Ticket Pricing and Attendance

A stadium can be enormously expensive to build and operate. Yet once the gates are open and the event is staffed, admitting one more spectator to an otherwise empty seat may add relatively little to the team’s operating cost. If cost determined price in a simple mechanical way, this combination would seem to produce a puzzle: high total cost but a low price for the next ticket.

Sports organizations do not solve the puzzle by dividing the stadium bill by the number of seats. They face demand. Fans differ in what they are willing to pay, games differ in appeal, seats differ in quality, and the number of seats is fixed on game day. A team with market power must decide which ticket products to offer, how many tickets to sell, and how much to charge for each part of the menu.

That decision also extends beyond the primary box office. Prices can change as information arrives. Tickets can be resold. Attendance depends on the team and opponent, but also on timing, weather, viewing alternatives, loyalty, and the full cost of attending. Ticket pricing is therefore an unusually rich application of demand, elasticity, market power, and information.

Learning Goals

After reading this chapter, you should be able to:

• distinguish fixed cost, marginal cost, and capacity scarcity in a stadium
• explain why low marginal cost does not imply a low ticket price
• explain why stadium construction costs and player payroll do not mechanically pass through to ticket prices
• analyze ticket pricing when stadium capacity is slack or binding
• connect elasticity to ticket revenue without confusing revenue with profit
• use income and cross-price elasticity to explain shifts in ticket demand
• distinguish product differentiation, market segmentation, and price discrimination
• explain first-, second-, and third-degree price discrimination
• explain two-part tariffs and personal seat licenses
• distinguish variable pricing from dynamic pricing by examining the pricing rule
• explain how primary and secondary ticket markets allocate tickets and surplus
• evaluate attendance evidence without treating correlation as causation

The Cost Puzzle

Suppose a team has already built or leased its stadium and committed to the staff, security, lighting, and other services needed for a game. Many of those expenses do not change when one additional fan occupies an otherwise empty seat. They are fixed for the relevant decision.

Other costs do change. An additional spectator may require more cleaning, ticket processing, security attention, or other services. Those are marginal costs. The practical distinction is not that marginal cost is always zero. It is that the cost of serving one more spectator can be small relative to the average cost created by the stadium and the event.

This distinction changes the pricing question. Once the event is going forward, the team does not ask, “What ticket price makes every seat pay an equal share of the stadium?” It asks, “How do price and quantity affect the additional revenue and cost associated with this event and ticket product?”

Fixed cost still matters. It affects whether a stadium should be built, whether the team can cover its obligations over time, and whether the business is sustainable. It simply does not enter the short-run pricing rule in the same way as marginal cost.

What Exactly Is The Product?

Before drawing a demand curve, we need to define what consumers are buying. “A ticket to the team” is usually too broad. A ticket is attached to a particular event, date, seat or section, purchase arrangement, and set of rules. A seat behind home plate for a rivalry game is not the same product as an upper-level seat for an ordinary weekday game.

This matters because a team rarely chooses one price for all spectators. It constructs a menu. The menu can vary along several dimensions:

• seat quality: location, view, comfort, club access, or included services
• event quality: opponent, expected team quality, star players, rivalry, or playoff importance
• timing: advance purchase, last-minute purchase, or a price that can be revised before game day
• purchase terms: single-game ticket, season plan, partial plan, group package, or refund rights
• buyer eligibility: student, youth, military, local resident, or another identifiable group

The demand curve in the basic model should therefore be interpreted as demand for a defined ticket product at a defined event, holding the rest of the menu fixed.

Three Ideas That Are Easy To Confuse

Product differentiation means that the products themselves differ. A premium club seat may include a better view and additional services. A higher price may partly reflect those differences.

Market segmentation means that the seller identifies groups of buyers or purchasing situations that differ in willingness to pay or responsiveness to price. The seller may use eligibility rules, timing, purchase history, or product design to keep those groups partly separate.

Price discrimination means charging different markups for the same or sufficiently similar product in ways not fully explained by differences in marginal cost. It requires some market power and some ability to limit arbitrage between buyers.

These concepts can operate together, but they are not synonyms. Observing different prices for a club seat and an upper-level seat is not enough to prove price discrimination because the products differ. At the same time, teams can design differences among seats, bundles, and purchase rules partly to encourage fans with different willingness to pay to select different options. A good analysis identifies the product difference, the segmentation device, and the pricing mechanism instead of applying one label to the entire menu.

