Compared to the previous season, overall attendance at MLB
regular season games was down by over 1.7 million fans with the largest declines
by Toronto (-575,137) and Seattle (-507,552).
Those two franchises represent over half of the total decline in MLB
regular season attendance alone. The
average decline in fan attendance was (-39,036) from the previous season. So, was this a (statistically) significant
decline in MLB attendance? To answer
that question, I will use a t-test, which is a statistical measure of the
relationship between two variables and then allows one to determine the degree
of confidence that the two variables have to each other. The t-test for regular season team attendance
from the 2018 to 2019 seasons results in 0.3944. The standard threshold for a t-test to be
statistically significant is 0.0500.
Thus since the t-test is above 0.0500, I conclude that while the change
in regular season team attendance declined from 2018 to 2019, it did not
decline in a statistically significant manner.
Showing posts with label MLB. Show all posts
Showing posts with label MLB. Show all posts
Tuesday, October 8, 2019
2019 MLB Attendance Analysis
Today I want to focus on MLB
fan behavior by looking at MLB team regular season attendance. To do this, I grabbed the data off of ESPN’s website. So let’s look at the 2019
regular season attendance data. First, a
total of 68,478,648 attended (or at least bought tickets) to regular season MLB
games this past season. The LA Dodgers
had the highest total attendance (3,974,309 fans) and the Miami Marlins had the
lowest total attendance (811,302 fans).
The average number of fans over the season was 2,282,622; with a
standard deviation of 762,879.
Monday, October 7, 2019
2011-2019 MLB Payroll and Performance
Recently, I have blogged about MLB. First, I looked at competitive balance between the American League and the National League. Then I blogged regarding
MLB team payroll inequality and I showed that since 2011, MLB team payroll
inequality is rather large and increasing.
So are those two facts related? Specifically, I am looking at team
performance (as measured by each team’s regular season winning percent) and
each team’s relative payroll from 2011 to 2019.
In order to analyze this, I run a linear regression with team regular
season winning percent (the dependent variable) on team relative payroll (the independent variable), using
robust standard errors. I find the
following: first, relative payroll is
positive and statistically significant, meaning that an increase in a team’s
relative payroll leads to an increase in the team’s regular season winning
percent; and statistically significant means that I am at least 95% confident
that this relationship between team performance and relative payroll was not a
random result (assuming that the underlying hypothesis is true). That is great that we can know the these two
variables are related. But it would also
be helpful to know the size and by how much they are related.
In terms of the amount, we can use the estimated coefficient
from the regression to answer this question.
In the regression that I ran, I find that the coefficient (marginal
effect) is equal to 0.0788935. This
number is interpreted as follows: a one
unit increase in relative payroll on average yields a 0.0788935 increase in
regular season winning percent. So how
much is a one unit increase in relative payroll? Relative payroll is average payroll during
each season. Over the 2011 to 2019
seasons, relative payroll equals $129,404,591.
So the regression tells us that if a team increases their team payroll
by $129,404,591 that the average team’s regular season winning percent
increases from 0.500 to 0.508, which over a 162 game regular season means that
teams would win an additional 12.78 games. Another way of looking at it, is
that each win would result in an additional $10,124,963 spent on payroll. Now for an average team that does not seem
like a good deal, but for teams “on the bubble” of making or not making the
post-season, this might be a serious consideration.
Finally, how much does relative payroll explain regular
season winning percentage? In other
words, even if relative payroll is positive and statistically significant, how
much does the variation in relative payroll relate to the variation in regular
season winning percent? To answer that
question, we use the regression’s R2. From the regression results, the R2 is equal
to 0.1365, which I interpret as relative payroll “explains” only 13.65% of
regular season winning percent. Hence,
the explanatory power of relative payroll seems to be not very strong.
Think of it this way, if the weather forecast
states there is a 14% chance of rain, will you wear rain boots, a rain coat and
carry an umbrella for just a 14% chance?
I would not. In the same way,
should MLB general managers spend an additional $129 million dollars to increase winning
percent nearly 8%, when the “weather forecast” of rain is 14%?
Saturday, October 5, 2019
2011-2019 MLB Team Payroll Inequality
Yesterday, I took a look at competitive balance in MLB and found that MLB competitiveness was declining this season as compared to last season. So now, what I want
to look at is the level of team payroll inequality using team payroll data.
In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2019. As you can see, team payroll is highly unequal. Want to know how to calculate the Gini coefficient? Look here.
