Have I got news for you!

An exciting announcement: some of my recent research was the subject of a news article on Sportico.com where I was interviewed for some sounds bites.

Bobbleheads Boost Attendance, Build Long-Term Fan Devotion, Study Finds https://t.co/xrAaaiGTPV via @sportico @HowieLongShort #SportsBiz

— Scott Soshnick (@soshnick) June 15, 2021

New Published Articles

Dear readers,

I have taken a short break (as I am wont to do from time-to-time) to focus on some research. I am happy to now announce the fruit of this labour:

First, "Doping in Sports: A Compliance Conundrum" has been included as a chapter in the textbook The Cambridge Handbook of Compliance. It is chapter 65 of 69, which leads me to believe they followed the age-old adage, 'save your best for [almost] last.' (I will update the link with a version of the text if/when I can find one).

Second, "Stadium Giveaway Promotions: How Many Items to Give and the Impact on Ticket Sales in Live Sports" has been published (online) at the Journal of Sport Management. Avid readers will find it takes a familiar spin on the bobblehead topic I have been taking about ad nauseum. 

Thank you for all your support and stayed tuned for more research updates and (most importantly) future posts on Sports and Economics (in that order).

Jeffrey

Ale-lasticity: How price sensitive are MLB beer drinkers?

TLDR: A recent poll came out stating that Chicago White Sox fans drink the most beer during baseball games. While also showing the number of drinks per attendee for each MLB team, this poll helps reveal something about the demand for beer and the relationship between fans and teams. 

I find the price elasticity of beer, or the ‘ale-lasticity,’ is -0.1. This means that a 10% increase in the price of a beer at the ballpark would result in a 1% decline in beers sold, making the demand for in-stadium beer relatively inelastic.

This inelasticity also means teams are not pricing beer at profit-maximizing levels: read on to find out why.

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As Major League Baseball ramps up its Spring Training, we head towards a fresh start for the 2021 regular season. It now appears, unlike last year, most teams will be allowed to host reduced numbers of fans in their respective stadia.* 

After a financially rough 2020, it has been reported the 30 teams of MLB lost a collective $3.1 billion. Looking to recoup these losses, teams may now be faced with decision to alter prices on tickets or concessions even with fewer fans.

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* As an aside, the last time the Chicago White Sox played a game in front of fans at Guaranteed Rate Stadium was back on 29 September 2019. That same day, while sitting in the stands, I received an email from my boss with a clip of Jason Benetti calling me out for 'man-splaining' to my then-girlfriend/now-fiancée (I guess she didn't mind hearing about the finer points of baseball, Jason).

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Back to economics: one way to sell larger quantities of an item is to lower its price - this isn't too difficult of a concept to understand. However, if I were to say that I lowered the price of an item by one dollar and sold three more units, you may or may not say, 'so what?' This would be an impressive feat if the items in question were, say, cars, but not if they were cans of corn.

Instead, a better measure would be to see the percent change in sales resulting from a percent change in price. This is known as the elasticity of demand (it applies to price increases just as it does with price decreases).

For example, if the price of an item increases by 10% and its sales decrease by more than 10%, the demand is considered to be elastic. There is a disproportionate reaction by consumers to the change in the price of a good causing some to stop buying and substitute to another good or wait until the price declines to something more reasonable.

Conversely, if the price of an item increases by 10% and its sales decrease by less than 10%, the demand is considered to be inelastic. When the percentage price increase has the exact opposite percentage decline in quantity sold, the demand is considered unit elastic. This is the point at which a seller cannot raise the price any higher without sacrificing profits through lower sales. It also cannot lower its price without leaving money on the table. Costs aside, pricing at the unit elastic point of the demand curve achieves profit maximization.

So, with all this in mind, we can calculate the change in the quantity of ballpark brews in response to a change in price and answer the question of are teams profit maximizing?

I take the price of beer per ounce for each MLB team from Team Marketing Report's Fan Cost Index and the average number of beers per fan from the survey published by NJ Online Gambling and calculate the elasticity of demand using a log-log OLS regression. I find the price elasticity of beer, or the ‘ale-lasticity,’ is -0.1. This means that a 10% increase in the price of a beer at the ballpark would result in only a 1% decline in beers sold, making the demand for in-stadium beer relatively inelastic.

So, if teams could raise beer prices and still not lose too many sales, why don't they?