Market Power And The Capacity Constraint

A team with market power faces a downward-sloping demand curve for a ticket product. To sell more tickets, it generally must offer a lower price, improve the product, or reach additional buyers. If we write inverse demand as

then price falls as the number of tickets sold rises. Total ticket revenue is

Marginal revenue, $MR$, is the additional revenue generated by selling one more ticket. Because a lower price may be needed to expand sales, marginal revenue lies below the demand curve for positive quantities. In the linear example,

The familiar monopoly rule is to expand ticket sales while marginal revenue exceeds marginal cost. If capacity does not interfere, the profit-maximizing quantity $Q^m$ occurs where

The team then reads the price $P^m$ from the demand curve at that quantity. The price does not come from the marginal-revenue curve.

Stadium capacity adds the constraint

where $K$ is the number of tickets that can be supplied for the relevant event and product.

Slack Capacity

In the left panel of Figure 4.1, capacity $K_S$ lies to the right of $Q^m$. The stadium has more seats than the team wants to sell at the profit-maximizing price. The team chooses $Q^m$, charges $P^m$, and leaves some capacity unused.

An unsold seat does not by itself prove that the team made a pricing mistake. Filling it may require lowering the price on other seats, changing purchase rules, or weakening the value of a package sold to other buyers. The relevant comparison is not “some revenue versus no revenue” for one isolated seat. It is the effect of a pricing change on the entire set of buyers affected by that change.

Binding Capacity

In the right panel, capacity $K_B$ lies to the left of $Q^m$. The team would like to sell more tickets at the margin because $MR(K_B)>MC$, but no additional seats are available. Quantity is fixed at capacity. The associated market-clearing price is $P(K_B)$ on the demand curve.

Binding capacity creates scarcity. That scarcity can support a higher price, but the capacity line does not replace demand. A small stadium does not guarantee a sellout, and a large stadium does not guarantee empty seats. Demand determines whether the constraint actually binds.

A Worked Pricing Example

Suppose demand for a game’s tickets is

where $P$ is the ticket price in dollars and $Q$ is measured in thousands of tickets. Marginal revenue is

Assume that the marginal cost of admitting and serving another spectator is $20. If the stadium holds 60,000 spectators, capacity is slack. Setting $MR=MC$ gives

so $Q^m=50$ thousand tickets. Reading from demand, the team charges $70. It leaves 10,000 seats unsold. Cutting the price to $60 would fill the stadium and raise ticket revenue from $3.5 million to $3.6 million, but serving the additional 10,000 spectators would add $200,000 in variable cost. Contribution toward fixed cost and profit would fall from $2.5 million to $2.4 million. The example shows why maximizing attendance, ticket revenue, and profit are three different objectives.

Now suppose the same game is played in a 40,000-seat stadium. At $Q=40$, marginal revenue is $40, which still exceeds the $20 marginal cost, but the team cannot sell another seat. Capacity binds. The market-clearing price at that capacity is $80. The smaller stadium changes the constraint, not the demand curve. Whether the team can actually charge $80 still depends on fans’ willingness to pay.

Elasticity In The Pricing Decision

Chapter 2 introduced the elasticity formulas, classifications, midpoint method, and total-revenue rule. Here elasticity becomes a decision tool. Over an inelastic range of demand, a price increase raises ticket revenue; over an elastic range, it lowers ticket revenue. A price cut reverses those revenue effects.

A team does not need to publish a textbook elasticity calculation to face this problem. Every proposed price embodies a forecast: how many tickets will sell at that price rather than at the alternatives? Historical sales, resale prices, experiments, surveys, and statistical demand models can inform the forecast. When the Bills choose prices across seats and games, for example, they must form some view of fan responsiveness even though we should not claim to know their undocumented internal method.

There Is No Single Team Elasticity

Elasticity belongs to a particular product, group, price range, and time. Demand for a rivalry game may be less price responsive than demand for an ordinary game. Students may be more responsive than corporate buyers. Club seats and promotional upper-level seats may face different demand. A fan deciding months in advance may have different alternatives than a visitor shopping on game day.

This is one reason teams build ticket menus rather than rely on a single teamwide estimate. Expected responsiveness can inform seat tiers, game tiers, discounts, and the timing of price changes. The useful question is not “What is the Bills’ elasticity?” but “How responsive is demand for this ticket product, among these buyers, over the price change being considered?”

