The Sloan Sports Analytics Conference showcases cutting-edge research that is frequently featured in top media outlets throughout the world and has even changed the way sports are analyzed. The Research Papers Competition is an ideal way to build your reputation within the field of sports analytics.

This year’s competition will feature six sports tracks – Basketball, Baseball, Soccer, Football, Business of Sports, and Other Sports.

Abstract Submissions for SSAC24 are now closed

Abstracts are selected based on the novelty, academic rigor, and impact of the research.

All submissions are required to be open-source and a link to the author's GitHub repository or other repository supporting the research will be required.

Please refer to our Research Papers Rules page for full details on the submission and evaluation process. We look forward to reading your contribution!


Competition Format

The competition consists of the following phases:

  1. Abstract Phase

Authors submit abstracts. Based on the judged merits of their abstract submissions, a select group of authors will be invited to submit full manuscripts.

  1. Full Manuscript Phase

Invited authors submit full manuscripts. Referees will evaluate every manuscript, and authors of the best submissions will be invited to give a presentation on their findings at the conference. The referees will also select a separate set of authors who will be invited to present their work during a poster session, as well as a final set of three authors to give a deep-dive of their work in an open-source workshop.

  1. Conference Phase I

     a. Presentations

Invited authors will present their findings during the first day of the conference. Based on the quality of the presentation and manuscript, one paper per sports track (see tracks below) and one wildcard will be selected to present at the conference in front of a panel of industry experts. The judge scores will be tabulated and the winners will be announced following presentations.

     b. Poster Competition

All posters selected for the conference will be entered into a competition for Best Poster, determined by a combination of a fan and judges vote during the weekend of the conference.

Note: this competition is independent of the presentation finals, and none of the posters will advance to the presentation finals.

Timeline (all times Eastern Time)

Abstract submission due – Oct. 01, 2023, 11:59 p.m. EST

Full paper requests sent out – Mid-October 2023

Full paper submission due (if selected) – Dec. 01, 2023, 11:59 p.m. EST

Finalists and posters announced – Mid-January 2024

Submission of poster (if selected) – Early-February 2024

Submission of presentation (if selected) – Mid-February 2024

Conference presentations (if selected) – Conference Day

Open-Source Requirement

For the Sloan Sports Analytics Conference, the Research Papers competition has been a tremendous opportunity for researchers to both share their work with the community and improve the application of analytics across sports. We are excited to continue requiring all papers to be open-source for SSAC 2024 to further the impact of the great work of researchers in the industry.

Open-source research helps advance our mission to democratize analytics in sports by allowing researchers to build on top of the models and methods of their peers, both amplifying the effect of their research and better enabling widespread adoption of their work. We strongly believe that continued research into sports analytics is what makes our games more exciting and participants more effective. 

All papers will be required to submit a link to the team's GitHub repository, or another open-source repository, with the data used to conduct the research. This should include any publicly available data or private data used in the research. For any private / proprietary data, please use your best judgement to anonymize any personal information before sharing publicly. The code running the models is not required to be submitted, but is encouraged, as it contributes to the communal spirit of open-source work by which researchers build off of each other's work to further the application of analytics across sports.

Sports Tracks

Based on abstract content, all submissions will be entered into one of the following Sports Tracks:

  1. Basketball – All submissions related to the sport of basketball.
  2. Baseball – All submissions related to the sport of baseball.
  3. Soccer – All submissions related to the sport of soccer.
  4. Football – All submissions related to the sport of American football.
  5. Business of Sports – All submissions related to the business of owning, managing, or marketing a sport, or to new technology or ideas which could change the face of the sport.
  6. Other Sports – All submissions related to the playing of a sport that is not included in the above Sports Tracks.

Abstract Guidelines

Abstract submissions should be submitted online, and must use the following guidelines:

  • Abstracts must contain fewer than 500 words, including title and body.
  • Abstracts may include up to two tables or figures combined (e.g.  1 figure and 1 table, or 2 tables).
  • Each abstract should contain the following sections:
  • Introduction – What question is this research trying to answer? Why is it an important question for the industry?
  • Methods – Description of relevant statistical methods used, including data sources or data collection procedures
  • Results – Description of actual (not promised) results along with relevant statistics
  • Conclusion – The overall takeaway from the study, including how the results will impact the sports industry

Evaluation of Submissions

The conference seeks submissions that report research pertaining to the use of analytics in the sports industry. We are open to contributions ranging from evaluating players and game strategies, to examining the success factors for sports business. In the abstract and full paper submission process, research will be evaluated on, but not necessarily limited to, the following criteria:

  • Novelty of research – Does the research provide interesting insight into new models or challenge existing beliefs?
  • Academic rigor / validity of model – Are the methodologies of the model and results fundamentally sound and appropriate?
  • Reproducibility – Can the model and results be replicated independently?
  • Application – What are the applications or potential applications of the insights from the research?

In evaluating presentation finalists at the 2024 SSAC, the above factors will be supplemented by the following criteria, as judged by a panel of academics and industry executives from team management and sports business operations:

  • Interest / impact – Is there significant interest in the proposed question in the field of study or the community at large? What are the benefits or impact of the model or application?

The Research Papers team will review all abstracts. The Review Committee will evaluate all manuscript submissions. The Review Committee consists of the Research Papers team, as well as academic professors and experts from top universities in fields including statistics, information sciences, and economics. The industry panel that makes the final winner selection will decide on the basis of the paper and the presentation at the 2024 Sloan Sports Analytics Conference. In these final evaluations, more weight will be given to the final presentation, specifically the highlighted application and impact of the research.

