baseball <- read.csv("baseball.csv")
moneyball <- subset(baseball, baseball$Year < 2002)
moneyball$RD <- moneyball$RS - moneyball$RA
str(moneyball)## 'data.frame': 902 obs. of 16 variables:
## $ Team : Factor w/ 39 levels "ANA","ARI","ATL",..: 1 2 3 4 5 7 8 9 10 11 ...
## $ League : Factor w/ 2 levels "AL","NL": 1 2 2 1 1 2 1 2 1 2 ...
## $ 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 ...Model for Run Scored using OBP and SLG
fit.runs <- lm(RS ~ OBP + SLG, data=moneyball)
summary(fit.runs)##
## Call:
## lm(formula = RS ~ OBP + SLG, data = moneyball)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.838 -17.174 -1.108 16.770 90.036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -804.63 18.92 -42.53 <2e-16 ***
## OBP 2737.77 90.68 30.19 <2e-16 ***
## SLG 1584.91 42.16 37.60 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.79 on 899 degrees of freedom
## Multiple R-squared: 0.9296, Adjusted R-squared: 0.9294
## F-statistic: 5934 on 2 and 899 DF, p-value: < 2.2e-16The list of players:
players <- data.frame(name=c("Eric", "Jeremy", "Frank", "Greg", "Carlos"),
OBP=c(.338, .391, .369, .313, .361),
SLG=c(.540, .450, .374, .447, .500),
salary1000=c(1400, 1065, 295, 800, 300))
players## name OBP SLG salary1000
## 1 Eric 0.338 0.540 1400
## 2 Jeremy 0.391 0.450 1065
## 3 Frank 0.369 0.374 295
## 4 Greg 0.313 0.447 800
## 5 Carlos 0.361 0.500 300Given $1.5M budget, which 2 players to choose?
for (i in 1:nrow(players)) {
for (j in i:nrow(players)) {
if (i != j) {
salary <- players[i,4] + players[j,4]
if (salary <= 1500) {
obp = (players[i,2] + players[j,2]) * 0.5
slg = (players[i,3] + players[j,3]) * 0.5
p <- predict(fit.runs, data.frame(OBP=obp, SLG=slg))
print(paste(players[i,1], players[j,1], p))
}
}
}
}## [1] "Jeremy Frank 888.707132850523"
## [1] "Jeremy Carlos 977.60530290546"
## [1] "Frank Greg 779.556817073763"
## [1] "Frank Carlos 887.263327652974"
## [1] "Greg Carlos 868.4549871287"In 2012 and 2013, there were 10 teams in the MLB playoffs: the six teams that had the most wins in each baseball division, and four “wild card” teams. The playoffs start between the four wild card teams - the two teams that win proceed in the playoffs (8 teams remaining). Then, these teams are paired off and play a series of games. The four teams that win are then paired and play to determine who will play in the World Series.
We can assign rankings to the teams as follows:
Rank 1: the team that won the World Series
Rank 2: the team that lost the World Series
Rank 3: the two teams that lost to the teams in the World Series
Rank 4: the four teams that made it past the wild card round, but lost to the above four teams
Rank 5: the two teams that lost the wild card round
teamRank <- c(1,2,3,3,4,4,4,4,5,5)
t2012 <- data.frame(name=c("SanFran",
"Detroit",
"NY", "StLouis",
"Baltimore", "Oakland", "Washington", "Cincinnati",
"Texas", "Atlanta"),
rank=c(1,
2,
3, 3,
4, 4, 4, 4,
5, 5),
wins=c(94,
88,
95, 88,
93, 94, 98, 97,
93, 94))
(t2012)## name rank wins
## 1 SanFran 1 94
## 2 Detroit 2 88
## 3 NY 3 95
## 4 StLouis 3 88
## 5 Baltimore 4 93
## 6 Oakland 4 94
## 7 Washington 4 98
## 8 Cincinnati 4 97
## 9 Texas 5 93
## 10 Atlanta 5 94t2013 <- data.frame(name=c("Boston",
"StLouis",
"LA", "Detroit",
"TampaBay", "Oakland", "Pittsburgh", "Atlanta",
"Cleveland", "Cincinnati"),
rank=c(1,
2,
3, 3,
4, 4, 4, 4,
5, 5),
wins=c(97,
97,
92, 93,
92, 96, 94, 96,
92, 90))
(t2013)## name rank wins
## 1 Boston 1 97
## 2 StLouis 2 97
## 3 LA 3 92
## 4 Detroit 3 93
## 5 TampaBay 4 92
## 6 Oakland 4 96
## 7 Pittsburgh 4 94
## 8 Atlanta 4 96
## 9 Cleveland 5 92
## 10 Cincinnati 5 90What is the correlation between teamRank and wins2012?
cor(t2012$rank, t2012$wins)## [1] 0.3477129What is the correlation between teamRank and wins2013?
cor(t2013$rank, t2013$wins)## [1] -0.6556945