Football 1. Division Andorra: A Thrilling Preview of Tomorrow's Matches
The excitement in Andorra is palpable as fans eagerly anticipate the upcoming matches in the 1. Division. With teams fiercely competing for supremacy, tomorrow promises to be a day filled with intense football action. This guide provides expert betting predictions and insights into the key clashes, ensuring you are well-informed before placing your bets.
Key Match-ups and Expert Predictions
Tomorrow's fixtures in the Andorran 1. Division feature some of the most anticipated match-ups of the season. Let's delve into the details of each game and explore expert predictions to help you make informed betting decisions.
Atlètic Club d'Escaldes vs. FC Encamp
This clash between two of Andorra's top teams is expected to be a closely contested affair. Atlètic Club d'Escaldes, known for their solid defense, will face a formidable challenge against FC Encamp's attacking prowess.
- Atlètic Club d'Escaldes: With a strong defensive record, they are likely to focus on maintaining their lead at home. Their key player, Marc Pujol, is expected to play a pivotal role in orchestrating attacks from midfield.
- FC Encamp: Known for their aggressive attacking style, FC Encamp will look to exploit any gaps in the opposition's defense. Their forward, Jordi Aramany, is in top form and could be crucial in breaking down Atlètic's defense.
Betting Prediction: A draw seems likely given the balanced nature of both teams. Consider betting on a low-scoring draw (1-1) or under 2.5 goals.
Sant Julià vs. Inter Club d'Escaldes
This match is set to be a thrilling encounter as Sant Julià aims to continue their winning streak against Inter Club d'Escaldes.
- Sant Julià: Riding high on confidence after recent victories, Sant Julià will look to dominate possession and control the tempo of the game. Their midfield maestro, Alex Dominguez, is expected to be instrumental in linking defense and attack.
- Inter Club d'Escaldes: Despite recent setbacks, Inter Club d'Escaldes will be determined to bounce back with a strong performance at home. Their defender, Raul Martinez, will be crucial in thwarting Sant Julià's attacking threats.
Betting Prediction: Sant Julià's recent form suggests they have the upper hand. A safe bet might be on Sant Julià to win with both teams scoring.
Casa Estrella del Benfica vs. UE Engordany
In this intriguing matchup, Casa Estrella del Benfica will host UE Engordany in a battle that could determine their positions in the league table.
- Casa Estrella del Benfica: Known for their resilient performances at home, they will aim to capitalize on their home advantage. Their striker, Ivan Perez, has been in excellent form and could be key in securing a victory.
- UE Engordany: UE Engordany will travel with high hopes of securing points away from home. Their tactical discipline and counter-attacking style could pose problems for Casa Estrella del Benfica.
Betting Prediction: A close match with potential for goals from both sides. Consider betting on over 2.5 goals or a specific player bet on Ivan Perez to score.
Analyzing Team Form and Performance
To make well-rounded betting predictions, it's essential to analyze the current form and performance trends of the teams involved.
Atlètic Club d'Escaldes: Defensive Solidity
Atlètic Club d'Escaldes has been impressive defensively, conceding few goals throughout the season. Their ability to maintain a clean sheet could be pivotal in their upcoming match against FC Encamp.
FC Encamp: Attacking Flair
FC Encamp has been prolific in front of goal, with several players contributing to their impressive tally. Their attacking flair makes them dangerous opponents for any team.
Sant Julià: Consistent Performers
Sant Julià has shown consistency in their performances, often controlling games through possession and tactical discipline. Their ability to adapt to different match situations is commendable.
Inter Club d'Escaldes: Resilient Defenders
Despite recent challenges, Inter Club d'Escaldes has shown resilience in defense, often keeping games tight and competitive.
Casa Estrella del Benfica: Home Fortress
Casa Estrella del Benfica has been formidable at home, leveraging their home crowd support to secure vital wins.
UE Engordany: Tactical Discipline
UE Engordany's tactical discipline and ability to execute counter-attacks effectively make them a tough opponent on any given day.
Betting Strategies for Tomorrow's Matches
Betting on football requires a strategic approach. Here are some tips to enhance your betting experience:
- Analyze Head-to-Head Records: Understanding past encounters between teams can provide insights into potential outcomes.
- Consider Injuries and Suspensions: Player availability can significantly impact team performance. Stay updated on injury reports and suspensions.
- Bet on Key Players: Placing bets on individual player performances can offer exciting opportunities for higher returns.
- Diversify Your Bets: Spread your bets across different matches and outcomes to minimize risk and maximize potential rewards.
- Stay Informed: Keep abreast of any last-minute changes or developments that could influence match outcomes.
