the 81st win as the difference in revenue for the team if it won 81, rather than 80 games for the season.

Figure 1 - Dollar Value of 81st Win
TEAM    $ VALUE
NYM     1.4
ATL     1.2
SF      1.2
HOU     1.2
SEA     1.2
BOS     1.2
NYY     1.1
LAA     1.1
CWS     1.0
STL     1.0

CHC     1.0
CLE     1.0
PHI     0.9
AVERAGE: 0.9
TOR     0.9
LAD     0.8
MIL     0.8
TB      0.8
COL     0.7
FLA     0.7

ARI     0.7
KC      0.7
DET     0.7
SD      0.6
BAL     0.6
MIN     0.6
OAK     0.6
TEX     0.5
CIN     0.5
PIT     0.5

$ Value in millions, based on 2006 win curve

When evaluating a team’s marginal revenue at the 81st win level, the value is nearly entirely related to fans’ immediate response to regular season wins. Since the probability of an 81-win team reaching the playoffs is 2%, only a small portion of the playoff revenue stream is included in the 81st win calculation. By contrast, if we look at the value of the 90th win (Figure 2), we incorporate more of the value of the postseason revenue stream, as well as capture a team’s fans response to a highly competitive winning ballclub. Some teams’ rankings change considerably when comparing the two different points on the win-curve. The Mets, Mariners, Giants, Yankees, Red Sox, White Sox, and Cardinals remain in the top one-third of revenue for all teams for both win levels. However, the Braves move from second highest ranking at 81 wins, to 23rd highest at 90 wins, possibly reflecting Braves fans’ weak response to appearing in the postseason and their relative apathy to a competitive team.

Figure 2 - Dollar Value of 90th Win
TEAM    $ VALUE
NYY     4.3
NYM     4.2
CHC     3.8
LAD     3.6
CWS     3.5
SF      3.5
PHI     3.4
STL     3.3
BOS     3.0

BAL     3.0
TOR     2.9
CLE     2.9
HOU     2.9
AVERAGE: 2.8
TEX     2.8
LAA     2.7
ARI     2.7
SD      2.6
DET     2.5

COL     2.4
TB      2.1
FLA     2.1
ATL     2.1
MIL     2.0
OAK     1.8
CIN     1.8
PIT     1.7
KC      1.7
MIN     1.6

It’s also interesting to evaluate the revenue change over five-win increments. For example, if we look at the marginal revenue when a team moves from the 86- to 91-win level, it sheds light on a team’s economics in a playoff contention mode. Alternatively, we can think of the marginal revenue from the 78- to 83-win level as the economics of teams’ quest for respectability. The marginal revenue totals in the latter case are more clustered than the marginal revenue for teams in playoff contention. To measure the dispersion of the value of wins for each category, we can look at the standard deviation across all teams. The standard deviation of the “respectability” category is $1.2 million, vs. $3.2 million for the “playoff contention” category. This difference may suggest that as a team moves up its win-curve to become a playoff contender, the rich get richer and the poor get poorer.

The implication is that weaker revenue teams can effectively compete with the “big boys,” as measured by the value of a win, when both have non-contending status. However, when teams strive to reach that 90-plus win zone in their quest for a playoff spot, it becomes more difficult for the economically challenged teams to compete for players with the high revenue, or high-fan-loyalty teams. Included in the 86- to 91-win playoff contention marginal revenue estimate for all teams is a portion of an anticipated revenue stream that would accrue to the team for reaching the playoffs. The economically advantaged teams typically expect a higher postseason revenue stream, further separating them from their weaker revenue counterparts and making it difficult for the disadvantaged teams to justify competing for players in the free-agent market.

A team’s location on the win-curve—their absolute level of wins—has a dramatic impact on the value of a win. To understand the power of the win-curve location, you only have to look as far as the marginal revenue of a Twins team in playoff contention. A five-win improvement for financially challenged Minnesota, from 86 wins to 91 wins, would yield $6.8 million in incremental revenue. When comparing this revenue estimate with teams who are striving for respectability (78- to 83-win category), their marginal revenue is greater than all teams, except the Mets. The location on the win-curve is so important that it often trumps market size as the key driver of a team’s marginal revenue opportunity.

Figure 3  $ Value of five wins
TEAM     78-83    86-91
CHC      4.8      15.9
NYM      7.1      18.0
LAD      3.8      15.1
BOS      5.8      13.3
LAA      5.2      11.8
CLE      5.0      12.5
TOR      4.3      12.5
CWS      5.2      14.9
SF       6.2      15.2
ATL      6.1      9.6

OAK      3.0      7.7
STL      5.1      13.9
PHI      4.5      14.5
HOU      5.9      12.5
SEA      5.8      16.1
MIL      3.8      8.5
TEX      2.7      11.4
MIN      3.0      6.8
DET      3.4      10.4
AVERAGE: 4.2      11.7

SD       3.3      10.8
ARI      3.6      11.3
BAL      3.1      12.3
NYY      5.6      18.4
CIN      2.6      7.5
PIT      2.3      7.3
TB       3.9      9.1
FLA      3.5      9.0
KC       3.6      7.4

The slope of the win-curve can vary across teams, reflecting the degree of fan responsiveness to changes in wins. Comparing the Yankees to the Braves illustrates two extreme examples. Yankees fans maintain a high expectation regarding their team’s competitiveness and expect the Yankees to win and reward them accordingly. To think about this in the reverse, or negative, we could say that if the Yankees don’t contend for the postseason, attendance and fan support would drop precipitously, as the team would fall far short of fan expectations. The result is a steep win-curve. Conversely, the Braves have a long-standing loyal fan base, which seem to be unfazed by their consistent success, leading to a flat win-curve. A comparison of the two win-curves shows the Braves generate more marginal revenue until 89 wins, when the two lines cross and the Yankees leave the Braves in the dust from a marginal revenue standpoint. Moving from the 89- to 99-win level on the win-curve, the Yankees generate nearly $16 million in marginal revenue more than the Braves.

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Player Value: The Last Piece of the Puzzle

Vince Gennaro — 2006-02-08T04:04:15+00:00

There is no simple answer to the question of how much a player is worth. A common, even if somewhat flawed, approach is to compare the salary of similar players, creating a set of “comparables” as if it were a real estate transaction. The problem with this approach is that it assumes there is some intrinsic value to a player who can generate six wins.

