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profb

profb

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  • mythbusting_1 OpenML100 study_1 study_123 study_135 study_14 study_144 study_15 study_20 study_34 study_41 study_52 unspecified_target_feature study_293 study_379
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Author: Hal Stern, Robin Lock Source: [StatLib](http://lib.stat.cmu.edu/datasets/profb) 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

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
Show all 10 features

107 properties

NumberOfInstances
672
Number of instances (rows) of the dataset.
NumberOfFeatures
10
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
1200
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
666
Number of instances with at least one value missing.
NumberOfNumericFeatures
5
Number of numeric attributes.
NumberOfSymbolicFeatures
5
Number of nominal attributes.
AutoCorrelation
0.55
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.92
Entropy of the target attribute values.
DecisionStumpAUC
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
48.79
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
66.67
Percentage of instances belonging to the most frequent class.
MajorityClassSize
448
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.65
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.56
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
90
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.03
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
28
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.85
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
9.97
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
2.47
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.36
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
28.8
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
130.19
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
12.6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.34
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
5.65
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
-0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.5
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
5.31
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
1
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.02
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.82
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
33.33
Percentage of instances belonging to the least frequent class.
MinorityClassSize
224
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
10
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
99.11
Percentage of instances having missing values.
PercentageOfMissingValues
17.86
Percentage of missing values.
PercentageOfNumericFeatures
50
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
50
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.14
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.37
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
7.1
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.01
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
2.07
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
2.61
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.12
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
16.86
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.42
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
4.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.65
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.52
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
56.48
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.65
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
9.62
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
14.1
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
-0.07
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

25 tasks

Supervised Classification on profb
13173 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Home/Away
Supervised Classification on profb
214 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Home/Away
Supervised Classification on profb
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Home/Away
Learning Curve on profb
88 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Home/Away
Supervised Data Stream Classification on profb
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Home/Away
Clustering on profb
0 runs - target_feature: Home/Away
Clustering on profb
0 runs
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Clustering on profb
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on profb
1304 runs - target_feature: Home/Away
Subgroup Discovery on profb
1301 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
Subgroup Discovery on profb
0 runs - target_feature: Home/Away
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