An observed sellout does not answer that question. It tells us that quantity demanded at the posted terms was at least as large as available capacity. It does not reveal how many buyers would have appeared at a lower price or how much more current buyers would have paid. When sales are capped by stadium capacity, the observed quantity conceals some of the underlying demand curve.

Income And Related Products

Own-price elasticity concerns a movement along a demand curve. Income and the prices of related products can shift the entire curve. Premium seating, ordinary single-game tickets, and low-price promotions need not respond to an income change in the same way. Teams should not assume that every sports ticket is a luxury good merely because some seats are expensive.

Related products are equally important. Parking and tickets may be complements, so an increase in parking prices can reduce ticket demand. Watching at home may substitute for attending a game, but media exposure can also deepen interest and make future attendance more attractive. A partial season plan may substitute for single-game tickets while complementing concessions or merchandise. The relevant relationship is an empirical question about defined products, buyers, and time horizons—not something the team can infer from the labels alone.

From One Price To A Ticket Menu

The single-price model isolates the main trade-off, but actual ticketing looks more like menu design. Teams offer combinations of seats, games, eligibility rules, and purchase commitments. Economists use the language of price discrimination to study how firms capture different amounts of surplus from buyers with different willingness to pay.

First-Degree Price Discrimination

Under first-degree price discrimination, the seller charges each buyer approximately that buyer’s maximum willingness to pay. Perfect first-degree discrimination is a benchmark rather than a realistic description of most sports ticket markets. Teams do not know every fan’s willingness to pay with precision, and resale, privacy, fairness concerns, and transaction costs limit individualized extraction.

Data and personalized offers may move pricing in this direction, but a price that varies across time or seats is not automatically first-degree discrimination.

Second-Degree Price Discrimination

Under second-degree price discrimination, the seller offers a menu and lets buyers sort themselves. Season plans, partial plans, premium packages, refund terms, and seat-service combinations can induce fans with different preferences to choose different options.

This is where product differentiation and screening overlap. The products may genuinely differ, but the menu may also be designed so that buyers reveal something about their willingness to pay through their choices.

Third-Degree Price Discrimination

Under third-degree price discrimination, the seller charges different prices to identifiable groups whose demand differs. Student or youth discounts are familiar examples. The strategy works only if the seller can identify eligibility and limit resale from the discounted group to other buyers.

The economic prediction is about relative elasticities, not about whether one group is more deserving. Holding other factors constant, a profit-seeking seller tends to charge a lower markup to a group with more elastic demand.

Two-Part Tariffs And Seat Licenses

A two-part tariff divides payment into an access charge and a usage charge. A fan might pay an initial fee for the right to buy tickets and then pay separately for the tickets themselves. This structure can transfer more consumer surplus to the seller while also creating a longer-term commitment between the buyer and the team.

The acronym PSL is not expanded uniformly. Carolina uses Permanent Seat License, while Buffalo uses Personal Seat License. Both can resemble a two-part tariff, but the controlling contract matters more than the label. As of August 2026, the Buffalo Bills describe a PSL for new Highmark Stadium as a one-time purchase giving the licensee the right and obligation to purchase season tickets; the team requires the PSL for season-ticket purchases.^[4] The license payment and the later season-ticket payment are economically distinct.

Variable And Dynamic Pricing

Prices can differ across games even when the seat is physically identical. A weekend rivalry game and an ordinary weekday game may face different demand. The seller can recognize those differences in advance or revise prices as information arrives.

For this chapter, the useful operational distinction is:

• variable pricing: prices differ across preassigned games, seats, or categories
• dynamic pricing: a price can be revised over time in response to new information, remaining inventory, or observed demand

The terminology used by organizations is not perfectly uniform. Students should therefore inspect the rule itself. Can the price change after the initial schedule is announced? What information triggers a revision? How often can it change? Is the process automatic, discretionary, or both?

Dynamic pricing can improve the match between price and current demand. It can also create uncertainty for buyers, change the value of purchasing early, and raise concerns about transparency or fairness. Those reactions are part of demand. A pricing system that extracts more revenue from one transaction may weaken trust or alter future purchase behavior.

