Tag Archives: MLB

Do New Stadiums Lead to an Increase in Business?

Unless you’re familiar with the literature in this arena (no pun intended) or you know about Betteridge’s law of headlines, the title of this post is actually still an unresolved question for you. Well, I won’t delay the inevitable: according to research published earlier this year, the answer is no — new stadiums do no lead to an increase in business.

There are two things I want to talk about as it relates to this research. The first is Richard Florida. If this area is an interest of yours, there’s a good chance that you’ve come across him. Florida has been a professor for the last 20+ years and has written extensively on cities. Here’s a post I found from him within the last year that talks about the very thing that the journal article discussed:

The overwhelming conclusion of decades of economic research on the subject is that using public funds to subsidize wealthy sports franchises makes zero economic sense and is a giant waste of taxpayer money. A wide array of studies have shown that professional teams add virtually no income to local economies. In fact, some of them find that large subsidies actually have a negative effect, taking money out of the local economy. Aside from the jobs generated by actually building the stadium, most jobs inside the stadium—selling food and beer or working at team concessions—are low-paying temp jobs. It’s even worse for football stadiums, which are used for games at most a dozen times a year, and maybe a few more times for concerts or large events. Public economic development dollars can be put to much better use on things besides subsidizing sports teams and their wealthy owners.

Ultimately, the burden of public subsides falls disproportionately on small cities that are the least able to bear the cost. For example, a $200 million public subsidy for a new stadium ends up costing a small city like Santa Clara roughly $1,650 per resident, compared to just $50 a person for L.A. And, of course, teams in bigger cities, with their bigger markets and more revenue, often do not need subsidies at all.

The reason I raise Florida’s name is because I was surprised that I didn’t see his name mentioned in the journal article. To be fair, I don’t think that Florida has done any primary research in this domain, but I would have thought that even in the opening introduction or literature review that there may have been some reference to Florida’s constant discussion of literature like this.

Anyhow, the second thing I wanted to talk about is something that might not be measurable. Well, it might not be measurable in a simple way. As a former amateur athlete, I have a special place in my heart for sports. Certainly, there are plenty of things that one could classify as “wrong” about sports, but part of me still wants to defend it/them and I’ll be upfront: that might be part of what’s going on with this section of this post.

Something I didn’t see in the article (and probably something I wouldn’t expect to find in any well-written article) is a measure of (or discussion of?) the positive externalities that result from a city’s team winning the championship or even the spillover effects from the possible positive externalities. Now that’s a tortured sentence. I’m talking about how the residents of a city feel after their team wins the championship (in a given sport). Naturally, not everyone would be watching (or care), but for those that are fans of the team that wins, there would certainly be elevated levels of joy and happiness immediately following the victory. If there were studies done on this, I suspect that there might be comparisons to those who have won the lottery in that a couple of months after, lottery winners return to a similar level of satisfaction/happiness that they had prior to the lottery win.

I wonder, though, could we measure the economic gains for a city from this positive externality and the resulting spillover effect (in this case, let’s say the spillover effect would be the “pay it forward”-ness of joy from the fans of the team to the non-fans that the fans will be interacting with in the weeks following the city’s team’s victory). Even if there is a tangible effect that can be measured, I’m sure that any reasonable cost-benefit analysis would still conclude that a new stadium isn’t worth it for a city.

ResearchBlogging.orgHarger, K., Humphreys, B., & Ross, A. (2016). Do New Sports Facilities Attract New Businesses? Journal of Sports Economics, 17 (5), 483-500 DOI: 10.1177/1527002516641168

Black Players Were Held to a Higher Standard After Jackie Robinson Broke the Colour Barrier

In (unintentionally) keeping with one of the themes from the last post — let’s talk about baseball after Jackie Robinson broke the colour barrier. Most people will tell you that even after Jackie Robinson broke the colour barrier in 1947, black players had to be that much better than white players before they were given a chance to play. Having not been alive in 1947, many might take the word of those that were. However, we don’t have to! Let’s take a quick detour.

In the mid-90s and especially the 2000s, sabermetrics began making its way into the fold. In short, sabermetrics is a more sophisticated way to analyze baseball statistics. From Fangraphs:

Sabermetrics is about trying to evaluate the sport more accurately. For decades, statistics like home runs, runs batted in, batting average, wins, and earned run average were all we had to determine which players were good, which were bad, and which were in between. But as gathering, collecting, and sharing information became easier, a group of baseball teams and analysts started to develop statistics that were slightly harder to track and disseminate, but ones that were a much better reflection of talent or performance.