Conflict of Interest Policy

Our objective is to ensure an unbiased evaluation of submissions throughout the process. We are aware that members of the evaluation committee may have had relationships with authors who have submitted papers. When possible, potential conflicts of interest are avoided by minimizing the review of research by the following:

  • Authors who have collaborated with the reviewer on previous submissions
  • Current or former students who worked with the reviewer
  • Colleagues from the same organization
  • Any other previous relationships with the author that may prevent an unbiased evaluation of the paper

All potential conflicts of interest will be managed as best as possible while still maintaining the quality of the review process. Final reviews will occur without knowledge of the names of the authors.

Rights and Permissions

All authors retain ownership rights to the research and the right to publish the research after the conference. Upon submission, authors grant access to 42 Analytics to make their research available for public viewing online and in print, for conference use for the Sloan Sports Analytics Conference. Authors are responsible for obtaining permission from third parties to reprint copyrighted information such as data, tables, or figures that may be protected by copyright.

SSAC 2024 Research Papers & Authors Profiles

2024 Research Paper Finalists

Approaching In-Venue Quality Tracking from Broadcast Video using Generative AI

Short Abstract:
Over the last 25 years, soccer tracking data has provided a deeper understanding of the ways that players and teams play the game. Although traditional tracking systems require in-venue installation, there is a current push to track players remotely from broadcast footage. However, tracking data obtained from broadcast footage is inherently incomplete due to players being out of the broadcast camera’s field of vision. We address this issue in this paper, leveraging generative AI to predict highly accurate locations of the players for the large portions of games where they cannot be visually perceived.

Harry Hughes

Harry Hughes is a doctoral student focusing on how modern artificial intelligence techniques can be applied to sports data. With an undergraduate degree in Software Engineering at the University of Queensland, he is currently working at Stats Perform developing the company's broadcast tracking system.

Michael Horton

Felix Wei

Michael Stokl

Harshala Gammulle

Clinton Fookes

Sridha Sridharan

Sateesh Pedagadi

Patrick Lucey

Estimating NBA Team Shot Selection Efficiency from Aggregations of True, Continuous Shot Charts: A Generalized Additive Model Approach

Short Abstract:
We develop a novel type of basketball shot chart, a true shot chart, that uses a generalized additive model (GAM) to estimate total shot proficiency continuously in the half-court as a continuous, 3-D surface ( Unlike conventional shot charts, which do not consider free throw scoring pursuant to a shot from a given location, true shot charts incorporate total points, from the field and free throw line, pursuant to each shot in an NBA game (from 2016-2022 in the study) toward improved explanatory power of offensive efficiency variation across NBA team-seasons. Whereas conventional shot charts show a league-wide three-point premium over the period of the data, true shot charts show a deepening dispremium since 2018, as the free throw rate for three-point attempts is substantially less than that for two-point attempts. Lastly, we develop a novel shot chart summary measure, shot selection efficiency, as the Pearson correlation between expected proportional volume and expected true points, from the field and free throw line, across the half court space; polynomial regression and XGBoost modeling suggest shot selection efficiency is not only win productive, but a “Moneyball” or partly supra-payroll source of wins.  

Justin Ehrlich

Dr. Justin A. Ehrlich is an associate professor specializing in sport analytics, machine learning, and computer science. His diverse research spans virtual reality, 3D human pose estimation, advanced visualization, ranking and rating in sports, the business of sport, and the analysis of risks associated with developing Chronic Traumatic Encephalopathy (CTE) in football players. As a faculty member in Syracuse University's Big Data Cluster, Dr. Ehrlich continues to contribute significantly to the field, focusing on big data, rating and ranking methodologies, on-field performance analysis, and advanced shot charts and visualizations. His dedication to advancing sport analytics is evident in the breadth and impact of his research contributions, including innovative approaches to understanding and visualizing player performance on the field.

Shane Sanders

Dr. Shane Sanders is a Professor of Sport Analytics at Syracuse University and an author at During the summer months, Sanders has done extensive consulting on player acquisition in professional basketball and has published leading academic work in sports economics, statistics, and game theory.  In total, he has authored or co-authored 80+ articles in leading journals of these fields, as well as a popular economics book, The Economic Reason. His work has been cited on NPR, in USA Today, in a U.S. Supreme Court sports antitrust case, and other prominent outlets. When not thinking about himself, Sanders–along with his wife, Bhavneet–helps coach his older daughter, Simran, for various middle school spelling, math, and science pursuits. Last year, Simran qualified for and placed well at the Scripps National Spelling Bee. Sanders also helps coach his younger daughter, Nanki, in soccer skills and in her budding academic interests. Hailing from Zionsville, Indiana–”land of Brad Stevens”– Sanders and his brethren have always been disproportionately crazy about basketball. Under the influence of this so-called “hysteria,” his parents, Dennis and Debby, actually bought and maintained an old high school gym for two decades strong.

Feeling the Pressure: A Unified Framework for Automating Pass Rushing Statistics in NFL Games

Short Abstract:
In spite of the importance of the pass rush in professional football, pass rushing statistics only include the final outcomes of a play, e.g., sack and pass-made. They do not capture the dynamics of the pass rush or fine-grained insights throughout a play on how much pressure a rusher generates during the rush. In this paper, we propose a unified framework that enables the estimation of defensive pressure scores throughout a play with high accuracy and performance for rusher and blocker identification, rusher-blocker match-up and pressure score estimation and show the real-world applications of our framework including enriched analytics.

Sungmin Hong

Sungmin Hong is an Applied Scientist at Amazon Generative AI Innovation Center where he helps expedite the variety of use cases of AWS customers. Before joining Amazon, Sungmin was a postdoctoral research fellow at Harvard Medical School. He holds a Ph.D. in Computer Science from New York University. Outside of work, Sungmin enjoys hiking, reading and cooking.