In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2019. As you can see, team payroll is highly unequal. Want to know how to calculate the Gini coefficient? Look here.
Friday, October 4, 2019
Competitve Balance in MLB
With the MLB's regular season in the books, I want to look at MLB over
the next few days with regard to: competitive balance, payroll
inequality, payroll and performance and attendance analysis. To begin, I
examine competitive balance in MLB using the regular season standings
and the Noll-Scully measure of competitive balance. For those not
familiar with this measure, start here. For those interested in calculating this on your own, try this step-by-step measure. For the calculations below, I am using data from Yahoo! Sports.
Major League Baseball has become less competitive as the Noll-Scully increased from 2.263 in 2018 to 2.452. In both leagues, competitive balance has decreased. The National League Noll-Scully increased from 1.579 in 2018 to 1.884, and in the American League the Noll-Scully increased from 2.793 in 2018 to 2.924.
MLB competitive balance waxes and wanes from season to season. Here is a graph of that in MLB since 2002.
Major League Baseball has become less competitive as the Noll-Scully increased from 2.263 in 2018 to 2.452. In both leagues, competitive balance has decreased. The National League Noll-Scully increased from 1.579 in 2018 to 1.884, and in the American League the Noll-Scully increased from 2.793 in 2018 to 2.924.
MLB competitive balance waxes and wanes from season to season. Here is a graph of that in MLB since 2002.
Friday, October 5, 2018
MLB Attendance Analysis
For the last three days I have blogged about Major League
Baseball. Today I want to focus on MLB
fan behavior by looking at MLB team regular season attendance. To do this, I grabbed the data off of ESPN’swebsite. The 2017 & 2018 data can be found here. So let’s look at the 2018
regular season attendance data. First, a
total of 69,649,736 attended (or at least bought tickets) to regular season MLB
games this past season. The LA Dodgers
had the highest total attendance (3,857,500 fans) and the Miami Marlins had the
lowest total attendance (811,104 fans).
The average number of fans over the season was 2,321,658, with a
standard deviation of 742,203.
Compared to the previous season, overall attendance at MLB
regular season games was down by over 3 million fans with the largest declines
by Toronto (-878,605) and Miami (-840,893).
Those two franchises represent over half of the total decline in MLB
regular season attendance alone. The
average decline in fan attendance was (-100,690) from the previous season. So, was this a (statistically) significant
decline in MLB attendance? To answer
that question, I will use a t-test, which is a statistical measure of the
relationship between two variables and then allows one to determine the degree
of confidence that the two variables have to each other. The t-test for regular season team attendance
from the 2017 to 2018 seasons results in 0.0954. The standard threshold for a t-test to be
statistically significant is 0.0500.
Thus since the t-test is above 0.0500, I conclude that while the change
in regular season team attendance declined from 2017 to 2018, it did not
decline in a statistically significant manner.
Thursday, October 4, 2018
MLB Payroll & Performance
The last two days I wrote about Major League Baseball. First, I took a look at competitive balance
between the American League and the National League and noted that while the
National League (in historical terms) was fairly competitive, the American League
was not. The following post looked at
MLB team payroll inequality and I showed that since 2011, MLB team payroll
inequality is rather large and increasing.
So are those two facts related? Specifically, I am looking at team
performance (as measured by each team’s regular season winning percent) and
each team’s relative payroll from 2011 to 2018. Data is found here.
In order to analyze this, I run a linear regression with team regular
season winning percent (the dependent variable) on team relative payroll (the independent variable), using
robust standard errors. I find the
following: first, relative payroll is
positive and statistically significant, meaning that an increase in a team’s
relative payroll leads to an increase in the team’s regular season winning
percent; and statistically significant means that I am at least 95% confident
that this relationship between team performance and relative payroll was not a
random result (assuming that the underlying hypothesis is true). That is great that we can know the these two
variables are related. But it would also
be helpful to know the size and by how much they are related.
In terms of the amount, we can use the estimated coefficient
from the regression to answer this question.
In the regression that I ran, I find that the coefficient (marginal
effect) is equal to 0.072997. This
number is interpreted as follows: a one
unit increase in relative payroll on average yields a 0.072997 increase in
regular season winning percent. So how
much is a one unit increase in relative payroll? Relative payroll is average payroll during
each season. Over the 2011 to 2018
seasons, relative payroll equals $128,432,967.