As a six-win closer, Billy Wagner’s “value”—the marginal revenue he generates for his team (relative to a replacement player)—can range from less than $1 million to more than $10 million, depending on his employer. In the rational world of economics and finance, a player’s dollar value is largely situational; it depends on the market, as well as on the team’s level of competitiveness.

The economics of winning dictate that a team’s level of competitiveness has a dramatic impact on a player’s value. There is little glory or marginal revenue from improving a 69-win team by six wins. The revenue gains from improving a 79-win team by six wins are somewhat larger, but they pale in comparison to improving an 89-win team by six wins. In the latter case, the player acquisition that generates the six-win improvement is considered the “last piece of the puzzle.”

This could be exactly what the Mets were thinking (and calculating) when they settled on their deal to bring Billy Wagner to New York. If you entertain the assumption that Omar Minaya’s offseason moves prior to the signing of Wagner elevated the Mets to, say, an 89-win team, Wagner becomes the potential “last piece of the puzzle” for the Mets to secure a berth in the postseason. Improving an 89-win team to 95 wins more than doubles the team’s chances of making the playoffs (93% vs. 44%).

Crediting Wagner and his expected six-win performance with the postseason revenue impact of thrusting his team into the playoffs brings his annual value to over $10 million. Figure 1 shows Wagner’s value to the Mets in three different scenarios: as a 74, 82 and 89-win team, prior to his arrival.

The “step-up” in value from each scenario is over $2 million. The Win Dollars (Win $) represent the incremental revenue that a player generates from improving his team by six wins (in the case of Wagner) and the resulting attendance, concessions and other revenue. The Postseason Dollars (PS $) represent the change in the expected value of the postseason revenue stream, based on how the player’s performance increases his team’s odds of reaching postseason paydirt. (For more on the methodology of calculating a player’s value see my article Player Value: The Postseason Effect.)

Conversely, if Wagner were the property of a “competitively challenged” 75-win Pittsburgh Pirates team, his value would be only $2.2 million. However, even a “small-market” team like the Pirates could justify paying $6.2 million for Wagner if he is their last piece of the puzzle, joining a hypothetical 89-win Pirates team and improving them to 95 wins.

There are three key leverage points in the player value equation: the player’s level of performance, the team/market and the competitiveness of the team. In order to understand the relative importance of each, I developed an example to allow a comparison. Regarding playing performance, I will compare Alex Rodriguez, a 10-WARP Yankee in 2005, and Brandon Inge, a six-WARP Tiger in 2005. (I’ve rounded their WARP values from the bp.com website to the nearest whole number to simplify the math for my illustration.)

For a team comparison I’ve chosen the mega-market Yankees, who earned about $400 million in revenue in ’05, vs. the Tigers, who generated about $100 million in revenue prior to any revenue-sharing distribution by the league. To demonstrate the power of the situation (i.e., the team’s level of competitiveness), I used my model’s estimates of marginal revenue at various win levels.

The first scenario looks at A-Rod as a 2005 Yankee and Inge as a 2005 Tiger. The 95-win Yankees’ revenues are $22.1 million higher, due to the on-field contribution of A-Rod, while the 71-win Tigers benefited by $1.6 million from Inge’s six-win contribution in ’05.

The dramatic difference in value has much to do with the win total of the two teams. The Yankees’ 95 wins placed them in the thick of the playoff hunt. About $8 million of A-Rod’s $22.1 million in value comes from revenue generated from the Yankees reaching the postseason, as A-Rod’s on-field contribution significantly elevates New York’s chances of reaching the playoffs. (Presently, the Yankees’ Net Present Value of reaching the playoffs are actually lower than the MLB average, due to the diminishing returns from reaching the postseason for 11 consecutive years.)

The limiting factor in Inge’s value is the win level of Detroit. Inge's on-field contribution generates six wins that are simply not as “important,” as measured by fans’ responsiveness to changes in a team’s win total. Attendance and other revenues do not increase much if a team improves to 71 wins vs. 65 wins.

Let’s look at how values might change if we switched A-Rod and Inge. As a Tiger, A-Rod would become a 10-win third baseman on a 75-win team, and Inge would be a six-win player on a 91-win Yankee team. With the switch of teams, Inge’s value of $13.2 million is more than three times greater than A-Rod’s $4.2 million. (See Figure 3.)

This comparison leads to the question: “Is it the team—the mega-market Yankees—that drives value, or is it the win total and playoff contention?” To illustrate the power of the win total, let’s look at the value of a six-win player across several teams (Yankees, Tigers, Twins and A’s) at various win totals. Figure 4 shows how the value of a six-win player increases as a team’s win total increases and it moves into contention for the postseason and its hefty revenue stream. (Note: “85 wins” refers to the value of a six-win player added to a 79-win team).

The chart clearly shows that there is a team or market effect—contrasting the Yankees with the Twins or A’s—particularly at 85 wins or more. However, the larger effect on player value may be the team’s level of competitiveness. As each team moves up the win curve, its revenues accelerate, peaking at about 95 to 98 wins.

There are several interesting conclusions shown in Figure 4. Note how the Yankees' economic advantage over small-market teams diminishes at lower win totals. As an 80-win ballclub, the Yanks hardly maintain any true economic advantage over Detroit or Minnesota in bidding for a six-win player. Also, a Tiger team in playoff contention (85 to 90 wins) will generate more revenue by adding a six-win player than an 80-win Yankee team. Even a contending Minnesota or Oakland ball club has as much economic justification to pay $6 million per year for a six-win player as a sub-.500 Yankee team.

While market size is an important variable in determining a player’s value, a team’s location on the win curve is also a significant factor. For the Twins, a six-win player’s value doubles for a 95-win team vs. an 80-win team, while for the A’s, his value would triple. In the ultra-competitive free-agent market, pricing tends to be set by teams adding the last puzzle piece—or at least teams that think they are adding the last piece to vault them into the postseason.

With the Blue Jays’ signing of BJ Ryan and AJ Burnett, one can only assume that General Manager JP Ricciardi believes these players will be the difference between mediocrity and a playoff berth. Even if he’s correct, for these contracts to “pay out," the Jays would need to reach the postseason three times and raise ticket prices by 20% over the five-year life of these two contracts. To protect the Jays on the downside, Ricciardi likely believes he will be able to unload Ryan to the Yankees (or another team grasping for the postseason) if his team turns non-competitive.