Laura Kulowski

Laura Kulowski is an Applied Scientist at Amazon’s Generative AI Innovation Center, where she works closely with customers to build generative AI solutions. She holds a PhD in Earth and Planetary Sciences from Harvard University. In her free time, Laura enjoys biking and skiing.

Dan Volk

Dan Volk is a Data Scientist at the AWS Generative AI Innovation Center, where he leverages generative AI to create novel solutions to complex problems. He has ten years of experience in machine learning, deep learning and time-series analysis and holds a Master’s in Data Science from UC Berkeley. Outside of work, Dan is a backpacker, snowboarder, mountain biker, drummer, and lifelong fan of all Seattle sports. Bring back the Sonics!

Henry Wang

Henry Wang is an Applied Scientist at the AWS Generative AI Innovation Center, where he builds innovative GenAI solutions and co-leads Sports vertical with Dan. He holds a Master’s in Computational Science and Engineering from Harvard University. Outside of work, he loves to play golf casually and compete in tennis at amateur level.

Keegan Abdoo 

Keegan Abdoo is a Manager of Research and Analytics in the Next Gen Stats department at the National Football League. He has helped build out the Next Gen Stats platform over the last six seasons and was promoted to his current role in January 2023. Coming from a background of charting football, Keegan has strived to expand the NGS toolbox to classify more schematic data. Outside of work, he enjoys skiing, live music, and exploring all of the great restaurants Los Angeles has to offer.

Conor McQuiston 

Conor McQuiston is a Research Analyst in the Next Gen Stats department at the National Football League. Since joining Next Gen Stats in October 2022, Conor has used his physics background to help the team to develop and communicate new tracking data metrics to NFL media and all 32 clubs. Prior to joining NGS, he interned as an analytics assistant with the Arizona Cardinals and as a football research intern with Pro Football Focus (PFF). Outside of work, he enjoys going to the beach, reading about history, and trying out new recipes.

Kyeong Hoon (Jonathan) Jung

Kyeong Hoon (Jonathan) Jung is a Principal Software Engineer at the National Football League. He has been with the Next Gen Stats team for the last eight years helping to build out the platform from streaming the raw data, building out microservices to process the data, to building API's that exposes the processed data. He has collaborated with the Amazon Machine Learning Solutions Lab in providing clean data for them to work with as well as providing domain knowledge about the data itself. Outside of work, he enjoys cycling in Los Angeles and hiking in the Sierras.

Mike Band

Mike Band is a Senior Manager of Research and Analytics for Next Gen Stats at the National Football League. Since joining the team in 2018, he has been responsible for ideation, development, and communication of key stats and insights derived from player-tracking data for fans, NFL broadcast partners, and the 32 clubs alike. Mike brings a wealth of knowledge and experience to the team with a master's degree in analytics from the University of Chicago, a bachelor's degree in sport management from the University of Florida, and experience in both the scouting department of the Minnesota Vikings and the recruiting department of Florida Gator Football.

Diego Socolinsky

Diego Socolinsky is a Senior Applied Science Manager with the AWS Generative AI Innovation Center, where he leads the delivery team for the Eastern US and Latin America regions. He has over twenty years of experience in machine learning and computer vision, and holds a PhD degree in mathematics from The Johns Hopkins University.

Measuring Individual Competitiveness and its Impact on Sporting Success

Short Abstract:
Although the sports industry pours millions of dollars into understanding talent, we do not know: how to measure individuals’ attitudes towards competition, when these attitudes are formed, how they vary both within individuals over time and across individuals, and, more fundamentally, how important competitiveness is for sporting success. We measure competitiveness and answer these questions by leveraging a rich, dynamic panel dataset on hundreds of top young prospects from a renowned professional soccer academy during the decade leading up to professionalism. The ideas and methods are applicable to all other sports.

Julene Palacios-Saracho

Born in Gorliz (Spain). Currently studying a joint degree BA in Business Economics and BSc in Industrial Engineering. I love two intersections: (1) between math, economics and engineering, and (2) between science and sports. Played my first game in Spain’s professional soccer leagues at age 15. My hobbies include reading science (particularly, physics) and music (soundtracks). I play soccer for Athletic Bilbao Women and I am a big fan of Athletic Bilbao.

Ander Palacios-Saracho

Born in Gorliz (Spain). Currently studying a BSc in Data Science and Artificial Intelligence. Fascinated by the power of data, analytics, and recent advances in technology to try to answer all types of scientific questions, in both the social and natural sciences. Hobbies include soccer, bodyboarding, and free-diving in underwater kelp forests to observe octopus. I am a big fan of Athletic Bilbao, Nottingham Forest, Liverpool FC, and Tadej Pogacar.

No More Throwing Darts at the Wall: Developing Fair Handicaps for Darts using a Markov Decision Process

Short Abstract:
Darts is a popular sport that caters to players of all different abilities, and it is therefore common for opponents to have mismatched skill levels. Handicaps are useful interventions that address this mismatch, keeping the game competitive by increasing the weaker player’s chances of winning. However, the design of handicaps in darts has historically been a lot like “throwing darts at the wall” with no rigorous approach. To fill this gap, we develop a framework to model the game of darts with different handicaps, allowing us to evaluate current approaches and design a novel, fairer handicap system.

Timothy C.Y. Chan

Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the Department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. Along with co-author Doug Fearing, he received the MIT Sloan Sports Analytics Conference research paper award in 2013. He recently got back into playing tennis and he is never going back.

