STEVE creates vectors for soccer teams based on past match data.
problem Creating meaningful representations of soccer teams.
method Learning real valued vectors using past match data.
result STEVE outperforms competitors in team market value estimation.
ContestTrade uses competitive teams to improve LLM trading performance.
problem High sensitivity to market noise in LLM-based trading systems.
method Internal competitive mechanism, data and research teams, real-time evaluation.
result Significantly outperforms other systems across various metrics.
New approach for open ad hoc teamwork using graph-based policy learning.
problem Designing autonomous agents to collaborate with changing teams without prior coordination.
method Graph-based policy learning to adapt to dynamic team compositions.
result Successfully models the effects of other agents, leading to robust adaptation and superior performance.
The paper offers algorithms for managing freelancers and in-house workers in online labor markets.
problem Managing freelancers and in-house workers in online labor markets efficiently.
method Developed algorithms for team formation with outsourcing in an online setting.
result Efficient online algorithms for minimizing costs in hiring and outsourcing.
In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 26 predictor variables and one binary response variable taking on a value …
CGAs estimate team performance from data, simplifying SV computation.
problem Predicting and rewarding team performance using game theory.
method Cooperative game abstractions (CGAs) for estimating characteristic functions from data.
result CGAs enable linear-time computation of Shapley Value for team contributions.
A Python approach minimizes risk in decentralized exchanges.
problem Minimizing risk in decentralized exchanges.
method Three-step approach: Kernel Ridge Regression, function minimization, and algorithmic trick.
result Reduced computational load and increased solution accuracy.
The team predicts foreign exchange rates using clustering and attention models.
problem Complexity and unexpected events in foreign exchange markets.
method Clustering and attention models applied to historical data.
result Improved event-driven price prediction for oversold scenarios.
Deep RL team defends payloads from obstacles.
problem Protecting high-value payloads from obstacles during navigation.
method Multi-agent deep reinforcement learning for coordinated escort teams.
result Escort teams increase navigation success by up to 75%.
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
problem Predicting future ticket prices from sparse and spatiotemporal data.
method Bi-level optimizing deep neural network with coarsening and refining layers, bi-level loss function.
result Our model outperforms other methods in real-world ticket price prediction.
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. This model was designed to be applied to sports such as soccer or hockey, in which contest outcomes take value in a limi…
Cricket betting is a multi-billion dollar market. Therefore, there is a strong incentive for models that can predict the outcomes of games and beat the odds provided by bookers. The aim of this study was to investigate to what degree it is possible to predict the outcome of cricket matches. The target competition was t…
Lognormal distribution used for predicting team rankings in an orienteering relay race.
problem Predicting final team rankings in an orienteering relay race.
method Used lognormal distribution and Fenton-Wilkinson approximations for order statistics.
result Accurate predictions of team rankings using order statistics.
Study analyzes financial distributions and inequality in professional cycling teams.
problem Financial inequality and concentration among cycling teams.
method Rank-size law and various inequality indices applied to Tour de France data.
result Financial gains distribution is hyperbolic with a decay exponent of about -1, contrary to Pareto principle.
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence o…
New algorithm for identifying Condorcet team in noisy comparisons.
problem Online learning with noisy comparisons of teams.
method Formalized dueling teams problem, developed algorithms for stochastic and deterministic settings.
result Identifies Condorcet winning team with reduced number of duels.
Researchers create a framework to value player actions in CSGO.
problem Lack of accessible data and analytical frameworks for esports players.
method Data model, graph distance measure, context-aware framework.
result Demonstrated framework's consistency and independence compared to existing methods.
Framework for real-time win probability and player ability in sports.
problem Real-time evaluation and player assessment in team sports.
method Continuous dominance indicator (T-score) and T-process formulation.
result Decomposes competitive advantage into interpretable statistical components.
Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underly…
Study uncovers tactical line-breaking passes in football using clustering.
problem Detecting and analyzing line-breaking passes in football matches.
method Unsupervised clustering-based framework using event and tracking data.
result Introduced tactical metrics to quantify pass effectiveness.
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
problem Analyzing the impact of FIFA World Cup matches on fan tokens.
method Event study and intraday analysis of blockchain-based fan tokens.
result Fan tokens experienced a surge in returns six months before the World Cup, followed by a decline during the matches, revealing asymmetries in performance.
Attackers can significantly reduce team rewards in cooperative multi-agent reinforcement learning.
problem Robustness of cooperative multi-agent reinforcement learning to adversaries.
method Novel attack method involving training a policy network and using targeted adversarial examples.
result Reduces team reward from 20 to 9.4 by attacking a single agent, reducing winning rate from 98.9% to 0%.
Paper studies competitive networks where teams aim to minimize their own objectives, adapting to each other's strategies.
problem Competitive networks where teams have conflicting objectives.
method Proposes diffusion learning algorithms for two classes of network games: zero-sum and non-zero-sum.
result Stability performance of proposed algorithms analyzed and demonstrated through experiments.
After the shocking series of bankruptcies started in 2008, the public does not trust anymore the classical methods of assessing business risks. The global economic severe downturn caused demand for both developed and emerging economies' exports to drop and the crisis became truly global. However, this current crisis of…
Hierarchical MARL learns complementary skills for team coordination.
problem Creating intelligent agents that coordinate like human sports teams.
method Two-level hierarchical MARL with unsupervised skill discovery.
result Emergence of useful and complementary skills in team games.