The most obvious example of this is the difference between batting average and on-base percentage. A walk is a positive outcome for the batter, and while it isn’t as valuable as a single or a double, it is much better than making an out. Batting average completely ignores walks, meaning that it is failing to capture important information about the hitter. Beyond that, batting average and on-base percentage assume that each hit or time on base is equally valuable, when we know that extra base hits lead to more runs than singles and walks. So there needs to be a way to credit hitters for getting on base, but also for how much their particular way of reaching base is worth. Sabermetrics, at its heart, is about making sure we capture as much of that as possible.

One of the statistics to come out of sabermetrics is called “Wins Above Replacement.” Once again, from Fangraphs:

Wins Above Replacement (WAR) is an attempt by the sabermetric baseball community to summarize a player’s total contributions to their team in one statistic. WAR basically looks at a player and asks the question, “If this player got injured and their team had to replace them with a minor leaguer or someone from their bench, how much value would the team be losing?” This value is expressed in a wins format, so we could say that Player X is worth +6.3 wins to their team while Player Y is only worth +3.5 wins.

Now that we know a bit more about sabermetrics and WAR, let’s get back to the 1950s and the colour barrier. As I said earlier, many might rely on the perceptions of those who were alive to witness baseball in the 1950s. However, we have a statistic like WAR that can help us better understand — empirically — whether black players really did have to play that much better to earn their spot on a team. From research published recently:

The data presented here provide support for anecdotal observations about racial bias in the major leagues. For decades, Black players who were promoted to the major leagues turned out to be more valuable players than White ones promoted at the same time.

Now that we know that there’s data to support the idea of this injustice, when do you think it ended? That is, when do you think that black players had to stop being that much better than white players? Before I read the research, I’m not sure what I would have guessed. Why don’t you take a second and think about what’s happened since 1950 and when you think this injustice has fallen away (or whether you think it’s still going on?) Again, from the research:

[Research] indicates that at least through 1975 (28 years after major league baseball was first integrated), Black players were still held to higher standards: simply put, they had to be better to reach the majors. After that point in time, the difference in eventual performance between White and Black players promoted to the major leagues was no longer significant.

So, it seems that in 1975, twenty-eight years after Jackie Robinson broke the colour barrier, the goal was finally realized. Of course, as the last sentence in the above-quoted research says, ‘no longer significant.’ That doesn’t mean that there still wasn’t an prejudicial effect, but just that in measuring that effect via WAR, it was no longer significant after 1975.

The Society for American Baseball Research (the same group behind sabermetrics) put together some helpful visualizations of the baseball demographics from 1947 to 2012. In reviewing some of them, there isn’t any ‘obvious’ reason for why the prejudicial effect is no longer significant post-1975. I’ve included one of the graphs and encourage you to read the whole article as it talks about the decline in black players in MLB.

ResearchBlogging.orgNewman, L., Zhang, L., & Huang, R. (2015). Prejudice in Major League Baseball: Have Black Players Been Held to a Higher Standard Than White Players? Journal of Sport & Social Issues DOI: 10.1177/0193723515594211

Travel and Sports: Timezones Used to Have an Effect on Winning Percentage in the NBA

It’s probably not surprising to you to learn that when an NBA team travels east of its “home” timezone, it’s more likely to win and when it travels west of its “home” timezone, it’s more likely to lose. However, you may be surprised that this effect only bears out for games played during the day and more importantly, not for games played at night. This finding surprised the researchers who conducted the study as they expected to find an effect for games played at night in concert with similar studies about the NFL.

It’s important to note that the time span for this research that found this effect was in the 90s. That is, this effect with regard to day games in the NBA only accounts for the time span in the 90s (1991 to 2002, to be exact). When the researchers conducted a similar study for the years between 2002 and 2013, they found no significant effect for either the day or night games. The researchers suggested that by the decade of the 2000s, teams had been better at preparing for day games (when travelling west).

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In thinking about this research, I wonder about this effect for other sports. The researchers mentioned the work conducted on the NFL and how West Coast teams benefit when travelling east for games, but what about baseball?

MLB is different from two of the major pro sports (NBA and NHL), as it’s leagues (or conferences, if you prefer) aren’t split up between the West and the East. That is, in the NBA, there’s the Western Conference and the Eastern Conference. Similarly, in the NHL, they have a Western Conference and and Eastern Conference. In baseball (much like football), the two “conferences” are split up, but not necessarily on geographic lines. While there are divisions that are split up regionally within the conference, it’s very common for NFL teams to have to travel across the US to play another team on a semi-regular basis. And in MLB, travel from the East Coast to the West Coast (or vice versa) happens regularly.