Craig Fernandes

Craig Fernandes is a third-year Operations Research PhD Candidate and Vanier Scholar at the University of Toronto supervised by Profs. Timothy Chan and Ningyuan Chen. His research focuses on optimization, game theory & AI/ML techniques applied in economics, emerging markets, and sports. His research has been featured at MIT's Sloan Sports Analytics Conference (SSAC), the New England Symposium on Statistics in Sports (NESSIS), and the Sport Innovation (SPIN) Summit hosted by Own the Podium. He also previously worked as a research data scientist at Amazon and will be conducting a research visit this summer at Dartmouth College's Tuck School of Business. He picked up golf during the pandemic and is now an enthusiast.

Rachael Walker

Rachael Walker graduated from the University of Toronto with a BASc in Industrial Engineering and a minor in Artificial Intelligence Engineering. For her undergraduate thesis, she researched the application of stochastic optimization models in sports. Specifically, she examined how Markov models can be used to design fair handicap systems in the game of darts. Rachael recently started a new role on the data science team at the private equity firm Birch Hill Equity Partners. She is also an undeterred fan of the Toronto Maple Leafs.

Optimizing Baseball Fielder Positioning with Consideration for Adaptable Hitters

Short Abstract:
This paper presents a novel approach to positioning baseball fielders to maximize expected outs or minimize expected runs allowed against an opposing hitter. We find evidence that our positioning approach is an improvement over MLB average positioning in terms of both hits and runs allowed. We then extend our approach to adaptable hitters who adjust their batted ball strategy in response to the defense’s positioning strategy by modeling the interaction as a zero-sum game and solving for an equilibrium pair of strategies. We demonstrate two examples where the game theory model is appropriate: against shift-beating hitters who pull the ball less frequently when the defense shifts against them and against pull-heavy left-handed hitters who threaten to bunt against an extreme shift.

William Melville

William Melville received his undergraduate degree in applied and computational mathematics at BYU in 2020 before starting a job as an analyst with the Texas Rangers. He returned to BYU in 2022 where he is currently pursuing a PhD in computer science. His research focuses on applications of game theory to baseball strategy. His passion for baseball runs much deeper than just the analytics; he also has a great appreciation for the equipment of the game and makes wooden bats in his home woodshop.

Samuel Wise

Samuel Wise is an undergraduate at Brigham Young University in his final semester as a Statistics major with an emphasis in Data Science and a minor in Economics. He is from Walnut Creek, California and enjoys exploring his passion for sports through data analytics. He recently has been interested in ranking algorithms and their applications in sports. He is an avid fan of football and the New Orleans Saints, as well as basketball and the UFC. When he is not watching sports, he is watching movies and playing video games with his friends. 

Grant Nielson

Grant Nielson is in his final undergraduate year studying Statistics at BYU. He has enjoyed this past year working on baseball projects with IDeA labs, helping him towards his goals of attending grad school and working with a major league team. Having Dallas roots, his favorite memory is watching his Texas Rangers win the World Series in person last year. He also enjoys biking along the Wasatch Front, watching Moneyball, and playing piano and basketball.

Tristan Mott

Tristan Mott grew up in Austin, TX and later moved to Alpine, UT. He enjoys spending his free time traveling, backpacking, and fly fishing. In his third year at BYU, he is working on undergraduate degrees in computer engineering (major) and computer science (minor). He is passionate about playing and watching sports and loves working on research projects for the Texas Rangers and BYU baseball teams. He aspires to someday receive a PhD in computer science so that he can make contributions to the fields of sports, machine learning, and game theory.

Christopher Archibald

Christopher Archibald is an Assistant Professor of Computer Science at BYU, where he has been since 2019.  His research focuses on Artificial Intelligence and Strategic Reasoning, including Sports Analytics. He received his undergraduate degree in Computer Engineering from BYU in 2006 and a PhD in Computer Science from Stanford University in 2011 under the supervision of Yoav Shoham.  From 2011 to 2013 he was a Postdoctoral Fellow at the University of Alberta under the supervision of Michael Bowling.  From 2013 to 2019 he was an assistant professor at Mississippi State University. He enjoys teaching, playing games with his kids, and learning about obscure sports. 

David Grimsman

David Grimsman is an Assistant Professor in the Computer Science Department at Brigham Young University. He completed BS in Electrical and Computer Engineering at Brigham Young University in 2006 as a Heritage Scholar, and with a focus on signals and systems. After working for BrainStorm, Inc. for several years as a trainer and IT manager, he returned to Brigham Young University and earned an MS in Computer Science in 2016. He then received his PhD in Electrical and Computer Engineering from UC Santa Barbara in 2021. His research interests include multi-agent systems, game theory, distributed optimization, network science, linear systems theory, and security of cyberphysical systems.

The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer

Short Abstract:
Player injuries in soccer significantly impact team performance, club financial stability and player welfare, with the ‘Big Five’ European soccer leagues experiencing a staggering £513 million in injury-related costs during the 2021/22 season. In this paper, we present a novel forward-looking team selection model, framed as a Markov decision process and optimised with Monte Carlo tree search, that balances team performance with the risk of long-term player unavailability due to injury. We demonstrate that real-world teams could reduce the incidence of player injury by ~13% and wages inefficiently spent on injured players by ~11% using our data-driven team selection model.

Gregory Everett

Gregory is a PhD candidate at the University of Southampton, where his research is focused on the use of AI to optimize team performance in soccer. Gregory has published papers previously in this field, focusing on optimizing collective team positioning and predicting player behaviour. He has also gained practical experience through an internship at SentientSports, and has a passion for the development of AI models to enhance the effectiveness of operational processes within soccer clubs.

