Major League Baseball Scheduling

Peter Theis and Jeremy Hastings, MBA students at my home base, the Tepper School of Business at Carnegie Mellon, have independently reminded me that I have been getting some press about the baseball schedule that I should be pointing to. Not all of it has been positive, but most of it talks about how difficult creating satisfying schedules is. For those who don’t know, some colleagues (Doug Bureman, George Nemhauser and Kelly Easton) and I, through our company the Sports Scheduling Group, created the 2005 and 2007 Major League Baseball Schedule. has an article on the 2007 schedule. They talk about some of the rough parts of the schedule:

For baseball players, grousing about the schedule is as routine as chewing sunflower seeds or making rookies wear cocktail dresses and high heels to the airport during the obligatory hazing trip. The average fan might regard it as just another case of millionaires whining, but fans don’t have to step in the box in front of 50,000 people and produce while bleary-eyed and jet-lagged.

Listen closely, and you’ll hear the Pittsburgh Pirates groaning en masse as they look at their schedule and contemplate that geographically challenged Houston-to-San Diego-to-Chicago trip in late September.

Or consider how thrilled the Texas Rangers must be looking forward to a nine-game Detroit-to-Oakland-to-Minnesota jaunt (with no day off) in the final month.

But also talks about the challenges:

Each team has its own unique circumstances. Cincinnati is always home on Opening Day, while Boston plays at Fenway Park each Patriots Day. The Mets have potential traffic and parking concerns when the U.S. Open tennis tournament is in town, and the Minnesota Twins share the Metrodome with the NFL’s Vikings.

And lest we forget, New York, Chicago, Los Angeles, the San Francisco Bay Area and Baltimore-Washington are all two-team markets. In a perfect world, one team will be at home while the other is on the road.

Throw in six pages of whys and wherefores governing scheduling in the collective bargaining agreement, and you have an extremely complicated jigsaw puzzle.

“You can take any short part of a team’s schedule and say, ‘That’s awful. Why would anybody schedule that?'” Feeney said. “But you can’t look at it that way. It’s not a two-week schedule. It’s a 26-week, 30-team schedule.'”

There have also been articles in the San Jose Mercury News and the Seattle Times. The News has the most depressing line (at least to me):

Starting next season, MLB will create the schedule within its offices. In other words, no more outside consultants.

Not happy news for the Sports Scheduling Group!

NCAA Tournament and Operations Research

Final Four LogoIt is time again for the year’s best sports event: the NCAA college basketball tournament. 65 teams in a single elimination tournament held over 3 successive weekends, with the winner taking all. Picking the teams for the tournament is a favorite conversation topic over coffee and beers (not that most of us can distinguish among the 336 eligible teams!). Joel Sokol, Paul Kvam and my research and business partner George Nemhauser have a system entitled the “Linear Regression/Markov Chain” (LRMC) to rank teams. You can get the pre-tournament rankings here. These rankings suggest that the four top teams in the land are North Carolina, Kansas, UCLA, and Texas A&M. The tournament committee picked Ohio State and Florida (number 5 and 6) instead of UCLA and Texas A&M.

If you are filling out your bracket, you can use the rankings to guide you (just pick the highest ranked team at each step). As an article in the Atlanta Journal Constitution writes:

LRMC’s most striking predictions this year: Picking No. 12 seed Arkansas over No. 5 seed Southern Cal and No. 10 seed Georgia Tech to knock off No. 7 seed UNLV.

The tournament committee this year had access to LRMC:

Nemhauser, a former Tech faculty athletics representative, arranged to provide a special version of LRMC to the selection committee for the first time this year. (The committee wouldn’t look at a ratings system that considered victory margin, so it got one with that component factored out.)

“It’s certainly one tool people could look at,” committee chairman Gary Walters said, but he went on to praise the RPI and the Sagarin ratings and to call the RPI “our primary quantitative tool.”

It will be interesting to see how some of the predicted upsets work out: perhaps the tournament committee will need a new primary quantitative tool.

MIT Conference on Sports Business

MIT is holding a conference this weekend on Sports Business (unfortunately it is sold out) with a focus on Analytical Sports Management. This workshop is an interesting mix of sports insiders, economists, operations researchers, media executives and more, with a strong emphasis on those in the business. No talks directly on scheduling (my particular emphasis) but a cool looking conference nonetheless.

Mark Cuban on Operations Research

Mark CubanMark Cuban is the owner of the Dallas Mavericks basketball team (and billionaire, having timed the dot-com boom and bust pretty well). He is certainly outspoken, amassing hundreds of thousands of dollars of fines for complaining about referees, opposition, and the league. But he also wants to bring a bit of analysis to the game. In a recent blog entry, he is practically begging for more operations research (as always, Steven Baker of BusinessWeek got to this before me in his Blogspotting blog):

The easiest thing in the world for anyone to do is Tivo a game and then break it down. What any of the 13 participants on the court did and how they did it is pretty easy to document for 99.9 pct of the time on the clock. The other .01 can be grey. It doesnt really matter. Aggregate data from a lot of games over a lot of seasons, and all of the sudden you have a database with value.

Once you have information, then you can add brainpower and try to do things better.

Once you have information, then you can start to define excellence and strive for it, measuring your progress along the way.

This certainly isnt a new concept. There are untold number of QC , Process Improvement and Optimization techniques out there. Pick one, pick them all.

Wayne Winston is one of the people working with Cuban, as I wrote in a previous entry.

More OR and Sports

I missed this earlier article from the Wall Street Journal entitled “The NBA Tries to Make Teamwork a Science” on how teams in the National Basketball Association are trying to measure “team” effects of their plays (thanks Otis Smith for pointing this out). Basketball, more than many sports, relies on smooth teamwork for a team to be successful. This leads to an interesting data mining issue. The article link expires in 7 days, but here are a few excerpts:

In a league long dominated by high-flying superstars, more teams are focusing this season on teamwork — and turning to surprisingly scientific methods to measure it. New technology makes it easier to track the performance of every combination of five players that steps on the court, in a long list of game situations, from out-of-bounds plays to pick-and-rolls to zone defenses. As different player mixes yield different results, teams are beginning to quantify the elusive concept known as chemistry.

[Wolf Pack: Eddie Griffin (far left) and Kevin Garnett (center) click on the court.]
Wolf Pack: Eddie Griffin (far left) and Kevin Garnett (center) click on the court.

Say, for example, that after a coach inserts two particular players into a game, the opposing team has trouble scoring. Getting ready for the next opponent, the coach might flip open his laptop, punch a few keys, and see how his team did defensively in other games when the same two players were on the court together. He’s able to do this because teams are increasingly turning to software that dissects plays, follows every pass and shot and tracks each player’s part in every possession.

Not surprisingly, it is people in Operations Research that come to the rescue. Wayne Winston is best known for the textbooks he has published on a range of operations research-related topics. But he also has been working with NBA teams on the issue of teamwork: Continue reading “More OR and Sports”

Tournament Time!

The NCAA Tournament is irresistable to OR types. Predicting the tournament has proven a rich area of application. Jay Coleman of the University of North Florida has a scorecard approach that gives probabilities of wins for every game in the first round. For three of the 32 games, his approach favors the lower seeded team (including number 10 Alabama over number 7 Marquette. As far as number 1 seeds go, his method gives a 99%+ chance to Villanova and Duke in their first game (a number 16 has never beat a number 1) but just 97% for UConn and 95% for Memphis.

Joel Sokol of Georgia Tech has done a lot of work in this area. He has some interesting comments on the 2006 brackets.

INFORMS has some other pointers in this area.

Anyone else like to talk about their OR approaches to this?