So the regression tells us that if a team increases their team payroll
by $128,432,967 that the average team’s regular season winning percent
increases from 0.500 to 0.507, which over a 162 game regular season means that
teams would win an additional 11.82 games. Another way of looking at it, is
that each win would result in an additional $10,860,711 spent on payroll. Now for an average team that does not seem
like a good deal, but for teams “on the bubble” of making or not making the
post-season, this might be a serious consideration.
Finally, how much does relative payroll explain regular
season winning percentage? In other
words, even if relative payroll is positive and statistically significant, how
much does the variation in relative payroll relate to the variation in regular
season winning percent? To answer that
question, we use the regression’s R2. From the regression results, the R2 is equal
to 0.1302, which I interpret as relative payroll “explains” only 13.02% of
regular season winning percent. Hence,
the explanatory power of relative payroll seems to be not very strong. Think of it this way, if the weather forecast
states there is a 13% chance of rain, will you wear rain boots, a rain coat and
carry an umbrella for just a 13% chance?
I would not. In the same way,
should MLB general managers spend $128 million dollars to increase winning
percent 7%, when the “weather forecast” of rain is 13%?
Wednesday, October 3, 2018
MLB Team Payroll Inequality
Yesterday, I took a look at competitive balance in MLB and found that while the leagues overall have been improving in terms of their competitiveness, the AL this past season did not. So now, what I want to look at is the level of team payroll inequality using team payroll data from www.spotrac.com.
In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2018. As you can see, team payroll is highly unequal. Want to know how to calculate the Gini coefficient? Look here.
In the chart below is the MLB Team Payroll Gini coefficients from 2011 to 2018. As you can see, team payroll is highly unequal. Want to know how to calculate the Gini coefficient? Look here.
So, if MLB team payroll is unequal as we can see from the Gini coefficients above, what is the relationship between payroll and performance? That I will address tomorrow.
Tuesday, October 2, 2018
Competitive Balance in Major League Baseball
With the MLB's regular season in the books, I want to look at MLB over the next four days with regard to: competitive balance, payroll inequality, payroll and performance and attendance analysis. To begin, I examine competitive balance in MLB using the regular season standings and the Noll-Scully measure of competitive balance. For those not familiar with this measure, start here. For those interested in calculating this on your own, try this step-by-step measure. For the calculations below, I am using data from Yahoo! Sports: https://sports.yahoo.com/mlb/standings/
First, let's start with the most recent MLB regular season: 2018. The Noll-Scully measure of competitive balance for the American League was: 2.793 and for the National League was: 1.579. Clearly, the National League was much more competitive this season than the American League. For MLB in total, the Noll-Scully was: 2.263. While this is indicative that MLB was not extremely competitive, is the 2018 regular season typical, more competitive or less competitive? To answer that, I calculate competitive balance since 2002 and display it on a graph below.
As you can see, for the National League, it is similar to previous seasons in terms of competitive balance, but for the American League, it is not.
First, let's start with the most recent MLB regular season: 2018. The Noll-Scully measure of competitive balance for the American League was: 2.793 and for the National League was: 1.579. Clearly, the National League was much more competitive this season than the American League. For MLB in total, the Noll-Scully was: 2.263. While this is indicative that MLB was not extremely competitive, is the 2018 regular season typical, more competitive or less competitive? To answer that, I calculate competitive balance since 2002 and display it on a graph below.
As you can see, for the National League, it is similar to previous seasons in terms of competitive balance, but for the American League, it is not.
Wednesday, October 11, 2017
2017 MLB Pay and Performance
Last week I blogged about competitive balance in MLB. Now I want to turn to the relationship between MLB payroll and MLB performance. I have looked at this using USA Today MLB team payroll data in the past (2015 and 2016), but now I want to use data from spotrac.com for MLB. I find that the data from spotrac is excellent and probably more accurate than the USA Today MLB team payroll data. The data from spotrac starts in 2011, so I have a smaller time frame to work with, but I thought it would be good to see if the relationship between payroll and performance differs using a different set of MLB team payroll data. If you are interested in doing this type of analysis, here is a step-by-step guide to payroll and performance analysis.