Dr. Ryan Beal

Ryan is the CEO and Co-Founder of SentientSports, an AI startup dedicated to helping sports brands unlock the full potential of AI. The company specializes in providing personalized content and real-time analytics for innovative fan experiences. Holding a PhD from the University of Southampton, his research focused on AI applications in team sports, a domain in which he has published numerous papers. At SentientSports, Ryan has collaborated with many leading sports brands, including leading clubs in the Premier League and NFL. He has been recognized with a Royal Academy of Engineering 1851 Enterprise Fellowship, and his work has been featured in media outlets such as The Athletic and The Times.

Dr. Tim Matthews

Tim is CTO and co-founder of SentientSports as well as holding a PhD in Computer Vision and Artificial Intelligence from the University of Southampton. His research includes creating SquadGuru, an AI system that surpasses 99% of human players in fantasy sports. At SentientSports, Dr. Matthews leads initiatives in developing AI tools for performance analysis and fan engagement, collaborating with various European football teams, including major English Premier League clubs. His work focuses on merging advanced AI techniques with sports analytics to enhance team performance and decision-making.

Prof. Sarvapali Ramchurn

Sarvapali is a leading expert in Artificial Intelligence at the University of Southampton, with a specific focus on trustworthy and responsible AI. He is a co-founder and the Chairman of SentientSports, and also serves as the CEO of Responsible AI UK, the UK government's flagship AI program. Additionally, Professor Ramchurn directs the UKRI Trustworthy Autonomous Systems Hub and has been recognized as a Turing Fellow. His significant academic contributions are evident in his over 7000 citations, and his work has gained widespread recognition, featuring in prominent media outlets like BBC News, New Scientist, Sky News, BBC Click, and Wired.

Prof. Timothy Norman

Professor Timothy Norman is the Head of Electronics and Computer Science at the University of Southampton and serves as the director of the UKRI Minds CDT. His extensive academic career is marked by a strong focus on broader AI topics, including multi-agent systems and AI planning and scheduling. He has been involved in sports research for the past 6 years with a number of his students.

2024 Poster Presenters

How to Predict the Performance of NBA Draft Prospects

Short Abstract:
We introduce a new mathematical system for predicting outcomes of NBA draft prospects based on the outcomes of other previously drafted players. This approach, which is completely general and applicable to any sport, forms predictions as relevance-weighted averages of prior outcomes using a precise and theoretically justified assessment of relevance derived from principles of information theory. Crucially, a measure called “fit” indicates in advance the unique reliability of each individual prediction and dynamically focuses each prediction on the combinations of predictive variables and previous players that are most informative for the prediction task. Relevance-based prediction addresses complexities that are beyond the capacity of conventional prediction models, but in a way that is more transparent, more flexible, and more theoretically justified than widely used machine learning algorithms.

David Turkington

David Turkington is a Founding Partner of Cambridge Sports Analytics. He is also Senior Managing Director and Head of State Street Associates, State Street Corporation’s Cambridge-based innovation hub. Dave is the author of more than 40 peer-reviewed scholarly articles and co-author of three books: Prediction Revisited, Asset Allocation: From Theory to Practice and Beyond, and A Practitioner’s Guide to Asset Allocation. His scholarly research has garnered numerous awards, including the prestigious Harry M. Markowitz Award for his research in relevance-based prediction. Dave graduated summa cum laude from Tufts University with a Bachelor of Arts degree in Mathematics and Quantitative Economics. 

Megan Czasonis

Megan Czasonis is a Founding Partner of Cambridge Sports Analytics. She is also Managing Director and Head of Portfolio Management Research at State Street Associates, State Street’s renowned innovation hub located at Harvard Square. Megan has published extensively in peer-reviewed journals and is a co-recipient of the prestigious Harry M. Markowitz Award for her research in relevance-based prediction. Megan is a coauthor of the acclaimed book, Prediction Revisited, which introduces an alternative mathematical system for forming predictions from data. She holds a Bachelor of Science degree in Economics and Finance from Bentley University.

Mark Kritzman

Mark Kritzman is a Founding Partner of Cambridge Sports Analytics. He is also a Founding Partner of Windham Capital Management and State Street Associates, and he teaches a graduate finance course at MIT Sloan. He has served on several boards including the Government Investment Corporation of Singapore, the Institute for Quantitative Research in Finance, the International Securities Exchange, The Investment Fund for Foundations, the MIT Sloan Finance Group, Protego Trust Corporation, and St. John’s University. He has published more than 100 articles in peer-reviewed journals and is the author or co-author of eight books including Prediction Revisited, Asset Allocation: From Theory to Practice and Beyond, The Portable Financial Analyst, and Puzzles of Finance. Mark has won numerous awards for his scholarly research including the Harry M. Markowitz Award for his research in relevance-based prediction. In 2004, Mark was elected a Batten Fellow at the Darden Graduate School of Business Administration, University of Virginia. Mark holds a Bachelor of Science degree in Economics from St. John’s University and a Master of Business Administration degree with distinction from New York University.

Cel Kulasekaran

Cel Kulasekaran is a Founding Partner of Cambridge Sports Analytics. He is also Managing Partner at Windham Capital Management, where he leads applied research for Windham’s asset management business and its strategic partnerships. ​Cel is the founding architect of Windham’s innovation arm, Windham Labs, which delivers portfolio optimization and risk management technology to investors worldwide. His research has appeared in peer-reviewed academic journals. Cel holds a Bachelor of Science degree in Mathematical Sciences with distinction from Worcester Polytechnic Institute and a Master of Arts degree in Mathematical Finance from Boston University. 

