SoccerCPD detects tactical changes in soccer matches using spatiotemporal tracking data.
problem Detecting consistent team formations in fluid sports like soccer.
method Two-step change-point detection: formation and role changes.
result Accurately detects tactical changes and estimates formation and role assignments.
Method reveals soccer teams' passing strategies from ball trajectories.
problem Inferring soccer teams' tactics from ball movement data.
method Dynamic Time Warping analysis of player and ball trajectories.
result Insights into passing strategies and individual player capabilities.
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.
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.
Paper uses GPS data and ML to forecast soccer injuries.
problem Injuries in soccer impact team performance and rehabilitation costs.
method Collects GPS training data, constructs injury forecaster using machine learning.
result Injury forecaster is accurate and interpretable, providing practical rules for injury prevention.
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.
Bayesian model improves win probability estimates in soccer.
problem Technical challenges in estimating win probability for soccer.
method Bayesian statistical framework using contextual game state features.
result Well-calibrated win, tie, and loss probabilities demonstrated.
RL controls small soccer robots in a real league, beating human-designed policies.
problem Training robots to play complex, real-world sports.
method Sim-to-Real RL approach, training in simulated environment, applying to real-world robots.
result Robots learned policies to compete effectively, beating human-designed strategies.
Study quantifies how soccer performance correlates with success.
problem Understanding the link between team performance and success in soccer.
method Analyzed 6,000 games and 10 million events from six European leagues.
result Team's position in final ranking is significantly related to its typical performance.
Optimal trading tactics for small asset purchases.
problem Finding the best way to buy a small amount of asset quickly.
method Order book model and optimization of expected utility function.
result The optimal trading tactic outperforms naive strategies.
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.
Paper introduces new metrics for evaluating player actions in soccer.
problem Traditional metrics focus on rare actions and ignore contextual information.
method New language and framework for valuing player actions considering context.
result Total offensive and defensive contributions can be quantified.
Predicts next actions in soccer possessions using path signatures.
problem Predicting next actions in soccer possessions with high accuracy.
method Leveraging path signatures to encode spatio-temporal structure of recent possessions, avoiding manual feature engineering.
result Our approach outperforms transformer-based benchmarks across various loss metrics and reduces computational cost.
TacticAI helps football coaches improve tactics by analyzing corner kicks.
problem Developing effective responses to rival teams' tactics algorithmically.
method TacticAI combines predictive and generative components to suggest player setups and position adjustments.
result TacticAI's model suggestions are favored over existing tactics 90% of the time by football domain experts.
Models predict soccer match outcomes with similar accuracy.
problem Predicting soccer match outcomes (win, draw, loss).
method Compared Bradley-Terry extensions and hierarchical Poisson log-linear model. Parameters estimated using log-likelihood or integrated nested Laplace approximations. Predictive performance assessed using temporal validation.
result Bradley-Terry extensions and hierarchical Poisson log-linear model perform similarly in predicting match outcomes.
Study predicts soccer player market values using machine learning and SHAP for interpretability.
problem Predicting accurate market values for professional soccer players.
method Ensemble machine learning models, SHAP for interpretability, Boruta for feature selection.
result GBDT model achieved high predictive accuracy (R-squared 0.901, RMSE 3,221,632.175).
This article provides a novel framework to evaluate limit order tactics that highlights expected fill price, adverse price selection cost, and opportunity cost. We formulate the problem of optimal execution of market orders with nonlinear market impact, power law decay kernel, and stochastic and deterministic liquidity…
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.
Machine learning automates proof generation in Coq proof assistant.
problem Automating proof construction in proof assistants.
method Developed ASTactic, a deep learning model generating tactics as ASTs.
result ASTactic can generate effective tactics and prove new theorems.
We propose a design for schedule-based execution trading strategies based on uncertainty bands. This formulation: 1) simplifies strategy specification and implementation; 2) provides for flexible allocation among passive, opportunistic, aggressive, and dark pool crossing execution tactics; 3) allows for rapid enhanceme…
We propose a reinforcement learning solution to the \emph{soccer dribbling task}, a scenario in which a soccer agent has to go from the beginning to the end of a region keeping possession of the ball, as an adversary attempts to gain possession. While the adversary uses a stationary policy, the dribbler learns the best…
The paper proposes a fast method to predict tactical solutions to operational problems under imperfect information.
problem Predicting tactical solutions to operational planning problems under imperfect information.
method Formulated as a two-stage optimal prediction stochastic program, solved with a supervised machine learning algorithm using training data from deterministic problems.
result Deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less).
Paper improves asset allocation using machine learning for regime detection.
problem Improving asset allocation strategies in uncertain economic conditions.
method Machine learning for regime detection, modified k-means algorithm, portfolio optimization.
result Significant portfolio performance improvements over traditional benchmarks.
xYOLO speeds up object detection on low-end hardware for humanoid soccer robots.
problem Real-time object detection on resource-constrained devices like Raspberry Pi.
method Adaptation of YOLO CNN model to achieve faster inference speed.
result Achieved 9.66 FPS on Raspberry Pi 3 B, 70x faster than Tiny-YOLO.
