Introduction to Tomorrow's IHL Italy Ice-Hockey Matches
Welcome to the ultimate guide on tomorrow's thrilling IHL Italy ice-hockey matches. As a local enthusiast, I'm thrilled to share expert predictions and insights into the games that will keep you on the edge of your seat. Whether you're a seasoned fan or new to the sport, this guide is packed with everything you need to know about the matches, teams, and betting tips to enhance your viewing experience.
Match Schedule Overview
Get ready for an exhilarating day of ice-hockey action as the IHL Italy teams take to the rink. Here's a quick overview of the matches scheduled for tomorrow:
- Match 1: Team A vs. Team B - Kick-off at 10:00 AM
- Match 2: Team C vs. Team D - Kick-off at 12:30 PM
- Match 3: Team E vs. Team F - Kick-off at 3:00 PM
- Match 4: Team G vs. Team H - Kick-off at 5:30 PM
Team Highlights and Key Players
Each team brings its unique strengths and star players to the ice. Here are some highlights:
Team A
Known for their aggressive playstyle, Team A has been dominating the league recently. Key player to watch: John Doe, renowned for his exceptional skating skills and goal-scoring ability.
Team B
Team B prides itself on a solid defense and strategic gameplay. Key player to watch: Jane Smith, a formidable goalie with a knack for making game-changing saves.
Team C
This team is celebrated for its fast-paced offense. Key player to watch: Mike Johnson, a dynamic forward known for his speed and precision in shooting.
Team D
With a balanced approach between offense and defense, Team D is a tough competitor. Key player to watch: Emily Brown, a versatile defenseman with excellent puck-handling skills.
Betting Predictions and Tips
Betting on ice-hockey can be exciting and rewarding if done wisely. Here are some expert predictions and tips for tomorrow's matches:
Match 1: Team A vs. Team B
Prediction: Team A is likely to win with a close scoreline.
Tip: Consider betting on Team A's first goal scorer.
Match 2: Team C vs. Team D
Prediction: A high-scoring game with Team C edging out.
Tip: Bet on the total goals being over three.
Match 3: Team E vs. Team F
Prediction: An evenly matched game, but Team E might have the upper hand.
Tip: Place a bet on both teams scoring in the first period.
Match 4: Team G vs. Team H
Prediction: Team H is expected to pull off an upset.
Tip: Consider betting on the underdog, Team H, winning.
In-Depth Match Analysis
Analyzing Match Dynamics
The IHL Italy matches are not just about physical prowess but also about strategy and teamwork. Here’s a deeper look into what makes these games fascinating:
- Tactical Formations: Teams often employ various formations like the box formation or offensive overload to gain an advantage.
- Puck Control: Maintaining control of the puck is crucial. Teams with superior puck control can dictate the pace of the game.
- Power Plays: Special teams play during power plays can be game-changers. Teams that capitalize on these opportunities often have an edge.
- Grit and Resilience: The ability to withstand pressure and bounce back from setbacks is vital in ice-hockey.
Fan Experience and Viewing Tips
To make the most out of your viewing experience, consider these tips:
- Venue Atmosphere: If attending live, immerse yourself in the electrifying atmosphere of the arena.
- Social Media Engagement: Follow teams and players on social media for real-time updates and behind-the-scenes content.
- Betting Communities: Join online forums or betting communities to exchange insights and predictions with fellow enthusiasts.
- Cheer Loudly!: Support your favorite team by cheering them on throughout the match!
Ethical Betting Practices
Betting should be approached responsibly. Here are some ethical practices to keep in mind:
- Budget Wisely: Set a budget for betting and stick to it.
- Avoid Impulsive Bets: Make informed decisions rather than betting impulsively.
- Educate Yourself: Learn about different types of bets and their odds before placing any wagers.
- Sportsmanship First: Remember that sports are about enjoyment and fair play above all else.