This analysis attempts to illustrate that player value is dynamic, not static. A player’s value is dependent on his performance, his team and their market and the team’s level of competitiveness. There may not be a simple answer to the question of how much a player is worth, but there is an answer for every player on any team at every win level.

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Player Value: The Postseason Effect

Vince Gennaro — 2006-02-07T05:09:15+00:00

In Parts 1, 2 and 3 of my recent article on measuring the dollar value of a player to his team, I took readers through my process of estimating a team’s marginal revenue attributable to a player's on-field performance. The core of my analysis is a series of regressions to estimate the all-important relationship between winning and revenues for each team. The highlights of my conclusions are:

There is a measurable relationship between winning and revenue. For each team, there is a statistically significant relationship between winning and attendance. Changes in attendance are a bellwether for fan interest and revenues, such as those from concessions, broadcasts, and ad and marketing sponsorships.

Significant variations occur across teams. Each team generates a different level of marginal revenue from an identical improvement in wins. Market size is only one factor in explaining the difference, as the Cubs and White Sox have different win-attendance-revenue relationships. Also, contrary to the market size argument, St. Louis has a greater revenue impact from improving their wins than does Detroit. Each team’s fans have a unique relationship with the team, loyalty factor and expectations, which shape the win-revenue relationship (win-curve).

Location on the win-curve has the greatest impact on value. The win-curve is not linear. The value of each win differs, depending on the degree to which it enhances a team’s competitiveness. If a 5-win player improves a team from 88 to 93 wins, the economic value is much greater than the 5-win player who improves a team from 78 to 83 wins.

In previous articles I made frequent reference to an important “open question” for player valuation – the revenue stream accruing to a team as a result of reaching the postseason. Since that time I have quantified the value of a playoff appearance for each team and assessed its impact on player value. As a result, I now have completed my estimates of the marginal revenue, and hence player value, for every win for each team.

This 2-part article will focus on quantifying and interpreting the value of the postseason and incorporating it into the dollar value of a player. Since the financial rewards to a team for achieving a playoff appearance can be equivalent to as much as 25% of a team’s annual local revenue, it can have a dramatic impact on the dollar value of the player(s) whose performance elevates his team into October baseball.

In fact, the value a player generates for his team can be as much as two or three times greater if he is the “last piece of the puzzle”—improving a team to say, 94 wins—as opposed to improving a team to a .500 level.

Before we delve into quantifying that value, let’s understand why the postseason comes with a hefty reward. As a competitive team strives to clinch a playoff berth, fan interest swells (as captured in the win-attendance curves presented in earlier articles), but once a team reaches the promised land, the mad scramble for playoff tickets ensues. Fans’ struggles range from the choice of seats remaining for markets where availability is less scarce, to the exorbitant price of tickets on the secondary market for teams like the Cubs or Yankees, where demand is already high.

The primary solution for some fans is to avoid the problem in the future by enlisting in some form of season ticket plan for the following season. As a result, the primary driver of value for a postseason appearance is season (or partial season) ticket sales to fans who want guaranteed access to playoff game tickets, now and in the future. (In mid-January the White Sox announced a new record high season ticket sales for 2006.) A second source of value is ticket pricing leverage.

Since 1995, when baseball returned from its work stoppage by adding a wild card spot to the postseason, teams appearing in the postseason have averaged ticket prices increases nearly double the price increases for teams that did not reach the playoffs. The pricing power that comes with the playoffs can be counted as “value”, as it’s an additional way to monetize the increased fan demand for tickets. Other sources of revenue from a playoff appearance include increased luxury suite demand, greater ad and sponsorship opportunities, advance ticket sales for the next season and broadcast bonuses or upsides.

It all adds up a financial bonanza for the ballclub—a revenue stream over a four to five year period—that needs to be incorporated into the marginal revenue estimate of value for players who elevate their team to the perch of the postseason, or at least increase their team’s chances of getting there.

By analyzing team attendance for the years immediately following a team’s initial postseason appearance, and adjusting for the team’s number of wins and other important factors, such as the new stadium effect, I was able to quantify the attendance and revenue effect from a postseason appearance. It is important to differentiate an “initial’ postseason appearance, defined as the first trip to the playoffs in a 3-year period, from successive postseason trips.

Many of the same benefits accrue from a second and third consecutive appearance, but the dollar value is diminished. There is ample evidence to indicate additional ticket packages are sold (which also drives concession revenues), and ticket pricing upside still exists, but at a reduced rate. So for a team whose initial postseason appearance generates a revenue stream with a net present value (NPV) of say, $20 million, a second consecutive playoff berth would yield approximately $13 million NPV, while a third would generate an incremental $9 million NPV.

As an example, Figure 1 shows the marginal revenue curve for the Phillies. I’ve layered the value of the postseason (PS $) on top of the estimation of the win-curve (Win $), the concept and principles of which were detailed in parts 1 and 2. While in my earlier articles I established the notion of the dollar value of a win increasing as the performance of the team improves, the effect is considerably more dramatic when adding in the postseason effect.

In order to translate the team’s postseason financial rewards into the dollar value of a player, I quantified the team’s probability of reaching the postseason, based on their win total. By looking at history of the wild card era (1995 to present) and applying statistical techniques, I was able to estimate the playoff probability associated with each win total.

I can quantify the value attributable to a player by simply calculating the expected value of the postseason—the probability x the dollar value. For example, an 85-win team has a 10% chance of qualifying for the postseason, while a 90-win team has a 57% probability. Using a hypothetical example, let’s say the Phillies win 90 games next year, and Jimmy Rollins duplicates his 5-win performance of this year. Jimmy Rollins’ value has two components: He gets credit for the 86th to 90th win, worth $3.5 million of value (Win $), plus his postseason effect.

He would elevate the playoff probability of the Phils by 47 percentage points (10% to 57%), increasing their chance at the $14 million revenue stream that would result from their first postseason appearance in over a decade. The math (Figure 2) behind Rollins’ value is $3.5 million over replacement value for helping the Phils improve from 85 to 90 wins, plus $6.6 million in postseason value for improving their playoff odds, for a total of $10.1 million of “value”.

Jimmy Rollins’ Value as a 5-win player to a 90-win Phillies team*

                           ∆ PS
                        Probability    PS NPV$         $ Value          
Win $ Value                 ---         ---           $3.5 million	    
Postseason $ Value          47%      $14 million      $6.6 million      		
Total $ Value                                        $10.1 million

*Assumes Rollins performs at a 5 WARP level

Before we make the leap to say that a player’s salary, in any one year, should be equal to his value, there are a few things to consider. First, it’s more sensible to evaluate a multi-year time horizon when trying to relate salary to “value” created. “Does the value a player can create over a four-year period, justify his salary demands over that timeframe?” is a better approach to matching salary and value.

