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Author: Source: Unknown - Date unknown Please cite: PRO FOOTBALL SCORES (raw data appears after the description below) How well do the oddsmakers of Las Vegas predict the outcome of professional football games? Is there really a home field advantage - if so how large is it? Are teams that play the Monday Night game at a disadvantage when they play again the following Sunday? Do teams benefit from having a "bye" week off in the current schedule? These questions and a host of others can be investigated using this data set. Hal Stern from the Statistics Department at Harvard University has made available his compilation of scores for all National Football League games from the 1989, 1990, and 1991 seasons. Dr. Stern used these data as part of his presentation "Who's Number One?" in the special "Best of Boston" session at the 1992 Joint Statistics Meetings. Several variables in the data are keyed to the oddsmakers "point spread" for each game. The point spread is a value assigned before each game to serve as a handicap for whichever is perceived to be the better team. Thus, to win against the point spread, the "favorite" team must beat the "underdog" team by more points than the spread. The underdog "wins" against the spread if it wins the game outright or manages to lose by fewer points than the spread. In theory, the point spread should represent the "expert" prediction as to the game's outcome. In practice, it more usually denotes a point at which an equal amount of money will be wagered both for and against the favored team. Raw data below contains 672 cases (all 224 regular season games in each season and informatino on the following 9 varialbes: . Home/Away = Favored team is at home (1) or away (0) Favorite Points = Points scored by the favored team Underdog Points = Points scored by the underdog team Pointspread = Oddsmaker's points to handicap the favored team Favorite Name = Code for favored team's name Underdog name = Code for underdog's name Year = 89, 90, or 91 Week = 1, 2, ... 17 Special = Mon.night (M), Sat. (S), Thur. (H), Sun. night (N) ot - denotes an overtime game Data were submitted by: Robin Lock (rlock@stlawu.bitnet) Mathematics Department, St. Lawrence University Data were compiled by: Hal Stern, Dept. of Statistics, Harvard University Information about the dataset CLASSTYPE: nominal CLASSINDEX: 1

10 features

Home/Away (target)nominal2 unique values
0 missing
Favorite_Pointsnumeric46 unique values
0 missing
Underdog_Pointsnumeric38 unique values
0 missing
Pointspreadnumeric32 unique values
0 missing
Favorite_Namenominal28 unique values
0 missing
Underdog_namenominal28 unique values
0 missing
Yearnumeric3 unique values
0 missing
Weeknumeric17 unique values
0 missing
Weekdaynominal4 unique values
560 missing
Overtimenominal1 unique values
640 missing

107 properties

672
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
1200
Number of missing values in the dataset.
666
Number of instances with at least one value missing.
5
Number of numeric attributes.
5
Number of nominal attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
14.1
Standard deviation of the number of distinct values among attributes of the nominal type.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
12.6
Average number of distinct values among the attributes of the nominal type.
-0.01
First quartile of skewness among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.34
Mean skewness among attributes of the numeric type.
2.07
First quartile of standard deviation of attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
66.67
Percentage of instances belonging to the most frequent class.
5.65
Mean standard deviation of attributes of the numeric type.
2.61
Second quartile (Median) of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Entropy of the target attribute values.
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
448
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
-0.12
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.65
Maximum entropy among attributes.
-1.5
Minimum kurtosis among attributes of the numeric type.
16.86
Second quartile (Median) of means among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.56
Maximum kurtosis among attributes of the numeric type.
5.31
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
90
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.42
Second quartile (Median) of skewness among attributes of the numeric type.
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
0.03
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
10
Percentage of binary attributes.
4.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
48.79
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
28
The maximum number of distinct values among attributes of the nominal type.
-0.02
Minimum skewness among attributes of the numeric type.
99.11
Percentage of instances having missing values.
4.65
Third quartile of entropy among attributes.
0.52
Third quartile of kurtosis among attributes of the numeric type.
0.55
Average class difference between consecutive instances.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.85
Maximum skewness among attributes of the numeric type.
0.82
Minimum standard deviation of attributes of the numeric type.
17.86
Percentage of missing values.
56.48
Third quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
9.97
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
50
Percentage of numeric attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.47
Average entropy of the attributes.
224
Number of instances belonging to the least frequent class.
50
Percentage of nominal attributes.
0.65
Third quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.36
Mean kurtosis among attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.14
First quartile of entropy among attributes.
9.62
Third quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
28.8
Mean of means among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.37
First quartile of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
7.1
First quartile of means among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
130.19
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.

11 tasks

0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Home/Away
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Home/Away
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Home/Away
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Home/Away
0 runs - estimation_procedure: 10% Holdout set - target_feature: Home/Away
0 runs - estimation_procedure: 33% Holdout set - target_feature: Home/Away
0 runs - estimation_procedure: Leave one out - target_feature: Home/Away
0 runs - estimation_procedure: Test on Training Data - target_feature: Home/Away
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Home/Away
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Home/Away
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Home/Away
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