Human-AI teaming suffers from calibration issues.
problem Human-AI teaming
method Assume calibrated models and humans
result Existing methods for combination do not preserve human's calibration.
Technology offers new ways to measure the locations of the players and of the ball in sports. This translates to the trajectories the ball takes on the field as a result of the tactics the team applies. The challenge professionals in soccer are facing is to take the reverse path: given the trajectories of the ball is i…
AI updates can disrupt human-AI teams; new objective improves compatibility.
problem AI updates can disrupt human-AI team performance.
method Introduce compatibility concept, propose re-training objective.
result Current machine learning algorithms do not produce compatible updates.
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches. This learning problem is complicated by the rise of complex tasks on these platforms, such as web development and product design, that require a team of worker…
The study uses unsupervised machine learning to identify top European football teams.
problem Selecting teams for the new European football Super League.
method Used Laplacian eigenmaps clustering on performance data.
result Successfully identified four clusters of teams based on performance metrics.
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introdu…
This paper introduces glocal explanations for expected goal models in soccer.
problem Limited interpretability of expected goal models trained with black-box methods.
method Proposes glocal explanations using aggregated SHAP values and partial dependence profiles.
result Extracts knowledge from expected goal models for teams and players, enhancing performance analysis.
Paper presents Transfer Portal model for accurate player performance predictions.
problem Predicting future player performance after a transfer.
method Personalized neural network and Bayesian updating framework.
result Model generates accurate predictions for player performance at new clubs.
ShapleyBO explains BO's decisions, enhancing human-AI collaboration in robotics.
problem Lack of explainability in BO's black box nature, especially in human-in-the-loop applications.
method Uses game-theoretic Shapley values to quantify and interpret BO's parameter contributions.
result ShapleyBO identifies exploration and exploitation contributions, disentangling aleatoric and epistemic uncertainty.
New method calibrates eSSVI volatility surfaces without arbitrage.
problem Sequential calibration of eSSVI surfaces lacks global view and guarantees no arbitrage.
method Global and arbitrage-free parametrization of eSSVI surfaces.
result Faster calibration always guarantees an arbitrage-free fit.
MERL uses evolutionary and gradient-based methods to optimize sparse team-based and dense agent-specific rewards in multiagent coordination.
problem Training multiagent reinforcement learning policies on sparse team-based rewards is difficult and relying solely on agent-specific rewards is sub-optimal.
method MERL employs a split-level training platform with an evolutionary algorithm and a gradient-based optimizer, transferring skills between the two processes.
result MERL significantly outperforms state-of-the-art methods on coordination benchmarks.
Two new exploration methods for multi-agent systems improve team performance.
problem Exploration in transition-dependent multi-agent settings.
method EITI and EDTI, using mutual information and VoI to encourage coordinated exploration.
result Significant improvement in multi-agent performance through coordinated exploration.
BC-Aligner maintains backward compatibility of embeddings after frequent updates.
problem Updating embeddings without requiring consumer teams to retrain their models.
method Learning backward compatible embeddings through BC-Aligner.
result BC-Aligner maintains backward compatibility with existing unintended tasks after multiple model version updates.
Introduces LTQL for factored policies in cooperative MARL.
problem Learning optimal joint policies in collaborative MARL scenarios.
method Logical Team Q-learning (LTQL) as a stochastic approximation to dynamic programming.
result LTQL provides factored policies for optimal joint behavior in cooperative MARL.
Deep learning predicts customer churn in retail.
problem Accurately predicting which customers are likely to stop purchasing.
method Survival model parameters learned by recurrent neural networks.
result Individual level survival models for purchasing behavior.
We consider a team of reinforcement learning agents that concurrently operate in a common environment, and we develop an approach to efficient coordinated exploration that is suitable for problems of practical scale. Our approach builds on seed sampling (Dimakopoulou and Van Roy, 2018) and randomized value function lea…
This review explores ML in predicting team sport outcomes, identifying successful strategies and themes.
problem Predicting outcomes in team sports using machine learning.
method Survey of studies from 1996 to 2019, analyzing ML algorithms and their accuracies.
result Identifies common themes and strategies for future research in predicting team sport outcomes.
AIMM-X monitors markets for suspicious behavior using transparent scoring.
problem Detecting market manipulation from benign mechanisms.
method Combines microstructure signals and public attention signals for anomaly detection.
result Transparent scoring allows tracing and understanding flagged windows.
Study fair team formation in online labor marketplaces.
problem Design fair algorithms for team formation in online labor marketplaces.
method Define and analyze the Fair Team Formation problem, provide inapproximability results, and develop four algorithms.
result Developed four algorithms for fair team formation in online labor marketplaces.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…
We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predic…
Study on academic finance evolution over 30 years.
problem Understanding changes in academic finance research over time.
method Analysis of 32 finance journals from 1992 to 2021 using Structural Topic Model.
result Most journals have become more generalist over time, covering more topics.
Framework analyzes physical metrics in soccer to link performance with value.
problem Understanding and quantifying the value of physical performance in soccer.
method Estimating physical indicators from tracking data, contextualizing runs, linking with possession-value model.
result Physical performance correlates with value generation in soccer.