Data
profb

# profb

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
• 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
Issue #Downvotes for this reason By

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) nominal 2 unique values 0 missing Favorite_Points numeric 46 unique values 0 missing Underdog_Points numeric 38 unique values 0 missing Pointspread numeric 32 unique values 0 missing Favorite_Name nominal 28 unique values 0 missing Underdog_name nominal 28 unique values 0 missing Year numeric 3 unique values 0 missing Week numeric 17 unique values 0 missing Weekday nominal 4 unique values 560 missing Overtime nominal 1 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.
0.34
Mean skewness among attributes of the numeric type.
2.07
First quartile of standard deviation of 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
5.65
Mean standard deviation of attributes of the numeric type.
2.61
Second quartile (Median) of entropy among attributes.
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.
0
Minimal entropy among attributes.
-0.12
Second quartile (Median) of kurtosis among attributes of the numeric type.
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.
-1.5
Minimum kurtosis among attributes of the numeric type.
16.86
Second quartile (Median) of means 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.
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.
0.33
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.
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
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.
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.
0.55
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.
-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.
0.37
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.
0.82
Minimum standard deviation of attributes of the numeric type.
17.86
Percentage of missing values.
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.
33.33
Percentage of instances belonging to the least frequent class.
50
Percentage of numeric attributes.
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.
224
Number of instances belonging to the least frequent class.
50
Percentage of nominal 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.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.14
First quartile of entropy among 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.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.37
First quartile of kurtosis among attributes of the numeric type.
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.
0.02
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.
0.5
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
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.
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
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
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

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