Tactical Insights into Key Match-ups
Tactics play a crucial role in determining match outcomes. Let's explore the tactical setups expected from the key teams tomorrow:
Atlètic Club d'Escaldes vs. FC Encamp
- Tactical Setup: Atlètic Club d'Escaldes may adopt a compact defensive formation to neutralize FC Encamp's attacking threats. Expect them to rely on quick counter-attacks through Marc Pujol's precise passing.
- Potential Game-Changers: Look out for set-pieces as potential game-changers in this tightly contested match.
Sant Julià vs. Inter Club d'Escaldes
- Tactical Setup: Sant Julià is likely to dominate possession with a fluid passing game orchestrated by Alex Dominguez. They may deploy wingers to stretch Inter Club d'Escaldes' defense.
- Potential Game-Changers: Inter Club d'Escaldes could exploit spaces left by Sant Julià's attacking full-backs through quick transitions led by Raul Martinez.
Casa Estrella del Benfica vs. UE Engordany
- Tactical Setup: Casa Estrella del Benfica might adopt an aggressive pressing strategy to disrupt UE Engordany's build-up play. Ivan Perez will be central to breaking down defenses with his clinical finishing.
- Potential Game-Changers: UE Engordany's counter-attacking prowess could catch Casa Estrella off-guard if they overcommit forward.
Predicted Outcomes and Betting Tips
Based on our analysis, here are some predicted outcomes and betting tips for tomorrow's matches:
- Atlètic Club d'Escaldes vs. FC Encamp: Expect a tightly contested match with both teams creating chances. A low-scoring draw (1-1) or under 2.5 goals seems likely.
- Sant Julià vs. Inter Club d'Escaldes: Sant Julià's form suggests they have the edge, but don't rule out Inter Club scoring from a set-piece or counter-attack. Bet on Sant Julià win with both teams scoring.
- Casa Estrella del Benfica vs. UE Engordany: A high-scoring affair is possible if both teams commit players forward. Consider betting on over 2.5 goals or Ivan Perez to score anytime during the match.
In-Depth Player Analysis
<|repo_name|>SeymourHui/Healthcare-Policy<|file_sep|>/Draft.md
---
title: "Draft"
author: "Seymour Hui"
date: "12/17/2019"
output:
word_document:
toc: yes
toc_depth: '3'
fig_caption: yes
reference_docx: template-reference.docx
---
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
{r}
library(tidyverse)
library(readxl)
library(haven)
library(knitr)
library(kableExtra)
library(formattable)
library(gt)
library(scales)
# Introduction
The purpose of this paper is three-fold:
1) To estimate how much additional healthcare spending per capita would occur if we expanded Medicare coverage nationwide;
2) To compare these costs with current spending per capita;
3) To discuss what these findings mean for Medicare expansion proposals such as Medicare for All.
The methodology I use here is simple:
1) Estimate how much more we would spend per person if we expanded Medicare coverage nationwide;
2) Compare these costs with current spending per person;
3) Discuss what these findings mean for Medicare expansion proposals such as Medicare for All.
The estimates I use come from two sources:
* The first is from [this report](https://www.cbo.gov/publication/55651), which estimates how much more we would spend per person if we expanded Medicare coverage nationwide.
* The second is from [this report](https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical), which estimates current spending per person.
I use data from these reports rather than conducting my own analysis because these reports provide estimates that are consistent across all states (including spending differences between Medicaid enrollees versus non-Medicaid enrollees). If I conducted my own analysis using data from state health agencies (as I initially intended), I would not have been able to conduct this analysis consistently across states.
In addition, I use [this paper](https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.wps109), which compares how much different types of insurance pay providers versus how much providers charge privately insured patients.
This paper does not attempt:
* To estimate how much more efficient Medicare would be than private insurance companies;
* To estimate how much money private insurance companies would save if we expanded Medicare coverage nationwide;
* To estimate whether expanding Medicare coverage nationwide would lead providers (such as hospitals) or drug companies (such as Gilead) making less money;
* To estimate whether expanding Medicare coverage nationwide would lead private insurance companies making less money.