So, I wonder, if because there’s more frequent travel to the East/West Coast for baseball teams, would we find an effect (regardless of day/night games)? If I had to hazard a guess, I suspect not. Although, I wonder, if like the researchers did with the NBA, there’d be an effect if we were able to look into the past. Maybe there’d be an effect in MLB if we went back to the 80s or maybe even the 70s.

ResearchBlogging.orgNutting, A., & Price, J. (2015). Time Zones, Game Start Times, and Team Performance: Evidence From the NBA Journal of Sports Economics DOI: 10.1177/1527002515588136

A Can’t-Miss Strategy for Making the MLB Playoffs

The baseball season is long — the regular season lasts more than half of the year. And that’s just the regular season. It doesn’t even include the preseason or postseason. As the season spans six months, one would think that it might be hard for some players to keep their focus during the middle of the summer.

In fact, this past Sunday while watching a Blue Jays game, I saw a graphic that depicted the wins/losses of the teams in the division during the last game of the series for the 2013 season. The graphic showed how the other teams were far more successful than the Blue Jays when it came to the last game in a series. As a result, it got me thinking about how to better incentivize players (maybe managers, too?)

My idea: incentivize winning series.

Before I get into the details, I want to preempt the argument that baseball players get paid too much. Grant Brisbee of SB Nation had an all-around great response:

The problem with these comparisons is that baseball isn’t the real world. There is no comparison for baseball. Try to invent one without devolving into ridiculousness. Okay, so there are 30 Walmarts in America. And there are laws that protect Walmart’s monopoly, which means there aren’t any Targets. But those 30 Walmarts can be run only by people with Ph.D.’s who graduate in the top one percent of their class from the top 10 universities. And the Walmarts are in competition only with each other, which means …

… a ridiculous scenario all around, of course. Baseball players shouldn’t be compared to the average American worker. They’re specialized, elite talents in an entertainment industry that’s sitting on a money spigot. And I feel like I should mention this at least once: If the players didn’t get the money, it would just go to the owners. You can argue that owners should get a larger share because they take the investment risk. I’m not sure I’d agree, but that’s at least a consistent argument. Saying that players should make less because it offends your sensibilities isn’t quite as compelling.

Now that we’ve gotten that out of the way, we can focus on how to incentivize players to win series. Well, just before that, let me talk a little bit about why I chose series as a unit of measurement. As there are 162 games in a season, it seemed like incentivizing a player to win every game might superfluous, as players always want to win the game. I chose a series because there are a little more than 60 of them and it seemed like a good intermediate goal (or project milestone, if you want to put it in the language of project management) between winning every game and making it to the playoffs.

Most series are 3 games long, so we can think of winning the series as winning 2 out of the 3 games. If the team wins two out of the three games, then the players all get a bonus. To guard against them mailing it in during the last game, there could be another bonus if they sweep the series and win all 3 games. What happens when the team loses the first 2 games of the series — what do you incentivize then? Well, you’d incentivize not being swept. That is, if the team loses the first 2 games, the players get a bonus if they win the 3rd game and avoid being swept.

For those series that are 4 games long, the same incentivizes for winning/sweeping a series still apply, but we’d add another one — tying a series. That is, if a team is down 2 games to 1 in the series, the players would get a bonus if the won the last game to tie the series 2-2.

Now, my first thought would be to use money as the incentive to win these games, but with the salaries that players have, one may wonder whether there could be enough money offered to actually make the incentives work. The more I thought about it, though, the more I thought that even players with massive salaries could be motivated by money.

Let’s use last year’s MLB salary figures as a basis. Fangraphs had an article that detailed the average MLB salary last season ($3.4 million) and the median ($1.1 million). The median salary is probably a better representation, so let’s use it. The median salary equates to approximately $20,000/week, assuming that players get paid every week of the calendar year. Let’s also assume that there are 60 series in a season. That means, there will be approximately 60 times to offer players this bonus incentive. There are also 25 players that are on the active roster. As a result, we’d have to decide whether we wanted to reward all players or just the players that played in the game.

With 25 players on the active roster, the calculation for offering a bonus of $1000 makes it quite the expense, but not as much as you might think. 25 players getting a bonus of $1000 across 60 games equates to an extra 1.5 million that needs to be budgeted. Given that this is approximately the median salary of an MLB player, one would think that teams could afford this. It’s also important to note that these calculations didn’t include the possibility that teams would win the series and sweep the series. In those cases, players could get a bonus for winning the second game of a three game series and then get another bonus if they win the third game of the three game series. A quick look at the total number of sweeps last year tells us that the average number of sweeps was 7. So, we can add another $175,000, which brings the total expense to $1.675 million. While certainly not a small amount of money, in the context of how much teams spend, it seems like it might be worth it to try and win a few extra games.

