Baseball findings l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 30

Baseball Findings PowerPoint PPT Presentation


  • 119 Views
  • Uploaded on
  • Presentation posted in: General

Baseball Findings . The statistics behind the game. Harlan Thompson Sungjin Cho Ryan Fagan. An Introduction.

Download Presentation

Baseball Findings

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Baseball findings l.jpg

Baseball Findings

The statistics behind the game

Harlan Thompson

Sungjin Cho

Ryan Fagan


An introduction l.jpg

An Introduction

  • Throughout its long history, baseball has been the subject of many statistical studies. It lends itself well to statistics because very careful records are kept of everything that happens in every game.

  • The topics that have been studied range from the affect of interleague play on team standings to the role of chance in streaks and slumps

  • Other topics of study include records and predicting the outcomes of games.

  • We thought that looking at home runs and salary would be interesting because the great number of home runs hit and the inflation of salaries are both controversial topics.


Slide3 l.jpg

Home Runs Per Year

-How has the total number of home runs in major league baseball changed from year to year?


Test 1 l.jpg

Test #1

  • We ran a regression with the year as the independent variable and the number of home runs as the dependent variable to find out the rate at which the number of home runs in the league is increasing.


Scatterplot l.jpg

Scatterplot


Results l.jpg

Results

Source | SS df MS Number of obs = 25

-------------+------------------------------ F( 1, 23) = 21.69

Model | .911664082 1 .911664082 Prob > F = 0.0001

Residual | .966758514 23 .042032979 R-squared = 0.4853

-------------+------------------------------ Adj R-squared = 0.4630

Total | 1.8784226 24 .078267608 Root MSE = .20502

------------------------------------------------------------------------------

hr | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

year | .0264817 .0056862 4.66 0.000 .0147189.0382445

_cons | 1.350108 .079607 16.96 0.000 1.185428 1.514787

------------------------------------------------------------------------------


Interpretation l.jpg

Interpretation

  • The 95% confidence interval for the coefficient of year is totally positive - this shows that the number of home runs is definitely increasing each year.

  • An R2 value of .4853 clearly shows a positive relationship, although not a very strong one. This could be because many other factors can affect the number of home runs hit -- weather, injuries to certain players, etc.

  • The coefficient of year is .0264817, so each year about .02648 more home runs are hit in each game. This is over 4 more home runs per year.


Test 2 l.jpg

Test #2

  • We split up the home run data into 2 separate groups 1976-1987 and 1988-1999.

  • Then we ran a hypothesis test on the two groups to find out if their variances are equal to determine whether or not we could use a paired t test on the data.

  • We used the following hypotheses:

H0 : var(HR (‘76 - ‘87)) = var(HR(‘88-’99))

HA : var(HR(‘76 - ‘87)) not= var(HR(‘88-’99))


Slide9 l.jpg

Results

------------------------------------------------------------------------------

Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

---------+--------------------------------------------------------------------

hr1 | 12 1.584108 .073678 .255228 1.421944 1.746272

hr2 | 12 1.775 .0807902 .2798653 1.597182 1.952818

---------+--------------------------------------------------------------------

Comb. | 24 1.679554 .0570527 .2794998 1.561532 1.797577

------------------------------------------------------------------------------

Ho: sd(hr1) = sd(hr2)

F(11,11) observed = F_obs = 0.832

F(11,11) lower tail = F_L = F_obs = 0.832

F(11,11) upper tail = F_U = 1/F_obs = 1.202

Critical values at .05 significance level: (.288, 3.47)

Because the F statistic does not lie outside of this region, we cannot reject the null hypothesis!!


Interpretation10 l.jpg

Interpretation

  • The variance in home run hitting has not changed significantly over the past 25 years.

  • Therefore we can use these two sets of data in a paired t test to determine whether or not the number of home runs hit has increased.


Test 3 l.jpg

Test #3

  • Because we found that the two groups did not have an appreciable difference in variance, we can use a paired t test to determine whether or not the number of home runs hit per year has risen from the period 1976-1987 to the period 1988-1999.

  • So we ran a hypothesis test on the two groups with the following hypotheses:

H0 : HR (‘76 - ‘87) = HR(‘88-’99)

HA : HR(‘76 - ‘87) not= HR(‘88-’99)


Results12 l.jpg

Results

Paired t test

------------------------------------------------------------------------------

Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

---------+--------------------------------------------------------------------

hr1 | 12 1.584108 .073678 .255228 1.421944 1.746272

hr2 | 12 1.775 .0807902 .2798653 1.597182 1.952818

---------+--------------------------------------------------------------------

diff | 12 -.1908917 .0665798 .230639 -.3374327 -.0443506

------------------------------------------------------------------------------

Ho: mean(hr1 - hr2) = mean(diff) = 0

Ha: mean(diff) < 0 Ha: mean(diff) ~= 0 Ha: mean(diff) > 0

t = -2.8671 t = -2.8671 t = -2.8671

P < t = 0.0077 P > |t| = 0.0153 P > t = 0.9923


Interpretation13 l.jpg

Interpretation

  • The mean for the years from 1976 to 1987 was 1.584108 HR/game vs. 1.775 HR/game from 1988 to 1999.

  • We can reject our null hypothesis because we found t = -2.8671 (much less than the critical value -1.96).

  • The the probability of Type I error is only .0153.

  • Therefore, the mean number of home runs per game from 1988 to 1999 was significantly greater than the mean number from ‘76 to ‘87.

  • So, the number of home runs per year does seem to be increasing over time.


