Good morning!
For today's statistical analysis, I decided to analyze the factors which I looked at in the previous statistical analysis but just for the teams who made the play-offs for the 2013-14 season. These teams were:
Anaheim Ducks
Boston Bruins
Chicago Blackhawks
Colorado Avalanche
Dallas Stars
Detroit Red Wings
Los Angeles Kings
Minnesota Wild
Montreal Canadiens
New York Rangers
Philadelphia Flyers
Pittsburgh Penguins
San Jose Sharks
St. Louis Blues
Tampa Bay Lightning
Results
1) Pearson Correlation between number of points and number of wins in the regular season: 0.963.
This
is a very strong positive correlation. Naturally this makes sense
because the number of wins you have correlates strongly with making the playoffs and earning a high amount of points.
2) Pearson Correlation between the number of points and average number of goals per game in the regular season: 0.695.
This shows that there is no correlation or relationship between the number of points accumulated and the goals per game for the play-off making teams. Oddly enough, there was a strong positive correlation for all of the teams.
3) Pearson Correlation between number of points and average number of goals against per game in the regular season-0.457.
This
is a rather weak negative correlation. Therefore, more goals you let
in has a negative effect on your success but it is not a strong relationship.
4) Pearson Correlation between number of points and average out-shot percentage in the regular season: 0.872.
Once again, the out-shot percentage strongly correlates with success of the playoff teams.
5) Pearson Correlation between number of points and average out-shooting percentage in the regular season: 0.604.
There
is still a a weak positive correlation between points and out-shooting opponents.
6) Pearson Correlation between number of points and average win percentage when a team scores first in the regular season: 0.648.
For the play-off making teams, there was no strong correlation with winning games when scoring first and the number of points accumulated in the regular season.
Next, I will do a new analysis on simply the play-offs from 2013-14 before moving on into past seasons.
Tuesday, 27 January 2015
Tuesday, 20 January 2015
January 20, 2015
Good evening, blog!
I have began part one of my statistical analysis! The main focus for today's analysis was "what correlates with the number of points a team accumulates during the regular season?"
For this analysis, I inputted my data from an Excel spreadsheet on SPSS and computed the Pearson Correlation for twelve tests. For those of you 'non-math' lovers, the Pearson Correlation basically finds the relationship between if one thing happens, does another thing happen. For example, if a team has a high out-shooting percentage during the regular season, does that mean they will end the season with more points?
For the sake of this first analysis, I focused on the regular season only. Also, for the math lovers out there, my alpha significance was 0.01 (for those of you who are confused, that means I computed with a margin of error of less than 1%).
Interesting Results
1) Pearson Correlation between number of points and number of wins in the regular season: 0.982.
This is a very strong positive correlation. Naturally this makes sense because the number of wins you have correlates strongly with the number of points you recieve.
2) Pearson Correlation between the number of points and average number of goals per game in the regular season: 0.834.
This is a rather strong positive correlation. Therefore, the more goals you score, the better chance you have of winning (makes sense).
3) Pearson Correlation between number of points and average number of goals against per game in the regular season-0.753.
This is a rather strong negative correlation. Therefore, more goals you let in has a negative effect on your success (obviously).
4) Pearson Correlation between number of points and average out-shot percentage in the regular season: 0.888.
This was the most interesting statistic I found. What I got from it is that there's a strong correlation between being out-shot and earning points. So I guess teams are more successful with less scoring chances. I want to look into this statistic in particular more to see if it is credible or a fluke.
5) Pearson Correlation between number of points and average out-shooting percentage in the regular season: 0.821.
There is still a positive correlation when out-shooting your opponent; however, it does not seem as strong as when you are out-shot.
6) Pearson Correlation between number of points and average win percentage when a team scores first in the regular season: 0.854.
This credits the 'first goal wins the game' theory since there is a strong positive correlation with winning and scoring first.
I found weak or no correlations when correlating number of points with face-off win percentages, trailing in the first period winning percentage, shots against average per game and shots per game.
Next Steps
I want to look into my findings further and support them with findings from past seasons as well as find other strong positive correlations which may not be as obvious.
I will start doing this later this week!
I have began part one of my statistical analysis! The main focus for today's analysis was "what correlates with the number of points a team accumulates during the regular season?"
For this analysis, I inputted my data from an Excel spreadsheet on SPSS and computed the Pearson Correlation for twelve tests. For those of you 'non-math' lovers, the Pearson Correlation basically finds the relationship between if one thing happens, does another thing happen. For example, if a team has a high out-shooting percentage during the regular season, does that mean they will end the season with more points?
For the sake of this first analysis, I focused on the regular season only. Also, for the math lovers out there, my alpha significance was 0.01 (for those of you who are confused, that means I computed with a margin of error of less than 1%).
Interesting Results
1) Pearson Correlation between number of points and number of wins in the regular season: 0.982.
This is a very strong positive correlation. Naturally this makes sense because the number of wins you have correlates strongly with the number of points you recieve.
2) Pearson Correlation between the number of points and average number of goals per game in the regular season: 0.834.
This is a rather strong positive correlation. Therefore, the more goals you score, the better chance you have of winning (makes sense).
