Data and plotting
Those ranking data are available on ESPN’s website (and they are well structured data and easy to grab). I subtracted the 6 computer rankings by the overall ranking and drew those differences on a scatter plot.
-Alabama seems to have more chance to take LSU’s place.
-Although Michigan State beat Wisconsin last week, the computers still don’t favor it.
-Auburn looks very promising.
-Oklahoma State is highly possible to become #2.
One complaint about the computer ranking
I don’t like the averaging method of the 6 computer rankings used by BCS. Transformation and factor analysis may make full use of the information - SAS’s user guide provided a detailed solution by PROC PRINQUAL and PROC FACTOR.
data week9; input @1 RK: 20. @5 TEAM: $40. AVG_bcs: PVS_bcs: $2. RK_hp: $2. PTS_hp pct_hp RK_usa: $2. PTS_usa pct_usa AVG_computer AH RB CM KM JS PW; cards; /* COPY AND PASTE DATA FROM http://espn.go.com/college-football/bcs */ ;;; run; data _tmp01; set week9; array rank ah--pw; do i = 1 to 6; if rank[i]= 0 then rank[i] = 25; rank[i] = rk - rank[i]; drop i; end; run; proc sort data=_tmp01; by team; run; proc transpose data=_tmp01 out=_tmp02; var ah--pw; by team; run; proc template; define Style HeatMapStyle; parent = styles.htmlblue; style GraphFonts from GraphFonts / 'GraphLabelFont' = (", ",6pt) 'GraphValueFont' = (", ",6pt) 'GraphDataFont' = (", ",6pt); end; run; proc template; define statgraph HeatMap.Grid; begingraph; layout overlay / border=true xaxisopts=(label='TEAM') yaxisopts=(label='COMPUTER ALGORITHM'); scatterplot x=team y=_name_ / markercolorgradient=col1 markerattrs=(symbol=squarefilled size=32) colormodel=threecolorramp name='s'; continuouslegend 's' / orient=vertical location=outside valign=center halign=right; endlayout; endgraph; end; run; ods html style=HeatMapStyle image_dpi=300 ; proc sgrender data=_tmp02 template=HeatMap.grid; run;