Let’s look at the Baltimore Orioles last season as an example. They finished 85-77, 6.5 games out of making the playoffs. Meaning, if they were to win 7 of the games that they lost, they would have made the playoffs. Looking at their streak data from last season, they were swept 5 times. In addition, they were stopped from sweeping a team 8 times. Together, that’s 13 games. If the Orioles could have won half of those (6.5, so let’s round it to 7), they would have made the playoffs.

Put differently, if they would have employed this strategy and it was successful at least 50% of the time just in the series where they almost swept a team and were swept, they would have made the playoffs.

How Big Data Can Make Watching Baseball More Fun

I like baseball. I played it all throughout my youth and my years as a teenager. So, not surprisingly, I also like to watch baseball. Watching baseball on TV has come quite a ways. While baseball was first televised in the 1930s, instant replay didn’t come along until almost 1960. Nowadays, you can’t watch a game without seeing just about every “key play” replayed. From the replay of the last double in the gap to the last pitch that was so close to being called a strike. And on that note about strikes, we can now see a makeshift strike zone on the screen next to the batter/catcher.

My post today is a pitch (pardon the pun) about how to improve the viewing experience in the context of that makeshift strike zone, which on some networks, is called pitch tracker.

On the pitch tracker, we can see a few things that have happened during the at bat. We can see where each pitch crossed the plate and at what height. We can also see if the pitch was fouled off and if the pitch was a ball. While all of this great, in my opinion, there is one major flaw to all of this — the “strike zone” isn’t universal. That is, as many players will tell you, each umpire has a different “strike zone.” Some umpires like to call a “wider” strike zone. Meaning, on the screen, it will appear as though the pitch is quite a few inches outside of the strike zone, the umpire calls that pitch a strike.

To the casual fan this may be confusing, but to a fan who watches baseball frequently, this may be frustrating. Especially as the game wears on, you might hear the announcer state that the last pitch was called a strike earlier in the game, but now it’s being called a ball. I’d like to eliminate the need for the announcer to tell me this. I’d also like to eliminate the confusion of the fan who sees a pitch that appears outside the strike zone, but is called a strike. How can we do this? Big Data.

Umpires go through a rigorous process before becoming an MLB umpire. As a result, their strike zone will probably be pretty much set in stone by the time they get to umpire their first MLB game. I propose that instead of using the “standard” or traditional strike zone on the screen during the game that networks show us the strike zone of the umpire. So, if an umpire usually calls strikes that appear 6 inches outside, we can see that because that’s the strike zone on the screen. We could even using a rolling average of the umpire’s career, such that only the last 3 seasons are taken into account when creating the strike zone on the screen.

The reason I suggested Big Data as the solution to this is because of all the sports, baseball is one of the ones with reams of data. Bill James did an excellent job of using data to allow us to better understand the success and failure of players, I think it’s time we use some of that data to make watching baseball just a bit more interesting.

Revisiting Using Pitchers on Short Rest: Long-Term Ramifications

A couple of weeks ago, I wrote about the Los Angeles Dodgers’ strategy of using their best pitcher (and one of the best pitchers in baseball) on short rest to pitch in a non-elimination game. The Dodgers ended up winning that game and the series, but the debate over the strategy doesn’t end there.

In my post from a couple of weeks ago, I compared the Dodgers’ decision to my younger years when I was playing baseball in double elimination tournaments. This wasn’t a perfect comparison, but I the spirit of the decision to use your best pitcher was there in both. A few nights ago, the Los Angeles Dodgers were eliminated from the postseason. All but one of the thirty teams are eliminated, so this isn’t earth-shattering news. However, the fashion in which they lost is.

In Game 6 of the National League Championship Series, the Dodgers started Clayton Kershaw. Yes, the same one who started in Game 4 for the Dodgers in the National League Divisional Series. This time, Kershaw’s start didn’t go so well. In fact, Kershaw only pitched 4 innings before pulled by the manager, Don Mattingly, but not before Kershaw gave up 7 runs. So, the question might be warranted: did using Kershaw on short rest affect his ability in Game 6? It turns out, this was a thought that had crossed some minds before Kershaw made the start in Game 4.

