SOCR EduMaterials Activities BaseballSalaryWins

Summary

Baseball Salary and Success

This activity will center around investigating how a MLB team's payroll correlates with success. The first step is to use simple regression to compare wins to payroll each year and overall. To do this, go to the mapping tab and select each year's payroll as the independent variable with the corresponding year's wins as the dependent variable.

Goals

The aims of this activity are to:

• investigate whether baseball salaries and team wins or losses are correlated.
• demonstrate the use of several SOCR exploratory and quantitative data analysis tools (e.g., SOCR analyses, SOCR charts).
• explore the relations between multiple variables.

Data

These data are collected from the following resources: Baseball Payroll, 2008, 2009, and 2010 standings.

Team	League	2010Wins	2010Losses	2010W-L%	2010Runs	2010RunsAgainst	2010Rundiff	2010Payroll	2009Wins	2009Losses	2009W-L%	2009Runs	2009RunsAgainst	2009Rundiff	2009Payroll	2008Wins	2008Losses	2008W-L%	2008Runs	2008RunsAgainst	2008Rundiff	2008Payroll	TotalWins	TotalPayroll
PHI	NL	97	65	0.599	4.8	4	0.8	141927381	93	69	0.574	5.1	4.4	0.7	113004048	92	70	0.568	4.9	4.2	0.7	98269880	282	353201309
TBR	AL	96	66	0.593	5	4	0.9	71923471	84	78	0.519	5	4.7	0.3	63313035	97	65	0.599	4.8	4.1	0.6	43820597	277	179057103
NYY	AL	95	67	0.586	5.3	4.3	1	206333389	103	59	0.636	5.6	4.6	1	201449289	89	73	0.549	4.9	4.5	0.4	209081577	287	616864255
MIN	AL	94	68	0.58	4.8	4.1	0.7	97559167	87	76	0.534	5	4.7	0.3	65299267	88	75	0.54	5.1	4.6	0.5	56932766	269	219791200
SFG	NL	92	70	0.568	4.3	3.6	0.7	97828833	88	74	0.543	4.1	3.8	0.3	82161450	72	90	0.444	4	4.7	-0.7	76594500	252	256584783
CIN	NL	91	71	0.562	4.9	4.2	0.6	72386544	78	84	0.481	4.2	4.5	-0.3	70968500	74	88	0.457	4.3	4.9	-0.6	74117695	243	217472739
ATL	NL	91	71	0.562	4.6	3.9	0.7	84423667	86	76	0.531	4.5	4	0.6	96726167	72	90	0.444	4.6	4.8	-0.2	102365683	249	283515517
TEX	AL	90	72	0.556	4.9	4.2	0.6	55250545	87	75	0.537	4.8	4.6	0.3	68646023	79	83	0.488	5.6	6	-0.4	67712326	256	191608894
SDP	NL	90	72	0.556	4.1	3.6	0.5	37799300	75	87	0.463	3.9	4.7	-0.8	42796700	63	99	0.389	3.9	4.7	-0.8	73677616	228	154273616
BOS	AL	89	73	0.549	5	4.6	0.5	162747333	95	67	0.586	5.4	4.5	0.8	122696000	95	67	0.586	5.2	4.3	0.9	133390035	279	418833368
CHW	AL	88	74	0.543	4.6	4.3	0.3	108273197	79	83	0.488	4.5	4.5	0	96068500	89	74	0.546	5	4.5	0.5	121189332	256	325531029
STL	NL	86	76	0.531	4.5	4	0.6	93540753	91	71	0.562	4.5	4	0.6	88528411	86	76	0.531	4.8	4.5	0.3	99624449	263	281693613
TOR	AL	85	77	0.525	4.7	4.5	0.2	62689357	75	87	0.463	4.9	4.8	0.2	80993657	86	76	0.531	4.4	3.8	0.6	97793900	246	241476914
COL	NL	83	79	0.512	4.8	4.4	0.3	84227000	92	70	0.568	5	4.4	0.5	75201000	74	88	0.457	4.6	5.1	-0.5	68655500	249	228083500
OAK	AL	81	81	0.5	4.1	3.9	0.2	51654900	75	87	0.463	4.7	4.7	0	62310000	75	86	0.466	4	4.3	-0.3	47967126	231	161932026
DET	AL	81	81	0.5	4.6	4.6	0	122864929	86	77	0.528	4.6	4.6	0	115085145	74	88	0.457	5.1	5.3	-0.2	137685196	241	375635270
FLA	NL	80	82	0.494	4.4	4.4	0	55641500	87	75	0.537	4.8	4.7	0	36814000	84	77	0.522	4.8	4.8	0	21811500	251	114267000
LAA	AL	80	82	0.494	4.2	4.3	-0.1	105013667	97	65	0.599	5.5	4.7	0.8	113709000	100	62	0.617	4.7	4.3	0.4	119216333	277	337939000
LAD	NL	80	82	0.494	4.1	4.3	-0.2	94945517	95	67	0.586	4.8	3.8	1	100458101	84	78	0.519	4.3	4	0.3	118588536	259	313992154
NYM	NL	79	83	0.488	4	4	0	132701445	70	92	0.432	4.1	4.7	-0.5	135773988	89	73	0.549	4.9	4.4	0.5	137793376	238	406268809
MIL	NL	77	85	0.475	4.6	5	-0.3	81108279	80	82	0.494	4.8	5	-0.2	80257502	90	72	0.556	4.6	4.3	0.4	80937499	247	242303280
HOU	NL	76	86	0.469	3.8	4.5	-0.7	92355500	74	88	0.457	4	4.8	-0.8	102996415	86	75	0.534	4.4	4.6	-0.2	88930414	236	284282329
CHC	NL	75	87	0.463	4.2	4.7	-0.5	146859000	83	78	0.516	4.4	4.2	0.2	135050000	97	64	0.602	5.3	4.2	1.1	118345833	255	400254833
CLE	AL	69	93	0.426	4	4.6	-0.7	61203967	65	97	0.401	4.8	5.3	-0.6	81625567	81	81	0.5	5	4.7	0.3	78970066	215	221799600
WSN	NL	69	93	0.426	4	4.6	-0.5	61425000	59	103	0.364	4.4	5.4	-1	59328000	59	102	0.366	4	5.1	-1.1	54961000	187	175714000
KCR	AL	67	95	0.414	4.2	5.2	-1	72267710	65	97	0.401	4.2	5.2	-1	70908333	75	87	0.463	4.3	4.8	-0.6	58245500	207	201421543
BAL	AL	66	96	0.407	3.8	4.8	-1.1	81612500	64	98	0.395	4.6	5.4	-0.8	67101667	68	93	0.422	4.9	5.4	-0.5	67196246	198	215910413
ARI	NL	65	97	0.401	4.4	5.2	-0.8	60718167	70	92	0.432	4.4	4.8	-0.4	73571667	82	80	0.506	4.4	4.4	0.1	66202712	217	200492546
SEA	AL	61	101	0.377	3.2	4.3	-1.1	98376667	85	77	0.525	4	4.3	-0.3	98904167	61	101	0.377	4.1	5	-0.9	117666482	207	314947316
PIT	NL	57	105	0.352	3.6	5.3	-1.7	34943000	62	99	0.385	4	4.8	-0.8	48743000	67	95	0.414	4.5	5.5	-0.9	48689783	186	132375783