Noisy Judgments: A probability surface-based analysis of umpiring variability

Short Abstract:
Darts is a popular sport that caters to players of all different abilities, and it is therefore common for opponents to have mismatched skill levels. Handicaps are useful interventions that address this mismatch, keeping the game competitive by increasing the weaker player’s chances of winning. However, the design of handicaps in darts has historically been a lot like “throwing darts at the wall” with no rigorous approach. To fill this gap, we develop a framework to model the game of darts with different handicaps, allowing us to evaluate current approaches and design a novel, fairer handicap system.

Emily-Anne Patt

Emily-Anne Patt is the manager for quantitative intelligence and methodologies supporting security and resilience at Alphabet, Inc. based in Washington, D.C. Her background is in econometrics and financial economics, with prior experience in several US government agencies. Fenway Park will always be home, but these days you can find her at Nationals Park or coaching the scholar athletes at the Washington Nationals Youth Baseball Academy. 

James Stockton

James Stockton, PhD. is the lead data scientist at Altamira Technologies supporting the United States Air Force Chief Data and AI Office based in northern Virginia. His background is in astronomy and astrophysics and he has worked as a data scientist in private industry, academia, and supported federal customers in the DoD/IC space for over a decade. He's an avid climber and mountaineer and will happily talk about mechanical advantage haul systems longer than any sane person should.

Player Pressure Map - A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts

Short Abstract:
In Noisy Judgments: A probability surface-based analysis of umpiring variability, we establish the size, shape, and position of the strike zone as called during an MLB game by building a prior probability surface from 5.3 million called balls and strikes between the 2008 and 2022 seasons. We used this surface to evaluate changes in the actual strike zone over time, stress-testing the reliability of the model with established baseball facts related to batter and pitcher handedness and seasonal shifts. A sensitivity study shows the validity of the surface at lower pitch counts, allowing us to evaluate individual player and umpire performance across games and within a game. This analysis leads us to propose a novel methodology for evaluating a catcher's framing ability--the Framing Induced Strike Zone (FISZ).

Chaoyi Gu

Aaron (Chaoyi Gu) received the MS degree (First class) in sports analytics and technologies from the Institute for Sport Business, Loughborough University, London, United Kingdom in 2020. Chaoyi is currently a Ph.D. from the Institute for Digital Technologies, Loughborough University, London. His research interests include machine learning, multi-agent systems, and sports analytics.

Jaming Na

Jiaming (Jamie) Na is a PhD student at Loughborough University's Institute for Digital Technologies. With a background in statistics and a long-time passion for sports, his research centers on advancing sports analytics through computer vision AI models. His work aims to revolutionize performance analysis and strategic decision-making in athletics.

Yisheng Pei

Yisheng Pei is a third-year PhD student at Loughborough University London and a loyal Arsenal fan. His research interest lies in machine learning and deep learning applications in sports analytics, particularly soccer. He is always looking forward to understanding and quantifying players’ efforts on the pitch.n should.

Varuna De Silva

Varuna De Silva is a Reader (Associate Professor) in Machine Intelligence at Loughborough University. He directs the machine intelligence lab at Loughborough University London, where his team works on multi agent reinforcement learning, multimodal machine learning and their applications in Sports Analytics and Autonomous Systems.

A model-based risk-impact analysis of dribble actions in women's soccer

Short Abstract:
Our paper presents a model-based approach to quantify individual dribbles in women's soccer based on value and risk attributes. By analyzing over 48,000 dribbles in the 2023 Women's World Cup using machine learning techniques, we measure the expected probability of success and the expected threat of each dribble. The results highlight players who outperform expectations, but can also be used to analyze the playing philosophy of different teams. The findings have implications for player recruitment and development as well as team tactics in women's football. The source code can be found on

Tobias Beckman

After completing his master's degree in mathematics, Tobias joined the European consultancy d-fine in 2019. Having played soccer enthusiastically from an early age, he quickly joined the company's sports analytics team. There, he now uses his knowledge to quantify the youth development work of soccer clubs, develop methods for match analysis and develop customized scouting platforms using modern open-source tools.

Gerhard Waldhart

Gerhard is a sports scientist and soccer analyst, currently serving as the Head of Match Analysis for VfL Wolfsburg Women, champion of the German Bundesliga in 2022 and UWCL Women’s Champions League Finalist 2023. Gerhard has been a licensed coach for 17 years and is currently pursuing a master's degree in match analysis at the University of Cologne – always looking for an added edge through data and analytics.

Stefan Thiem

Stefan is a Senior Consultant at the European consultancy d-fine, where he works as a data scientist. In his projects, he supports sports clubs gaining insights from data for match analysis and talent evaluation. He graduated from the University of Münster with a PhD in sports economics and with a bachelor’s degree in mathematics. In addition, he has been holding a soccer coaching license from the UEFA for seven years. Stefan is very passionate about sports in general and he maintains his own sports podcast, where he covers and discusses interesting scientific questions related to sports.

Oliver Wohak

Oliver is a Senior Manager at the European consultancy d-fine, where he supports his clients in tackling the most complex challenges in digital transformation. Driven by his passion for sports and complemented by his master’s degrees in physics and Business Administration, Oliver built up the Sports Analytics team within d-fine and has been supporting clubs and associations in developing internal data analytics and IT solutions for over 5 years. While his main focus is on scouting, match analysis and talent development in soccer, his experience also includes ice hockey, basketball and handball. Stemming from his time in the US, Oliver is an avid American Football fan, roots for the Eagles and especially enjoys Fantasy Football season.

Using Tracking Data to Build Offensive Line Development Tools

Short Abstract:
American football has in recent years made drastic shifts towards the quantitative. The proliferation of charting and tracking data has given us the ability to better evaluate and value players, but player development has been left wanting. In this paper we use NFL's Next Gen Stats data to build tools for offensive linemen in pass protection, which will help teams more efficiently watch film, monitor performance and fitness, build rosters, and game plan, with player development as the central focus. 

