School of Information Sciences

Computational Sports Informatics Speaker Series

Friday, April 21

10:00 a.m.
IS Building, 3rd Floor Theatre

Wayne Winston, Visiting Professor, Bauer College of Business, University of Houston

“Sports Analytics: Past, Present, and Future”

Abstract: We will discuss the past, present , and future applications of sports analytics to baseball, football, and basketball. Promising areas for future research will be discussed as well as the current applications of analytics to these sports.

Bio: Wayne is a Visiting Professor at the Bauer College of Business at the University of Houston and Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University. He holds a BS in mathematics from M.I.T. and a PH. D in operations research from Yale. He won over 40 teaching awards at Indiana University, including the Top MBA teaching award (6 times) and an EMBA teaching award at the Bauer College of Business at the University of Houston. He has written over a dozen books including Marketing Analytics, Data Analysis and Decision Making, Operations Research, Practical Management Science, Excel 2016 Data Analysis and Business Modeling, and Mathletics. He has also authored 25 refereed. Articles. He has taught classes or consulted for many organizations including, Abbott, SABRE, BROADCOM, Cummins Engine, Eli Lilly, James Hardy, MGM,Pfizer, Sabre, Verizon, Microsoft, Cisco, US Navy, US Army, Ford, 3M, and GM. He is also a two-time Jeopardy! Champion and ha s consulted for the NBA’s Dallas Mavericks and New York Knicks.


Friday, March 31

1:00 p.m.
IS Building, 3rd Floor Theatre

Peter Carr, Research Scientist, Disney Research, Pittsburgh

“Modeling Sequential Decision Making in Team Sports Using Imitation Learning”

Abstract: Current state-of-the-art sports statistics compare players and teams to league average performance, such as “Expected Point Value” (EPV) in basketball. These measures have enhanced our ability to analyze, compare and value performance in sport. But they are inherently limited because they are tied to a discrete outcome of a specific event. For example, EPV for basketball focuses on estimating the probability of a player making a shot based on the current situation. In this work, we explore how teams control time and space by examining sequential decision making. 

We have developed an automatic "ghosting" system which illustrates where defensive players should have been (instead of where they actually were) based on the locations of the opposition players and ball. We employ a machine learning method called deep imitation learning, and modify standard recurrent neural network training to consider both instantaneous and future losses, which enables ghosted players to anticipate movements of their teammates and the opposition. Our approach avoids the man-years of manual annotation need to train existing ghosting systems, and can be fine tuned to mimic the behavior of specific teams or playing styles. 

Bio: Peter Carr is a Research Scientist at Disney Research, Pittsburgh. His research interests lie at the intersection of computer vision, machine learning and robotics. Peter joined Disney Research in 2010 as a Postdoctoral Researcher. Prior to Disney, Peter received his PhD from the Australian National University in 2010, under the supervision of Prof. Richard Hartley. Peter received a Master's Degree in Physics from the Centre for Vision Research at York University in Toronto, Canada, and a Bachelor's of Applied Science (Engineering Physics) from Queen's University in Kingston, Canada.


Friday, February 17

1:00 p.m.
IS Building, 3rd Floor Theatre

Mike Ressler, Chief Engineering Officer, Diamond Kinetics, Inc.

“The Internet of Swings”

Abstract: As a connected device company, Diamond Kinetics is collecting sensor data from the swings of players ranging from 8 years old to professional athletes. Baseball, as a sport, is filled with fantastic statistical analysis of on-field results. The SwingTracker sensor gives the kinematic "cause" to the on-field statistical "effect". Armed with this data, Ressler discuss how Diamond Kinetics approaches visualizing its data for comprehension and action as well as how Diamond Kinetics is approaching machine learning. Connected devices are an inevitable part of the future and the statistics-driven world of baseball will likely find a way to unlock the potential of such a rich data set.

Bio: Mike Ressler is a native of East Brunswick, New Jersey. Carnegie Mellon University brought him to Pittsburgh, where he studied Computer Science. Combining his passion for sports and computer science, Ressler started StatEasy in 2010, a statistics and video company based. Ressler is currently the Director of Engineering for Diamond Kinetics, Inc. a connected device company focusing on the movement data of athletes in baseball. His hobbies, aside from playing and coaching volleyball, include board games, biking, scuba diving, and quadcopter (drone) racing.


Friday, January 20

1:00 p.m.
IS Building, 3rd Floor Theatre

Michael Trick, Professor, Carnegie Mellon University

“Scheduling Major League Baseball”

Abstract: Since 2005, I (as part of a small firm, the Sports Scheduling Group) have been involved with putting together Major League Baseball's team and umpire schedules.  I will talk about what goes into such schedules and the role optimization and "big data" plays in creating schedules. 

Bio: Michael Trick is the James H. and Harry B. Professor of Operations Research and Senior Associate Dean, Faculty and Research at the Tepper School of Business, an institution he joined in 1989.  He is a researcher in computational integer programming, with interests in sports scheduling and computational social choice.  He was President of the Institute for Operations Research and the Management Sciences (INFORMS) in 2002 and will be President of the International Federation of Operational Research Societies (IFORS) in 2016-2019.  He has consulted for Major League Baseball and many college conferences on scheduling issues and with the FCC, IRS, and United States Post Office on optimization approaches.  He is a Fellow of INFORMS.


Friday, January 13

1:00 p.m.
IS Building, 3rd Floor Theatre

Samuel Ventura, Visiting Assistant Professor, Carnegie Mellon University

“Winning in Sports with Statistics”

Abstract: The path to becoming successful in the sporting world is substantially different from that of more traditional fields. Whether you're an athlete or an analyst, the best way to be successful working in sports is to effectively demonstrate your ability to have a positive impact on a team or organization. In this talk, I will discuss the skills that can be easily acquired in an academic program that are extremely important to having success as a data scientist in the sporting world, such as programming, data visualization, and professional communication (writing and speaking). This will be framed in my personal experiences from working with professional sports teams, doing research on statistics in sports, reviewing papers on sports analytics, coaching ice hockey, and teaching students about statistics. Although my own work is primarily in hockey, I will also discuss the use of statistics and data science in football, baseball, and basketball.

Bio: Sam Ventura is a Visiting Assistant Professor of Statistics at Carnegie Mellon, with research modeling the spread of infectious diseases, developing new tools for record linkage, and developing new statistical methodology for supervised and unsupervised learning.  In addition to his academic appointments, Sam is a member of the statistical advisory board for the Houston Astros and is currently a statistical consultant for the 2016 Stanley Cup Champion Pittsburgh Penguins.  Sam has published papers on sports analytics, most notably in the Annals of Applied Statistics, and has presented his work at numerous statistics and sports analytics conferences.  He is an associate editor for the Journal of Quantitative Analysis in Sports and a reviewer for the Journal of Sports Analytics.  In 2014, along with Andrew Thomas, he created, the most popular online resource for modern hockey statistics.  Sam is a Pittsburgh-native and earned his PhD in Statistics from Carnegie Mellon in 2015.


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