3) Pearson Correlation between number of points and average number of goals against per game in the regular season-0.753.
This is a rather strong negative correlation. Therefore, more goals you let in has a negative effect on your success (obviously).
4) Pearson Correlation between number of points and average out-shot percentage in the regular season: 0.888.
This was the most interesting statistic I found. What I got from it is that there's a strong correlation between being out-shot and earning points. So I guess teams are more successful with less scoring chances. I want to look into this statistic in particular more to see if it is credible or a fluke.
5) Pearson Correlation between number of points and average out-shooting percentage in the regular season: 0.821.
There is still a positive correlation when out-shooting your opponent; however, it does not seem as strong as when you are out-shot.
6) Pearson Correlation between number of points and average win percentage when a team scores first in the regular season: 0.854.
This credits the 'first goal wins the game' theory since there is a strong positive correlation with winning and scoring first.
I found weak or no correlations when correlating number of points with face-off win percentages, trailing in the first period winning percentage, shots against average per game and shots per game.
Next Steps
I want to look into my findings further and support them with findings from past seasons as well as find other strong positive correlations which may not be as obvious.
I will start doing this later this week!
Wednesday, 14 January 2015
January 14, 2015
Good Afternoon, readers!
Throughout the past week, I have spent time just inputting some preliminary data in excel sheets to further analyze. For today's blog post, I will explain to you my decision making process thus far.
Stanley Cups First, I have looked at who has won the Stanley Cup since 2000. It is important to note first however that there was a lockout in 2005 which resulted in no season and a lockout in 2013 which resulted in half of a season. However, the Chicago Blackhawks, Detroit Red Wings, Los Angeles Kings and New Jersey Devils have won the most Stanley Cups in that time period (each winning 2). In particular, Los Angeles has won two in the last 3 years. The Anaheim Ducks, Boston Bruins, Carolina Hurricanes, Colorado Avalanche, Pittsburgh Penguins and Tampa Bay Lightning have all also won one Stanley Cup in this time period. Therefore, I will be placing an emphasis on all of these teams to see if there were any hockey statistics which positively correlate with their success.
2013-2014 Statistics
So far, I have inputted data for the 2013-14 year. When choosing data to find correlations with, so far I have decided on focusing on regular season trends in the following categories:
- Number of Wins in the Regular Season (highest chance of making the play-offs, correlates with regular season success)
- Average point percentage (how well the team does in regular season, indicates their success)
- Average goals per game (in order to win games,you need to score goals obviously)
- Average goals against per game (do they score enough goals to win games?)
- Average shots per game (how many attempts do they have during a game, do they put pressure on their opponents?)
- Average shots against per game (can they out-shoot their opponents?)
- Percentage of winning when scoring in the first period (first period advantage effect)
- Percentage of winning when trailing in the first period (first period disadvantage effect)
- Percentage of winning when out-shot (do they score on less attempts?)
- Percentage of winning when out-shooting (do they score on more attempts?)
- Face-off win percentage (face offs in their own zone and their opposing zone)
- Even strength win percentage (when playing 5 v. 5)
Where Do We Go From Here?
I am also hoping to look at the power play percentages and see if I find any other correlations.
At the moment, I am hoping to conduct MANCOVA tests as well as looking at the Pearson Correlation on SPSS to find correlations and significance.
After focusing on the 2013 regular season, I am hoping to move into the 2013-14 play off seasons and conducting similar tests.
Later on this week, I will have the results from my SPSS tests inputted. Right now, I just want to go through the various statistics to decipher which ones I feel may have a significant effect on a team's success.
So far, I am enjoying this project! I love doing data collection and am hoping to find positive results soon. My goal by the end of the week is to have enough data to find what statistics may contribute to a team's success. In the future, this will be expanded upon for past seasons.
Throughout the past week, I have spent time just inputting some preliminary data in excel sheets to further analyze. For today's blog post, I will explain to you my decision making process thus far.
Stanley Cups First, I have looked at who has won the Stanley Cup since 2000. It is important to note first however that there was a lockout in 2005 which resulted in no season and a lockout in 2013 which resulted in half of a season. However, the Chicago Blackhawks, Detroit Red Wings, Los Angeles Kings and New Jersey Devils have won the most Stanley Cups in that time period (each winning 2). In particular, Los Angeles has won two in the last 3 years. The Anaheim Ducks, Boston Bruins, Carolina Hurricanes, Colorado Avalanche, Pittsburgh Penguins and Tampa Bay Lightning have all also won one Stanley Cup in this time period. Therefore, I will be placing an emphasis on all of these teams to see if there were any hockey statistics which positively correlate with their success.
2013-2014 Statistics
So far, I have inputted data for the 2013-14 year. When choosing data to find correlations with, so far I have decided on focusing on regular season trends in the following categories:
- Number of Wins in the Regular Season (highest chance of making the play-offs, correlates with regular season success)
- Average point percentage (how well the team does in regular season, indicates their success)
- Average goals per game (in order to win games,you need to score goals obviously)
- Average goals against per game (do they score enough goals to win games?)
- Average shots per game (how many attempts do they have during a game, do they put pressure on their opponents?)