Also, paying a player the full value he generates may not adequately compensate the ballclub for bearing the risk of a potential injury or sub-par year. To return to our example, Rollins cannot guarantee a 5-win performance to the Phillies, which may entitle the Phils to some “discount” to his value as the team bears the risk of variations in Rollins’ performance. Incentive-laden contracts have become an effective method of defraying the team’s risk, while still rewarding the player for his performance.

Furthermore, if Rollins’ teammates perform below expectations, then Rollins’ value declines. The reality is that there is less economic value created by improving an 82-win team versus an 85-win team. Five wins added to an 82-win team only improve the playoff odds by 21%, less than half the improvement of five wins added to an 85-win team. Improving a 79-win team by five wins creates even less value (See Figure 3).

In my final installment on player dollar value, I will delve deeper into the “last piece of the puzzle” economics and explore how a player’s value can differ dramatically, depending on his team’s win total. I will also discuss the relative importance of three important variables in the player value equation: the player’s performance, his team/market and his team’s win total.

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Measuring the Dollar Value of a Player: Part 3

Vince Gennaro — 2005-11-16T04:10:15+00:00

In Part 1, I defined the process, the thinking and some of the math behind my approach to player valuation. In Part 2, I focused on the win-attendance-revenue relationship for various teams. In this final part, I’ll shift my attention to showing the dollar values for selected players for the 2005 season and drawing some conclusions about the results. I define player value as the amount of revenue a player generates for his team. I quantify the dollar value of a player to his team by converting a player’s on-field performance into its impact on the team’s wins, and then translating those wins into attendance and revenue.

The model includes six factors that affect a player’s value to his team:

• The player’s on-field performance – I’m using WARP1 from Baseball Prospectus as the measure to convert a player’s offense, defense and pitching performance into wins for his team, although alternative measures could be used.

• The effect of wins on attendance for his team – Each team has a unique wins-attendance relationship driven by market size, fan loyalty and fan expectations about the team’s performance.

• The number of games the team wins – My analysis shows that the more games a team wins (up to 98 wins), the higher the “value” of the win. More additional fans attend games when the team reaches contention for the post-season. If a player is responsible for taking his team from 85 to 90 wins, he’s more valuable than if he takes his team from 70 to 75 wins.

• Ticket prices – An extra 5,000 fans per game at the Mets’ average ticket price is worth nearly $400,000 more than the same 5,000 incremental fans at the Royals’ average ticket price.

• The mix of broadcast and “all other” revenue – Teams that supplement their in-stadium attendance with strong broadcast revenues and all other (advertising, sponsorships, parking, concessions, etc) revenue, get a “bigger bang for the buck” when they improve their team. The Yanks have about 70% of their total local revenues in broadcast and all other, compared to less than 50% for the A’s.

• The team’s revenue sharing tax – The marginal tax on a team’s revenues range from the low of 39% to over 45%+, based on how the team is classified in the 2002 Collective Bargaining Agreement. (See Andrew Zimbalist’s book , May the Best Team Win) Even a tax rate difference of 10 or 15 percentage points can have a significant impact on a player’s value. Each team pays a marginal tax, even if they separately receive a revenue sharing check from the from MLB office.

Each of these factors was included in the calculation of a player’s dollar value. It is important to note that each player’s dollar value is defined as marginal value. The model calculates the revenues of the team with- vs. without the player. In the Top 10 table below, Alex Rodriguez and his 10.2 WARP1 get credit for taking the Yankees from an 85-win to a 95-win team. If a replacement player was installed at 3B instead of A-Rod, the 2005 Yanks would have 10.2 less wins, and according to the model, $14.2 million less in revenue.

Consequently, the Mariano Rivera valuation places A-Rod back on the Yankee roster and calculates the Yankees’ marginal revenue with- and without Rivera. This means that the same portion of the revenue curve is being utilized to measure each player, reflecting the approach of truly measuring the impact of one player in isolation. I will discuss the merits of this approach, but first let’s get to the results.

The following is the Top 10 list of the MO$T Valuable Players of 2005:

Rank     Player            WARP1   Team         Value
1.       Alex Rodriguez     10.2    NYY   $14,226,561
2.       Mariano Rivera      9.1    NYY    13,018,862
3.       Derek Jeter         8.8    NYY    12,671,245
4.       Derek Lee          12.3    CHC    12,478,310
5.       Rafael Furcal       8.2    ATL    10,527,742
6.       Roger Clemens      10.4    HOU    10,392,508
7. Tie   Randy Johnson       6.8    NYY    10,205,675
7. Tie   Gary Sheffield      6.8    NYY    10,205,675
9.       Andruw Jones        7.9    ATL    10,184,500
10.      Hideki Matsui       6.7    NYY    10,073,079

It’s no surprise that six of the Top 10 are Yankees. Combine talent laden, high performing players, with one of the highest marginal revenue per win teams, and the result is a domination of the Top 10 of player value. The key to high value is the combination of the team, its win-level, and the high performance of a star player. A-Rod hit the trifecta—an economic juggernaut team, a 95-win playoff bound team and an MVP season—resulting in the highest value player for 2005.

Derrek Lee is the only member of the Top 10 whose team did not reach the post-season—a tribute to his extraordinary season and the strong economics underlying the Cubs. If Lee were a Yankee, and performed identically, he would be valued at over $17 million. By contrast, if A-Rod were a Cub, he would be valued at less than $11 million, ranking him 7th in the Top 10.

The approach of valuing each player using the same portion of his team’s marginal revenue curve could raise some concerns among those who would prefer to allocate players’ value across a broader spectrum of wins, particularly when valuing multiple players at the same time. While I have a preference of valuing each player on a truly marginal basis, I recognize the legitimacy of the debate. Some may think that the sum of the marginal values of an entire team, using my marginal approach, would lead to some unwieldy, unexplainable results.

As a quick test, I summed the marginal value of every player from four teams (with a positive WARP1 and ignored the negative WARP1 values) and compared it with the team payroll for 2005—not that payroll is necessarily any indication of “reasonableness”. Of the four teams I sampled, comparing the sum of marginal values vs. their payroll, two are greater and two are less. While this does not definitively answer the question, it may indicate that major league teams do not value players consistently higher or consistently lower than this approach.

