baseball – The OM Blog by Heizer, Render, & Munson

Video Tip: How Baseballs Are Made

Despite its uncomplicated appearance, the baseball is in fact a precision-made object, and one that has often been the subject of heated controversy throughout its history.

An official Major League baseball consists of a round cushioned cork center called a “pill,” wrapped tightly in windings of wool and polyester/cotton yarn, and covered by stitched cowhide. Approximately 600,000 baseballs are used by all Major League teams combined during the course of a season. The average baseball remains in play for only 5-7 pitches in a Major League game. Each ball must weigh between 5 and 5.25 ounces and measure between 9 and 9.25 inches in circumference to conform to Major League standards. Your students will enjoy this 5 minute video showing the manufacturing process.

Such uniformity was nonexistent in the early years of baseball’s history, when balls were either homemade or produced on a custom-order basis as a sideline by cobblers, tanners and other small business owners. In 1872, the modern standard for the baseball’s weight and size was established. The production of balls became more consistent during the remainder of the decade, thanks largely to the demands made on manufacturers by the newly formed National League, the first professional baseball league.

At the turn of the century, the baseball had a round rubber core. This gave way in 1910 to the livelier cork-centered ball, which was itself replaced two decades later by the even more resilient cushioned cork model. The baseball has undergone only one significant change since that time, when a shortage in the supply of horses in 1974 prompted a switch from horsehide to cowhide covers.

OM in the News: Need a Poka Yoke at San Francisco’s Baseball Stadium?

$99 batter's box template — $99 batter’s box template

Though it doesn’t take long for them to get destroyed and disappear, the batter’s boxes originally drawn in for this week’s Reds-Giants baseball game at San Francisco’s AT&T Park “may be the most crooked and awkward looking we’ve ever seen,” reports Yahoo Sports (July 25, 2013).

Of course that also means they were not up to Major League Baseball standards — or sandlot ball standards, for that matter — but no one on the field seemed to notice or care — most notably home plate umpire Chris Guccione — as the game started and carried on without the lines being corrected. The umpires collectively missed it or ignored it.

Most hitters, like Miguel Cabrera, can sense when a line is an inch too long or too close to the pitcher’s mound. A lot of that is based on routine. A lot of that is based on instincts. But, in this case the errors weren’t even marginal. Both sides were angled so noticeably. The cure is a poka yoke like the one shown here, available for less than $100 on-line. (For more on poka yoke, see Chapter 6, Managing Quality).

Discussion questions:

1. Why does the crooked batter’s box make a difference?

2. Name some other popular poka yokes.

Teaching Tip: How Technology Almost Ruined the World Series for the Cardinals

Everyone loves a good baseball story, but how often can you find one to tie into your OM lecture? In case you don’t follow the sport, here is a quick summary of how a lack of technology (a topic in Ch.7) cost the St. Louis Cardinals Game 5 of the 2011 World Series, as reported in The Florida Union-Times (Oct.26,2011). In an age of texting, email, iChat and Skype, baseball, it seems, remains mired in the Civil War era of flannel uniforms. St. Louis manager Tony La Russa conveyed his decision to the bullpen to change pitchers (in the 8th inning with the score tied 2-2 against opponent Texas Rangers) using the old-fashioned dugout phone (shown in the photo).

La Russa’s call to bullpen coach Derek Lillquist to warm up two pitchers, Rzepcynski and Motte, was perhaps drowned out by the screaming crowd of 51,459 fans. So Lillquist thought La Russa only said Mark Rzepczynski. La Russa called back to confirm and asked for Motte again. This time, Lance Lynn started to throw, even though he was supposed to be resting from the previous game and used only in an emergency. The series of miscommunications wound up putting Rzepczynski on the mound against Mike Napoli with the bases loaded, a lefty-righty matchup that clearly favored Texas. The Rangers catcher delivered with a two-run double that sent Texas to a 4-2 victory.

For all the magnificent scoreboards in each ballpark, and all the computers that track each pitch, it seems baseball is stuck with land lines. “It’s amazing “, says TV commentator Keith Olbermann. “With all the technology here, they can’t get a call completed from one part of the building to another? You go to an Apple store, the communications device the salesman is carrying is capable of launching a nuclear device”.