Data Analysis

Exploratory data analysis (EDA)

The correlations between payroll and team success for 2008, 2009, 2010, and overall are 0.327, 0.501, 0.349, and 0.520, respectfully. This indicates that the strength of the relationship varies from year to year.

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Quantitative data analysis (QDA)

ANOVA

The next step is to compare the average payroll each year in the American League and National League. To do this, use ANOVA and select league as the independent variable and each year's payroll as the dependent variable. The p-value for each year is 0.328 for 2008, 0.442 for 2009, 0.434 for 2010 and 0.381 overall. This suggests that there is no significant difference between the two leagues.

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T-Test

Another thing to check is how the average payroll changed from year to year. To do this, select the Two-paired sample T test and choose 2008 and 2009 as the variables, then repeat with 2009 and 2010 as the variables. The test for 2008 and 2009 has a p-value of .315. The test for 2009 and 2010 has a p-value of .167. This means there isn't evidence of a significant increase in payroll each year.

Multiple linear regression

As a team's record is heavily influenced by the number of runs they score and the number of runs they allow, it would be interesting to check the relationship between each of those and wins as well as the relationship between them and payroll. To do this, first use multiple regression with wins each year as the dependent variable and runs and runs against the independent variables.

Conclusion

• For 2008, the slope estimates are 17.421 for runs and -16.904 for runs against. For 2009, the slope estimates are 15.715 for runs and -19.459 for runs against. For 2010, the slope estimates are 15.538 for runs and -14.809 for runs against. This suggests that scoring more runs was slightly more important in 2008 and 2010 while allowing fewer runs was more important in 2009. Next, use simple regression to compare the correlation and plot the relationship between payroll and runs and between payroll and runs against each year.

• In 2008, the correlation between payroll and runs was .281, and the correlation between payroll and runs against was -.261. For 2009 they were .372 and -.290 respectively. For 2010 they were .364 and -.111 respectively. This suggests that it is easier to use payroll spending improve hitting than it is to improve pitching. This suggests that the ability to still get good pitching is what allows some teams with lower payrolls to remain competitive.

See also

• Baseball Player Height, Weight and Age Dataset

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• SOCR Home page: http://www.socr.ucla.edu

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