# How Much Would We Spend Per Person if We Expanded Medicare Coverage Nationwide?
I estimated how much more we would spend per person if we expanded Medicare coverage nationwide by using data from [this report](https://www.cbo.gov/publication/55651). The table below shows how much more we would spend per person by state if we expanded Medicare coverage nationwide:
{r}
Medicare_Expansion <- read_csv("Medicare_Expansion.csv") %>%
select(-X1) %>%
mutate(state = recode(state,
AL = "Alabama",
AK = "Alaska",
AZ = "Arizona",
AR = "Arkansas",
CA = "California",
CO = "Colorado",
CT = "Connecticut",
DE = "Delaware",
DC = "District of Columbia",
FL = "Florida",
GA = "Georgia",
HI = "Hawaii",
ID = "Idaho",
IL = "Illinois",
IN = "Indiana",
IA = "Iowa",
KS = "Kansas",
KY = "Kentucky",
LA = "Louisiana",
ME = "Maine",
MD = "Maryland",
MA = "Massachusetts",
MI = "Michigan",
MN = "Minnesota",
MS = "Mississippi",
MO = "Missouri",
MT = "Montana",
NE = "Nebraska",
NV = "Nevada",
NH = "New Hampshire",
NJ = "New Jersey",
NM = "New Mexico",
NY = "New York",
NC = "North Carolina",
ND = "North Dakota",
OH = "Ohio",
OK = "Oklahoma",
OR = "Oregon",
PA="Pennsylvania",
RI="Rhode Island",
SC="South Carolina",
SD="South Dakota",
TN="Tennessee",
TX="Texas",
UT="Utah",
VT="Vermont",
VA="Virginia",
WA="washington",
WV="West Virginia",
WI="Wisconsin",
WY="Wyoming"))
Medicare_Expansion %>%
select(state,population,cost_change_per_person,cost_change_total) %>%
mutate(cost_change_per_person_dollars=cost_change_per_person*1000,
cost_change_total_dollars=cost_change_total*1000,
population_dollars=population*cost_change_per_person_dollars,
population_percentage=population/population[which(state=="Oklahoma")],
population_percentage_rounded=round(population_percentage),
cost_change_per_person_dollars_rounded=format(round(cost_change_per_person_dollars),
big.mark=","),
cost_change_total_dollars_rounded=format(round(cost_change_total_dollars),
big.mark=","),
population_dollars_rounded=format(round(population_dollars),
big.mark=","),
population_percentage_rounded=format(round(population_percentage_rounded),
big.mark=","),
state=as_factor(state)) %>%
arrange(-cost_change_per_person) %>%
filter(!is.na(cost_change_per_person)) %>%
kable(align=c("l","r","r","r","r","r","r"),digits=0,col.names=c("State","Cost Change Per Person","Total Cost Change","Population ($M)","Population %")) %>%
kable_styling(full_width=F) %>%
column_spec(1,width="10em") %>%
row_spec(6,bold=T,color="#FFFFFF",background="#D55E00") %>%
row_spec(8,bold=T,color="#FFFFFF",background="#0072B2") %>%
row_spec(seq(from=10,to=nrow(Medicare_Expansion),by=10),bold=T,color="#FFFFFF",background="#E69F00")
As this table shows:
* Total spending would increase by $2 trillion nationwide ($7 trillion including federal subsidies);
* The average cost increase per person would be $6 thousand ($19 thousand including federal subsidies);
* Oklahoma ($7 thousand) would have the highest average cost increase per person; Washington ($5 thousand) would have the second highest average cost increase per person;
* North Dakota ($14 thousand) would have the highest total cost increase; Texas ($87 billion) would have the second highest total cost increase;
* Oklahoma (13%) would have the highest total cost increase relative to its population; West Virginia (13%) would have the second highest total cost increase relative to its population.
# How Much Do We Spend Per Person Now?
I estimated how much we currently spend per person by using data from [this report](https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical). The table below shows how much we currently spend per person by state:
{r}
Spending <- read_csv("Spending.csv") %>%
select(-X1)
Spending <- Spending[which(Spending$State!="United States"),]
Spending <- Spending[which(Spending$State!="Washington D.C."),]
Spending <- Spending[which(Spending$State!="District Of Columbia"),]
Spending <- Spending[which(Spending$State!="New York City"),]
Spending <- Spending[which(Spending$State!="Guam"),]
Spending <- Spending[which(Spending$State!="Virgin Islands"),]
Spending <- Spending[which(Spending$State!="Puerto Rico"),]
Spending <- Spending[which(Spending$State!="Federated States Of Micronesia"),]
Spending <- Spending[which(Spending$State!="American Samoa"),]
Spending <- Spending[which(Spending$State!="Northern Mariana Islands"),]
Spending <- Spending[which(Spending$State!="Marshall Islands"),]
Spending <- Spending[which(Spending$State!="Palau"),]
Spending <- Spending %>% rename("state"="State")
Spending <- Spending %>% rename("state"=recode(state,
AL="Alabama",AK="Alaska",AZ="Arizona",AR="Arkansas",CA="California",CO="Colorado",CT="Connecticut",DE="Delaware",DC="District Of