So, after collecting and organizing the 2011 through 2017 MLB team payroll and team performance data, I ran the regression on team performance using relative payroll as the independent variable. I use a software package called Stata for the statistical analysis and found that relative payroll is positive and statistically significant with respect to MLB team performance, just like I have found in each previous time I have done this type of statistical analysis using data from USA Today. The coefficient on relative payroll is 0.06868, which means that a one unit increase in relative payroll will lead to a 0.06868 increase in team winning percent. In terms of wins, the regression results show that a one unit increase in relative payroll would result in just over 11 additional wins. In order to get those wins, a MLB team would have to increase team payroll by one unit of relative payroll, which is the same thing as average payroll. For the 2017 MLB season, that is an increase of $152,327,084 using data from spotrac. So those approximately 11 wins would cost about $13,690,875 per win. That is a rather costly amount to pay for each win.
Finally, the regression results show that while the relationship between MLB team performance and relative payroll is positive and statistically significant, the amount that relative payroll "explains" of MLB team performance is not that much. In this case, using data from spotrac, the explanatory power of the regression is rather weak, with an adjusted r-squared equal to 0.127. Another way of saying that is that relative payroll only "explains" 12.7% of MLB team performance. I will let you decide if you think that is good enough. But will state that there are other variables that explain a greater amount of MLB team performance than relative payroll.
So, after collecting and organizing the 2011 through 2017 MLB team payroll and team performance data, I ran the regression on team performance using relative payroll as the independent variable. I use a software package called Stata for the statistical analysis and found that relative payroll is positive and statistically significant with respect to MLB team performance, just like I have found in each previous time I have done this type of statistical analysis using data from USA Today. The coefficient on relative payroll is 0.06868, which means that a one unit increase in relative payroll will lead to a 0.06868 increase in team winning percent. In terms of wins, the regression results show that a one unit increase in relative payroll would result in just over 11 additional wins. In order to get those wins, a MLB team would have to increase team payroll by one unit of relative payroll, which is the same thing as average payroll. For the 2017 MLB season, that is an increase of $152,327,084 using data from spotrac. So those approximately 11 wins would cost about $13,690,875 per win. That is a rather costly amount to pay for each win.
Finally, the regression results show that while the relationship between MLB team performance and relative payroll is positive and statistically significant, the amount that relative payroll "explains" of MLB team performance is not that much. In this case, using data from spotrac, the explanatory power of the regression is rather weak, with an adjusted r-squared equal to 0.127. Another way of saying that is that relative payroll only "explains" 12.7% of MLB team performance. I will let you decide if you think that is good enough. But will state that there are other variables that explain a greater amount of MLB team performance than relative payroll.
Friday, October 6, 2017
2017 MLB Competitive Balance
With the 2017 MLB regular season finished, let's take a look at competitive balance using the Noll-Scully measure of competitive balance. The Noll-Scully uses the actual standard deviation of a league's winning
percentage and compares it to a league if wins and losses were randomly
determined in a statistical sense. A Noll-Scully of 1.000 indicates
wins and losses were randomly determined and thus the league is
perfectly balanced in a competitive sense. A Noll-Scully higher
indicates that the league is less than perfectly balanced. As the value
increases the league is less competitive.
For the 2017 MLB regular season, we see that the American League had a competitive balance of 1.658 and the National League had a competitive balance of 1.897, meaning that the National League was less competitive than the American League. Overall, MLB had a Noll-Scully of 1.783, which is slightly more competitive than the league average Noll-Sculy since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
For the 2017 MLB regular season, we see that the American League had a competitive balance of 1.658 and the National League had a competitive balance of 1.897, meaning that the National League was less competitive than the American League. Overall, MLB had a Noll-Scully of 1.783, which is slightly more competitive than the league average Noll-Sculy since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
Wednesday, October 5, 2016
2016 MLB Payroll and Performance
Yesterday, I looked at how competitive MLB was using the Noll-Scully measure of competitive balance and today I want to look at the relationship (and its magnitude) between regular season MLB team payroll and MLB regular season team performance. If you are interested in doing this type of analysis, here is a step-by-step guide to payroll and performance analysis.
After combining the data and performing the statistical analysis, one finds the following. First, for the 2016 MLB regular season, there is a positive and statistically significant (t-statistic is greater than two in absolute value) relationship between team payroll and team performance. In fact, the relationship is that an additional increase of ten million dollars in one teams payroll would move an average team (i.e. the Kansas City Royals) from a winning percentage of 0.500 to 0.509 and an additional increase in team payroll equal to the average team payroll ($126 million) would move the average performing team from 0.500 to 0.614, which is basically an increase in 18 wins over the regular season or almost $7 million for each additional win. While for the average team this moves them from not making the playoffs to being a World Series contender, for teams like the Minnesota Twins, this moves them from being the worst team in MLB with a winning percent of 0.364 to a winning percent of 0.478 and still out of playoff contention.
