Research Paper Competition
Details
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.
Submissions for SSAC26 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!
Rules
Competition Format
The competition consists of the following phases:
- 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.
- 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.
- Conference Phase
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, 2025, 11:59 p.m. EST
Full paper requests sent out – Late-October 2025
Full paper submission due (if selected) – Dec. 7, 2025, 11:59 p.m. EST
Finalists and posters announced – Late-January 2026
Submission of poster (if selected) – Early-February 2026
Submission of presentation (if selected) – Mid-February 2026
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 2025 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:
- Basketball – All submissions related to the sport of basketball.
- Baseball – All submissions related to the sport of baseball.
- Soccer – All submissions related to the sport of soccer.
- Football – All submissions related to the sport of American football.
- 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.
- 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.
2026 Research Papers
This research introduces a novel method for modeling visual perceptual behavior in sports as a two-dimensional top-down plane using pose-enhanced positional tracking data. It integrates pitch control and pitch value with vision maps to quantify controlled and observed space. We show that players who observe higher quantities of occupied space while awaiting passes exhibit larger gains in their spatial positioning after subsequent on-ball actions. Unlike traditional visual exploratory action counts (i.e. rapid head movements), this method is position independent, requires no manual annotation, and offers continuous measurements compatible with existing analytics models.
This paper introduces PAUSA, a metric that evaluates pass timing optimality rather than speed. By decomposing decisions into 'Temporal Judgment' and 'Spatial Selection' using the OBSO model, the study demonstrates that PAUSA correlates more strongly with team performance than traditional speed-based metrics. This framework effectively identifies elite players who utilize strategic delay ("La Pausa"), shifting the analytical focus from execution speed to the optimality of timing.
This paper presents an interference-aware causal inference framework for evaluating defensive performance in American football, utilizing player tracking data. We model all 22 players jointly with a multi-agent Transformer to estimate counterfactual Expected Points Saved (EPS) for individual defenders while explicitly accounting for coordination, substitution, and other violations of independence. Using doubly robust estimation, adversarial representation balancing, and Bayesian uncertainty quantification, we recover causal defensive value that traditional tackle-based and predictive models systematically miss. Extensive synthetic validation, sensitivity analyses, and real-game examples demonstrate that explicitly modeling defensive interdependence substantially reduces bias and reveals hidden contributions across positions.
Professional boxing has long been plagued by judging controversy, driven largely by the sport's subjective scoring criteria. Leveraging new computer vision technology, we map round-by-round statistics to judges' scores at unprecedented scale (7,323 rounds). We introduce a points-based scoring system that achieves accuracy within the range of professional judges while remaining transparent, consistent, and bias-free. We also reveal which specific performance factors most strongly drive judges’ scores, offering actionable insights for improving scoring standards and competitive fairness in combat sports.
This paper introduces a deep reinforcement learning framework for evaluating NBA players that learns context-dependent player value directly from game outcomes. Using temporal-difference learning with a distributional win-probability model, the approach estimates how actions and player presence influence expected outcomes across game states. We combine this with a neural Shapley value attribution method to fairly decompose team success into individual contributions while capturing interaction and synergy effects. Empirical results show improved predictive accuracy, greater stability than RAPM, and systematic identification of defensive value and player synergies that traditional metrics fail to capture.
This paper introduces a novel framework for characterizing and controlling perceived competitive balance in sports leagues using the observed turning point, which measures how long a tournament remains statistically indistinguishable from a perfectly balanced competition. We propose an unbiased modification of this metric that mitigates scheduling effects, enabling fair comparisons across leagues and sports. We further present heuristics for influencing how balanced a competition appears solely through schedule design. This approach suggests that carefully crafted schedules can shape public perception of competitive balance without altering the underlying competitive structure.
The impact of managers on team records is one of the final holdouts to the influence of the sports analytics revolution on professional baseball. This paper applies simulation and Bayesian methods to a sample of over 500 managers spanning the history of AL/NL seasons since 1900. It develops a manager estimator equivalent to player WAR and finds that a substantial fraction of managers (including current and recently active ones) have over their careers influenced team “winning percentages” ≥ ± 0.012, the equivalent of ± 2 wins per 162 games.
