arXiv research

The study analyzes games and social hierarchies, incorporating luck and depth of competition.

Bayesian rating system for large competitions improves prediction and efficiency.

New methods for skill rating in sports using state-space models.

Framework for real-time win probability and player ability in sports.

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…

The paper proposes using experts' insights in machine learning tasks.

Algorithm beats sports betting markets, showing inefficiencies.

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…

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…

FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.

The paper analyzes sports commentary to automatically recognize events and extract insights.

This review explores ML in predicting team sport outcomes, identifying successful strategies and themes.

Study proposes new methods to convert betting odds into accurate probabilities for sports forecasting.

GNNRank uses neural networks to learn global rankings from competition match data.

This paper optimizes sports betting strategies using neural networks and portfolio theory.

Investigates sports betting strategies using modern portfolio theory and Kelly criterion.

New method ranks competitors from multiple types of comparisons.

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…

Paper simplifies complex sports analytics models for better understanding.

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…

Blockchain fan tokens boost sports fan engagement by 50%.

Study confirms mispricing in sportsbooks but finds data issues affect results.

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,…

A simplified Bayesian approach for online sports rating.

Rugby-Bot predicts multiple metrics from a single source using fine-grain data.

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6,000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's…

Improved trajectory prediction for team sports using sparse outputs.

Random forest model predicts tennis match outcomes with 80% accuracy.

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition t…

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical propo…

BBE simulates betting exchanges to generate synthetic data for AI research.

Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. Although vision-based object tracking techniques have been developed to analyze sport competition videos, it is still challenging to recognize and position a high-speed …

Machine learning predicts US will win most Olympic medals in 2020.

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,…

Given a measurement graph $G= (V,E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measurements of the form $r_i - r_j$; $\{i,j\} \in E$. This problem arises in a variety of applications, such as ranking teams in sports data and time synchronization of distribute…

PredictionMarketBench benchmarks trading agents on prediction markets.

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 …

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector $w^* \in \mathbb{R}^d$ that represents the "qualities" of the $d$ ite…

BBE simulates sports betting exchanges for data generation.

Hierarchical MARL learns complementary skills for team coordination.

Decentralized prediction markets use AMMs to pool and withdraw liquidity, improving financial properties.

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…

Study uses complex networks and machine learning to predict soccer match outcomes.

Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we pr…

Kernel-based machine learning approaches are gaining increasing interest for exploring and modeling large dataset in recent years. Gaussian process (GP) is one example of such kernel-based approaches, which can provide very good performance for nonlinear modeling problems. In this work, we first propose a grey-box mode…

We generalize Mallows model to learn distance metrics from data.

Study shows news from various topics impacts Nifty 50 index.

Bayesian taut splines estimate modes in probability densities.