A hybrid machine learning model predicts soccer match scores for FIFA Women's World Cups.
problem Forecasting soccer match scores for FIFA Women's World Cups.
method Combining bookmaker consensus and team strength parameters in a random forest model.
result The model favors the defending champion USA over the host France.
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.
A reinforcement learning framework combining value function and tree search planner for strategic and tactical decisions.
problem Strategic and tactical decision-making in discrete environments.
method Combines value function and tree search planner, using uncertainty modeling and risk measurement.
result Improves performance and learning speed on hard exploration environments.
GamePad uses machine learning to help prove theorems in Coq proof assistant.
problem Automating theorem proving in interactive proof assistants.
method Synthesizes proofs and trains models for tactic prediction and position evaluation.
result Trained models predict proof steps and tactics in Coq theorem proving.
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.
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…
The paper proposes a fast method to predict operational solutions under uncertainty.
problem Predicting tactical descriptions of operational solutions in two-stage stochastic programming.
method Formulated as a stochastic optimal prediction program, solved with supervised machine learning.
result Deep learning models produce accurate predictions in milliseconds, close to lower bounds.
Two tactics to attack deep RL agents using adversarial examples, reducing reward and luring to target states.
problem Adversarial attacks on deep reinforcement learning agents to reduce their performance or guide them to specific states.
method Strategically-timed attack and enchanting attack methods using adversarial examples.
result Strategically-timed attack reduces reward by 4 times less often than uniform attack.
Fairness GAN generates fair datasets for decision making.
problem Creating fair datasets for allocative decision making.
method Novel auxiliary classifier GAN aiming for demographic parity or equality of opportunity.
result Improves demographic parity and equality of opportunity in generated images.
Investigates sports betting strategies using modern portfolio theory and Kelly criterion.
problem Mitigating risk in sports betting investments.
method Modern portfolio theory and Kelly criterion, with modifications for practical risk control.
result Adaptive fractional Kelly method is suitable across various sports settings.
Paper presents a method to efficiently learn ordered representations of multi-agent data.
problem Challenges in learning consistent representations of multi-agent interactions.
method Dynamic alignment method to order multi-agent data for faster representation learning.
result Representation learning of multi-agent data is significantly accelerated.
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
This paper compares forecasting techniques for sales data, focusing on profit-driven models.
problem Choosing the best forecasting technique for sales data is challenging.
method Compares various forecasting methods including ML, statistics, and econometrics.
result Simple seasonal models consistently outperform other methodologies.
Method reduces complexity of spatial interaction networks.
problem Complex spatial interaction networks.
method Empirical Bayes approach with tree partitioning and generalized double Pareto prior.
result Compact vectorial representations and interpretable visualizations.
New algorithms tackle multi-agent problems with hybrid action spaces.
problem Applying deep reinforcement learning to multi-agent problems with discrete-continuous hybrid action spaces.
method Proposed two novel algorithms: Deep MAPQN and Deep MAHHQN, using centralized training and decentralized execution.
result Empirical results show both algorithms significantly outperform existing methods.
Simultaneously estimates travel times and route choice model parameters.
problem Interdependent estimation of arc travel times and route choice model parameters.
method Maximum likelihood estimation for any differentiable route choice model.
result Strong performance in real-world data, even compared to arc travel time estimation methods.
Twitter has been proven to be a notable source for predictive modelling on various domains such as the stock market, the dissemination of diseases or sports outcomes. However, such a study has not been conducted in football (soccer) so far. The purpose of this research was to study whether data mined from Twitter can b…
Model for optimal execution with passive market impact.
problem Optimizing execution strategies in markets with passive price impact.
method Developed a mesoscopic model incorporating empirical price impact features.
result Obtained a passive impact rate that decays exponentially with quote distance.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
We prove that every spherical football (also known as a spherical soccer ball) is a branched cover, branched only in the vertices, of the standard football made up of 12 pentagons and 20 hexagons. We also give examples showing that the corresponding result is not true for footballs of higher genera. Moreover, we classi…
Geometric Brownian motion (GBM) is a model for systems as varied as financial instruments and populations. The statistical properties of GBM are complicated by non-ergodicity, which can lead to ensemble averages exhibiting exponential growth while any individual trajectory collapses according to its time-average. A com…
New method detects outliers in object-relational data.
problem Detecting outliers in complex object-relational data.
method Exceptional Model Mining framework applied to object-relational data with probabilistic models and Bayesian networks.
result Novel metric based on likelihood ratio improves outlier detection accuracy.
Minimalistic attacks reveal deep RL policies' vulnerabilities with little perturbation.
problem Tackling the vulnerability of deep reinforcement learning policies to minimal perturbations.
method Three key settings: black-box policy access, fractional-state adversary, and tactically-chanced attack. Formulated adversarial attacks on six Atari games.
result Deep RL policies can be significantly fooled by minimal perturbations, even in 0.01% of the input state.
AlphaZero assesses new chess variants for balance and dynamics.
problem Designing engaging and balanced game rules, especially for chess variants.
method Used AlphaZero to learn near-optimal strategies for nine chess variants.
result AlphaZero reveals novel strategic and tactical patterns in chess variants.