Well, research has shown time and time again that teams intentionally don't profit maximizing on every sale at a ballpark. Whether to avoid the consumer backlash from perceived price-gouging or to leave money on the table (also known as consumer surplus) for fans, teams often choose to invest in fan goodwill. In fact, a forthcoming co-authored paper of mine called "Stadium Giveaway Promotions: How Many Items to Give and the Impact on Ticket Sales in Live Sports" discusses how some teams give away promotions, like bobbleheads, to everyone just to avoid not giving a bobblehead to anyone. The teams that are most generous with their bobbleheads also have the most devote fans (e.g., Philadelphia Phillies, St. Louis Cardinals, San Francisco Giants, etc.).

And fans are certainly aware of the full cost of attending a live sports event. For example, an owner of a now-defunct soccer team, San Francisco Deltas, from a now-defunct league, North American Soccer League, issued a plea to his fans to continue coming to games and supporting the team. A survey revealed that 10% of Deltas fans did not return to the soccer matches due to the price of tickets and concessions. However, the comment section of the linked article above would suggest that 10% likely understates the actual size of the group feeling priced-out of the market (or maybe that its just a very vocal minority).

So, there you have it. MLB teams are doing you a favour by keeping beer prices lower than profit-maximizing levels. Therefore, if you are fortunate enough to attend a live baseball game this upcoming season and can do so safely, make sure you thank your team by purchasing a drink or two or three.

After all, beer doesn’t have much vitamins, that’s why you need to drink lots of it. 

New Research - an update!

Updated 3 April 2020

After exhaustive work to satisfy the toughest, but fair, referees, I have successfully published my research in the Journal of Sports Economics.

Please feel free to take a look at it here and I highly recommend downloading it through your educational institute.

Thank you all for your support,
Jeffrey

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Originally posted 1 December 2018

Dear readers,

I have taken a short break from Sports and Economics (in that order) to focus on a project that has been sitting on the back-burner for quite some time: a publication entitled "Impacts of Performance-Enhancing Drugs on the Demand for Major League Baseball"

The results of the paper are loosely based on my post from February 2018, "Consumer balk when cheaters are revealed: more evidence from steroid suspensions in Major League Baseball"

I would love to hear any and all feedback from you on the current state of the paper before I send it to a publisher in early 2019.

Please find a link to it HERE

Thank you all!

Bobbleheads 2020

It's the most wonderful time of the year!

Players have reported to camp, pitchers are stretching out, and Spring Training is underway.

But the best part is that teams have announced their 2020 promotional schedules which gives way to the question: has Major League Baseball begun to appreciate the potential of the bobblehead?

Hint, the San Diego Padres may have figured it out...

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There are two things team must consider when making decisions about promotional giveaways (items that I loosely consider to have a resale value of at least $10) at sporting events:

1. Promos increase ticket sales by a significant amount; and,
2. Promos allow the team to charge more for tickets.

To understand the promotional impacts on attendance, I undertake the following steps, following the same procedure that I have done for the previous four years:

• Scrape each team's website for their promotional giveaway schedule for 2020,
• Parse through the text of each schedule to identify dates with promos such as:
• replica World Series rings;
• collectible coins;
• collectible pins; and
• Bobbleheads!
• Predict attendance at each game with and without a hypothetical promo using a simple regression model,
• Choose the best dates for which promos would provide the most value to the team; and,
• Compare my hypothetical promo schedule to the actual promo schedule and estimate the additional revenue from my promo schedule.

There are currently 223 promo days scheduled for the 2020 MLB season. While a few teams either have not yet published their promotional schedule or are not offering meaningful promotions this year (Miami and Colorado), 2020 should ultimately feature a similar number of promo days as we are accustomed to seeing.

In 2020, the model says there are 172 games that have been sub-optimally chosen to feature a promo. I therefore reassign 77% of promo days (172/223) and find that the entire league may be foregoing ticket sales to the tune of ...

... $6.4 million!

Here's this years number in some historical context.

Year		Total Promos		Reassigned Promos		Forgone Revenue ($)
2016		232		166		3.2M
2017		231		170		4.0M
2018		254		185		3.8M
2019		227		180		5.0M
2020		223		172		6.4M

As seen in past years, the St. Louis Cardinals, Los Angeles Dodgers, and Chicago Cubs lead the league in terms of foregone revenue, but an appearance at the top of the list of losers is largely in part due to the fact that these teams have either a) above-average number of promos; or, b) above-average ticket price (i.e., Chicago Cubs).

