Operations Research Management Science - Issues In Education

<BODY bgcolor="#ffffff" link="cc0000" vlink="cc0000" text="#000000"> <img src="../cleardot.gif" width=1 height=1 vspace=4 border=0><br>   <table border=0 cellspacing=0 cellpadding=0 width=480><tr valign=top> <td width=10><img src="../cleardot.gif" width=1 height=1 hspace=4 border=0></td> <td width=470> <FONT face="verdana, arial, helvetica" SIZE=2><B><I><A HREF="../../ORMS.shtml" TARGET="_top">OR/MS Today</A> - February 2004</I></B></font><BR> <hr size="1"> <img src="cleardot.gif" width=1 height=1 vspace=4 border=0><br> <font face="times new roman, times"> <img src="../cleardot.gif" width=1 height=1 vspace=4 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=2><B>Issues In Education</B></FONT><br> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=8 border=0><br> <FONT SIZE="+2"><B>Drafts, Dynasties and Dance Cards</B></FONT><BR> <img src="../cleardot.gif" width=1 height=1 vspace=6 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=2><B>By Keith A. Willoughby</B></FONT><br> <hr size="1"> <img src="../cleardot.gif" width=1 height=1 vspace=12 border=0><br>    A lot of our students love sports. Regrettably, many are terrified of quantitative problems. Enhancing a student's analytical problem-solving skills while capitalizing on their inherent interest in sports would seem like a worthy objective. <P>Moreover, for those of us who teach quantitative methods, we may occasionally use a sports example to crystallize a key concept. For example, one could determine descriptive statistics and develop histograms for the number of runs in a baseball game. Decision analysis could be utilized to address two-point conversion strategies in football. Undeniably, sports provide a solid forum for the application of quantitative approaches. This provides the rationale for a senior-level undergraduate course we have developed at Bucknell University, "Quantitative Methods in Sports: Drafts, Dynasties and Dance Cards."<BR> <img src="../cleardot.gif" width=1 height=1 vspace=12 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=3><B>"Pre-Game Chat":<BR> The Impact of Quantitative Modeling in Sports</B></FONT><BR> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=2 border=0><br> There is a long and illustrious history of quantitative modeling in sports. Mottley [1] was an early contributor, specifically declaring that "football presents may intriguing possibilities for the talents of the OR worker." Schutz [2] and Gerchak [3] added survey papers. Ladany and Machol ([4], [5]) edited two books devoted to quantitative analysis of sports. More recently, three academic journals published special issues on these topics <I>(The Statistician, 2002; European Journal of Operational Research, 2003; Computers and Operations Research, 2004).</I> <P>Prominent professional franchises use quantitative models to aid in strategic decision-making. The Boston Red Sox restructured their bullpen based on the statistical findings of Bill James [6] and the Dallas Mavericks adopted a player-rating system developed by Wayne Winston and Jeff Sagarin [7]. In particular, the latter article opined that such models are "part of sports franchises' emerging interest in hard-core quantitative analysis."<BR> <img src="../cleardot.gif" width=1 height=1 vspace=12 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=3><B>"The Whistle Sounds":<BR> Course Operation</B></FONT><BR> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=2 border=0><br> Our primary goal in developing this course was to introduce students to the process of quantitative reasoning and its applications in the analysis of sports. We have sought to couple a student's interest in sports with training in basic quantitative methodologies like regression analysis and optimization methods. Ideally, we desired to have students learn critical statistical concepts within a base of applications that is both rigorous and interesting. <P>Our course was offered for the first time during the fall 2003 semester. Its one prerequisite was an introductory statistics class. Since the course fulfilled the Capstone course requirement (one aspect of Bucknell's Common Learning Agenda), it was restricted to 18 students. We had no trouble reaching this limit! <P>We met twice weekly in 80-minute sessions. During these classes, we discussed a variety of sports applications involving various quantitative tools. In order to provide students with necessary conceptual understanding, we did spend two class periods presenting overviews of our quantitative approaches; namely, regression analysis and optimization methods. Although a number of our applications involved "big name" sports like football, baseball and basketball (to wit, we explored regression analysis in determining basketball's March Madness winners, and optimization methods in investigating performance evaluation in Major League Baseball), we were able to find relevant articles dealing with, among others, tennis, curling and gymnastics. Besides specific sports studies, we explored broad topics such as anxiety-inducing situations in sports, moral hazard in professional sport draft lotteries and sports economics. (The interested reader is invited to contact me for a complete reference list of articles discussed in the course.) Indeed, we have assembled a database of more than 175 articles combining quantitative analysis and sports. With such a vast array of potential subjects, the instructor has a certain flexibility in being able to select appropriate articles for course discussion. <P>Statistics and sports? C'mon, is this just an excuse for guys to meet and discuss their favorite teams/players/games during the past week? No. The particular applications, although captivating, did involve a certain amount of rigor. While we did have more males than females in our fall 2003 offering (15 to 3), the female students were every bit the equal of their male counterparts. We found that students came to class prepared to discuss the assigned articles and willing to participate in thoughtful discussion. Any student, given an inherent interest in sports, would find such a course insightful and relevant.