Second, the performance of the regression is better than in years past, with the amount that the variation in team payroll "explains" the variation in team performance being around 39%.
After combining the data and performing the statistical analysis, one finds the following. First, for the 2016 MLB regular season, there is a positive and statistically significant (t-statistic is greater than two in absolute value) relationship between team payroll and team performance. In fact, the relationship is that an additional increase of ten million dollars in one teams payroll would move an average team (i.e. the Kansas City Royals) from a winning percentage of 0.500 to 0.509 and an additional increase in team payroll equal to the average team payroll ($126 million) would move the average performing team from 0.500 to 0.614, which is basically an increase in 18 wins over the regular season or almost $7 million for each additional win. While for the average team this moves them from not making the playoffs to being a World Series contender, for teams like the Minnesota Twins, this moves them from being the worst team in MLB with a winning percent of 0.364 to a winning percent of 0.478 and still out of playoff contention.
Second, the performance of the regression is better than in years past, with the amount that the variation in team payroll "explains" the variation in team performance being around 39%.
Tuesday, October 4, 2016
2016 MLB Competitive Balance
With the MLB regular season in the books, let's take a look at a measure of competitive balance using Noll-Scully measure of competitive balance. The Noll-Scully uses the actual standard deviation of a league's winning percentage and compares it to a league if wins and losses were randomly determined in a statistical sense. A Noll-Scully of 1.000 indicates wins and losses were randomly determined and thus the league is perfectly balanced in a competitive sense. A Noll-Scully higher indicates that the league is less than perfectly balanced. As the value increases the league is less competitive.
From the 2016 MLB regular season, we see that the American League had a competitive balance of 1.586 and the National League had a competitive balance of 1.713, meaning that the National League was less competitive than the American League. Overall, MLB had a Noll-Scully of 1.657, which is slightly more competitive than the league average Noll-Sculy since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
From the 2016 MLB regular season, we see that the American League had a competitive balance of 1.586 and the National League had a competitive balance of 1.713, meaning that the National League was less competitive than the American League. Overall, MLB had a Noll-Scully of 1.657, which is slightly more competitive than the league average Noll-Sculy since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
Wednesday, November 18, 2015
MLB Regular Season Attendance Analysis
Today I want to look at regular season MLB attendance and see if there has been any statistically significant changes in attendance from 2005 to 2015. I think this is a fairly stable time period in MLB, with no changes in team locations. So what has happened to regular season attendance?
In order to answer that question, I am going to look at average regular season home attendance for each team and compare it to average regular season home attendance for each team in the prior season. I grabbed the data from ESPN. I am using average regular season attendance since teams do play slightly different number of regular season home games from one season to another. To determine if average regular season home attendance is different from one season to the next, I will use a t-test.
After calculating the t-test for each pair of seasons (i.e. 2015 average home attendance for all 30 teams to 2014 average home attendance for all 30 teams) I find that there is no statistically significant difference in any pair of seasons. In other words, statistically for each season there is no difference in league attendance since 2005. In fact, there is no statistically significant difference between average regular season attendance between the 2005 season and the 2015 season.
In order to answer that question, I am going to look at average regular season home attendance for each team and compare it to average regular season home attendance for each team in the prior season. I grabbed the data from ESPN. I am using average regular season attendance since teams do play slightly different number of regular season home games from one season to another. To determine if average regular season home attendance is different from one season to the next, I will use a t-test.
After calculating the t-test for each pair of seasons (i.e. 2015 average home attendance for all 30 teams to 2014 average home attendance for all 30 teams) I find that there is no statistically significant difference in any pair of seasons. In other words, statistically for each season there is no difference in league attendance since 2005. In fact, there is no statistically significant difference between average regular season attendance between the 2005 season and the 2015 season.
Thursday, October 8, 2015
2015 MLB Pay and Performance
Yesterday I posted on competitive balance in the MLB and showed that competitive balance in the MLB is getting slightly better in the 2015 season. So the next step is to look at payroll and performance in MLB during the 2015 season. I have looked at payroll and performance previously and here is a blog from the end of last season in MLB. As you can see from that blog that the payroll and performance relationship has been declining since the mid-1990's. In fact, just taking a look at last season, there is no statistically significant relationship between team payroll and team regular season performance in MLB for the 2014 season. So, was 2014 a fluke or a trend. Let's find out by looking at the 2015 MLB regular season.