Excluding the aforementioned clubs without promos, the San Diego Padres had the least foregone revenue. This is partially because of their more reasonably priced tickets (3rd cheapest in the league) but also because the two games they chose to feature promos were ranked the 4th and 5th best options by the model: the model chose to reassign the promo games but picked games that would sell only ~109 more tickets each.

Way to show them how it's done, Padres!

Below are the results for each individual team. How did your team do?        

	Team		Total Promos		Reassigned Promos		Forgone Tickets (#)		Forgone Revenue ($)
1	St. Louis Cardinals		19		13		34,635		1,230,942
2	Los Angeles Dodgers		18		15		20,469		872,393
3	Chicago Cubs		7		6		14,007		833,292
4	San Francisco Giants		6		5		13,562		519,681
5	Milwaukee Brewers		20		11		18,055		513,493
6	Atlanta Braves		18		15		14,224		418,746
7	Washington Nationals		10		9		5,630		248,415
8	New York Yankees		8		6		4,910		233,829
9	Toronto Blue Jays		8		7		7,631		226,562
10	Boston Red Sox		3		3		2,604		154,445
11	Philadelphia Phillies		5		4		3,650		131,529
12	New York Mets		11		8		4,743		130,916
13	Minnesota Twins		6		5		3,277		107,104
14	Houston Astros		5		4		1,822		90,822
15	Seattle Mariners		4		4		2,165		81,762
16	Chicago White Sox		5		5		2,401		68,133
17	Pittsburgh Pirates		10		8		2,861		65,253
18	Texas Rangers		4		3		2,281		58,740
19	Cleveland		6		4		1,776		55,344
20	Cincinnati Reds		11		7		2,468		52,175
21	Baltimore Orioles		9		5		1,618		48,465
22	Tampa Bay Rays		8		7		2,144		48,314
23	Los Angeles Angels		4		4		1,545		47,773
24	Arizona Diamondbacks		5		4		1,492		31,132
25	Detroit Tigers		6		4		1,076		30,461
26	Oakland A's		3		3		1,116		27,127
27	Kansas City Royals		2		1		774		25,433
28	San Diego Padres		2		2		217		4,818
T-29	Colorado Rockies		0		0		0		0
T-29	Miami Marlins		0		0		0		0
	Total MLB		223		172		173,153		6,357,099

The Houston Astros' 'Home-Field Advantage'

In case you have missed it, much like the title of the classic New York Yankees movie, Bang the Drum Slowly, the Houston Astros have been accused of, well, banging just a trash can.

More specifically, the Houston Astros have been accused of having a camera set up at their 2017 home games to watch the signals sent from the opposition's catcher to the opposition's pitcher. The catcher's signals are used to communicate to the pitcher the type and location of the next pitch to be thrown. The camera would send a live video feed to an Astros' staffer who allegedly bangs a trashcan based on the signals observed, thereby relaying to the Astros' batters the information on the type of pitch that is about to be thrown.

There is lots of evidence, as uncovered by the new internet sensation, Jomboy, that I am not going to go into. Instead, I want to see if there is any evidence of harm, i.e., that the Astros gained a competitive advantage by 'cheating.'

Therefore, IF there was an impact on the outcome of the pitch, at-bat, inning, and ultimately game, how exactly would we see it? Based on the reports, the batter allegedly is relayed a signal indicating the next pitch is to be a slider and/or change-up - the absence of a signal indicates the next pitch is fastball. With this knowledge, a batter may gain a slight advantage as to how fast the pitch is coming and whether it will break as it nears the batter. The batter can then decide when and where to swing, if at all.

Thus, a plausible place to look for an impact is whether the 2017 Astros are any better at making contact (i.e., not swinging and missing) in home games versus away games. Note that it does not imply that the contact was productive (e.g., more home runs and less groundouts) but that the batter did not make a swinging strike. This measure of contact versus a swinging strike is known as the whiff rate: the number of times a batter swings and misses divided by the number of time the batter swings. 

Since teams would never intentionally signal their pitches, it would likely take time for an observer to study the catcher's signals and decipher the code of which signal indicates which pitch. Therefore, we will likely see no advantage until such an observer is able to correctly predict the next pitch from the catcher's signals: it's likely safe to say this would occur sometime after the 1st inning. But since pitching ability of obviously differs between pitchers, we would have to look at times the same pitcher is throwing the same pitch, but in different innings.