<BR> <img src="../cleardot.gif" width=1 height=1 vspace=12 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=3><B>"At the Buzzer":<BR> Some Concluding Remarks</B></FONT><BR> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=2 border=0><br> Our experience with this course has been most gratifying. As far as we can tell, students gained an appreciation <P>of the applicability of quantitative methodologies in sports, and developed some skill in building and interpreting statistical models. A major element of course evaluation involves a term project. In pairs, students are required to investigate an important quantitative issue in sports and prepare a written report. Oral presentations of their findings are made to the entire class. The variety of term projects serves as testament to their initiative in discovering topics and analyzing data sets. A sampling of projects from the recent semester include: "Momentum in Sports," "Is Winning Really Everything? A Revamping of the Major-League Win Statistic," and "Battle of the Sexes: A Comparison of Men's and Women's Performances in Athletics." Their presentations were, almost without exception, innovative and creative. <P>Many reports involved sophisticated (for an undergraduate level) regression analysis. The students in the "Momentum in Sports" project examined successful franchises in football and basketball. They determined a regression model for those "box-score" statistics critical to team success. Then, they compared these results with similar regression models for teams that have poorly performed. This permitted them to suggest reasons why certain teams continue to build on the momentum of recent successes, while other franchises flounder. Moreover, the students coupled this technical model with a survey of Bucknell student-athletes, asking them to qualitatively describe momentum-inducing situations in their particular sports. This sparked a lively discussion of momentum during their presentation. <P>Another student group developed a regression model to highlight key game statistics in predicting major league baseball players' salaries. Armed with these results, they could clearly identify overpaid and underpaid players, as well as those "superstars" who are truly worth the money. <P>We feel our course has met its goals, and we eagerly anticipate future offerings.<BR> <img src="../cleardot.gif" width=1 height=1 vspace=12 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=3><B>References</B></FONT><BR> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=2 border=0><br> <OL> <font size=1> <LI>Mottley, C.M., 1954, "The Application of Operations-Research Methods to Athletic Games," <I>Journal of the Operational Research Society,</I> Vol. 2, pgs. 335-338. <LI>Gerchak, Yigal, 1994, "Operations Research in Sports," Handbooks in OR & MS (S.M. Pollock et al., Eds.), Vol. 6. <LI>Schutz, Robert W., 1980, "Sport and Mathematics: A Definition and Delineation," <I>Research Quarterly for Exercise and Sport,</I> Vol. 51, No. 1, pgs. 37-49. <LI>Ladany, S.P. and Machol, R.E., 1976, "Management Science in Sports," North-Holland, New York. <LI>Ladany, S.P. and Machol, R.E., 1977, "Optimal Strategies in Sports," North-Holland, New York. <LI>Antonen, M., 2003, "Red Sox: Stats the way to go," <I>USA Today,</I> Feb. 14, page 10C. <LI>Leonhardt, D., 2003, "Mavericks Depend on Decimal Points," <I>The New York Times,</I> April 27, page 4.</font> </OL> <img src="../cleardot.gif" width=1 height=1 vspace=4 border=0><br> <IMG SRC="../barbluehook.gif" WIDTH="468" HEIGHT="4"><br> <img src="../cleardot.gif" width=1 height=1 vspace=2 border=0><br> <FONT face="verdana, arial, helvetica" SIZE=2><I><B>Keith A. Willoughby</B> (<A HREF="mailto:kwilloug@bucknell.edu">kwilloug@bucknell.edu</A>) is an assistant professor in the Department of Management at Bucknell University.</i></font><BR>  <img src="../cleardot.gif" width=1 height=1 vspace=5 border=0><br>  <img src="../barblue.gif" width=468 height=1 border=0 alt=" "><br> <img src="../cleardot.gif" width=1 height=1 vspace=8 border=0><br> <font face="verdana, arial, helvetica" size="1"> <CENTER><script type="text/javascript"></script> <script type="text/javascript" src="http://pagead2.googlesyndication.com/pagead/show_ads.js"> </script></CENTER><br> <img src="../cleardot.gif" width=1 height=1 vspace=8 border=0><br> <LI> <A HREF="tocfr.html" Target="_top"><I>Table of Contents</A><br> <img src="../cleardot.gif" width=1 height=1 vspace=4 border=0><br> <LI> <A HREF="../../ORMS.shtml" TARGET="_top"><I>OR/MS Today</I> Home Page</A><br>  <img src="../cleardot.gif" width=1 height=1 vspace=8 border=0><br> <img src="../barblue.gif" width=468 height=1 border=0 alt=" "><br> <b><i>OR/MS Today</i></b> copyright © 2004 by <A HREF="http://www.informs.org/" target="_new">the Institute for Operations Research and the Management Sciences</a>. 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