To do so, first i need the two variables: MLB regular season winning percent, which I grabbed from ESPN and MLB team payrolls which I got from USA Today's MLB team payroll page and second, I need to run the payroll and performance statistical analysis. (If you are interested in performing your own payroll and performance analysis, here is a step-by-step guide to analyzing payroll and performance.)
What I find is that team payroll for the 2015 MLB season is statistically insignificant, meaning that MLB team payroll has zero effect on MLB team regular season performance during this last season, just like in the 2014 season.
To do so, first i need the two variables: MLB regular season winning percent, which I grabbed from ESPN and MLB team payrolls which I got from USA Today's MLB team payroll page and second, I need to run the payroll and performance statistical analysis. (If you are interested in performing your own payroll and performance analysis, here is a step-by-step guide to analyzing payroll and performance.)
What I find is that team payroll for the 2015 MLB season is statistically insignificant, meaning that MLB team payroll has zero effect on MLB team regular season performance during this last season, just like in the 2014 season.
Wednesday, October 7, 2015
2015 MLB Competitive Balance
With the end of the Major League Baseball season, I have calculated the Noll-Scully measure of competitive balance
and find that MLB was 1.612 for the 2015 season. Below is a chart of
the Noll-Scully measure of competitive balance since 1982. For the
entire time period the average Noll-Scully measure of competitive
balance is 1.697, slightly more competitive than the average of 1.700 from 1982 to 2014. So for this time period, the 2015 MLB season was
more competitive than the average since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
Tuesday, February 24, 2015
2014 MLB Player Production with Runs Created
In the previous post I looked at MLB team runs created productivity. While the model does an excellent job of explaining how runs are created, here I want to focus on the individual batters for the 2014 season and look at their individual contributions to the team's runs.
To do that, I take the estimated coefficients from the MLB team Runs Created model and multiply them by the actual player statistics for each player in 2014. Calculating this yields an value of the players Runs Created. (If you are interested, I have blogged previously on how to calculate MLB Player Production using MLB Team Runs Created).
So, for the past season, the most productive player in terms of Runs Created was Mike Trout. The top 10 players for 2014 are listed in the table below.
For comparison purposes, here is the link for MLB player Runs Created in 2012 and 2013.
To do that, I take the estimated coefficients from the MLB team Runs Created model and multiply them by the actual player statistics for each player in 2014. Calculating this yields an value of the players Runs Created. (If you are interested, I have blogged previously on how to calculate MLB Player Production using MLB Team Runs Created).
So, for the past season, the most productive player in terms of Runs Created was Mike Trout. The top 10 players for 2014 are listed in the table below.
| playerID | teamID | Runs Created | ||
| troutmi01 | LAA | 127.23 | ||
| mccutan01 | PIT | 116.21 | ||
| bautijo02 | TOR | 113.96 | ||
| cabremi01 | DET | 112.95 | ||
| martivi01 | DET | 112.61 | ||
| brantmi02 | CLE | 111.68 | ||
| abreujo02 | CHA | 110.99 | ||
| altuvjo01 | HOU | 107.00 | ||
| stantmi03 | MIA | 105.63 | ||
| cruzne02 | BAL | 104.04 |
For comparison purposes, here is the link for MLB player Runs Created in 2012 and 2013.
Monday, February 23, 2015
2014 MLB Team Runs Created Productivity
In my Sports Economics course we look at how to estimate the
productivity of MLB players. In order to do that, we first look at
estimating the productivity of MLB teams using a model created by Asher
Blass that was published in 1992. Almost every year I update his model
using MLB data from Sean Lahman's database. So I did the MLB Team Runs Created analysis (step-by-step procedure to estimate MLB Team Runs Created)
for the 2014 season, and here are the results. In step 8, I talk about
the statistical analysis called a linear regression adjusting for
heteroskedasticity, which is what I am reporting below.
Here are the estimated results for these 15 seasons of data (2000-2014).
A few observations: first, other than grounded into double plays and caught stealing (GIDPCS) (along with the constant term) each of the variables is statistically significant at the 99% confidence level and of the correct sign. Only OUTS are negative and statistically significant. Second,the coefficient on HR's is greater than the coefficient on Singles, which means that a HR will on average generate more runs than a single. All the other coefficients seem to make sense as well.
