Correlation between mlb player age and sprint time
Scott Weiner
2023-02-28
https://rpubs.com/ScottWeiner19/1009344
Purpose
The objective of this document is to assess the correlation between the age of an mlb player and their 90 foot sprint time, which is the distance between bases.
#assessing relationship between baseball player age and 90 ft sprint (aka split) time
Speedofplayers <- read.csv(file ='running_splits_R.csv')
head(Speedofplayers)## last_name first_name player_id name_abbrev team_id position_name age bat_side
## 1 Abrams CJ 682928 WSH 120 SS 21 L
## 2 Abreu José 547989 CWS 145 1B 35 R
## 3 Acuña Jr. Ronald 660670 ATL 144 RF 24 R
## 4 Adames Willy 642715 MIL 158 SS 26 R
## 5 Adams Riley 656180 WSH 120 C 26 R
## 6 Adell Jo 666176 LAA 108 LF 23 R
## seconds_since_hit_000 seconds_since_hit_005 seconds_since_hit_010
## 1 0 0.53 0.82
## 2 0 0.57 0.92
## 3 0 0.54 0.85
## 4 0 0.56 0.88
## 5 0 0.56 0.89
## 6 0 0.55 0.85
## seconds_since_hit_015 seconds_since_hit_020 seconds_since_hit_025
## 1 1.07 1.30 1.51
## 2 1.20 1.45 1.68
## 3 1.10 1.33 1.54
## 4 1.14 1.39 1.61
## 5 1.16 1.41 1.64
## 6 1.11 1.34 1.55
## seconds_since_hit_030 seconds_since_hit_035 seconds_since_hit_040
## 1 1.71 1.90 2.09
## 2 1.90 2.11 2.32
## 3 1.74 1.93 2.12
## 4 1.82 2.02 2.21
## 5 1.86 2.07 2.27
## 6 1.75 1.94 2.13
## seconds_since_hit_045 seconds_since_hit_050 seconds_since_hit_055
## 1 2.27 2.44 2.62
## 2 2.52 2.71 2.90
## 3 2.30 2.48 2.65
## 4 2.40 2.59 2.77
## 5 2.47 2.66 2.86
## 6 2.31 2.48 2.65
## seconds_since_hit_060 seconds_since_hit_065 seconds_since_hit_070
## 1 2.79 2.95 3.12
## 2 3.09 3.27 3.46
## 3 2.82 2.99 3.16
## 4 2.94 3.12 3.29
## 5 3.04 3.23 3.42
## 6 2.82 2.99 3.15
## seconds_since_hit_075 seconds_since_hit_080 seconds_since_hit_085
## 1 3.29 3.45 3.63
## 2 3.64 3.83 4.02
## 3 3.32 3.49 3.65
## 4 3.47 3.64 3.81
## 5 3.60 3.79 3.97
## 6 3.31 3.47 3.64
## seconds_since_hit_090
## 1 3.82
## 2 4.22
## 3 3.82
## 4 3.99
## 5 4.17
## 6 3.81Age <- (Speedofplayers$age)
##Split_Time is time in seconds to sprint 90 ft.
Split_Time <- (Speedofplayers$seconds_since_hit_090)
x <- lm(Split_Time ~ Age)
summary(x)##
## Call:
## lm(formula = Split_Time ~ Age)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34066 -0.11242 -0.02036 0.09920 0.62404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.573538 0.049397 72.34 <2e-16 ***
## Age 0.017649 0.001756 10.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1551 on 572 degrees of freedom
## Multiple R-squared: 0.15, Adjusted R-squared: 0.1485
## F-statistic: 101 on 1 and 572 DF, p-value: < 2.2e-16resx <-residuals(x)
cf <-coef(x)
int <- cf[1]
sl <- cf[2]
int## (Intercept)
## 3.573538sl## Age
## 0.01764916Correlation and Residual Plots
You can also embed plots, for example:
plot(Split_Time ~ Age, main="Age vs 90 Foot Split Time",
ylab="Split Time (seconds)",
xlab= "Age slope = 0.0176 seconds R^2 = 0.155"
)
abline(x,lwd=6)
plot(resx, main= "Residual Plot")
abline(0,0)