Ice hockey, from a statistical mindset, involves the analysis and interpretation of numerical data to understand various aspects of the sport. Here’s a breakdown of how statistical thinking can be applied to ice hockey:
Scoring Metrics: Evaluate players based on scoring metrics such as goals, assists, and points. Analyze which team’s allow for continous success.
Wins and Losses: Analyze team performance by examining the win-loss record. Identify patterns or trends in how teams perform over different periods or against specific opponents.
Tailoring Fan Experiences: Understand fan preferences and engagement through statistical analysis of viewer data. Tailor fan experiences by identifying trends in viewer demographics, preferences, and behaviors during games. (which can be view to prepare by understanding the history of the team)
In essence, ice hockey provides a data-driven approach to understanding and optimizing player and team performance, making informed decisions, and enhancing the overall experience for players and fans alike.
Question Line of Inquiry
After playing Ice hockey during highschool and in middle school, I have always been asked who my favorite team is. I always struggled with the answer as in Wisconsin we do not have a team. Therefore my question is, which team should I choose? Mainly, how does a team’s history of success, recent championships, or playoff appearances affect the decision to support that team?
How this will be Accomplished
To determine which ice hockey team to support, particularly considering Wisconsin’s lack of a local team, I plan to adopt a multifaceted approach. Initially, I will compile a list of all NHL and other hockey teams to gather comprehensive and up-to-date information.
To assess a team’s historical success, I will define success metrics such as championships won, playoff appearances, and overall performance. The decision-making process will involve establishing criteria, assigning weights to different metrics, and ultimately my choice will be an informed and meaningful one, blending statistical insights with personal preferences to select a team that resonates both historically and experientially.
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First by identifying the HTML source. This is the page for ice hockey all the NHL Teams & Other Hockey Teams.
Rows: 110 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Franchise, Lg
dbl (15): From, To, Yrs, GP, W, L, T, OL, PTS, PTS%, Yrs Plyf, Div, Conf, Ch...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Make sure when working on this proect that the working directory is set to a folder. The working directory is the folder on your computer where R will look for files and where it will save files if you don’t specify a path.
Set Working Directory: You use setwd("path/to/your/directory") to set the working directory to a specific folder on your computer.
Check Working Directory: You can use getwd() to check the current working directory.
[1] "C:/Users/krajn/OneDrive/Documents/Quarto"
[1] FALSE
Data Dictionary
For the Hockey data frame there are a couple things to understand.
Franchise: A unique identifier assigned to a hockey franchise within a league
LG: League
From: Start of team year
To: Last time used year
Yrs: total years that team has been used ( To-From)
GP: games played
W: Wins
L: losses
T: Ties
OL: overtime losses
PTS: Points
PTS%: Points percentage ( points divided by maximun points)
Yrs Plyf : Year team made the playoff
Div: Year team finished first (or tied for first) in the division
Conf: Years team won the playoff conference championship. This begins in 1981-82
Champ: Years team won the league championship
St Cup: Years team won the Stanley Cup
Visualizations:
Is the wins-to-losses ratio for each team a crucial visualization that can significantly contribute to making an informed decision?
Analyzing the wins-to-losses ratio for each ice hockey team provides valuable insights into their overall performance. This ratio is a fundamental metric reflecting a team’s success on the ice.
The wins-to-losses ratio serves as a benchmark for assessing a team’s competitiveness and consistency throughout a season or over multiple seasons. By examining this metric for various teams, one can identify those with a higher ratio, signifying stronger performance, while lower ratios may indicate areas for improvement or variability in success. Such an analysis enables fans, analysts, and team management to gauge and compare the relative strengths of different teams in the dynamic and competitive landscape of ice hockey.
In view of the graph, we are able to see that the reds which represent the NHL have a higher win to loss ratio than the WHA. This can be view as NHL has a higher wins than losses which lead to a stronger ratio, or that WHA does not play that many games comparison.
Does a correlation exist between the total number of points scored and the overall number of wins for teams?
In exploring the relationship between the number of points scored and the total wins for ice hockey teams, several key aspects warrant attention. Firstly, a detailed analysis of the data is essential to identify patterns and trends. Understanding whether an increase in points directly corresponds to a higher number of wins.
Additionally, examining outliers or instances where high point totals do not translate into significant win counts could unveil unique team dynamics or playing styles. The strength of any correlation being positive should be thoroughly assessed to gauge the predictive value of points in determining a team’s success.
Ultimately, focusing on these key elements will contribute to a nuanced and insightful interpretation of the connection between points scored and total wins, providing valuable insights for both enthusiasts and analysts in the realm of ice hockey.
Is there a discernible relationship between Stanley Cup wins and total wins specifically within the NHL context?
Analyzing the relationship between Stanley Cup wins and total wins within the NHL context reveals several key insights into the dynamics of success in professional ice hockey. The paramount aspect is the examination of whether teams that consistently secure higher total wins in the regular season also exhibit a propensity for clinching the prestigious Stanley Cup. Notably, such a correlation underscores the importance of teams maintaining competitiveness throughout the entire season to position themselves favorably for playoff contention.
Conversely, the absence of a strong correlation may suggest that factors beyond regular-season victories, such as playoff strategies, player performance under postseason pressure, or sheer unpredictability, play a more significant role in securing the ultimate championship. The analysis of this relationship provides valuable insights for fans, analysts, and team management, guiding expectations and strategies for sustained success in the challenging NHL landscape.
What is the relationship between wins_losses_ratio and Point percentage?