Primary And Secondary Ticket Markets

The primary market creates the initial allocation. The team, venue, league, or authorized seller determines the original ticket products, prices, and purchase rules. The secondary market reallocates some of those tickets after the initial sale.

Resale exists because information and circumstances change. A buyer may learn that a ticket is more valuable than expected, while another buyer may become unable to attend. A secondary market can move the ticket toward the buyer with the higher willingness to pay.

That does not mean resale has only benefits. Transactions can involve search costs, platform fees, fraud risk, restrictions, and uncertainty about ticket validity. Resale also changes who captures the surplus. If a ticket is initially sold below the amount some buyers are willing to pay, part of the resulting resale value may go to the initial buyer or an intermediary rather than to the team.

Economists therefore separate at least three questions:

1. Allocation: Does resale move tickets toward people who value attendance more highly?
2. Distribution: Do the team, initial buyer, reseller, platform, or final buyer capture the resulting surplus?
3. Transaction design: How do fees, refund options, release timing, transfer restrictions, and price disclosure affect the market?

Consider a ticket originally sold for $60. The first buyer later cannot attend and resells it for $110. If a platform retains a $10 fee, the original buyer receives $100 and nets $40 relative to the original purchase price. A final buyer willing to pay $140 receives $30 of consumer surplus, while the platform receives $10. The ticket reaches someone who values attending more highly, but the team does not capture the resale markup in this simple example. Different fees, refund rights, resale restrictions, or revenue-sharing rules would change the distribution and could also affect whether the reallocation occurs.

The welfare answer is not automatic. A flexible resale market may improve allocation, but high transaction costs or deceptive price presentation can absorb some of that gain. A centralized refund or exchange system may reduce flexibility while improving control or lowering certain risks. Courty’s economic analysis of ticket resale and later work comparing resale with centralized partial refunds illustrate these competing effects.^{[11] [12]}

Price transparency is now part of the institutional setting. The Federal Trade Commission’s rule on unfair or deceptive fees took effect in May 2025 and applies upfront total-price requirements to live-event ticketing, including secondary markets.^[13] The rule does not determine the economically correct fee. It changes how mandatory fees must be disclosed when sellers advertise a price.

What Determines Attendance?

Attendance is an observed outcome after price, capacity, and the other features of the event interact. It is not a pure measure of underlying demand, and it is not generated by ticket price alone.

The dependent variable itself requires care. A reported attendance figure may count tickets sold or distributed, while a turnstile measure counts people who entered the venue. A ticket holder who does not attend appears in the first measure but not the second. A no-show for a paid ticket does not change primary ticket revenue, but it can matter greatly for concessions, stadium atmosphere, staffing, and any attempt to estimate the demand to be physically present.

The research literature identifies several recurring categories of demand shifters, but it does not produce one universal attendance equation for every sport and market. Borland and Macdonald’s review emphasizes contest quality, viewing quality, and uncertainty of outcome while warning that the evidence does not yield simple conclusions that apply everywhere.^[14] A later scoping review documents a large literature but also finds important gaps, including limited work on women’s and niche sports, emerging markets, and disaggregated attendance behavior.^[15]

Association, Prediction, And Causation

Suppose high-priced games also have high attendance. It would be a mistake to conclude that raising price causes attendance to rise. Teams may charge more precisely when a rivalry, star opponent, or playoff race has shifted demand outward. The common demand shift raises both the price selected by the team and the number of tickets fans want to buy.

That is an association: price and attendance move together in the observed data.

A model may still use price, opponent, weather, and team quality to predict attendance. Good prediction means the model performs well on observations it did not simply memorize. It does not by itself tell us what would happen if the team changed price while everything else remained the same.

A causal claim asks that counterfactual question. How would attendance change if the price changed but the other determinants of demand did not? Answering it requires a research design that separates the price change from the demand conditions that caused the team to choose that price.

Capacity creates another complication. At a sellout, recorded attendance is capped. Demand may rise substantially without producing any increase in tickets sold. A dataset containing many sellouts can therefore hide changes in underlying demand.

These distinctions matter for management and policy. A variable can improve forecasts without being a useful policy lever, and a strong correlation can disappear once the underlying selection process is considered.

