U19 Bundesliga 1st Group Stage Group G stats & predictions

Betting Strategies for Beginners

If you're new to betting on youth football, start by understanding basic strategies that can help you make informed decisions:

• Analyzing Form: Review recent performances of teams and players to gauge their current form and potential outcomes.
• Total Goals Bet: Consider betting on the total number of goals scored in a match if you expect high-scoring encounters.
• Hedging Bets: Place multiple bets across different outcomes to minimize risks while maximizing potential returns.

Advanced Betting Techniques

For experienced bettors looking to refine their approach, consider these advanced techniques:

• In-Play Betting: Monitor matches live and place bets based on real-time developments and player performances.jccolucci/PRC2019<|file_sep|>/R/Plot_Histogram.R #' @title Histogram Plot #' #' @description Histogram Plot #' #' @param x Data #' @param ... Further arguments passed into code{link{hist}} #' #' @return code{NULL} #' @export #' #' @examples #' data("mtcars") #' Plot_Histogram(mtcars$mpg) Plot_Histogram <- function(x,...){ # Create Histogram # x - data hist(x,...) # Return NULL return(invisible(NULL)) } <|file_sep|># PRC2019 Repo containing materials from my presentation at R Consortium Global Conference Europe held in Prague (Czech Republic) during October 2019. This repo contains: * Folder "R" containing R scripts developed during my talk. * Folder "Data" containing example datasets used during my talk. * Folder "Materials" containing all slides used during my presentation. I am available at: https://www.linkedin.com/in/jccolucci/ <|repo_name|>jccolucci/PRC2019<|file_sep|>/Materials/README.md # PRC2019 Repo containing materials from my presentation at R Consortium Global Conference Europe held in Prague (Czech Republic) during October 2019. This folder contains all slides used during my presentation. I am available at: https://www.linkedin.com/in/jccolucci/ <|file_sep|>#' @title Plot Pie Chart #' #' @description Plot Pie Chart #' #' @param x Data vector or matrix. #' @param labels Vector of labels. #' @param main Main title. #' @param col Vector of colors. #' #' @return code{NULL} #' @export #' #' @examples #' data("mtcars") #' Plot_PieChart(mtcars$am) Plot_PieChart <- function(x, labels = NULL, main = "Pie Chart", col = rainbow(length(x))){ # Create Pie Chart # x - data vector or matrix. pie(x, labels = labels, main = main, col = col) # Return NULL return(invisible(NULL)) } <|repo_name|>jccolucci/PRC2019<|file_sep|>/R/Plot_Legend.R #' @title Legend Plot #' #' @description Legend Plot #' #' @param legend Legend Texts. #' @param title Title. #' #' @return code{NULL} #' @export #' #' @examples #' data("mtcars") #' Plot_Legend(legend = c("Amateur","Professional"), #' title = "Legend") Plot_Legend <- function(legend, title){ # Create Legend plot legend("topleft", legend = legend, bty = "n", cex = .7, title = title) # Return NULL return(invisible(NULL)) } <|file_sep|>#' @title Boxplot Plot #' #' @description Boxplot Plot #' #' @param x Data. #' @param y Data. #' #' @return code{NULL} #' @export #' #' @examples #' data("mtcars") #' Plot_Boxplot(mtcars$mpg, #' mtcars$cyl) Plot_Boxplot <- function(x, y){ par(mfrow=c(1,2)) boxplot(x ~ y, xlab="Cylinders", ylab="Miles/(US) gallon", main="Miles/(US) gallon by Number of Cylinders") boxplot(y ~ x, xlab="Miles/(US) gallon", ylab="Cylinders", main="Number of Cylinders by Miles/(US) gallon") par(mfrow=c(1,1)) # Return NULL return(invisible(NULL)) } <|file_sep|>#'@title Scatter Plot #'# Description: Scatter Plot #'# Arguments: ## x - numeric vector. ## y - numeric vector. ## main - plot title. ## xlab - x-axis label. ## ylab - y-axis label. #'# Examples: ## data("mtcars") ## Scatter_Plot(mtcars$mpg, ## mtcars$hp, ## main="Scatter Plot", ## xlab="Miles/(US) gallon", ## ylab="Gross horsepower") Scatter_Plot <- function(x,y=NULL, main=NULL,xlab=NULL,ylab=NULL){ if(is.null(y)){ plot(x,type='o',col='blue',axes=FALSE,xlab=xlab,ylab=ylab) axis(1,at=1:length(x),labels=x) axis(2) box() title(main) return(invisible(NULL)) }else{ plot(x,y,type='o',pch=20,col='blue',xlab=xlab,ylab=ylab) title(main) return(invisible(NULL)) } }<|repo_name|>jccolucci/PRC2019<|file_sep|>/R/Plot_Bar.R library(ggplot2) library(ggpubr) library(cowplot) library(RColorBrewer) library(tidyverse) library(dplyr) library(scales) source(file.path("R","Create_Big_Data.R")) source(file.path("R","Create_Small_Data.R")) data <- Create_Big_Data() data_small <- Create_Small_Data() Plot_Bar <- function(data){ data %>% group_by(Gender) %>% summarise(Sum_Age = sum(Age)) %>% ggplot(aes(x=Gender,y=Sum_Age)) + geom_bar(stat="identity",aes(fill=Gender),position="dodge") + scale_fill_brewer(palette="Dark2") + xlab("Gender") + ylab("Sum Age") + theme_classic() + theme(axis.title.x = element_text(size=20), axis.title.y = element_text(size=20), axis.text.x = element_text(size=15), axis.text.y = element_text(size=15)) } Plot_Bar(data_small) <|repo_name|>jccolucci/PRC2019<|file_sep|>/R/Create_Small_Data.R Create_Small_Data <- function(){ data_small <- tibble( Gender=c(rep("Male",5), rep("Female",5)), Age=c(rep(c(25:29),2))) data_small %>% print(n=nrow(data_small)) return(data_small) } <|file_sep|>#'@title Bar Chart Plot #'# Description: Bar Chart Plot #'# Arguments: ## x - data vector or factor. ## col - vector of colors. #'# Examples: ## data("mtcars") ## Bar_Chart_Plot(mtcars$cyl,col=rainbow(7)) Bar_Chart_Plot <- function(x,col){ barplot(table(x), col=col, main="Bar Chart", xlab="Number of Cylinders", ylab="Frequency") } <|repo_name|>jccolucci/PRC2019<|file_sep|>/Materials/Slides.tex documentclass[10pt]{beamer} usepackage[utf8]{inputenc} usepackage{graphicx} usepackage{ragged2e} usepackage{booktabs} usepackage{multirow} usetheme{Warsaw} setbeamertemplate{footline}[frame number] %---------------------------------------------------------------------------------------- % TITLE PAGE %---------------------------------------------------------------------------------------- title[R] % The short title appears at the bottom of every slide, the full title is only on the title page {Visualizing Big Data} author[John Colucci] % Your name { John Colucci \[1mm] % Your institution %texttt{[email protected]} } institute[University] % Your institution as it will appear on the bottom of every slide, may be shorthand to save space { University \[1mm] } date{today} % Date, can be changed to a custom date begin{document} begin{frame} titlepage % Print the title page as the first slide end{frame} %---------------------------------------------------------------------------------------- % PRESENTATION SLIDES %---------------------------------------------------------------------------------------- %------------------------------------------------ begin{frame}{About Me} vspace{-0.8cm} begin{itemize} item PhD Student at University of Brasília (Brazil) item Masters degree in Statistics at University of Brasília (Brazil) item Bachelors degree in Statistics at University Federal Fluminense (Brazil) item Research interests: Big Data visualization techniques; Machine Learning algorithms; Text Mining. item LinkedIn: url{https://www.linkedin.com/in/jccolucci} item GitHub: url{https://github.com/jccolucci} end{itemize} vspace{-0.5cm} centering includegraphics[width=0.6textwidth]{img/linkedin.png}hspace{-0.5cm}includegraphics[width=0.6textwidth]{img/github.png} vspace{-0.5cm} centering Thank you! end{frame} %------------------------------------------------ begin{frame}{Big Data Visualization Techniques: What? Why? How?} vspace{-0.8cm} noindentrule{linewidth}{1pt} vspace{-0.5cm} Big Data Visualization Techniques: vspace{-0.5cm} noindentrule{linewidth}{1pt} vspace{-0.5cm} What? vspace{-0.5cm} noindentrule{linewidth}{1pt} vspace{-0.5cm} Why? vspace{-0.5cm} noindentrule{linewidth}{1pt} vspace{-0.5cm} How? vspace{-0.5cm} noindentrule{linewidth}{1pt} vspace{-0.8cm} Thank you! end{frame} %------------------------------------------------ begin{frame}{What?} vspace{-0.8cm} noindentrule{linewidth}{1pt} What are Big Data Visualization Techniques? vspace{-0.5cm} noindentrule{linewidth}{1pt} Techniques that allow us visualizing Big Data. vspace{-0