Eric Eager

Eric Eager is the VP of Research and Development at SumerSports. He holds a doctorate in mathematical biology from the University of Nebraska - Lincoln, and has published over 30 papers, including three at the MIT Sloan Sports Analytics Conference. He is the co-author of the book: Football Analytics with Python and R: Learning Data Science Through the Lens of Sports. 

Tej Seth

Tej Seth is a data scientist at SumerSports with a focus on football analytics. He graduated from the University of Michigan, where he worked with the football team. He is an avid Lions fan and outside of football loves listening to podcasts and going to the gym.

 Ben Brown 

Ben Brown is a data scientist at SumerSports. Prior to joining Sumer, he was the Head of Betting Innovation at PFF, where he won numerous DFS tournaments at both the NFL and NCAA level and hosted the PFF Daily Betting Podcast and various live streams.

Haley English

Haley English is currently a Football Information Intern with the Detroit Lions. She recently graduated from Villanova University and previously interned at Pro Football Focus. Outside of football analytics, she enjoys taking trips to the lake and watching her former sport, gymnastics.

Geoff Schwartz

From his pre-Bar Mitzvah stuttering days to being drafted 241st (out of 252) in the 2008 NFL draft, Geoff Schwartz has overcome adversity to exceed all expectations and impressively succeed in life. His eight-year career included stints with the Carolina Panthers, Minnesota Vikings, Kansas City Chiefs and New York Giants, where he signed a 4-year deal to become a starting guard on the Eli Manning-led team. Unfortunately, injuries derailed Geoff’s career and caused his retirement from the NFL in February 2017. Before his retirement Geoff began a seamless transition to a career in media. He can be heard daily on Sirius XM Radio 373 hosting PAC-12 Today and on the weekend hosting Fox Sports Radio. Geoff is a Fox Sports NFL gambling analyst, providing digital and written content. He hosts his own podcast, Geoff Schwartz Is Smarter Than You, where Geoff makes the average football fan smarter. He appears routinely on sports radio talk shows and podcasts throughout the country offering his insights on football, other sports plus a wide variety of non-sports related topics.

How Much Do Faceoffs Matter? Translating Faceoffs to Goals, Wins, and Championships in Hockey

Short Abstract:
In hockey, faceoffs have long been acknowledged as important drivers of puck possession, but their actual impact on scoring outcomes remains inadequately measured. It is acceptedly evident that a center winning 54% of their faceoffs outperforms one with a 51% success rate, but the tangible extent of this advantage in terms of goals, wins, and losses remains underexplored. This research fills the void by continuing the effort to translate faceoff results to scoring outcomes, measuring faceoff performance in goals, wins, and losses in a novel manner. We explore evidence that faceoffs are an undervalued championship-caliber market inefficiency and offer models enabling General Managers to see role-specific projections of how different personnel and usage could maximize offense, defense, and championship chances.

Tad Berkery

Tad is a senior at Johns Hopkins University majoring in Computer Science and Economics and minoring in Computational Medicine and Applied Mathematics & Statistics. He has completed research or worked directly for teams spanning the Big Ten, MLB, NHL, and NFL. Tad has also been featured in The Washington Post and is the author of What’s the “Right” Career?. In his free time, you can find him hanging out with family and friends, playing games, or trying his hand at becoming an at-home barista.

Chase Seibold

Chase is a recent graduate of Syracuse University, holding Bachelor's degrees in Economics and Sport Analytics, along with a Master's degree in Applied Data Science. During his time at Syracuse, he interned with Wasserman in the baseball department and contributed to the Washington Nationals R&D group as an intern. He is currently an R&D Analyst for the Nationals.

Max Stevens

My name is Max Stevens, and I am currently a student at Johns Hopkins University, set to graduate in May 2024 with degrees in Applied Mathematics & Statistics and Economics. Professionally, I have gained valuable experience as a Data Analyst intern at Attain Sports, a sports holding company with interests in baseball and soccer. My role involved predictive modeling for game scenarios and applying my analytical skills to enhance the business aspects of their sports portfolio. Previously, I was a member of the Johns Hopkins football team. I am a dedicated fan of Boston sports having grown up in Lexington, Massachusetts. Most recently, I have discovered a passion for European football and have pledged myself to the Tottenham Hotspur football club, COYS.

Justin Nam

Justin is a recent graduate from Johns Hopkins who was a part of the Johns Hopkins University Sports Analytics Research Group for three years. During his time working under Dr. Anton Dahbura, he worked on projects in baseball, football, and hockey, including projects for the Baltimore Orioles and Ravens. Since graduating in May with degrees in Computer Science and Applied Mathematics & Statistics, he has been working on the AWS Cloudfront Team as a Software Developer Engineer.

Anton Dahbura

Anton (Tony) Dahbura received the BSEE, MSEE, and PhD in Electrical Engineering and Computer Science from the Johns Hopkins University in 1981, 1982, and 1984, respectively. He served as a researcher at AT&T Bell Laboratories, was an Invited Lecturer in the Department of Computer Science at Princeton University, and served as Research Director of the Motorola Cambridge Research Center in Cambridge, Massachusetts. In January, 2012 he was named Executive Director of the Johns Hopkins University Information Security Institute in Baltimore and joined the faculty of the Johns Hopkins University Department of Computer Science as an Associate Research Scientist. In September 2018 he was named Co-Director of the Johns Hopkins University Institute for Assured Autonomy. Tony has had a love for baseball since childhood. He was named to the National Under-18 Baseball Team of El Salvador, where he lived for most of his early years, and was an outfielder for Johns Hopkins as an undergraduate. In 2010 he became co-owner of the Hagerstown Suns, the low-A affiliate of the Washington Nationals.

