In association football, predicting the likelihood and outcome of a shot at a goal is useful but challenging. Expected goal (xG) models can be used in a variety of ways including evaluating performance and designing offensive strategies. This study proposed a novel framework that uses the events preceding a shot, to improve the accuracy of the expected goals (xG) metric. A combination of previously explored and unexplored temporal features is utilized in the proposed framework. The new features include; “advancement factor”, and “player position column”. A random forest model was used, which performed better than published single-event-based models in the literature. Results further demonstrated a significant improvement in model performance with the inclusion of preceding event information. The proposed framework and model enable the discovery of event sequences that improve xG, which include; opportunities built up from the sides of the 18-yard box, shots attempted from in front of the goal within the opposition’s 18-yard box, and shots from successful passes to the far post.

XG

## Overview

XG is a dataset for object detection tasks - it contains Objects annotations for 340 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Test data results for comparison between expected goals statistic and traditional metrics.

Dataset

This dataset was created by ShyamSUBEDI

Released under MIT

Contents

The AUC ROC for the optimal model in this research used test data, and used players’ FIFA ratings as a proxy for player ability.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

These are the measurements (S-parameters and farfield patterms) of the prototype discussed in Chapter 4 of the PhD thesis Advanced Electromagnetic Modelling of the Next Generation (XG) Wireless Communication Systems.

League positions resulting in specific consequences for teams in each league.

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

The postglacial rebound is component of the glacial isostatic adjustment which causes the Earth's crust to rebound in regions formerly covered by or adjacent to ice sheets, and subside beneath ocean basins. In North America, the observed postglacial rebound is mainly the result of the Laurentide Ice Sheet deglaciation after it reached its maximum thickness and extent at the Last Glacial Maximum (26.5-19 ka). Global-scale numerical models of glacial isostatic adjustment faithfully reproduce past and current changes in postglacial rebound, but the integration their predictions in a geographic information system to facilitate high-resolution paleotopographic reconstructions remains challenging. We therefore present high-resolution raster datasets of land-deformation and ice-free paleotopography of glaciated North America for several time slices since the Last Glacial Maximum to support geological, paleoenvironmental and archeological studies.

Lasi

## Overview

Lasi is a dataset for object detection tasks - it contains XG annotations for 384 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A comparative analysis of DC, CTGAN-DC, XGBoost, CTGAN-XG, and TVAE-XG models in Kawasaki Disease experiments.

Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

The data and script has been uploaded to GitHub. It can be accessed using the following link: https://github.com/AzazHassankhan/Machine-Learning-based-Trading-Techniques/. (IPYNB)

In this work, the fractional mathematical model of an unsteady rotational flow of Xanthan gum (XG) between two cylinders in the presence of a transverse magnetic field has been studied. This model consists of two fractional parameters α and β representing thermomechanical effects. The Laplace transform is used to obtain the numerical solutions. The fractional parameter influence has been discussed graphically for the functions field distribution (temperature, velocity, stress and electric current distributions). The relationship between the rotation of both cylinders and the fractional parameters has been discussed on the functions field distribution for small and large values of time.

The influence of the inner region Rin and peak on the velocity at α = β = 1 for different time t.

The constants of the problem.

Comparative analysis of previous and proposed study.