Home runs by position l.jpg

Home Runs by Position

First we looked at last year’s home runs by position for each team.

The following is a sample of the data we accumulated...

TeamSS HR1B HR2B HR3B HR C HRLF HRCF HRRF HRTOT HR

Anaheim63694714352534206

NY Mets422252413151718138

San Fran2019331014491224181

Next we calculated the total number of home runs and at bats as well as the average

number of home runs per at bat from each position for the whole league

(in order of performance)...

PositionHR/AB HRs ABs

First Base0.051925 752 14737

Left Field0.047185 629 13098

Right Field0.0462273 667 14314

Center Field0.0391384 627 15623

Third Base0.038288 523 13154

Catcher0.0348063 381 10681

Shortstop0.0243771 354 14050

Second Base0.0239095 300 14535


Do some positions hit significantly more than the average l.jpg

Do some positions hit significantly more than the average?

  • The league average of home runs per at bat is .0384.

  • For each position, we used binomial hypothesis tests to test whether or not the number of home runs per at bat from that position differs significantly from the mean.

  • For each position,

Ho : HR/AB = .0384

HA : HR/AB not= .0384

(Reject if |z| > 1.96)


Results16 l.jpg

Results

SIGNIFICANTLY BETTER (reject null)

  • First Base: z = 7.978

  • Left Field: z = 5.731

  • Right Field: z = 5.104

ABOUT AVERAGE (accept null)

  • Center Field: z = 1.127

  • Third Base: z = 0.812

  • Catcher: z = -1.468

BELOW AVERAGE (reject null)

  • Shortstop: z = -8.145

  • Second Base: z = -11.143


Interpretation17 l.jpg

Interpretation

  • So, we’ve proven that first basemen, left fielders and right fielders are significantly above the mean in home run hitting.

  • Shortstop and second basemen are significantly below the mean in home run hitting.

  • Center fielders, third basemen and catchers are about average.

  • This makes sense - the players at positions that require the most mobility (shortstop, second base) would obviously not be as powerful as those who play positions require less speed and agility.

  • It is interesting that center fielders are significantly different from the other outfielders - they do have to have a lot more flexibility and speed.


Does salary affect performance l.jpg

Does salary affect performance?

  • We looked at team salary vs. number of wins to see if the amount of money paid to the players has a significant affect on a team’s performance. Below is some of the data we used.


Wins vs payroll for 2000 l.jpg

Wins vs. Payroll for 2000


Wins vs payroll for 1999 l.jpg

Wins vs. Payroll for 1999


Wins vs payroll for 1998 l.jpg

Wins vs. Payroll for 1998


Results for 2000 l.jpg

Results for 2000


Results for 1999 l.jpg

Results for 1999


Results for 1998 l.jpg

Results for 1998


Interpretation25 l.jpg

Interpretation

  • The R2 value for the year 2000 (.1952) did not reflect a significant correlation, however years 1998 (.5442) and 1999 (.4691) reflect a relationship between total payroll and number of wins

  • Because the coefficient of the number of wins is roughly 1 for all three years, we can conclude that an additional win costs about a million dollars.


Salary and home run hitting l.jpg

Salary and home run hitting

  • Finally, we thought we’d combine these two studies of salary and home run hitting and analyze how the changes in average salary have been resulted in changes in the number of home runs hit per person. Exactly how many more home runs are we getting per $1?

  • We looked at data from 1969 to 2000.

  • We found average salary but we could not find average number of home runs/player. However we thought the leader in home run percentage might give some kind of portrayal of the number of home runs being hit.


Salary vs home runs l.jpg

Salary vs. Home Runs

YearSalary(thousands)HR Pct LeaderYearSalary(thousands)HR Pct Leader

196924.99.161985371.67.92

197029.37.951986412.57.08

197131.59.491987412.58.8

197234.17.571988438.77.18

197336.610.21989497.38.66

197440.86.931990597.58.9

197544.77.171991851.57.69

197651.57.8119921028.78.99

197776.18.4619931076.18.58

197899.97.1819941168.39.75

1979113.69.0219951110.812.3

1980143.88.761996112012.29

1981185.78.7619971336.69.29

1982241.57.4519981398.813.75

1983289.27.4919991611.212.48

1984329.46.8720001895.610.21


Regression l.jpg

Regression


Results29 l.jpg

Results

Source | SS df MS Number of obs = 32

-------------+------------------------------ F( 1, 30) = 22.14

Model | 40.3836608 1 40.3836608 Prob > F = 0.0000

Residual | 54.7139269 30 1.82379756 R-squared = 0.4247

-------------+------------------------------ Adj R-squared = 0.4055

Total | 95.0975877 31 3.06766412 Root MSE = 1.3505

------------------------------------------------------------------------------

hrpct | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------

sal | .002094 .000445 4.71 0.000 .0011852 .0030029

_cons | 7.760353 .3369654 23.03 0.000 7.072178 8.448528

------------------------------------------------------------------------------


Interpretation30 l.jpg

Interpretation

  • The coefficient of salary is .002094 and the entire confidence interval for this value is positive. So it seems that an increase in salary may produce an increase in home run hitting.

  • For every additional hundred thousand dollars in average salary, the leading home run hitter would hit home runs .2% more.

  • We found an R2 value of .4247, which is fairly significant. However, from 1969 to 1976, salary stayed fairly standard (compared to the inflation today), so this may have hurt our regression since home runs were increasing at the time, although not as rapidly as recently.

  • This suggests that home runs and salary may be increasing independently through time. There may not be an actual relationship between the two. Further study would be needed to determine if they are related.


  • Login