If we expand our list to include the Top 25 MO$T Valuable Players of 2005:

Rank     Player            WARP1   Team         Value
11.      Marcus Giles        8.2    ATL   $ 9,831,649
12.      John Smoltz         7.6    ATL     9,714,032
13.      Roy Oswalt          9.5    HOU     9,537,398
14.      Andy Pettitte       9.4    HOU     9,441,447
15.      David Ortiz         8.0    BOS     9,434,126
16.      Vladimir Guerrero   7.8    LAA     9,169,549
17.      Jason Giambi        5.9    NYY     9,005,143
18.      Bartolo Colon       7.4    LAA     8,725,807
19.      Morgn Ensberg       8.4    HOU     8,470,288
20.      Albert Pujols      10.7    STL     8,410,461
21.      Pedro Martinez      7.9    NYM     8,285,276
22.      Cliff Floyd         7.8    NYM     8,186,194
23.      Jhonny Peralta      9.2    CLE     8,146,908
24.      Tim Hudson          6.1    ATL     8,025,858
25.      Carlos Zambrano     8.0    CHC     8,008,808

While only four teams are represented in the Top 10, nine teams are represented in the Top 25 MO$T Valuable Players. Only five of the Top 25 did not appear in this year’s post-season—The Cubs’ Lee and Zambrano, the Mets’ Martinez and Floyd and the Indians’ Peralta. The values for Giambi and Hudson benefit the most from the strong economics of their teams, as many players from smaller market clubs have stronger on-field performance for 2005.

One factor that I would like to address is the current revenue sharing system. In its present form it has a dramatic negative impact on player value and very little positive impact, if any, on competitive balance. Since each team pays a marginal tax (and the poorest teams at a higher marginal rate), its primary function is to depress the marginal value of players to a team. Just examining the top end of the player market, the above analysis shows that 10 players have a value greater than $10 million in 2005. Without a marginal revenue sharing tax, A-Rod would have generated $23.5 million in value for the Yanks, and 56 players would have generated value in excess of $10 million for their teams.

The downward pressure on salaries is not a necessary consequence of any revenue sharing system. It is a design flaw in the current system—or possibly intentional if you believe the owners’ motivation in implementing revenue sharing is to reduce players’ salaries. The current system reduces the marginal value of a win to all teams, but reduces it more to the poorest teams.

An alternative to the current system would be to tax teams a fixed dollar amount each year based on their revenue opportunity—their market size or other relevant variables—rather than actual revenues. Such a program would be more efficient at moving dollars from the high resource base teams to the low resource base teams, without sapping the economic motivation to grow revenues. The ideal program would also provide a bonus for “wins” by the disadvantaged teams. This would provide a further incentive to invest in payroll, scouting and development, by increasing the marginal value of a win to the poorest teams and help them compete more effectively in the free agent market.

Let’s look at the Top 10 performers (as measured by WARP1) who were not on the Top 25 MO$T Valuable Player list:

Rank     Player            WARP1   Team        Value*
 -       Dontrelle Willis   11.4    FLA    $4,791,947
 -       Jason Bay          10.0    PIT     2,708,296
 -       Todd Helton         9.6    COL     5,274,055
 -       Johann Santana      9.3    MIN     4,136,380
 -       Mark Teixeira       9.3    TEX     2,639,471
 -       Brian Roberts       8.9    BAL     1,517,396
 -       Jim Edmonds         8.9    STL     6,944,696
 -       Miguel Cabrera      8.7    FLA     4,306,355
 -       Brian Giles         8.4    SDP     3,615,201
 -       Kenny Rogers        8.1    TEX     2,344,811

* For players whose teams had low win totals, a modification was necessary to establish a value for the player. Since my model does not impute marginal value below 70 wins, for low win teams, I credited a player with the lowest value wins equal to his WARP1.

The dramatic effect of the team on a player’s value is evident when comparing Brian Roberts and Edmonds. With the equivalent on-field performance, there is a difference of more than $5 million in their marginal value. The Edmonds-Roberts differential is attributable to two factors: St. Louis’ league leading 100 wins vs. a ho-hum 74 wins for the Orioles; and much higher marginal revenue per win for the Cardinals. The bar chart below shows the marginal value of the last win for the four teams represented in the Top 10 MO$T Valuable and four teams that had high WARP1 players, which did not qualify for the Top 25 MO$T Valuable.

An interesting and topical case study is Rafael Furcal, who is currently testing the free agent market. If Furcal can perform at or near his 2005 performance over the next four years (which could be asking too much), his value to the Braves would be in the neighborhood of $45 to $50 million. (This assumes modest increases in ticket prices and other revenues over the next four years). The only other teams that 1) have a space for a starting shortstop and 2) could compete financially with the Braves are the Marlins and potentially the Cubs.

The Marlins have a steep sloping revenue curve (similar to the Yankees), meaning that they need to be in contention for the post-season to drive significant revenue growth. If the Marlins replace Burnett and assemble enough talent to have a legitimate chance at 90+ wins, they could justify a value comparable to the Braves’ value for Furcal. Conversely, the Cubs have a relatively flat revenue curve due to their limited capacity and strong base of loyal fans, even in poor performing seasons. Furcal’s value to the Cubs would be several million dollars per year less than his value to Atlanta or Florida, but this gap could be mitigated if Furcal proved to be the missing link to carry Chicago into the post-season.

In my latest attempt to measure a player’s dollar value, I have been able to quantify many of the relevant variables that attribute a team’s marginal revenues to a player. Clearly there are some unanswered questions, such as the “bonus value” or carry-over effect of reaching the post-season or winning a World Series, or the value of a star player who creates a special bond with local fans, creating revenue beyond his playing performance. While not part of the base model, these effects can be customized to the analysis of a specific player or team.

At a time when sabermetric tools can diagnose a player’s performance better than an MRI can detect a rotator cuff tear, it is reasonable to think that player dollar valuation is a void that needs to be filled. This comprehensive, analytical approach to estimating the dollar value of a player to his team could arm GMs with additional information to help manage the largest component of their cost structure—player salaries. In today’s game, information and insight can be the difference between winning and losing.