Analyzing NBA Player Positions and Interactions with Density-Functional Fluctuation Theory

Short Abstract:
Player tracking data can enhance the quantification of player abilities and our understanding of team composition and broader team strategies. In this work, we demonstrate how density-functional fluctuation theory (DFFT), an extension of a Nobel Prize-winning physics approach, can process basketball tracking data by treating players as interacting densities. By training the interactions on different play outcomes, we can evaluate play-outcome likelihoods based on player positions, determine which players are in strong or weak positions, and understand which players consistently instigate strong responses from the opposing team (i.e., ‘player gravity’). We find that our approach not only identifies the overall strengths of a player, but also identifies subtleties such as those who are left-handed (e.g., D. Russell) or who instigate changes non-locally through frequent passes (e.g., N. Jokic). 

Boris Barron

Boris Barron is a PhD candidate in Theoretical Physics at Cornell University, where he is affiliated with the Cornell Population Center. He holds a Master of Science in Mathematical and Theoretical Physics from the University of Oxford and a Bachelor of Science in Biophysics from York University. His research experience has spanned microfluidics, laser stability physics, machine learning, and increasingly complex systems in the broadest sense. Recently, he has been developing a novel framework for understanding residential segregation, with work that has included presentations at the U.S. Census Bureau, American Sociological Association (ASA), American Physical Society (APS), and the Population Association of America (PAA). His work has been funded in part by NSERC PGS-D. Beyond academia, Boris used to be a competitive ballroom dancer and is a speed typist, averaging over 120 words per minute –  height limitations have prevented him from being a serious contender in basketball.

Nathan S. Sitaraman

Nathan Sitaraman is a postdoctoral associate in the Cornell Laboratory for Accelerator-based ScienceS and Education (CLASSE) and a part-time consultant for the Dallas Mavericks. He graduated from Yale University cum laude with a bachelor's degree in physics, and completed his PhD in physics at Cornell. His research focuses on improving teamwork among superconducting electrons, as well as the much more complicated problem of improving teamwork among basketball players.

Tomas A. Arias

Tomás Arias, a Professor of Physics at Cornell University, holds SB and PhD degrees in Physics from the Massachusetts Institute of Technology, where he also briefly served on the faculty in the late 1990s before joining Cornell. He is an expert in developing novel density-functional theory (DFT) methods, with thirty years of experience and more than 60 publications on this topic alone, contributing to a total of 91 publications with over 25,000 citations. His recent work involves the development of joint density-functional theory (JDFT), a specialized method for analyzing the equilibrium of liquids and solids, and density functional fluctuation theory (DFFT), which extends the theory to understand the collective behaviors of human crowds and animal groups, as well as human residential segregation and group sports.

Previous Research Paper Finalists

Full Paper Submission Form

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Open Source Competition - Voting

Review our SSAC 2022 open source finalists below and cast your vote for your favorite submission HERE!

2022 Open Source Finalists

Understanding why shooters shoot - An AI-powered engine for basketball performance profiling
Short Abstract:
In professional basketball, it is crucial for the coaching staff of a team to analyze an opposing team and develop an effective strategy. Understanding player shooting profiles is an essential part of this analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents, while understanding where their players are most comfortable can lead them to developing more effective offensive strategies. We present a tool that can visualize player performance profiles in a timely manner while taking into account factors such as play-style and game dynamics, generating interpretable heatmaps that allow us to identify and analyze how these non-spatial factors affect the performance profiles. Our methods provide an effective and efficient tool that can provide insight into how certain players and teams play, without requiring the time-consuming process of reviewing hours of film, and could potentially be applied to other sports with adaptations.
GitHub Link (Open Source)

Alejandro Rodriguez Pascal, Ishan Mehta, Muhammad Khan, Rose Yu, Frank Rodriz

Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage
Short Abstract:
As an amateur goalkeeper playing grassroots soccer, who better to learn from than top professional goalkeepers? In this paper, we harness computer vision and machine learning models to appraise the save technique of professionals in a way those at lower levels can learn from. We train an unsupervised machine learning model using 3D body pose data extracted from broadcast footage to learn professional goalkeeper technique. Then, an “expected saves” model is developed, from which we can identify the optimal goalkeeper technique in different match contexts.
GitHub Link (Open Source)

Matthew Wear, Ryan Beal, Tim Matthews, Gopal Ramchurn, Tim Norman

Winning duels in VALORANT, a visualization of optimal positioning
Short Abstract:
This paper applies traditional sports analytics metrics with novel machine learning models in a brand new competitive Esport. By leveraging in-game positional data, we are able to evaluate the difficulty of a particular gun fight and assign a win probability to both sides. We use these predictions to identify players who are performing above or below expected, and identify strengths and weaknesses for NRG’s player development. We are hopeful for more analytics in Esports from current working professionals and the younger generation.
GitHub Link (Open Source)

DeMars DeRover

Using Machine Learning to Describe how Players Impact the Game in the MLB
Short Abstract:
This paper draws upon recent advances in Natural Language Processing (NLP) and Computer Vision (CV) to learn to describe the way in which players impact the game in the MLB. In particular, this work views the game as a sequence of events - instead of a set of summary statistics describing said events - and trains machine learning models to describe the impact that a given sequence of events has on the game. The models describe a sequence of events for a single player over a relatively small time period; so we refer to the model output as player form embeddings - descriptions of how they have impacted the game in the short term. We demonstrate how these embeddings can be used to describe players over the short- and long-term, and contain signals useful for predicting the outcome of games.
GitHub Link (Open Source)

Connor Heaton, Prasenjit Mitra