With all this said, let us examine a simple example of the 2017 Houston Astros whiff rate on all breaking balls (i.e., curveballs and sliders) by starting pitchers in the 1st and 5th innings:

What can be observed here is in the 1st inning, the 2017 Astros have a whiff rate of around 10% in both home and away games. By the fifth inning, the Astros whiff rate drops to 6.7% in away games. This can be explained by many factors such as pitcher fatigue and/or the Astros becoming more familiar with the pitcher (recall that these are the same pitchers who started in the 1st inning).

But what becomes shockingly apparent is the whiff rate for home games is less than half of what is for away games. The 2017 Astros were able to decrease their whiff rate by 4.1 percentage points in 5 innings for away games whereas this decrease is a whopping 7.6 percentage points for home games!

This can be stated alternatively as a net benefit of a 3.5 percentage point lower whiff rate seen only at home games: (-7.6) - (-4.1) = -3.5.

But what happens with other pitches, such as fastballs and change-ups? This 'net benefit' described above is known as the 'difference-in-differences,' and it gives a more comprehensive estimate of the observed effect of the Astros 'home-field advantage.' Below is a graph of these difference-in-difference calculations for various pitches the 2017 Astros had encountered.

When put into this perspective, the 3.5 percentage point benefit pails in comparison to the 6.1 or 5.7 percentage point benefit seen with respect to change-ups and fastballs. Note, a large "other" category did not have enough observations to say definitively whether or not there was some sort of benefit.

Perhaps the Los Angeles Dodgers (who lost to the Houston Astros in game seven of the 2017 World Series) should plan a retro-active 2017 World Series parade?

How to Pump Up Your Bobblehead Revenue: it’s not a question of when, but of how many

As you have come to know by now, there is perhaps no subject I am better versed in than the bobblehead.

I have argued ad nauseam about how Major League Baseball (MLB) teams are not revenue maximising when it comes to scheduling which home games will feature the cherished novelty. 

And while I have suggested that teams are losing out on as much as $5 million on this inefficiency in 2019 only, I now have evidence to suggest that teams are leaving even more money on the table by offering the wrong number of bobbleheads on the days they choose to offer them.

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Scarcity is the concept that there are finite resources yet possibly infinite desires. It is the decisions and actions made by individuals in the face of scarcity makes up the very root of the study of economics. In the supply and demand framework, an item's scarcity impacts it's supply and thereby it's price.

Alternatively, scarcity bias in behavioural economics suggests that individuals value a good more if they perceive it to be scarce. Mixed with human's inane sense of loss aversion, or Fear Of Missing Out (FOMO), scarcity can be used to induce demand. Think of the times you have heard the phrase 'Act Now' or 'Limited Time Only' or the McDonald's McRib.

Major League Baseball teams have been known to use scarcity on their promotional giveaways to induce demand for tickets. You will see conditions attached to their promotional giveaway schedules such as "first 20,000 fans," or "while supplies last," used to signal their rareness of the item. While teams may deploy various strategies, one thing that becomes clear is the 20,000 bobbleheads promised by the Los Angeles Dodgers would not satiate the same share of fans as it would if promised by, say, the Miami Marlins: 20,000 bobbleheads would imply on average 40% of Dodgers fans get one, while nearly every Marlins fan could have two.

I calculate the probability of a consumer receiving a bobblehead as the number of bobbleheads promised divided by the capacity of the stadium. If a team does not provide a number of bobbleheads, I assume they plan to give everyone a bobblehead, or that the probability is 100%. Below is a histogram of these probabilities.

What can be observed from this histogram is that there is a slightly skewed distribution of the probability of receiving a bobblehead, with a giant spike at 90-100% (note that no team signaled what could be interpreted as 'false' scarcity - a limit of bobbleheads that is greater than it's capacity - although it could be a very interesting strategy). Ignoring the 100% values, the average bobblehead day sees a 40% probability a fans receives one. Including the 100%, the average is closer to 50%.

