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.
Study examines heart and football-shaped metrics, verifying geometric structure.
Study uncovers tactical line-breaking passes in football using clustering.
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…
The study uses unsupervised machine learning to identify top European football teams.
The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the rec…
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research Football Environment, a new reinforcement learning environment wher…
Compact Ricci solitons on surfaces have at most two cone points, and are known as Hamilton's footballs. In this note we completely describe the degenerations of these footballs as one or both of the cone angles approaches zero. In particular, we show that Hamilton's famous non-compact cigar soliton is the Gromov--Hausd…
Optimizes football play calls using reinforcement learning.
New framework values football players based on in-game interactions.
TacticAI helps football coaches improve tactics by analyzing corner kicks.
AI benchmarks evaluate football team performance using generative models.
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…
Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and adv…
Bray's football theorem (\cite{bray2009penrose}) is a weakening of Bishop theorem in dimension 3. It gives a sharp volume upper bound for a three dimensional manifold with scalar curvature larger than and Ricci curvature larger than . This paper extends Bray's football theorem in high dimensions, …
Paper finds new realizable data for maps with three branch points.
A risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricing are applied to this set-up which enables us to derive novel arbitrage-free valuation formulæ for c…
SciSports is a Dutch startup company specializing in football analytics. This paper describes a joint research effort with SciSports, during the Study Group Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main challenge that we addressed was to automatically process empirical football players' traject…
Improved fantasy football performance predictor using human feedback.
We show that spheres of positive constant curvature with () conic points converge to a sphere of positive constant curvature with two conic points (or called an (American) football) in Gromov-Hausdorff topology when the corresponding singular divisors converge to a critical divisor in the sense of Troyanov.…
New method learns time-varying home field advantage in football.
To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from elite athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and …
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the cost of experiments compared to current methods. We achieve this with a …
The study predicts pass completion probability in NFL games.
Rational maps structure theorem with geometric decomposition and realizability proof.
This paper studies the normalized Ricci flow on surfaces with conical singularities. It's proved that the normalized Ricci flow has a solution for a short time for initial metrics with conical singularities. Moreover, the solution makes good geometric sense. For some simple surfaces of this kind, for example, the tear …
Paper presents Transfer Portal model for accurate player performance predictions.
In this paper, we study the discrete Morse flow for the Ricci flow on football, which is the 2-sphere with removed north and south poles and with the metric of constant scalar curvature, and and for Porous media equation on a bounded regular domain in the plane. We show that with a suitable assumption about $g(0)…
Study on spherical conical metrics and their reducibility on compact Riemann surfaces.
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…
Counting HCMU sphere components using weighted trees.
Study proposes new methods to convert betting odds into accurate probabilities for sports forecasting.
Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outsi…
Solves Cheltsov-Rubinstein problem for complex surfaces with two boundary components.
Geomstats introduces shape module for analyzing shapes of objects.
A new shape space allows optimization of non-smooth shapes in fluid mechanics.
Introduces Star-Shaped deviation measures for risk analysis.
Optimizes shapes in uncertain Navier-Stokes flow problems.
New method reconstructs 3D shapes from 2D images using Kendall's shape space.
Newly discovered Eguchi-Hanson metric arises from edge metrics.
A novel method predicts shape development using Riemannian shape spaces.
This paper tackles shape denoising in computer vision and medical imaging.
In shape analysis, the concept of shape spaces has always been vague, requiring a case-by-case approach for every new type of shape. In this paper, we give a general definition for an abstract space of shapes in a manifold. This notion encompasses every shape space studied so far in the literature, and offers a rigorou…
Paper analyzes shapes of brain arterial networks using statistical methods.
Develops new shape metrics for high-dimensional objects.
Paper tackles shape graph registration using neural networks.
In this paper, we describe a novel shape classification method which is embedded in the Bayesian paradigm. We discuss the modelling and the resulting shape classification algorithm for two and three dimensional data shapes. We conclude by evaluating the efficiency and efficacy of the proposed algorithm on the Kimia sha…
Defines finite type Multivalued Shape using hyperspaces.
The paper explores three methods to assign a metric to shape spaces.