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Measuring the Dollar Value of a Player: Part 2

Vince Gennaro — 2005-11-15T04:05:15+00:00

In Part 1, I defined the process, the thinking and some of the math behind my approach to player valuation. In this part of the series, I’ll focus on the team-to-team differences in the value of a win and discuss how the attendance and revenue curve estimates impact the value of players. There are four key drivers affecting a player’s value:

• The player’s on-field performance
• The responsiveness of his team’s fans to changes in winning percentage
• The relative success of his team—generally, improving an 85-win team is more lucrative than improving a 72-win team
• The strength of the revenue streams his team has established—the level of ticket prices, concessions, parking, advertising and sponsorships

The attendance curves below show changes in per game attendance for selected teams at various win levels. For example, the Angels would draw about 10,000 more fans per game during an 87-win season vs. a 70-win season. At the same 87-win level, the Twins would draw an incremental 8,400, the White Sox +7,600, The A’s +5,600 and the Yanks +4,400. (While these numbers are an estimate of how attendance responds to winning, the baseline attendance levels—attendance for a 70-win season—of these teams are very different. The Yanks baseline level of attendance is over 40,000 per game and is more than double that of Oakland, Minnesota and the White Sox.) Even though the Yankees appear to gain less incremental attendance from winning, this graph tells only one piece of the story. Don’t feel sorry for the Yanks yet, as later I’ll show how higher ticket prices, stronger revenue streams and enormous broadcast revenues ultimately place the Bronx Bombers in a dramatically advantaged position in terms of player value.

Incremental or marginal attendance is only one piece of the “value” story. Dissecting these estimates further, we can look at the portion of the curve a player affects. In valuing a player who improves his team by 5 wins, it is important to consider the team’s win total. Below is a bar chart of the models’ estimates of attendance gains that result from 5-win improvements. Each bar represents the increase in per game attendance from the specific 5-win improvement.

Of the five teams selected, the Yankees and Angels have the steepest curves—the change in attendance increases (or decreases) at a greater rate as the win level increases (or decreases). It is also interesting to note that the Yanks have the lowest gain in attendance when “improving” from 77 to 82 wins. While most teams’ fans consider this improvement worthy of additional support, Yankee fans’ lofty expectations cause them to make little distinction between 77 or 82 wins, reflecting their “post-season or bust” attitude. Oakland has the flattest attendance curve—there is no more than a 400 fans-per-game difference across all 5-win increments. At the other extreme, the Yanks nearly triple their attendance increase by being in the “sweet spot” (92 to 97 wins) on their attendance curve.

From Attendance Gains to Marginal Revenue

Translating the per game attendance gains into revenue yields a different picture. The Yankees emerge as the team that converts playoff contention win totals (87+ wins) into the highest revenue gains. The chart below also shows the White Sox generating marginal revenue at a greater rate than the Twins and A’s. The efficient conversion of attendance gains into revenue gains for the Yanks and White Sox is partly attributable to ticket prices approximately 50% higher than the other three teams listed.

The Yanks also benefit from the highest value broadcast arrangement of any MLB team. In order to quantify teams broadcast revenue, I had to account for teams which have created their own regional sports network, or teams owned by a media entity that may leave a portion of the “fair value” of their broadcast rights on the income statement of the media company, rather than the ballclub. The Yankees’ YES network, the Red Sox’ affiliation with NESN, among others, can be considered to fall into this category of teams that have “related party transactions”. (I relied heavily on the detailed treatment of this topic in Zimbalist, “May the Best Team Win”). Where teams have a relationship with a media entity, the model assumes a somewhat higher portion of broadcast revenues are variable. If a team improves and attracts more viewers and commands more advertising dollars, the model reflects a more efficient pass-through to the ballclub, instead of the upside being “pocketed” by the broadcast entity. The end result is that teams strong enough to start their own regional network, get the added benefit of also profiting from an upside in revenues from improvement in the team— boosting their marginal revenue per win. This is consistent with the perception that the Mets are prepared to aggressively invest in payroll to improve the team on the eve of the launch of their own network.

Finally, to convert the model’s attendance results into total local revenue (attendance + broadcast + all other), I also had to make assumptions regarding the amounts of broadcast and “all other” that would vary with attendance—the classic fixed vs. variable dilemma. From speaking to several experts, the general conclusion is that broadcast revenues are largely fixed (90%+ fixed and up to 10% variable) and therefore, less dependent on a team’s performance in any given year. Categories like parking and concessions, however, move in tandem with attendance.

The Value of a Win

So how much is a win worth? While I will explore this more in Part 3 of this series, there is a wide range from highest to lowest team. Since not all wins are created equal, I’ll use the 88-win level as a point of comparison. The highest marginal revenue team (after the revenue sharing tax is deducted, which I will discuss in more detail in Part 3) is the Braves at over $1.3 million for the 88th win, with the lowest being the Orioles at less than $300,000 for their 88th win.

Another way to look at the marginal revenue estimates is to calculate the value of each team’s last win. For 2005, that means the Yankees’, Angels’, and Red Sox’ 95th win, the Mets’, Marlins’, and Twins’ 83rd win. After the revenue sharing tax is taken from the gross revenue estimates, we’re left with the following marginal value of a win for the above mentioned teams for 2005:

It is important to note that the model defines “value”—the value of a win and the value of a player—as marginal value. Each team also has a baseline level of revenue which does not fluctuate based on the team’s on-field performance. These baseline revenues, defined as the amount of revenue a team is likely to generate in a season even if it wins 70 or less games, reflect the value of the team as a “brand”, or the popularity of baseball in their respective market. (In the short-run, a poor performing season or two should not affect the baseline revenue estimates. However, over the long-term, a team’s baseline revenues are not a “given”. A string of poor performing seasons can erode a team’s equity with its fans and lower its baseline revenues.) Baseline revenues vary widely from team-to-team with the Yankees, Red Sox and Cubs at the top end of the scale, with baseline revenues of $200 million or more. At the other end of the scale are the Devil Rays, Royals, Twins and A’s with baseline revenues (excluding any payments they would receive from the revenue sharing or luxury tax pool) equivalent to about 25% of the top tier.