To determine what sort of effect the scarcity generates in ticket sales, I create a model to predict the paid attendance at MLB games during these years. Similar to other studies by yours truly, the unit of observation is the attendance at a single game and I include controls for the following:

• Home team fixed effects,
• Away team fixed effects,
• Day of the week fixed effects,
• Month fixed effects,
• Year fixed effects,
• Day/evening game indicator,
• Divisional rival indicator,
• Interleague indicator,
• Opening day (team's first home game of the season) indicator, and
• Predicted Season Wins.¹

Then, if all is done correctly, the only thing I do not control for is idiosyncratic shocks and ... the bobblehead scarcity. (Yes, I understand that there are likely more things I do not control for than things that I do, but this is a free blog).

The leftover attendance that is not explained by the list of controls from above is known as the residual. If the residual is positive then the actual attendance is higher than what the model predicts. Conversely, if the residual is negative, the model predicts a higher attendance than is actually observed.

If we plot the residuals, we begin to see a pattern emerge for bobblehead days that is correlated with the probability of consumers receiving one. Computers are much better at recognising these types of patterns, so just in case you are not able to see it, I have drawn in a non-parametric line of best fit.

What this non-parametric pattern reveals is that there appears to be a local maximum at around 40%, a local minimum at around 80%, and an uptick at the 100% mark, although there is lots of variation in between. 

What is also illustrates is that perhaps a parametric approximation is appropriate - this is beneficial sa it allows us to calculate the actual value of the local minimum and maximum, as well as test what the maximum value is over the range of bobblehead probabilities [0,1]. I therefore fit a cubic function in order to test this hypothesis. This is accomplished by adding four more controls to the model described above:

• Bobblehead Day indicator (the cubic's constant term)
• Probability of a Bobblehead,
• Probability of a Bobblehead-Squared, and
• Probability of a Bobblehead-Cubed.

I graph cubic function returned by the regression below. The function tells us what percent increase in fans we should expect at a bobblehead game on the Y-axis given the probability of receiving a bobblehead on the X-axis.

The results suggest  the most fans attend when the probability of receiving a bobblehead is 38.7%. This is also known as the revenue maximising strategy and it will bring in about 13.6% more fans, all else equal. This means that 65% of bobblehead days give out too many bobbleheads.

The strategy of offering 100% is not quite as lucrative, although it is very close, bringing in about 10.5% more fans on average. Conversely, a bobblehead probability of 80.9% is revenue minimising, and it is not predicted to bring in any additional fans.

But how much does choosing the bobblehead probability actually matter? To answer this, we could calculate the change in attendance if every team chose the revenue maximising value of bobbleheads.

Similarly to how I reassign bobblehead days, I reassign the number of bobbleheads given out on the bobblehead days to 38.7%, and predict attendance. Because the predicted attendance will be higher than the observed attendance, I cap the predicted attendance at the stadium's capacity to avoid overestimating the impact.

Then I take the difference of revenue-maximising predicted and observed attendance and multiple it by the team's average ticket cost from Team Marketing Report. The end result is the forgone revenue due to an inefficient bobblehead strategy. Below is a graphical depiction with the forgone revenue from bobblehead day reassignment added for comparison (I estimated the latter back in March - see here).

The sum total of revenue gained from changing the bobblehead day schedule is approximately $16M from 2016 to 2019. But the sum total from handing out the correct number of bobbleheads is ...

$25.6 million!

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A sobering thought ...

If my model is correct, most teams do not revenue maxmise when deciding the number of bobbleheads to give out on promo days. About 65% of days promise too many bobbleheads. Therefore, from 2016 to 2019, more than 4 million extra bobbleheads were handed out than would
have otherwise been suggested.

At 18cm (7 inches) in height, 4 million bobbleheads is enough to stretch from San Franscisco to San Diego!
(or, for my readers in Canada, from Winnipeg to the Paris of the Prairies, Saskatoon)

But in other not-so-fun terms, a bobblehead weighs about 700g (~1.5lbs) and a garbage truck holds 12.5 tonnes (~14 US tons) meaning MLB produced 227 extra garbage trucks worth of bobbleheads and lost $25.6 million at the same time.

While a bobblehead day can be a fun way for teams to attract fans, it has also been shown that not getting the timing and quantity correct may be quite costly and not just in forgone revenue...

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[1] Predicted Season Wins is the number of wins a team can expect to end the season with given their play in prior games and the probability of winning in future games. It is calculated as the actual number of wins prior to the observation plus the expected future number of wins from the observation to the end of the season (using the moneyline odds for information on the probability of winning each future game).