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.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
problem Complexity of sporting events requires multiple metrics for accurate analysis.
method Multi-task learning with fine-grain spatial data and wide-and-deep learning.
result Predictions are consistent and can be in distribution form.
Improved trajectory prediction for team sports using sparse outputs.
problem Challenging to train deep learning models for player trajectory prediction.
method Sparse trajectory prediction and constant acceleration interpolation.
result Interpolation improves performance for all tested models.
Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of par…
This paper optimizes sports betting strategies using neural networks and portfolio theory.
problem Optimizing betting strategies in sports gambling.
method Combining neural network models with portfolio optimization, integrating Von Neumann-Morgenstern Expected Utility Theory and the Kelly Criterion.
result Achieved 135.8% relative profit during the English Premier League season.
Paper simplifies complex sports analytics models for better understanding.
problem Difficulty in interpreting deep learning models for sports analytics.
method Developed a linear model tree that mimics deep learning models and explains its knowledge.
result The linear model tree achieves high accuracy and provides actionable insights.
Algorithm beats sports betting markets, showing inefficiencies.
problem Inefficiencies in sports betting markets.
method Created a betting algorithm using a novel dataset and win probability model.
result Above market returns for various sports betting markets.
Technology has had an unquestionable impact on the way people watch sports. Along with this technological evolution has come a higher standard to ensure a good viewing experience for the casual sports fan. It can be argued that the pervasion of statistical analysis in sports serves to satiate the fan's desire for detai…
Decentralized prediction markets use AMMs to pool and withdraw liquidity, improving financial properties.
problem Creating a fair and efficient decentralized prediction market.
method Developed a liquidity-based AMM structure for prediction markets, studied liquidity management, and proposed trading fees.
result The decentralized AMM structure satisfies financial properties and can be managed with liquidity withdrawal.
The paper analyzes sports commentary to automatically recognize events and extract insights.
problem Automatically recognizing and categorizing major actions in sports events from commentary.
method Used multiple Natural Language Processing techniques for classification and sentiment analysis.
result Identified insights from analyzing live sport commentaries and classifying major actions.
Machine learning predicts US will win most Olympic medals in 2020.
problem Improve Olympic medal forecasting accuracy.
method Two-stage Random Forest machine learning model.
result Model outperforms traditional forecasts for three previous Olympics.
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.
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
problem Analyzing patterns of wins and losses in games and social hierarchies.
method Generalized probabilistic models incorporating luck and depth of competition.
result Social competition tends to be deeper with many distinct levels, but there is often a chance of upset victories.
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.
Bayesian rating system for large competitions improves prediction and efficiency.
problem Rating systems for large, competitive events like online programming contests.
method Developed a Bayesian rating system for many participants, proving robustness and runtime.
result The system outperforms existing systems in accuracy and computation speed.
The paper proposes using experts' insights in machine learning tasks.
problem Lack of reliance on experts' opinions in machine learning tasks.
method A three-step framework for integrating experts' insights into machine learning models.
result Proposed models achieve more stable and lower log loss results compared to top solutions.
Wearables like smartwatches which are embedded with sensors and powerful processors, provide a strong platform for development of analytics solutions in sports domain. To analyze players' games, while motion sensor based shot detection has been extensively studied in sports like Tennis, Golf, Baseball; Table Tennis and…
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.
Blockchain fan tokens boost sports fan engagement by 50%.
problem Low fan engagement in sports decisions.
method Analyzed 3,576 fan token polls to identify determinants of participation.
result Fan tokens engage 4,003 participants per poll, 50% of token holders.
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.
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replacing the static parameters of a class of popular pairwise-compari…
Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal Élő update still remains, after more than 50 years, a valuable baseline which is difficult to…
A simplified Bayesian approach for online sports rating.
problem Rating players/teams based on game outcomes.
method Bayesian approach using Kalman filter principles.
result Known algorithms like Elo and TrueSkill are special cases.
Comparison data arises in many important contexts, e.g. shopping, web clicks, or sports competitions. Typically we are given a dataset of comparisons and wish to train a model to make predictions about the outcome of unseen comparisons. In many cases available datasets have relatively few comparisons (e.g. there are on…
New methods for skill rating in sports using state-space models.
problem Improving skill rating in competitive sports.
method State-space models, sequential Monte Carlo, discrete hidden Markov models.
result Advantages of state-space models for time-varying player skills.