For comparison purposes, here is the blog for 2013 MLB Team Runs Created.
Here are the estimated results for these 15 seasons of data (2000-2014).
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| SINGLE | 0.499 | 0.02 | 24.87 | 0.00 |
| DOUBLE | 0.718 | 0.04 | 16.86 | 0.00 |
| TRIPLE | 1.137 | 0.12 | 9.32 | 0.00 |
| HR | 1.463 | 0.04 | 37.62 | 0.00 |
| NBB | 0.288 | 0.02 | 14.31 | 0.00 |
| SB | 0.103 | 0.04 | 2.61 | 0.01 |
| GDIPCS | -0.028 | 0.06 | -0.48 | 0.63 |
| HBP | 0.398 | 0.08 | 4.88 | 0.00 |
| SF | 0.774 | 0.17 | 4.63 | 0.00 |
| OUTS | -0.102 | 0.02 | -4.19 | 0.00 |
| C | -10.644 | 106.03 | -0.10 | 0.92 |
A few observations: first, other than grounded into double plays and caught stealing (GIDPCS) (along with the constant term) each of the variables is statistically significant at the 99% confidence level and of the correct sign. Only OUTS are negative and statistically significant. Second,the coefficient on HR's is greater than the coefficient on Singles, which means that a HR will on average generate more runs than a single. All the other coefficients seem to make sense as well.
For comparison purposes, here is the blog for 2013 MLB Team Runs Created.
Friday, October 10, 2014
MLB Pay and Performance 1988 to 2014
With the MLB regular season in the books and the playoffs underway, I thought that I would get back to looking at MLB pay and performance. As we state in our book, The Wages of Wins, while there is a positive and statistically significant relationship between relative payroll and team performance (as measured by winning percent); the variation that is common between the two variables is not a lot. So what I will do here is measure both the relationship and the amount of common variation (using r-squared) for the MLB regular season over a few different time periods.
If you are interested in performing your own payroll and performance analysis, here is a step-by-step guide to analyzing payroll and performance.
First, the two variables are MLB regular season winning percent, which I grabbed from ESPN and MLB team payrolls which I got from USA Today's MLB team payroll page.
Second, as I have explained previously, I am using relative payroll because average payrolls have been increasing over time and average winning percent is constant over time. Hence if I use total payroll instead of relative payroll, I will find a weaker relationship since MLB total payrolls are increasing and MLB winning percent is constant. If I use relative payroll (a team's total payroll divided by that seasons average payroll) then I will be comparing two variables that are stationary to each other and get better results.
How much in common is the variation in relative payroll and the variation in winning percent by season?
1995-2014: 18.1%
2000-2014: 14.8%
2005-2014: 13.7%
2010-2014: 8.4%
2014: relative payroll is statistically insignificant.
Over different time periods we see that the explanatory power of relative payroll on team winning percent has been declining since 1995.
If you are interested in performing your own payroll and performance analysis, here is a step-by-step guide to analyzing payroll and performance.
First, the two variables are MLB regular season winning percent, which I grabbed from ESPN and MLB team payrolls which I got from USA Today's MLB team payroll page.
Second, as I have explained previously, I am using relative payroll because average payrolls have been increasing over time and average winning percent is constant over time. Hence if I use total payroll instead of relative payroll, I will find a weaker relationship since MLB total payrolls are increasing and MLB winning percent is constant. If I use relative payroll (a team's total payroll divided by that seasons average payroll) then I will be comparing two variables that are stationary to each other and get better results.
How much in common is the variation in relative payroll and the variation in winning percent by season?
1995-2014: 18.1%
2000-2014: 14.8%
2005-2014: 13.7%
2010-2014: 8.4%
2014: relative payroll is statistically insignificant.
Over different time periods we see that the explanatory power of relative payroll on team winning percent has been declining since 1995.
Sunday, September 28, 2014
Competitive Balance in MLB
With the end of the Major League Baseball season, I have calculated the Noll-Scully measure of competitive balance and find that MLB was 1.509 for the 2014 season. Below is a chart of the Noll-Scully measure of competitive balance since 1982. For the entire time period the average Noll-Scully measure of competitive balance is 1.700. So for this time period, the 2014 MLB season was more competitive than the average since 1982.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
If you are interested in doing this on your own, here is a step-by-step guide to calculate the Noll-Scully measure of competitive balance using Microsoft Excel.
Subscribe to:
Posts (Atom)