<|repo_name|>yale-linguistics-2021/exploration<|file_sep|>/analysis/lexicality_verb_noun.Rmd
---
title: "Lexicality Verb Noun"
output:
html_document:
toc: true
toc_depth: '4'
toc_float:
collapsed: false
---
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggplot2)
library(broom)
library(lme4)
library(lmerTest)
library(dplyr)
library(tidyr)
library(emmeans)
library(knitr)
## Lexicality Verb Noun
### Data cleaning
{r}
#read in data
raw_data <- read.csv("/Users/jaydeshmukhsachdeva/Desktop/Yale Linguistics/exploration/data/data_exploration.csv")
#make factors
raw_data$group <- factor(raw_data$group,
levels = c("control", "lexicality"),
labels = c("Control", "Lexicality"))
raw_data$condition <- factor(raw_data$condition,
levels = c("verb", "noun"),
labels = c("Verb", "Noun"))
#filter data
data <- raw_data %>% filter(!is.na(verb_noun_mean))
#data exploration
head(data)
#check data
str(data)
### Descriptives
{r}
#descriptives
data %>% group_by(group) %>% summarise(mean = mean(verb_noun_mean), sd = sd(verb_noun_mean))
data %>% group_by(group) %>% summarise(mean = mean(verb_noun_mean), sd = sd(verb_noun_mean)) %>%
kable()
### Linear mixed effects model
{r}
#fit model
model <- lmer(verb_noun_mean ~ condition*group + (1|participant), data = data)
#summary table
summary(model)
#get p-values
anova(model)
#get emmeans table
emmeans_table <- emmeans(model, pairwise ~ condition*group)
#output table
emmeans_table %>%
summary(infer = T) %>%
as.data.frame() %>%
kable()
### Visualize data
{r}
ggplot(data,
aes(x = condition,
y = verb_noun_mean,
fill = group)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = .15) +
stat_summary(fun.y = mean,
geom = "point",
shape = "dotdash",
size = .75,
color = "black") +
theme_bw() +
labs(x = "Condition",
y = "Mean Verb Noun Score") +
theme(text=element_text(size=16),
axis.text.x=element_text(size=12),
axis.text.y=element_text(size=12))
<|repo_name|>yale-linguistics-2021/exploration<|file_sep|>/analysis/lexicality_participant.Rmd
---
title: "Lexicality Participant"
output:
html_document:
toc: true
toc_depth: '4'
toc_float:
collapsed: false
---
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggplot2)
library(broom)
library(lme4)
library(lmerTest)
library(dplyr)
library(tidyr)
library(emmeans)
## Lexicality Participant
### Data cleaning
{r}
#read in data
raw_data <- read.csv("/Users/jaydeshmukhsachdeva/Desktop/Yale Linguistics/exploration/data/data_exploration.csv")
#make factors
raw_data$group <- factor(raw_data$group,
levels = c("control", "lexicality"),
labels = c("Control", "Lexicality"))
raw_data$condition <- factor(raw_data$condition,
levels = c("verb", "noun"),
labels = c("Verb", "Noun"))
raw_data$participant <- factor(raw_data$participant)
#filter data
data <- raw_data %>% filter(!is.na(participant))
#data exploration
head(data)
#check data
str(data)
### Descriptives
{r}
#descriptives
data %>% group_by(group) %>% summarise(mean_participant_verb_noun_mean_per_participant =
mean(participant_verb_noun_mean_per_participant),
sd_participant_verb_noun_mean_per_participant =
sd(participant_verb_noun_mean_per_participant))
data %>% group_by(group) %>% summarise(mean_participant_verb_noun_mean_per_participant =
mean(participant_verb_noun_mean_per_participant),
sd_participant_verb_noun_mean_per_participant =
sd(participant_verb_noun_mean_per_participant)) %>%
kable()
### Linear mixed effects model
{r}
#fit model
model <- lmer(participant_verb_noun_mean_per_participant ~ group + (1|participant), data =
data)
#summary table
summary(model)
#get p-values
anova(model)
#get emmeans table
emmeans_table <- emmeans(model, pairwise ~ group)
#output table
emmeans_table %>%
summary(infer=T) %>%
as.data.frame() %>%
kable()
### Visualize data
{r}
ggplot(data,
aes(x=group,
y=participant_verb_noun_mean_per_participant)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(width=.15) +
stat_summary(fun.y=mean,
geom="point",
shape="dotdash",
size=.75,
color="black") +
theme_bw() +
labs(x="Group",
y="Mean Participant Verb Noun Score per Participant") +
theme(text=element_text(size=16),
axis.text.x=element_text(size=12),
axis.text.y=element_text(size=12))
<|repo_name|>yale-linguistics-2021/exploration<|file_sep|>/README.md
# Exploration Study
This repository contains all analyses conducted for our exploration study.
## Data collection
The data collection script can be found [here](https://github.com/yale-linguistics-2021/exploration/blob/main/data_collection/main.py).
The stimuli used in this study can be found [here](https://github.com/yale-linguistics-2021/exploration/blob/main/data_collection/stimuli.xlsx).
## Data analysis
The scripts used for our analyses can be found [here](https://github.com/yale-linguistics-2021/exploration/tree/main/analysis).
The RMarkdown files used for our analyses can be found [here](https://github.com/yale-linguistics-2021/exploration/tree/main/analysis).
The final tables containing our results can be found [here](https://github.com/yale-linguistics-2021/exploration/tree/main/results).
<|file_sep|># -*- coding:utf-8 -*-
import os.path as osp
import numpy as np
import pandas as pd
def main():
'''
Main function that runs through all stages of analysis.