- Average shots against per game (can they out-shoot their opponents?)
- Percentage of winning when scoring in the first period (first period advantage effect)
- Percentage of winning when trailing in the first period (first period disadvantage effect)
- Percentage of winning when out-shot (do they score on less attempts?)
- Percentage of winning when out-shooting (do they score on more attempts?)
- Face-off win percentage (face offs in their own zone and their opposing zone)
- Even strength win percentage (when playing 5 v. 5)
Where Do We Go From Here?
I am also hoping to look at the power play percentages and see if I find any other correlations.
At the moment, I am hoping to conduct MANCOVA tests as well as looking at the Pearson Correlation on SPSS to find correlations and significance.
After focusing on the 2013 regular season, I am hoping to move into the 2013-14 play off seasons and conducting similar tests.
Later on this week, I will have the results from my SPSS tests inputted. Right now, I just want to go through the various statistics to decipher which ones I feel may have a significant effect on a team's success.
So far, I am enjoying this project! I love doing data collection and am hoping to find positive results soon. My goal by the end of the week is to have enough data to find what statistics may contribute to a team's success. In the future, this will be expanded upon for past seasons.
Thursday, 8 January 2015
Welcome to my Genius Hour
Hello and welcome to my classmates, colleagues and all others who chose to peruse my blog (including but not limited to family, friends, hockey enthusiasts, math lovers and the entire Internet world!). My name is Alicia and I am excited to have you on board as I embark on my first ever Genius Hour Journey.
For those of you new to Genius Hour, it was created by Google and allows their employees to research any particular thought-provoking question which they are passionate about. A thought-provoking question cannot simply be answered by a quick Google search or one sentence.
As a future teacher exploring the realms of Genius Hour in an instructional strategies and computer technology course, I am anticipating what the next few weeks have in store for me regarding my Genius Hour question as well as the different forms of technology that I will be using throughout the course.
You may now be wondering what my Genius Hour question is. My question is: Who is likely to win the NHL 2014-2015 Stanley Cup?
As a Math student and teacher who lives and breathes numbers, formulas and algorithms, I am hoping to use my general knowledge of statistics and probabilities as well as previous attempts found on the internet to predict the winner using team statistics from previous years. There may or may not be a rhyme or reason to the statistical tests I choose since I am no hockey analyst and all of my testing will be done based on my knowledge of the statistical tests used and will be explained as I go along. By analyzing the trends of the past decade or so of NHL seasons, I also hope to notice patterns among the data collection and previous winners to further help my winning choice. After concluding a winner based on dating back a few seasons (since you know, history repeats itself), I will compare these results with the current NHL standings. In the end as a fun addition will include an EA Sports NHL 15 Season Simulation on my Xbox 360 to see how my team does in the simulated standings.
To measure my progress, it is essential for this kind of project to be structured with time management. I will be keeping weekly reflections and blog posts here to track my journey. Similarly, I will ensure that each of the 'checkpoints' in this class are completed on a timely manner. My goal is before week 6 to be done the statistical analysis, week 7 and 8 will be devoted to commentary and comparisons to the current season at the time, week 9 will be when I conduct the NHL 15 simulation and week 10 will be a reflection on the journey.
Well, now is the time to get started! Thank you for reading and feel free to leave any thoughts, questions and comments as I go along.
For those of you new to Genius Hour, it was created by Google and allows their employees to research any particular thought-provoking question which they are passionate about. A thought-provoking question cannot simply be answered by a quick Google search or one sentence.
As a future teacher exploring the realms of Genius Hour in an instructional strategies and computer technology course, I am anticipating what the next few weeks have in store for me regarding my Genius Hour question as well as the different forms of technology that I will be using throughout the course.
You may now be wondering what my Genius Hour question is. My question is: Who is likely to win the NHL 2014-2015 Stanley Cup?
As a Math student and teacher who lives and breathes numbers, formulas and algorithms, I am hoping to use my general knowledge of statistics and probabilities as well as previous attempts found on the internet to predict the winner using team statistics from previous years. There may or may not be a rhyme or reason to the statistical tests I choose since I am no hockey analyst and all of my testing will be done based on my knowledge of the statistical tests used and will be explained as I go along. By analyzing the trends of the past decade or so of NHL seasons, I also hope to notice patterns among the data collection and previous winners to further help my winning choice. After concluding a winner based on dating back a few seasons (since you know, history repeats itself), I will compare these results with the current NHL standings. In the end as a fun addition will include an EA Sports NHL 15 Season Simulation on my Xbox 360 to see how my team does in the simulated standings.
To measure my progress, it is essential for this kind of project to be structured with time management. I will be keeping weekly reflections and blog posts here to track my journey. Similarly, I will ensure that each of the 'checkpoints' in this class are completed on a timely manner. My goal is before week 6 to be done the statistical analysis, week 7 and 8 will be devoted to commentary and comparisons to the current season at the time, week 9 will be when I conduct the NHL 15 simulation and week 10 will be a reflection on the journey.
Well, now is the time to get started! Thank you for reading and feel free to leave any thoughts, questions and comments as I go along.
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