In delving into the correlation between Stanley Cup wins and total wins within the exclusive context of the NHL, several key focal points emerge. Identifying whether teams with higher total wins are more likely to secure coveted Stanley Cup victories sheds light on the significance of regular-season success. This curve backs this statement up as it demonstrate that the more wins the high the point percentage. Which is logical.
The strength and consistency of this correlation merit close scrutiny, as it provides insights into the factors contributing to sustained excellence in the highly competitive NHL landscape.
How can the distribution of the number of times a team in the first position in the division be effectively visualized in relation to the years in which teams secured victories in the playoff conference championship?
In exploring the visual representation of the distribution of the number of times a team achieved first place in the division juxtaposed with the years of playoff conference championship victories, several key considerations come to the forefront.
Firstly, a detailed analysis of the data is crucial to unveil patterns in teams’ performances over different years. Understanding whether teams consistently securing the top position in the division also tend to excel in playoff conference championships is a primary focus. This analysis helps discern any correlation between regular-season dominance and postseason success.
Additionally, the visualization should highlight any anomalies or instances where a high number of divisional victories does not correspond with success in the playoff conference championship. In this graph, their are not outlyer that would suggest this.
Sentiment Analysis
After viewing this data, we are able to see the importance of how teams are. In viewing this pervious data, I want to see how within rivalries which two teams would have a better sentiment.
In being a hockey player is it abvioius the NHL is just better than WHA (instert personal bias) From there, it I decided to see another element to make a personal decioson which a team I choose cheer for. IN that, factors to decide that depend on your view. Some suggestions are with the highest win to lost ratio, the oldest team, or even the highest amount of Stanley Cup’s.
In choosing two teams to compaire, I decied to focus on who has the higest amount of Stanley Cup’s wins. Montreal Canadians (Bell Centre) vs. Toronto Maple Leafs (Scotiabank Arena). Let’s do sentiment analysis of these areas because thats where the fans gather. It is a chance to view the team spirit. In this, I use tripadvisor and use the reviews.
Table of words used
Here is a table that shows the most common words use it the first ten reviews.
Joining with `by = join_by(word)`
# A tibble: 171 × 2
word n
<chr> <int>
1 arena 8
2 game 8
3 hockey 4
4 concerts 3
5 food 3
6 raptors 3
7 seats 3
8 subway 3
9 watch 3
10 2 2
# ℹ 161 more rows
Joining with `by = join_by(word)`
# A tibble: 245 × 2
word n
<chr> <int>
1 centre 8
2 bell 7
3 hockey 7
4 game 6
5 arena 5
6 building 5
7 canadiens 5
8 expensive 5
9 ice 5
10 seats 5
# ℹ 235 more rows
Question 1:
Are fans of Montreal more satisfied with experience than fans of Maple Leafs?
`summarise()` has grouped output by 'sentiment'. You can override using the
`.groups` argument.
The color-coded analysis indicates that the Bell Centre tends to receive more positive reviews compared to Scotiabank Arena. Specifically, the Bell Centre exhibits higher positive scores, especially in categories such as joy, trust, and overall positivity. Despite a slightly elevated score in sadness, the significant positive differences in various aspects suggest that the overall experience at the Bell Centre, as reflected in the reviews, is more favorable than that at Scotiabank Arena.
Question 2:
Are the reviews of the leafs or Montreal more positive?
`summarise()` has grouped output by 'arena'. You can override using the
`.groups` argument.
Joining with `by = join_by(word)`
For, sensitivity analysis involves assessing how changes in input values affect the output, and the language used should convey the meticulous examination of these variations. As we are intrested in the fan base, we are trying to learn what type of an environment envouages a more postive outlook.
Positive words are commonly used to express praise, approval, or admiration. They contribute to a constructive and uplifting communication style. Negative words are employed to convey criticism, dissatisfaction, or concerns. They play a role in addressing issues or expressing discontent.
From this analysis, we are able to see the difference next to each other which words show as well as it if is positive or negative. From this table we are able to conclude that Bell has more postive words than negative, similar to the graph above.
Question 3:
How does time affect reviews?
`geom_smooth()` using formula = 'y ~ x'
Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
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This graph means shows when most reviews where taken into account time, over the years, that with more reviews made in
In this graph, we are able to take into the element about how time is a factor to the reviews. In this graph, we are able to see that their were more reviews made in 2023. This does not factor in if they were good reviews or bad but states that more people had things to say about their experience.
Understanding of the sentiment:
When comparing two distinct hockey towns, we gain insight into the nuances that differentiate them. This exploration allows us to delve into the unique cultures surrounding each area and comprehend the reasons why people passionately support specific teams. Upon reviewing the data set, it becomes clear that the differences in these hockey towns contribute significantly to the varied fan experiences and loyalties.
In this analysis, it becomes evident that, even within two Canadian towns, the Bell Centre and the Scotiabank Arena represent the home arenas of the Montreal Canadiens and the Toronto Maple Leafs, respectively, in the National Hockey League (NHL). The Canadiens are based in Montreal, Quebec, Canada, while the Maple Leafs are based in Toronto, Ontario, Canada. Despite their close proximity, the Bell Centre holds a higher favorability than the Scotiabank Arena.
Conclusion
Choosing a hockey team to support involves a thoughtful consideration of various factors, each contributing to the unique identity and appeal of a franchise. A team’s history of success, recent championships, and playoff appearances play pivotal roles in shaping its allure for fans.
Recent championships reflect a team’s ability to navigate difference challenges. Playoff appearances, marked by intense battles and emotional highs, contribute to the team’s narrative, creating a sense of shared experience for fans. Ultimately, the decision to support a team can be decided as well with your own personal journey, blending statistical achievements with emotional resonance, and finding a team whose legacy and current performance align with individual preferences and values.