'''
# load csv file with all responses from participants (response file from Google Forms).
response_file_path = osp.join('..', 'data', 'responses.csv')
response_df = pd.read_csv(response_file_path)
# load csv file with all stimuli.
stimuli_file_path = osp.join('..', 'data', 'stimuli.csv')
stimuli_df = pd.read_csv(stimuli_file_path)
##########
## STAGE ##
## stage one:
## calculate mean verb-noun scores per item (item-level scores).
item_scores_df_one_stage_one(response_df=response_df,
stimuli_df=stimuli_df)
## stage two:
## calculate participant-level scores.
item_scores_df_two_stage_two(response_df=response_df)
if __name__ == '__main__':
main()
def item_scores_df_one_stage_one(response_df=None,
stimuli_df=None):
'''
This function calculates mean verb-noun scores per item (item-level scores).
Input:
response_df (pd.DataFrame): DataFrame containing responses from participants.
stimuli_df (pd.DataFrame): DataFrame containing stimuli.
Output:
None.
'''
##########
## STAGE ##
## stage one:
## filter out irrelevant columns.
response_df_filtered_columns_one_stage_one_columns_to_keep_list=[
'participant',
'item',
'condition',
'response',
'reaction_time_ms',
]
response_df_filtered_columns_one_stage_one_columns_to_drop_list=[
col for col in response_df.columns.tolist() if col not in response_df_filtered_columns_one_stage_one_columns_to_keep_list]
response_df_filtered_columns_one_stage_one=response_df.drop(columns=response_df_filtered_columns_one_stage_one_columns_to_drop_list)
## get rid of missing values.
response_df_filtered_missing_values_stage_one=response_df_filtered_columns_one_stage_one.dropna()
## filter out items where participants did not respond.
response_df_filtered_missing_responses_stage_one=response_df_filtered_missing_values_stage_one[response_df_filtered_missing_values_stage_one['response'] != '']
## get list of unique participants.
participants_list_unique=response_df_filtered_missing_responses_stage_one['participant'].unique().tolist()
## get list of unique items.
items_list_unique=response_df_filtered_missing_responses_stage_one['item'].unique().tolist()
## get list of unique conditions.
conditions_list_unique=response_df_filtered_missing_responses_stage_one['condition'].unique().tolist()
## create empty dataframe where each row corresponds to one item.
item_scores_list=[]
for item_index,item_item in enumerate(items_list_unique):
item_scores_list.append({'item':item_item})
item_scores=pd.DataFrame(item_scores_list)
## calculate mean reaction time per item per condition per participant.
for participant_index,participant in enumerate(participants_list_unique):
print(f'Calculating reaction times per item per condition for participant {participant_index+1} out of {len(participants_list_unique)}...')
## create temporary dataframe where each row corresponds to one item per participant.
## this will allow us to calculate mean reaction time per item per condition per participant more easily later on.
participant_temporary_dataframe=pd.DataFrame()
for item_index,item_item in enumerate(items_list_unique):
## get all rows corresponding to current participant where current item was shown.
participant_item_rows=response_df_filtered_missing_responses_stage_one[(response_df_filtered_missing_responses_stage_one['participant']==participant)&(response_df_filtered_missing_responses_stage_one['item']==item_item)]
## add temporary column where we add current participant identifier (this will allow us later on to use groupby).
participant_item_rows['participant_temporary']=participant+'_'+str(item_index+1)+'_'+str(len(items_list_unique))
## append rows corresponding to current participant where current item was shown into temporary dataframe.
participant_temporary_dataframe=pd.concat([participant_temporary_dataframe,participant_item_rows])
## calculate mean reaction time per item per condition per participant.
participant_temporary_dataframe_grouped_means=(participant_temporary_dataframe.groupby(['item','condition','participant_temporary']).agg({'reaction_time_ms':'mean'})).reset_index()
## rename temporary column created above so that we know it corresponds to current participant identifier.
participant_temporary_dataframe_grouped_means.rename(columns={'participant_temporary':'participant'},inplace=True)
## merge calculated means into empty dataframe containing only items.
item_scores=pd.merge(left=item_scores,right=participant_temporary_dataframe_grouped_means,on=['item','condition','participant'],how='outer')
item_scores.replace(np.nan,'',inplace=True)
print('Calculating correct responses...')
## calculate correct responses per item per condition per participant (each cell either contains '' or True).
for participant_index,participant in enumerate(participants_list_unique):
print(f'Calculating correct responses per item per condition for participant {participant_index+1} out of {len(participants_list_unique)}...')
## create temporary dataframe where each row corresponds to one item per participant.
## this will allow us to calculate correct responses more easily later on.
participant_temporary_dataframe=pd.DataFrame()
for item_index,item_item in enumerate(items_list_unique):
## get all rows corresponding to current participant where current item was shown.