SPEAKERS & TALKS
COMPLEXITY AND SPORTS
Sports Satellite at CCS 2026
14th October 2026
Embedded Files
MEET OUR
KEYNOTE SPEAKERS!
MEET OUR KEYNOTE SPEAKERS!
Prof. Brandon Ogbunu (Yale University)
BIO: tbd
TALK: "Games of Life: Sports as a Paradigm for Complexity Science"
ABSTRACT: tbd
Prof. Kerianne N. Rubenstein (Syracuse University)
BIO: Keri Rubenstein is an Assistant Professor of Sports Analytics at Syracuse University, where she teaches courses in sport economics. Her research uses her background in economics to understand how rules and institutional structures shape player behavior, player movement, and market outcomes in professional sports. She earned her BA in Economics from Southern Methodist University in 2017 and her PhD in Economics from West Virginia University in 2021. Prior to joining Syracuse University, she was an Assistant Professor of Economics at North Dakota State University from 2021 to 2024. Keri lives in Syracuse, New York, with her husband, son, and dog. An avid sports fan, she roots for the Cincinnati Bengals, Seattle Storm, and Milwaukee Bucks.
TALK: "Not Just a Smaller NBA: Institutional Differences and Research Design Considerations for Comparing the NBA and WNBA's Labor Markets"
ABSTRACT: The WNBA's rapid growth in visibility, valuation, and labor market activity has drawn increasing attention from sport economists and sport management scholars, who frequently benchmark their findings against the NBA. We argue that such comparisons, when made without careful institutional grounding, are prone to systematic bias. We document structural and institutional differences between the NBA and WNBA across seven dimensions: league governance and ownership structure, NBA affiliation and shared infrastructure that persists independent of formal ownership, salary cap architecture and compensation levels, the dual labor market created by players' overseas competition, season length and schedule structure, transaction rules and player movement, and the history of structural breaks within the WNBA itself. We show how each institutional difference manifests in the data and why common NBA-derived modeling assumptions, including treating salary as a proxy for player value, assuming competitive bidding for talent, or pooling data across collective bargaining eras, are frequently violated in the WNBA context. We conclude with guidance for researchers studying the WNBA in isolation or in comparison with the NBA and discuss the relevance of these considerations for other women's professional leagues and their male counterparts, such as the NWSL/MLS and PWHL/NHL pairs.
Prof. Scott Powers (Rice University)
BIO: At Rice University, Scott serves as Assistant Professor of Sport Analytics and of Statistics, as well as Director of the Hutchinson Leadership Initiative in Sports Analytics. Prior to academia, Scott served as analytics director for the Los Angeles Dodgers and as assistant general manager for the Houston Astros, winning one World Series ring with each team.
TALK: "Safer, Not Slower: Actionable insights from a large-scale survival analysis of baseball pitching mechanics"
ABSTRACT: Baseball pitching mechanics is a complex system in which deficiencies in one part of the system can manifest compensation in other parts of the system, ultimately leading to structural failure. Professional baseball pitcher injuries are a longstanding problem, and they have only continued to rise over the past decade, while technological advances have enabled large-scale collection of data tracking player and ball movement in competitive games. From Major League Baseball (MLB), we obtained pitch-tracking data from 8 years of the major leagues and 4 years of the minor leagues, as well as biomechanics data from 2 years of the major leagues. Taking pitcher-season as our unit of observation, we estimate proportional hazards regression models for survival times until IL placement and UCL surgery conditional on demographic, pitch-tracking, and biomechanics features. We show that non-fastball (particularly slider) usage associates positively with injury hazard and that peak glove-arm momentum associates negatively with injury hazard but positively with fastball velocity. Predicted injury probability over a full season ranges from below 20% to above 50%.
MEET OUR
INVITED SPEAKERS!
MEET OUR INVITED SPEAKERS!
Emma Strawbridge (UMass Amherst)
BIO: Emma Strawbridge (they/them) is a second-year PhD student at UMass Amherst interested in sports, complex relationships, and finding new ways to make teaching intro statistics exciting.
TALK: "Good Catch! Measuring Catcher Effectiveness with Pitchers"
ABSTRACT: Baseball is a very particular team sport where players interact with each other significantly less than dynamic sports like soccer or ice hockey. It’s still a team sport, though, and one of the most interesting defensive relationships to investigate is between catchers and pitchers. This presentation explores the effect good catchers have on pitchers, how we can measure the difference a starter versus a backup makes on a pitcher’s performance, and how we might select a new starting catcher, given a team of pitchers we want to improve.
Dr. Max Jerdee (Santa Fe Institute)
BIO: TBD
TALK: "Groups and Strengths in Sports Ranking"
ABSTRACT: TBD
Dr. Martín Saldías (Gambeta)
BIO: Martín Saldías is the founder of Gambeta, a football intelligence startup based in Lisbon, Portugal, combining football expertise, data analytics and network science to support tactical and strategic decision-making. He is also Head of the Financial Intermediation Division at Banco de Portugal, where his research has focused on networks, interconnectedness and systemic risk. A former youth football coach, his work reflects a long-standing interest in complex systems, connecting practical experience on the pitch with quantitative analysis across football and financial markets.
TALK: "Aggregate or granular? What football passing networks preserve when spatial information is removed"
ABSTRACT: Football passing networks are commonly built from aggregate passer–receiver data, reducing a spatial and temporal interaction system to a schematic graph. This allows the method to be applied across many teams and matches, but it is unclear which tactical properties survive the loss of resolution. This presentation examines this through the Controlled Progression Index (CPI), which combines circulation, penetration and hub dependency. We compute the same features from two representations of the same passes: an aggregate passing matrix using fixed positional roles, and a spatially enriched event-and-tracking representation containing measured positions, possession phases and defensive context. We then treat the comparison as an agreement problem: how much does aggregation change the measurement, and how often does it reverse the ordering of teams or performances? The aim is to map the informative value of each data resolution: what clubs can learn from widely available event data across leagues, what additional tactical insight tracking provides, and where those gains justify the greater data and analytical requirements.
Prof. Elizabeth Upton (Williams College)
BIO: Elizabeth Upton is an Associate Professor of Statistics at Williams College. Her research focuses on applied statistical methods, with particular interests in network analysis. Her work has included applications in public health, social networks, and professional and amateur sports. She is also deeply engaged in statistics education. She received her Ph.D. in Statistics from Boston University and her M.Ed. from Harvard University.
TALK: "Hypergraphs And Hoops: Uncovering NBA Player Interactions Through APM"
ABSTRACT: In team sports, standard ranking metrics do not consider how smaller groups of players interact. Individual performance measures rarely reflect the synergy or friction among pairs or trios, while full-lineup assessments can’t pinpoint the exact contributions of these lower-order combinations. To bridge this gap, we propose a novel adjusted plus-minus (APM) framework that simultaneously evaluates individuals, smaller groups, and entire lineups. Underlying our approach is a link between APM and the hypergraph representation of a team, which captures these overlapping interactions. In this talk, we’ll demonstrate how this perspective applies to NBA data from 2012–2022 and highlight the insights gained from viewing a team as a network.
Page updated
Google Sites
Report abuse