A happy ending, by the way, for the Cardinals, winning the 7th game by 6-2.

OM in the News: Scheduling Major League Baseball Umpires

Here is an interesting scheduling application you may want to share with your class when you teach Chapter 15. Scientific American (Aug.18,2011) reports on how four B-School profs have formulated the “travelling umpire problem” to develop solutions to get umpire crews to every major league baseball game . Given that the Major League baseball (MLB) season lasts 6 months, such scheduling is a daunting task. During the season, 30 teams play a total of 2,430 games in 27 different cities. The umpires in the league are part of a 4-member group called a crew and each umpire handles about 142 games/year.

Here are some constraints: (1) minimize travel time and distance for the crews; (2) crews should visit each MLB city at least once; (3) they should work each team at home and on the road; (4) they should work no more than 21 days in a row; (5) they should not ump any one team’s games for more than 4 series all year–just to name a few of the rules.

The mathematical model proved successful in generating a high quality schedule in a short amount of time and MLB has used it over the past 3 seasons. Before the profs (who are at U. Miami, Carnegie, and Michigan State) built their computerized method, the schedule was created manually–and took weeks– by a retired umpire. As Scientific American puts it: “That guy is out“!

Researcher Tallys Yunes (at Miami) explains, “We not only reduced the time necessary to create the schedule, we also improved the overall quality of the schedule, in the sense that it better satisfies both the MLB and umpire union rules”.

If you want to provide a humourous side-bar to this class discussion here is a link to a 4- minute video clip about umpiring. It features Leslie Nielson playing a detective going undercover as an ump.

Discussion questions:

1. Why is this an important OM issue?

2. Besides major league sports, what other fields could benefit from math scheduling models like this?

OM in the News: Yield Management Turns to Sports

We discuss the subject of yield (revenue) management in detail in Chapter 13, Aggregate Planning. Examples are provided from the airlines(American), hotels (Marriott), car rental companies (Hertz), and even Disney’s theme parks. But the latest issue of Operations Management/Management Science (OR/MS) Today (Oct., 2010) turns to an interesting and relatively new application of revenue management that may interest your students, namely, major league baseball.

Ticket prices to sporting events have always been priced to depend on the seat’s location. But the San Francisco Giants have discovered that dynamic pricing of game tickets has increased 2010 revenues 6%. Ticket costs now depend on the opposing teams, pitching match-ups, day of the week, and even the weather forecast.

For example, a ticket in the Field Club, behind home plate, for the Oct. 1st game between the San Diego Padres and the host Giants cost $68 at the start of the season. It went to $92 on Aug. 1st,$121 a week later, $145 on Sept. 4th, and $175 just before the game!

Discussion questions:

1. Are other sports and teams replicating this concept of dynamic pricing ?

2. Will there be fan pushback to the idea?

3. How did the 2010 pennant race impact on the Giant’s decision to use yield management?

Teaching Tip: Baseball and Correlation Analysis

When you are covering the subject of correlation analysis (Chapter 4) and want to provide an example that may interest your students (especially the sports-oriented ones), here is a 2 paragraph quote from a recent WSJ article (Sept.17, 2010,p.W-8). The article suggests that more than any major league baseball season in recent memory, the size of a team’s payroll isn’t tied to winning.

“According to estimated figures updated throughout the season, the correlation between a team’s player payroll and its winning percentage is 0.14, a number that makes the relationship almost statistically irrelevant. That figure is 67% below last year’s mark and is easily the lowest since the strike.”

“This outcome represents a stark reversal from the state of affairs a decade ago. In 1998, the correlation between payrolls and wins was 0.71, a figure that suggests a strong and significant tie. And in the 1999 season, when the correlation was 0.5, all eight teams that reached baseball’s playoffs were among the top ten spenders.”

This can make for a nice class discussion. First, it shows that terms from the text show up even on the sports page. But let the students compute the R squares for these correlations and interpret the relationships for those values. If the R-square was 0.504 in 1998, and 0.25 in 1999,what explains the rest of the variation?

I love the Journal’s sports section and hope you also find some of the statistics on that page interesting.