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…
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,341 papers · 148 categories
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48 results for “team strategy”
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.
The study develops methods to summarize team passing strategies from soccer data.
problem Modeling spatial passing networks across multiple games with varying positions.
method Multiresolution tensor decomposition and Poisson nonnegative block term decomposition.
result Automatic production of network motifs at different levels of detail.
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.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
problem Improving AI cooperation in teams with shared rewards.
method Proposed Partner-Aware strategy extending Upper Confidence Bound for decentralized MAB.
result Achieves logarithmic regret in collaborative decision-making.
Algorithm for decentralized competition among adaptive agents.
problem Decentralized competition among adaptive networks.
method Developed an algorithm for decentralized competition among teams of adaptive agents.
result Algorithm enables decentralized competition among adaptive agents.
QPass evaluates soccer players' passes based on intrinsic value.
problem Lack of quantitative evaluation of players' contributions to team strategy.
method Developed QPass methodology to quantify player pass value.
result Players losing ball possession can lead to better chances of winning.
Investment challenge study finds luck and strategy equally important.
problem The role of luck and strategic considerations in M6 investment challenge performance.
method Introduced a stylized model to derive and analyze a portfolio strategy.
result Improving chances of winning without attaining abnormal returns possible.
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.
Soccer Team Vectorscs.LG
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.
Finding Common Characteristics Among NBA Playoff and Championship Teams: A Machine Learning Approachstat.ML
Machine learning identifies key defensive factors for playoff and championship teams.
problem Characterize NBA playoff and championship teams using machine learning.
method Used classification trees, random forests, and neural networks to analyze 17 seasons of NBA data.
result Defensive factors, particularly made three-point shots and perimeter defense, are crucial for playoff and championship success.
The paper analyzes spatio-temporal behavior in DotA 2, finding skill differences in team dynamics.
problem Understanding skill differences in MOBA team dynamics.
method Data-driven measures of spatio-temporal behavior, including zone changes and time series clustering.
result Professional teams show smaller within-team distances and more zone changes than amateur teams.
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.
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.
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%.
The paper analyzes team formation on online platforms, tackling the exploration vs. exploitation dilemma.
problem Matching workers with tasks on online platforms, especially complex ones.
method Analyzed two settings: strongest member vs. weakest member, using regret bounds and optimal algorithms.
result Established fundamental regret bounds and designed near-optimal algorithms for team matchings.
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%.
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.
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.
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
problem Allocating heterogeneous resources among multiple agents in a decentralized manner.
method Liquid-Graph-Time Clustering-IPPO, integrating dynamic cluster consensus.
result LGTC-IPPO achieves more stable rewards, better coordination, and robust performance.
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.
Human strategies improve deep reinforcement learning for multiple agents.
problem Limited exploration capacity of deep reinforcement learning agents.
method Integrates human strategies into multiple deep reinforcement learning agents.
result Significant performance improvement of multiple agents working together.
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.
Tangent Works won GEFCom 2017 using automatic model building.
problem Forecasting time series with historical temperature shuffling.
method Automatic model building using Tangent Information Modeller (TIM) with historical temperature shuffling and decision on trend variable.
result Automated model building setup won the competition.
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.
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
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.
Corporate VC firms struggle with internal conflicts despite growth.
problem Internal conflicts among corporate VC investment teams.
method Conducted interviews with corporate VC firms to identify issues.
result Executive-level attention and commitment are lacking, leading to conflicts.
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.
Study uses complex networks and machine learning to predict soccer match outcomes.
problem Predicting soccer match outcomes with complex networks and machine learning.
method Complex network metrics and match statistics were used to build machine learning models.
result Models based on passing networks were as effective as traditional models using match statistics.
MedAusbild team won ISIC challenge by classifying seven skin diseases.
problem Skin disease classification using medical images.
method Employed deep learning, specifically convolutional neural networks (CNNs), for seven skin disease classification.
result MedAusbild team won ISIC challenge 2018.
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.
Target market risk evaluationq-fin.RM
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…
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.
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.
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.
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…
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…
Thirty Years of Academic Financeq-fin.GN
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.
Algorithm learns to switch control among agents in a team.
problem Learning to switch control among reinforcement learning agents.
method 2-layer Markov decision process, upper confidence bounds, shared confidence bounds.
result Sublinear total regret with shared confidence bounds.
MOBA-Slice evaluates team advantage in MOBA games using neural networks.
problem Difficulty in evaluating and predicting MOBA game outcomes.
method Time slice based evaluation framework using neural networks.
result MOBA-Slice outperforms existing methods in predicting game results and remaining time.
AI benchmarks evaluate football team performance using generative models.
problem Evaluating human performance in complex interactive tasks is error-prone and unreliable.
method Trained Conditional VRNN Model on player and ball tracking data to imitate and predict team interactions.
result Trained model as a useful benchmark for evaluating team performance in football.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
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.
Proposes RPG-RT for red-teaming T2I models without internal access.
problem Evaluating T2I models' security through red-teaming is challenging due to their closed-source nature and unknown defense mechanisms.
method Integrates LLM and rule-based preference modeling to dynamically adapt to unknown defense mechanisms.
result Demonstrates superior and practical approach for red-teaming T2I models.