The Connected Pricing Decision

The chapter began with a simple question about one ticket price. The real decision is connected:

1. Define the ticket products and the market for each one.
2. Estimate how different buyers respond to price and product features.
3. Determine whether capacity is likely to bind.
4. Build a menu that balances revenue, marginal cost, complementary spending, and longer-run demand.
5. Decide which prices are committed in advance and which may change as information arrives.
6. Anticipate how resale, fees, and transfer rules affect allocation and surplus.
7. Evaluate attendance evidence without confusing observed correlations with causal effects.

This connected view also explains why the same team may use several pricing strategies at once. It may sell season plans through an access commitment, offer differentiated seat products, discount to an identifiable group, adjust some single-game prices over time, and permit resale through a platform. Each part addresses a different economic problem.

Big Picture

The central lesson is not that teams always charge the highest imaginable price. It is that ticket prices emerge from a constrained choice.

• Fixed costs affect long-run viability, but marginal decisions depend on marginal revenue and marginal cost.
• Capacity matters when it prevents the team from selling the unconstrained profit-maximizing quantity.
• Elasticity determines how a price change affects ticket revenue over a particular range.
• Different prices may reflect product differences, segmentation, price discrimination, or a combination of the three.
• Variable and dynamic pricing differ in how the pricing rule responds to categories, time, and new information.
• Secondary markets can improve allocation while changing transaction costs and the distribution of surplus.
• Attendance evidence must separate demand mechanisms from prediction and causal claims.

Ticket markets lead naturally to the next chapter. A fan can attend in person, watch through a media platform, follow highlights, or divide attention across several products. Once the market extends beyond the stadium, the scarce resource is no longer only seating capacity. It is also attention.

Review Questions

1. Why can a team face high fixed stadium costs and still have a low marginal cost for an additional spectator?
2. Under what condition does stadium capacity bind? Why does a small stadium not guarantee a sellout?
3. Why can an unsold seat be consistent with profit maximization under single-price monopoly pricing?
4. How does own-price elasticity determine the direction of the total-revenue response to a price change?
5. How could income and the price of a related product change a team’s ticket menu even if its own ticket price has not changed?
6. Distinguish product differentiation, market segmentation, and price discrimination.
7. Why is a PSL a two-part tariff? What does alienability add to the contract?
8. What feature of the pricing rule distinguishes variable pricing from dynamic pricing?
9. How can resale improve ticket allocation while leaving its welfare effect ambiguous?
10. Why can a positive association between ticket prices and attendance fail to identify the causal effect of price?

Problems And Applications

1. A team estimates demand as $P(Q)=150-2Q$, where $P$ is dollars and $Q$ is thousands of tickets. Marginal cost is $30 and stadium capacity is 40,000. Derive marginal revenue, find the unconstrained monopoly quantity and price, and determine whether capacity binds.
2. A club estimates that the own-price elasticity of demand for an upper-level ticket product is $-1.4$ over the price range it is considering. If the club raises the price by 5 percent, approximate the percentage change in quantity demanded and predict the direction of the ticket-revenue effect. Why is this estimate alone insufficient to determine what happens to total profit?
3. A team’s demand analysis estimates the cross-price elasticity between stadium parking and tickets at $-0.25$. Are the products behaving as substitutes or complements? Explain why raising the parking price could reduce total event profit even if parking revenue increases.
4. A PSL costs $4,000, gives the holder the right and obligation to purchase season tickets for $1,200 per year, and can be resold subject to team approval. Identify the access and usage charges. Then explain how transferability, future ticket-price increases, and expected team quality could affect the PSL’s initial and resale values.
5. A ticket is sold by a team for $75, resold for $160, and purchased by a fan willing to pay $190. The platform keeps $15 and remits the rest to the reseller. Calculate the final buyer’s consumer surplus, the reseller’s net gain before other costs, and the platform’s revenue. Which party does not capture the resale markup under these assumptions?
6. A dataset shows that games with higher ticket prices also draw larger crowds. Give a plausible omitted demand shifter that could produce this association. Then describe what additional evidence would be needed to estimate the causal effect of price on attendance.

Suggested Assignment

Choose one team and one future home game. Record the access date and compare the same ticket product in the team’s primary market and one authorized secondary market. Define the seat, purchase terms, advertised total price, and applicable fees carefully. In 500-750 words, explain what the price difference can reveal about demand and allocation, what it cannot establish, and how the conclusion could change before game day. Include links or screenshots and separate factual observations from your economic interpretation.

Source Notes