Cae 2-Baseball - Batting - Averages - With - Analysis
Cae 2-Baseball - Batting - Averages - With - Analysis
Cae 2-Baseball - Batting - Averages - With - Analysis
The variables for this analysis consist of yearly statistics on batting averages (number of hits
divided by number of at-bats), for major league baseball players with at least 400 at-bats in
a given year and also at least 400 at-bats in the previous year, for years between 1960 and
2004. This sample is further restricted to players whose names fall in the alphabetical
range from Aaron to Brock merely in order to keep it relatively small (588 rows) for
purposes of demonstration..
The objective is to predict a player's batting average in a given year from his batting average
and/or cumulative batting average in the previous year.
A larger version of the file, with the full set of players and many more statistics (4535 rows
and 82 columns) is also available on the web site.
.
BattingAverage (n=588, mean=0.277) BattingAverageLAG
80 80
60 60
40 40
20 20
0 0
Mi n = 0.185 Mi dpoint = 0.288 Ma x = 0.390 Mi n = 0.185 Mi
.
CumulativeAverageLAG1 (n=588, mean=0.276)
100
50
0
Min = 0.198 Mi dpoint = 0.277 Ma x = 0.357
0.288 0.288
0.185 0.185
0.185 0.277 0.368 0.198 0.277 0.357
End of Output
Maximum Skewness Kurtosis
Here are the results of a descriptive analysis of the 3 batting av
0.390 0.169 0.099 mean batting average is 0.277, and the correlation of batting av
0.368 0.094 0.053 ("LAG1") batting average is 0.481, and the correlation of batting
0.357 0.352 0.813 cumulative average is a little higher, 0.581. So, last year's cumu
slightly better predictor than last year's actual average as a pred
average.
age vs.
erageLAG1
ared = 0.290
77 0.357
25.000 12/13/18 6:24 AM on FACDS414 - Stats 1 -
0.15
0.18 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34 0.36 0.38
CumulativeAverageLAG1
0.4
0.35
0.3
0.25
0.2
0.15
0 100 200 300 400 500 600 700
Histogram of Residuals
Model 1 for BattingAverage (1 variable, n=588)
80
60
40
Actual
20 Normal
0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 9 0 0 8 1 0 7 2 0 6 3 0 5 4 0 4 5 0 3 6 0 2 7 0 1 8 0 0 9 0 0 0 00 9 0 1 8 0 2 7 03 6 04 5 0 5 4 0 6 3 07 2 0 8 1 0 9 0
. . . . . . . . . . . . . . . . . . . . .
-0 -0 -0 -0 -0 -0 -0 -0 -0 -0 0 0 0 0 0 0 0 0 0 0 0
End of Output
White No Font NoHeaders With P-value
Model 1 (#var
R code: Model.1 <- lm(BattingAverage ~ CumulativeAverageLAG1, data = BattingData)
Confidence
In the regression of batting average on previous cumulative batting average, the slope coefficient is abo
95.0% a player's batting average regresses to the mean by about 30% in a given year, relative to his prior cumu
standard error of the regression is 0.026, and the mean batting average for all players is 0.277 so roughl
Std. Coeff.
batting average between 0.250 and 0.300 (plus-or-minus 1 standard error, in round numbers) and rough
0.000
average between 0.225 and 0.325 (plus or minus 2 standard errors, in round numbers). The error distri
0.538
perfectly normal, as might be expected from the Central Limit Theorem, because the batting average st
large numbers of independent random variables (namely, 1 hit or 0 hits in each at-bat).
In this analysis, the optional teaching notes feature in RegressIt was turned on, so teaching notes that e
outputs are stored as comments in the cells with red flags in the title rows. Hover the mouse over a cel
the teaching note. You can also go to the Review menu on the Excel ribbon and click the Next and Prev
through all the cell comments, whether or not their flags are displayed.
Editable No Comment Notes 12/13/18 6:27 AM on FACDS414 - Model 1 -
No preceding m
Model 1 last Model 1 following model is Model 2 (#vars=2, n=588, AdjRsq=0.3): BattingAverage << BattingAverageLAG1, CumulativeAverageLA
0.4
0.35
0.3
0.25
0.2
0.15
0 100 200 300 400 500 600 700
Histogram of Residuals
Model 2 for BattingAverage (2 variables, n=588)
80
60
40
Actual
20 Normal
0
0 4 8 2 6 0 4 8 2 6 0 6 2 8 4 0 6 2 8 4 0
0 8 6 0 7 7 0 6 8 0 6 0 0 5 1 0 4 3 0 3 4 0 2 5 0 1 7 0 0 8 0 0 0 00 8 0 1 7 0 2 5 03 4 04 3 0 5 1 0 6 0 06 8 0 7 7 0 8 6
. . . . . . . . . . . . . . . . . . . . .
-0 -0 -0 -0 -0 -0 -0 -0 -0 -0 0 0 0 0 0 0 0 0 0 0 0