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unknown (from Weka repository)

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arff slurped Weka

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Integer,Floating Point,String

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# Instances: 672 / # Attributes: 10
HDF5 (163.2 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

profb

Version mldata

0

Comment

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

Names

Home/Away,Favorite_Points,Underdog_Points,Pointspread,Favorite_Name,Underdog_name,Year,Week,Weekday,Overtime,

Types

1. nominal:at_home,away
2. numeric
3. numeric
4. numeric
5. nominal:ATL,BUF,CHI,CIN,CLE,DAL,DEN,DET,GB,HOU,IND,KC,LAA,LAN,MIA,MIN,NE,NO,NYG,NYJ,PHA,PHX,PIT,SD,SEA,SF,TB,WAS
6. nominal:ATL,BUF,CHI,CIN,CLE,DAL,DEN,DET,GB,HOU,IND,KC,LAA,LAN,MIA,MIN,NE,NO,NYG,NYJ,PHA,PHX,PIT,SD,SEA,SF,TB,WAS
7. numeric
8. numeric
9. nominal:Monday_Night,Saturday,Sunday_Night,Thursday
10. nominal:yes

Data (first 10 data points)

Home...	Favo...	Unde...	Poin...	Favo...	Unde...	Year	Week	Week...	Over...
away	27	24	4.0	BUF	MIA	89	1	nan	nan
at_h...	17	14	3.0	CHI	CIN	89	1	nan	nan
away	51	0	2.5	CLE	PIT	89	1	nan	nan
at_h...	28	0	5.5	NO	DAL	89	1	nan	nan
at_h...	38	7	5.5	MIN	HOU	89	1	nan	nan
at_h...	34	20	6.0	DEN	KC	89	1	nan	nan
away	31	21	6.0	LAN	ATL	89	1	nan	nan
at_h...	24	27	2.5	NYJ	NE	89	1	nan	nan
away	16	13	1.5	PHX	DET	89	1	nan	nan
at_h...	40	14	3.5	LAA	SD	89	1	nan	nan
...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing StatLib datasets (statlib-20050214.tar.gz, 12,785,582 Bytes)

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Data Source

http://lib.stat.cmu.edu/datasets/

Measurement Details

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revision 1

by mldata on 2011-09-14 15:05

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