We provide scientific foundations for athletic performance prediction on an individual level, exposing the phenomenology of individual athletic running performance in the form of a low-rank model dominated by an individual power law. We present, evaluate, and compare a selection of methods for prediction of individual …
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…
New method learns time-varying home field advantage in football.
problem Discovering causal factors behind home field advantage in sports.
method DYNAMO: a novel causal discovery method for non-stationary processes.
result Time-varying home field advantages influenced by referee bias.
Study shows news from various topics impacts Nifty 50 index.
problem Lack of analysis on news impact on Nifty 50 index.
method Analyzed Nifty 50 index movement with sentiments from diverse news topics.
result Sentiment scores from different topics significantly impact Nifty 50 index.
Bayesian taut splines estimate modes in probability densities.
problem Estimating the number of modes in probability density functions.
method Bayesian inference with flexible kernel estimators and compositional splines.
result The new method provides more accurate results than traditional approaches.
This short note is intended as a "Letter to the Editor" Perspective in order that it serves as a contribution, in view of reaching the physics community caring about rare events and scaling laws and unexpected findings, on a domain of wide interest: sport and money. It is apparent from the data reported and discussed b…
BBE simulates betting exchanges to generate synthetic data for AI research.
problem Lack of real data for AI/ML in betting exchanges.
method Agent-based simulation model of a sports-betting exchange.
result Generates large, high-resolution synthetic data for AI/ML.
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end…
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).
In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…
PredictionMarketBench benchmarks trading agents on prediction markets.
problem Evaluating trading agents on prediction markets with realistic conditions.
method Deterministic replay of historical data, execution-realistic simulator, agent interface.
result Fee-aware algorithmic strategies outperform naive agents in volatile episodes.
Study proposes new methods to convert betting odds into accurate probabilities for sports forecasting.
problem Convert betting odds to accurate outcome probabilities for sports forecasting and market efficiency analysis.
method Proposes two methods: Odds-Only-Equal-Profitability-Confidence (OO-EPC) and Favourite-Longshot-Bias-Adjusted Generalised Linear Model (FL-GLM).
result Proposed methods outperform existing methods in empirical tests and real-world applications.
A new optimizer, MVO, improves nonlinear regression performance.
problem Finding optimal coefficients in nonlinear regression models.
method Multi-Verse Optimizer (MVO) compared to Particle Swarm Optimizer (PSO).
result MVO statistically outperforms PSO in 10 nonlinear regression problems.
Accurately predicting the outcome of sporting events has been a goal for many groups who seek to maximize profit. What makes this challenging is that the outcome of an event can be influenced by many factors that dynamically change across time. Oddsmakers attempt to estimate these factors by using both algorithmic and …
BBE simulates sports betting exchanges for data generation.
problem Creating synthetic data for betting strategy testing.
method Agent-based model (ABM) for sports betting exchange simulation.
result Simulation runs up to 1000 times faster with GPU.
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.
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…
Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (GP) as a prior for the time dynamics rather than the Markovian dynamics usually assumed. In addition,…
Predicts next item in sequential bundles using Transformers.
problem Predicting next item in sequentially consumed bundles.
method Used custom Transformers, GPT-3, LSTM, reinforcement learning, Markov models.
result Custom Transformer with decoder-only architecture most accurate.
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system's constituent parts. In this work, we introduce the neural relational inference (NRI…
Deep learning predicts baseball home runs with better accuracy.
problem Predicting player performance in baseball.
method Long Short-Term Memory (LSTM) neural network.
result LSTM outperformed other models in predicting home runs.
Olympic Games consistently exceed budgets, leading to unpredictable costs.
problem High costs and unpredictability of the Olympic Games.
method Statistical analysis of historical data to explain cost risks.
result Olympic costs follow a power-law distribution with infinite mean and variance.
Study confirms mispricing in sportsbooks but finds data issues affect results.
problem Mispricing and inefficiency in online sportsbooks.
method Replication using Wikipedia buzz factor metric and backtesting.
result Data quality issues affect betting results; only one strategy remains profitable.