** Recommended:** Listen to the Podcast Episode that accompanies this analysis

* Goal:* To investigate the interactive effect of throwing hand and batting stance on hitting performance using Major League Baseball data from 1992-2015

*Data source:* *http://www.baseball-reference.com/play-index/*

*Descriptive Statistics:*

(values in black are proportions)

*Key takeaway:*

- The majority of players (62% of lefties and 89% of righties) hit with their non-dominant hand closest to the knob of the bat

*Batting Average:*

(text in white shadow boxes is R code)

[shadowbox]>attach(BattingDataALL.new)

>boxplot (BA~Type, xlab=”Hitter Type”, ylab=”Batting Avg”)[/shadowbox]

*ANOVA*

[shadowbox]>aov1<- aov(lm(BA~Throw*Bat))

*t-tests*

*Left-handed players*

[shadowbox]

>lhanddat <- battingdataall[which(battingdataall$Throw=”=’L’),]

>t.test(BA ~ Bat, data=lhanddat)

[/shadowbox]

t = 2.9203, df = 11.385, p-value = 0.01348*

mean in group L mean in group R

0.2667065 0.2517000

*Right-handed players*

[shadowbox]

>rhanddat <- battingdataall[which(battingdataall$Throw=”=’R’),]

>t.test(BA ~ Bat, data=rhanddat)

[/shadowbox]

t = -13.435, df = 349.25, p-value < 2.2e-16***

mean in group L mean in group R

0.266295 0.285635

*Key takeaway:*

- Over this period, right-handed batters have a higher batting average than left-handed batters
- For both left-handed and right-hand batters, batting average is significant higher for batters that hit with their non-dominant hand closest to the knob of the bat

*On Base Percentage (OBP)*

[shadowbox]>boxplot (OBP~Type, xlab=”Hitter Type”, ylab=”OBP”)[/shadowbox]

*ANOVA*

[shadowbox]> aov2 <- aov(lm(OBP~Throw*Bat))

*t-tests*

*Left-handed:*

t = 1.8323, df = 9.9128, p-value = 0.09708

mean in group L mean in group R

0.337413 0.318600

*Right-handed:*

t = -3.799, df = 397.12, p-value = 0.000168**

mean in group L mean in group R

0.337390 0.346675

*Key takeaway:*

- Over this period, right-handed batters have a higher OBP than left-handed batters
- There is no significant effect of batting side for left-hand dominant players
- For right-hand dominant players, OBP is higher for right-handed batters

**Slugging Percentage (SLG)**

[shadowbox]>boxplot (SLG~Type, xlab=”Hitter Type”, ylab=”SLG”)[/shadowbox]

[shadowbox]> aov3 <- aov(lm(SLG~Throw*Bat))

**t-tests**

*Left-handed*

t = 0.42752, df = 10.761, p-value = 0.6774

mean in group L mean in group R

0.4206413 0.4150000

*Right-handed*

t = -6.4881, df = 397.3, p-value = 2.59e-10***

mean in group L mean in group R

*Key takeaway:*

- Over this period, right-handed batters have a higher SLG than left-handed batters
- There is no significant effect of batting side for left-hand dominant players
- For right-hand dominant players, SLG is higher for right-handed batters

**Stolen Bases (SB**)

[shadowbox]> aov4 <- aov(lm(SB~Throw*Bat))

> summary(aov4)[/shadowbox]

*Key takeaway:*

- Surprisingly, the number of stolen bases also seems to be related to the batting stance and hand dominance