If a baseball team scores 763 runs and allows 614 runs, how many games do we expect the team to win?

Using the linear regression model constructed during the lecture, enter the number of games we expect the team to win:

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# Read in data
baseball = read.csv("C:/Users/17862/Documents/SPORTS_ANALYTICS/Intro_to_R/baseball.csv")
str(baseball)
## 'data.frame':    1232 obs. of  15 variables:
##  $ Team        : chr  "ARI" "ATL" "BAL" "BOS" ...
##  $ League      : chr  "NL" "NL" "AL" "AL" ...
##  $ Year        : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
##  $ RS          : int  734 700 712 734 613 748 669 667 758 726 ...
##  $ RA          : int  688 600 705 806 759 676 588 845 890 670 ...
##  $ W           : int  81 94 93 69 61 85 97 68 64 88 ...
##  $ OBP         : num  0.328 0.32 0.311 0.315 0.302 0.318 0.315 0.324 0.33 0.335 ...
##  $ SLG         : num  0.418 0.389 0.417 0.415 0.378 0.422 0.411 0.381 0.436 0.422 ...
##  $ BA          : num  0.259 0.247 0.247 0.26 0.24 0.255 0.251 0.251 0.274 0.268 ...
##  $ Playoffs    : int  0 1 1 0 0 0 1 0 0 1 ...
##  $ RankSeason  : int  NA 4 5 NA NA NA 2 NA NA 6 ...
##  $ RankPlayoffs: int  NA 5 4 NA NA NA 4 NA NA 2 ...
##  $ G           : int  162 162 162 162 162 162 162 162 162 162 ...
##  $ OOBP        : num  0.317 0.306 0.315 0.331 0.335 0.319 0.305 0.336 0.357 0.314 ...
##  $ OSLG        : num  0.415 0.378 0.403 0.428 0.424 0.405 0.39 0.43 0.47 0.402 ...
# Subset to only include moneyball years
moneyball = subset(baseball, Year < 2002)
str(moneyball)
## 'data.frame':    902 obs. of  15 variables:
##  $ Team        : chr  "ANA" "ARI" "ATL" "BAL" ...
##  $ League      : chr  "AL" "NL" "NL" "AL" ...
##  $ Year        : int  2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
##  $ RS          : int  691 818 729 687 772 777 798 735 897 923 ...
##  $ RA          : int  730 677 643 829 745 701 795 850 821 906 ...
##  $ W           : int  75 92 88 63 82 88 83 66 91 73 ...
##  $ OBP         : num  0.327 0.341 0.324 0.319 0.334 0.336 0.334 0.324 0.35 0.354 ...
##  $ SLG         : num  0.405 0.442 0.412 0.38 0.439 0.43 0.451 0.419 0.458 0.483 ...
##  $ BA          : num  0.261 0.267 0.26 0.248 0.266 0.261 0.268 0.262 0.278 0.292 ...
##  $ Playoffs    : int  0 1 1 0 0 0 0 0 1 0 ...
##  $ RankSeason  : int  NA 5 7 NA NA NA NA NA 6 NA ...
##  $ RankPlayoffs: int  NA 1 3 NA NA NA NA NA 4 NA ...
##  $ G           : int  162 162 162 162 161 162 162 162 162 162 ...
##  $ OOBP        : num  0.331 0.311 0.314 0.337 0.329 0.321 0.334 0.341 0.341 0.35 ...
##  $ OSLG        : num  0.412 0.404 0.384 0.439 0.393 0.398 0.427 0.455 0.417 0.48 ...
# Compute Run Difference
moneyball$RD = moneyball$RS - moneyball$RA
str(moneyball)
## 'data.frame':    902 obs. of  16 variables:
##  $ Team        : chr  "ANA" "ARI" "ATL" "BAL" ...
##  $ League      : chr  "AL" "NL" "NL" "AL" ...
##  $ Year        : int  2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
##  $ RS          : int  691 818 729 687 772 777 798 735 897 923 ...
##  $ RA          : int  730 677 643 829 745 701 795 850 821 906 ...
##  $ W           : int  75 92 88 63 82 88 83 66 91 73 ...
##  $ OBP         : num  0.327 0.341 0.324 0.319 0.334 0.336 0.334 0.324 0.35 0.354 ...
##  $ SLG         : num  0.405 0.442 0.412 0.38 0.439 0.43 0.451 0.419 0.458 0.483 ...
##  $ BA          : num  0.261 0.267 0.26 0.248 0.266 0.261 0.268 0.262 0.278 0.292 ...
##  $ Playoffs    : int  0 1 1 0 0 0 0 0 1 0 ...
##  $ RankSeason  : int  NA 5 7 NA NA NA NA NA 6 NA ...
##  $ RankPlayoffs: int  NA 1 3 NA NA NA NA NA 4 NA ...
##  $ G           : int  162 162 162 162 161 162 162 162 162 162 ...
##  $ OOBP        : num  0.331 0.311 0.314 0.337 0.329 0.321 0.334 0.341 0.341 0.35 ...
##  $ OSLG        : num  0.412 0.404 0.384 0.439 0.393 0.398 0.427 0.455 0.417 0.48 ...
##  $ RD          : int  -39 141 86 -142 27 76 3 -115 76 17 ...
# Regression model to predict wins
WinsReg = lm(W ~ RD, data=moneyball)
summary(WinsReg)
## 
## Call:
## lm(formula = W ~ RD, data = moneyball)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.2662  -2.6509   0.1234   2.9364  11.6570 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 80.881375   0.131157  616.67   <2e-16 ***
## RD           0.105766   0.001297   81.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.939 on 900 degrees of freedom
## Multiple R-squared:  0.8808, Adjusted R-squared:  0.8807 
## F-statistic:  6651 on 1 and 900 DF,  p-value: < 2.2e-16

IN_ACTIVITY_CLASS_7

runs difference

763-614
## [1] 149

RD = 149

#numbers of wins
Wins = 80.88 +0.105766*(149)
Wins
## [1] 96.63913

The team would win approximately 96 games.