As they say, hindsight is 20-20, but it does seem a bit prescient. McCarthy’s hypothesis makes sense, but it’d be hard to test. One may point to Kershaw’s start in Game 2 of the NLCS. He tossed 6 innings and allowed 1 unearned run. Shouldn’t he have unravelled in that game if he were fatigued from the short rest start in the NLDS? One could argue that, but the way that McCarthy’s argument is setup leads one to believe that the “fatigue” could happen later and later. So, if the Dodgers won Game 6 and won Game 7, would McCarthy have expected Kershaw to unravel during one of his starts in the World Series?

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Let’s see if we can apply this lesson to decisions in other arenas. The analogy I used for the other post doesn’t really hold anymore. We’d be better off thinking about a decision for a business. Using Kershaw in the way the Dodgers did was almost like using a certain machine in the factory to fill some rush orders. The machine might only be able to fill a certain number of orders per week, but because it’s the holiday season, the boss thinks that running it on overdrive is necessary. Initially, the machine churns out the widgets just the way the boss would have expected, but next week when you use the machine, the widgets aren’t as high a quality. And then the week after that, the widgets aren’t even of a high enough quality to give away. It’s clear, the machine needs a break and some recalibration. In weighing the risk, the boss thought that using the machine more than usual was worth it for it potentially shutting down.

There are other ways we can map out this scenario, but I want you to think about how you might be overdoing it. Maybe the machine you use at work is fatiguing. Maybe you are fatiguing from working too hard.

Should the Los Angeles Dodgers Have Started Clayton Kershaw on 4 Days Rest?

A few days ago, there was a bit of a hullabaloo as the Los Angeles Dodgers decided they were going to start their star pitcher, Clayton Kershaw, in Game 4 on short rest. Let me back up for a second and explain a few things. Typically, starting pitchers in MLB get 5 days between starts. Meaning, if you pitched on Monday, you wouldn’t pitch again until Saturday. As we’re now into postseason baseball, some of the typical norms aren’t followed very closely. For example, last night in the elimination game between the Rays and the Red Sox, the Rays’ manager, Joe Maddon, changed the pitcher after the first inning even though the Red Sox hadn’t scored any runs! This is highly unorthodox. The Rays went on to lose last night, but as to whether that was a result of Maddon’s strategy is a post for another. Getting back to Kershaw and the Dodgers…

The Dodgers were up 2-1 in the series against the Atlanta Braves. Game 4 was to be played in Los Angeles. If the Dodgers won, they would move onto the next round of the playoffs. If the Braves won, there would be another game in Atlanta — Game 5 — to decide which of the two teams would advance. Kershaw pitched in Game 1 of the series, October 3rd, (and won). It was now October 7th, and the Dodgers’ manager, Don Mattingly, had decided that Kershaw was going to pitch in Game 4 that night.

There were many opinions about whether this was a good idea. There’s the “we’ve always done it this way” opinion that says you shouldn’t start Kershaw on short rest because that’s not how you do things. There’s also the mathematical opinion that starting Kershaw in Game 4 increased the Dodgers chances of winning Game 4.

In thinking about this decision that faced Mattingly, I was reminded of playing baseball when I was younger and being in double elimination tournaments. When it gets down near the end of the tournament, your pitchers are tired and some rules won’t let you pitch certain players more than a certain number of innings (depending on the league you’re playing in). So, coaches are often faced with the decision of starting their best pitcher in the semi-final game (or quarter-final) game to get onto the next round, where, quite possible, they won’t have anyone left to pitch. I’ve seen the strategy employed where one pitcher is held back in the “just in case” scenario. I understand why some coaches do this, but I don’t know that it’s the optimal strategy in most cases.

Elimination games are slightly different from games where you’re not facing elimination, but similar principles are used. Mattingly chose to use Kershaw in Game 4 instead of Game 5 because he thought it gave him the best chance to win. I totally respect that and if I were in his shoes, I think it’s the right call and the call that I would have made.

As it turns out, the Dodgers went on to win Game 4, so Mattingly’s use of Kershaw was vindicated. Even if the Dodgers lost, I still think that Mattingly would have made the right call in that situation. The mathematics supported Mattingly using Kershaw in Game 4 (to increase the Dodgers’ chances of winning Game 4).

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I wanted to use this sports example as a way to pivot towards strategy and decisions in your own life — personal or professional. I want you to think about decisions that are coming up in your life. Are you holding back your “Clayton Kershaw” for the “do-or-die” situation later or are you using him/her to close the deal or make the change right now? There’s not necessarily a right or wrong way to do it, but in reading this post, I hope that you’re able to map this scenario onto your own life to identify those instances where you might not be putting your best foot forward in the here and now because you’re saving it for tomorrow.