Translating Team Revenue into Player Value

Using Vladimir Guerrero as an example, let’s look at his value to the Angels for the 2005 season. Guerrero’s WARP1 value (from Baseball Prospectus) is listed at 7.8 for the 2005 season&mdahs;implying that Guerrero’s offense and defensive skills were responsible for 7.8 of the Angels’ 95 wins. In other words, if the Angels gave his 594 plate appearances and innings in right field to a replacement player, they would have won only 87 games. By referencing a table of revenue estimates, derived from my regression model, I can calculate the amount of additional revenue attributable to Guerrero. This estimate includes attendance revenue and other revenues at the ballpark (parking, concessions, etc) and a small percentage of broadcast revenue, all of which is adjusted for an estimate of the revenue sharing tax. The dollar value of the player also takes into account the increasing value of each win—the 95th win is worth more revenue than the 90th win.

Working backwards from the Angels’ 95th win, Guerrero’s WARP1 of 7.8 earns him credit for wins 89 through 95 and partial credit for the 88th win. Taking all of these factors into consideration, the estimate of Vlad’s value to the 2005 Angels is $9,169,549. If we look at the 2004 season, Guerrero’s first year with the Angels, WARP1 credits him with 9.1 of the Angels 92 wins. Using 2004 ticket prices and making all the necessary adjustments, the model says that Vlad was worth $10,298,351 to the Angels for his 2004 AL MVP season. In both instances, these numbers do not account for any additional revenue form reaching the post-season, something that Guerrero was instrumental in helping the Angels achieve in his first two years in Anaheim.

In Part 3, the final part of this series on player valuation, I will show a ranking of the top value players of 2005 and discuss the degree to which the Yankees and other well endowed teams maintain an advantage in signing and justifying high priced talent.

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Measuring the Dollar Value of a Player: Part 1

Vince Gennaro — 2005-11-14T04:05:15+00:00

Defining Player Value

With the development of dozens of insightful and innovative statistical measures, baseball’s forward thinking general managers and managers are working to create an edge for their ballclubs and get the most from the talent they put on the field. The weakest link in the analytical chain is the ability to place an objective dollar value on an individual player’s performance, measured by his contribution to his team’s revenues.

While others may define value based on relative salaries of players at the same position, or free agent contract values or arbitration settlement figures, I am defining player value as the amount of revenue a player generates for his team. By converting a player’s on-field performance into his impact on his team’s wins and then translating those wins into attendance and revenue, I quantify the dollar value of a player to his team.

My model answers the question, “How much more revenue do the Angels earn because Vladimir Guerrero is their right fielder (and sometimes designated hitter)?” The model estimates the Angels’ revenue with Guerrero’s 594 plate appearances vs. what the Angels’ revenue would have been without Guerrero and with a “replacement player” playing right field and taking his 594 plate appearances.

The first half of the player valuation equation—measuring player performance in terms of its impact on team wins—has been the subject of much debate in the sabermetric world. As a result, numerous measures have been developed by innovative statisticians to convert a player’s offensive, defensive, and pitching stats into team wins.

The second half of the player valuation equation—converting a team’s wins into a team’s dollar revenue—has received far less attention by the math experts. While there has been much academic research to determine the impact of winning on attendance, I am unaware of any comprehensive, team-specific analyses which quantify the value of a player to his team.

How could a player valuation analysis help a major league team? Just as a manager can adjust his on-the-field strategy by using statistical tools like the expected runs matrix, general managers and owners (and player agents) could now determine if a player’s “price is right” by using the output of a comprehensive player valuation model. It provides one more tool in the arsenal of the general manager to play the “what if” game before making an irreversible decision. The model can convert the results of innovative sabermetric decision tools into the bottom line common denominator of dollar value to the team.

Process of Assigning a Dollar Value to a Player

The objective is to add numbers to the thought process that a general manager intuitively follows when determining how to assemble and compensate talent. By quantifying the process, a general manager can divide a complicated thought process into tiny, digestible components which reduce the risk of overlooking any factor that would ultimately make a difference in a free-agent signing, a trade decision, an arbitration submission, or a waiver claim. This is how the process works:

Choose a performance metric that translates the on-the-field performance into contributions to “wins.” While there are many viable choices, such as Win Shares (Bill James, modified by Dave Studeman), WRAP (Lonergan and Pollack), and Win Expectancy (Tangotiger), I elected to use WARP1 (Wins Above Replacement Player) from the Baseball Prospectus website as the primary input into the player valuation calculations.

WARP1 attempts to measure a player’s offensive, defensive and pitching contributions to his team’s wins. By anchoring the baseline to “replacement level,” it simplifies the valuation process. Replacement level is considered to be a widely available commodity, such as a journeyman Triple-A player, and has an objective dollar cost—the major league minimum salary.

Quantify the relationship between winning and attendance revenue for each team (except the Washington Nationals, as there is insufficient data), within a consistent analytical framework. Attendance at home games is the bellwether revenue source for major league clubs. In many cases it is the single largest portion of local revenue and where it is not, it still plays a key role in building broadcast, advertising and sponsorship, parking, and concession revenue.

Much of the past statistical work in this area has merged the data across all teams and tried to define the relationship between attendance and wins based on market size. That method ignores the fact that even teams in the same market have different fan loyalties and responses to its team’s success. Another important nuance of my analytical framework is its focus on capturing the non-linear relationship between wins and attendance—not every incremental win is of equal value. This is discussed in more detail below.

Translate the “winning-attendance revenue” relationship into implications for total local revenue, which includes broadcast and “all other” (parking, concessions, etc.) revenue. While attendance and other ballpark revenues benefit from immediate “feedback” from fans, broadcast revenues are largely under multi-year contracts, with minimal variability from changes in a team’s viewing audience within a given year. (Later I will discuss the implications of team’s having an ownership stake in their own TV network.)

Account for the revenue sharing provision in the 2002 Collective Bargaining Agreement. A relatively recent wrinkle (post-1996) in the player value equation is the redistribution of net revenues that were legislated in the last two CBAs. The current revenue sharing includes an onerous tax on revenues for each team. While some “disadvantaged” clubs also receive a hefty check, every team still pays a portion of their revenues into the fund. This tax on revenue can range from 39% (for the wealthiest teams) to 47% (for the poorest teams).

If you wonder why some small market teams are reluctant to reinvest their revenue sharing windfall into players’ salaries, look no further than the revenue sharing tax. A team with a 47% tax needs to grow gross revenues by nearly $19 million for every $10 million it spends, just to break even. To make matters worse, the current CBA calls for the small market teams to pay the highest marginal tax rate. This ill-conceived revenue sharing program and its tax have the dramatic effect of directly reducing the marginal value of players, by reducing a team’s dollar value of a win by 39% to 47%.