2026 Research Papers
This study evaluates modern playoff and tournament designs across global sports, an important aspect of League and Tournament success. Using axiomatic theory, Bayesian modeling, and large-scale simulation, we assess tournament formats against core economic criteria—efficacy, fairness, and attractiveness—showing that the ADBC classic single-elimination final-four bracket is optimal within its class but dominated once hybrid designs, such as the Page Playoff System, are permitted. The PPS has been gaining traction in major sports leagues such as the NBA (Play-In Tournament) and top professional cricket leagues. It adds a top-bracket, double-elimination provision to a single-elimination final-four format of middling efficiency to dominate the efficiency of all single-elimination formats. Our analysis combines closed-form theoretical probability derivations with Monte Carlo simulation experiments—up to one million tournament realizations—parameterized using Bradley–Terry and Thurstone–Mosteller models of team strength. Results show that the Page Playoff System strictly increases the probability that the strongest teams win the tournament while preserving ordinal fairness, even under moderate seeding inefficiencies. These gains arise from top-bracket double-elimination concessions, which sharply reduce premature elimination of elite teams relative to optimized single-elimination formats. Applying the same simulation logic to the College Football Playoff, we find that the 2024 conference-champion bye rule induced a bimodal distortion in championship probabilities, weakening both fairness and efficacy. Simulated counterfactuals confirm that the CFP’s 2025 shift to straight seeding restores a meritocratic mapping between team strength and postseason success. This research highlights the need for robust probabilistic modeling in the design process. By conducting simulation analyses that adjust constraints, administrators can balance entertainment value, revenue, and fairness, ensuring the tournament maintains the sport's integrity while satisfying fan expectations.
This paper introduces a probabilistic framework for identifying basketball player archetypes that can be applied across leagues, including those without access to advanced data. While player segmentation is not new in basketball analytics, we propose a fundamentally different approach based on Archetypal Analysis, which identifies extreme and interpretable player profiles and models players as probabilistic mixtures of these archetypes, explicitly capturing hybrid roles and stylistic flexibility. We demonstrate the method on both European league data and an enriched NBA dataset, showing that the resulting archetypes are coherent and directly actionable for scouting, roster construction, and meaningful cross-league player comparison. Designed to work with widely available play-by-play and box-score statistics, the framework is accessible to teams across leagues and resource environments, and is accompanied by a detailed use case illustrating real-world applications for scouting, roster construction, and talent identification decisions.
Evaluating defensive performance in soccer remains challenging, as effective defending is often expressed not through visible on-ball actions such as interceptions and tackles, but through preventing dangerous opportunities before they arise. To address this issue, this paper proposes DEFCON, a framework that quantifies player-level defensive contributions across all attacking situations in soccer. DEFCON estimates the expected value of each attacking option and each defender's responsibility for stopping it using Graph Attention Networks. Then, it assigns positive or negative credits to defenders according to how much they reduce or increase the opponent’s expected value. Experiments on Eredivisie event and tracking data demonstrate strong correlations between DEFCON’s defensive credits and player market valuations, highlighting its potential for real-world scouting and performance analysis.
In 2023, Major League Baseball increased the width of bases from 15 to 18 inches and limited the pitcher’s pickoff attempts to two, with a third failed attempt resulting in a balk. These rule changes led to an increase in stolen base attempts and a reduction in caught stealing rates. We present a comprehensive analysis of stolen base and pickoff strategies under the 2023 rule changes. We model the interaction between pitcher and base stealer as an extensive-form game called the pitcher’s dilemma, and we define optimal base stealing, pickoff, pitchout, and hit and run strategies by solving for equilibria in various versions of the pitcher’s dilemma.
This paper presents HoopEval, a deep reinforcement learning framework for evaluating individual player actions in basketball using spatio-temporal tracking data. The approach models game dynamics and player interactions to estimate the value of both on-ball and off-ball decisions within their tactical context. By decomposing possession-level outcomes into fine-grained action evaluations, HoopEval provides interpretable measures of decision quality beyond traditional statistics. The results demonstrate its potential to support tactical analysis, player development, and data-driven coaching.
“Fast Hybrid Search for Red-Zone Play Recommendations” treats NFL red-zone playcalling as an information-retrieval problem: given the current game situation and recent play context, it searches a large corpus of historical red-zone snaps to surface similar precedents a coach might recall from film. Each play is converted into a compact, coach-like text “play card,” and a hybrid search stack combines keyword matching with semantic embeddings to retrieve candidates quickly. A constrained lightweight language model then synthesizes those candidates into a concise “play family” recommendation that fits existing coaching workflows and can be linked back to real historical analogues.
This paper presents a field-deployable framework that converts synchronized IPhone video into predictive neuromuscular performance insights for track and field athletes. Using markerless motion capture and musculoskeletal optimization, the study reconstructs internal joint torques, muscle activations, and ground-reaction forces from video alone. These neuromechanical features are integrated with machine learning models, (XGBoost. RNN, LSTM-NN) to predict javelin throw distance with high accuracy (R² = 0.92). The results demonstrate that scalable, video-based biomechanics can move sports analytics from descriptive observation to predictive, athlete-specific intelligence.