Another factor influencing a player’s value that is not presently included in the model is the carryover effect of reaching the post-season, or winning the World Series. When a team reaches the post-season (particularly for several consecutive years) there seems to be a carryover effect that raises the profile of the team in its city and serves as a boost to attendance. The Royals experienced this in the early ‘80s, the Blue Jays in the early ‘90s, and the Indians in the late ‘90s. In these instances wins alone do not fully explain the surge in attendance. Despite this premise, the data and modeling have yet to provide a consistent confirmation of the value of reaching the post-season.

One effect of reaching the postseason that can be quantified is the impact on ticket prices. Dating back to 1990, teams reaching the postseason raised their ticket prices the following year by an average of 10.1%, compared to 5.9% for teams that did not reach the post-season. World Champions averaged a 10.4% ticket price increase in the year after they earned their rings. I continue to work on a method to quantify the postseason carryover effect, as it can have a large effect on player valuation, particularly if the player in question puts a team over the threshold (i.e., takes a team from 88 to 95 wins and puts his team into the playoffs).

Also, the model does not explicitly include the rare situation where a star player has “gate appeal” and the capacity to attract fans to the ballpark beyond his impact on team performance. This effect is easier to measure for a starting pitcher rather than an everyday player, as his appearances are isolated. The gate appeal factor is not part of the base calculation of the player valuation model.

By building a player valuation model for each team, and factoring in the impact of the revenue sharing tax, I can convert the playing performance of any player into the marginal dollar value to his team – in a sense, the ceiling of what the player is worth to the team for that season. Alternatively, a general manager can use the model by plugging in his performance expectations for future years and thereby generating a marginal dollar value for any player.

The Relationship Between Winning and Attendance

The key missing link in the chain to place a dollar value on individual playing performance is quantifying the relationship between a team’s on-the-field success and their attendance. I used multiple regressions to quantify this key relationship for each team. Five key hypotheses were confirmed (sometimes through trial and error) by the results of my modeling.

1) Winning affects attendance and a team’s revenue.

The data verifies that fans respond to winning. For every team, the model shows that fans are inspired and motivated to attend their team’s games based on the success of the team—the more a team wins, the higher the attendance. However, this relationship does not hold up on the low end of the win range. I validated the assumption that an improvement in wins, below 70 wins, did not generally have a statistically significant impact on attendance. We can think of the 70-win threshold as representing a baseline level of attendance for each major league club.

2) The effects differ not only by market, but also by team.

Different income levels, entertainment options and competing sports in each MLB market, affect the attendance-win relationship. Even within a market, Cubs and White Sox fans respond differently to their team’s on-the-field success, as do Yankees and Mets fans, Giants and A’s fans and Dodgers and Angels fans. Each team is a “regional brand,” with unique brand equities and fan loyalties. It was important to develop individual models for each ballclub, to quantify the relationship for each team and its fans, but equally important to maintain a consistent framework across all the models.

3) Current year attendance is impacted by both current year and previous year winning percentage.

Fan interest in the team and demand for season tickets and advance ticket sales can be greatly influenced by the previous year’s performance. However, as the season evolves and fans form perceptions regarding the quality of the team, current performance will become a primary driver of attendance. To capture this effect, my models include the combination (equally weighted) of previous and current year wins.

4) The attendance-wins relationship is non-linear ... improving from 85 to 90 wins, will benefit attendance more than improving from 70 to 75 wins.

When a team is in contention for a postseason spot, fan interest and attendance rise disproportionately. The hardcore fan is more likely to attend more games and more casual fans are jumping on the bandwagon. While an improvement from 70 to 75 wins still has a positive impact, it is less than when a team approaches contention. To capture this non-linear effect for each team, I estimated the attendance-win relationship by converting wins to an exponential power based on which exponent yielded the best fit.

5) Wins in the “sweet spot” have the highest “value” to a team.

Wins beyond the high end of the sweet spot diminish in value. This sweet spot is approximately 85-98 wins. Any improvement in the team within this range generally has the highest impact on attendance. The bottom end of this range begins to legitimize the club as a post-season contender. Beyond the top of the range (beyond 98 wins) a team generally decreases the suspense of their appearance in the postseason and while they may attract more fans, they do so at a lesser rate.

The Yankees in 2004 and 2005 help illustrate this point. In 2004, the Yanks won 101 games and earned a post-season spot by nine games (92 wins would still have won the Wild Card). As a result, post-Labor Day attendance, when their fate was already decided, declined significantly, averaging 7,000 fewer fans for the last 12 home games. By contrast in 2005, when the Yanks did not clinch a playoff birth until October 1, their post-Labor Day attendance increased 1,500 fans per game over their pre-Labor Day average. In this specific case, the Yankees actually generated more attendance revenue by winning 95 vs. 101 games the previous season.

The graph below is an example the model’s estimate of the attendance-wins relationship for the Yankees. The attendance gains from 70 to 80 wins are modest, but accelerate as the season win total reaches the mid-80’s and through 98 wins. Beyond 98 wins the shape of the curve changes and attendance increases, but at a lesser rate.

Examining five-win increments we see the shape of the curve reflected in the model’s estimates:

Improving from 77 to 82 wins	+1,453 fans per game
Improving from 82 to 87 wins	+2,219 fans per game
Improving from 87 to 92 wins	+2,983 fans per game
Improving from 92 to 97 wins	+3,749 fans per game
Improving from 97 to 102 wins	+2,096 fans per game

Other Factors Influencing Attendance

In order to quantify the relationship between winning and attendance, a number of other factors had to be included in my models. The variables included the impact of new stadiums, the effects of the various work stoppages, a “new franchise effect” to account for the attendance levels in the early years of an expansion club, when fans are excited about the new game in town and have little expectation of winning.

It was also important to account for unique, team-specific events such as the record setting home run chase of Mark McGwire in 1998, which attracted fans for reasons other than the quality of the ballclub. The most unexpected result of my modeling efforts was the absence of ticket price as a determinant of attendance. I am not suggesting there is no impact, as fans clearly have some degree of price sensitivity, but it consistently failed to be a statistically significant variable in my attendance models.

Part 1 was intended to be an introduction to the concept of player valuation and an overview of my conceptual and statistical approach. In Part 2, I will focus on team-to-team differences in the value of a win and discuss the estimated revenue curves for selected teams. In Part 3, I will show specific player valuations and a ranking of the highest value players in 2005—including the MO$T Valuable Player in each league.

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