Final Margin vs Points Scored for NBA teams through January 2015

Final Margin vs Points Scored for NBA teams through January 2015
Final Margin vs Points Scored for NBA teams through January 2015

This set of small multiples shows the Final Margin vs Points Scored for NBA teams through January 2015.

Also shown is the linear fit with (tightly spaced) error bars.

Teams in the bottom row do better in higher scoring games.

Teams at the top not so much.

The half-circle icons at the small multiple edges make extrema easy to spot.

The Pelican have the highest score of the season at 140; the Pelican the lowest with 65.

The largest margin was 53 when the Mavs beat the 76ers.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

points,margin@self.nice_team(team) and (season=2014) as ‘Final Margin vs Points ScorednNBA teams through January 2015’?polyfit=1&polyfit_show=1&transparency

Second Half Margin v First Half Margin for NFL teams through January 2015

Second Half Margin v First Half Margin for NFL teams through January 2015
Second Half Margin v First Half Margin for NFL teams through January 2015

This scatter plot shows the second half margin vs the first half margin for NFL teams through January 2015.

Also shown to guide the eye is the 2nd order polynomial fit and shaded region of normalcy.

Teams to the top outplayed their opponents in the second half and teams to the right did better in the second half.

Teams towards the upper left are second half teams while teams towards the lower right are first half teams.

The Packers dominated the first half and lost ground in the second.

The Seahawks are the best second half team.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

A(sum(quarter scores[:2])-sum(o:quarter scores[:2])),A(sum(quarter scores[2:])-sum(o:quarter scores[2:])),R(team)@team and season=2014|$1 as Average First Half Margin,$2 as Average Second Half Margin,$3@1 as ‘Second Half Margin v First Half MarginnNFL teams through January 2015’?polyfit=1&marker_size=50&polyfit_show=0&polyfit_error_scale=10&ysymmetric&symmetric&ymax=10&ymin=-10

How Long Royals Starters Lasted in 2014

How Long Royals Starters Lasted in 2014
How Long Royals Starters Lasted in 2014

This set of small multiple histograms shows the distribution of how long each of the Royals starters lasted in 2014.

The finger-like structure is due to starters often being pulled after a complete number of innings.

Shields lasted mostly for 7 or 6 full innings and was only knocked out before the 5th one time.

Shields, Vargas and Guthrie each had one complete game.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

int(3*innings pitched) as Outs @ name and (team=Royals and season=2014 and order=1 and name[0] not in ‘AL’) as ‘How Long Royals Starters Lasted in 2014’?height=500

– Points Allowed v – Points Allowed in the First Half

Points Allowed v Points Allowed in the First Half
Points Allowed v Points Allowed in the First Half

 

This scatter plot show (the negative) or total points allowed v (the negative) of first half points allowed.

Also shown is the seconds order polynomial fit and region of normalcy.

Teams to the upper left of the fit line are good second half defensive teams – teams to the lower right tend to do worse in the second half.

The Seahawks are the best second half defensive team; the Patriots are a bit better than average.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

A(-o:S2),A(-o:points),R(team)@team and season=2014|$1 as (minus) Average Points Allowed in the First Half — good defense — >,$2 as (minus) Average Final Points Allowed — good defense –>,$3@1 as ‘Points Allowed in the First Half v Final Points AllowednNFL teams through January 2015’?polyfit=2&ysymmetri&symmetri&marker_size=50&polyfit_show=1&polyfit_error_scale=0.01

Final Margin v Margin at the Half for NBA Teams Through Jan 21, 2015

Final Margin v Margin at the Half for NBA Teams Through Jan 21, 2015
Final Margin v Margin at the Half for NBA Teams Through Jan 21, 2015

This scatter plot shows the average final margin v the margin at the half for NBA teams through Jan 21, 2015.

Also show in the linear fit with error-bar swath.

The positive slope of the fit line (1.4) shows that teams leading at the half generally tend to extend their lead. That the slope is less than two indicates that the winning team doesn’t generally double their lead.

Teams above the fit line tend to do better in the second half while teams below the fit line tend to fold. The Blazers, Bulls and Jazz look to be the best 2nd half teams while the Heat, Magic and Lakers tend to do worse in the 2nd half.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

A(margin at the half),A(margin),R(team)@team and season=2014|$1 as Average Margin at the Half,$2 as Average Final Margin,$3@1 as ‘Final Margin v Margin at the HalfnNBA teams through Jan 21, 2015’?polyfit=1&ysymmetric&symmetric&marker_size=40&polyfit_show=1

Points v Assists for NBA Teams Through Jan 20, 2015

Points v Assists for NBA Teams Through Jan 20, 2015
Points v Assists for NBA Teams Through Jan 20, 2015

This set of scatter plots show points v assists for each NBA team.

Also given is the linear fit with estimated errors shown as shaded regions.

The Raptors points scales the least with assists: that is, the score about the same over a range of assists.

At the opposite end of the spectrum, the Thunder stick way out with 2.2 points per assist.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

assists, points @ self.nice_team(team) and (season=2014) as ‘Points v AssistsnNBA teams through Jan 20, 2015’?polyfit=1&polyfit_show=1

Points Differential v Scored for NFL Teams in 2014

Points Differential v Scored for NFL Teams in 2014
Points Differential v Scored for NFL Teams in 2014

The set of small multiples shows points differential v points scored.

This is kind of fun since the horizontal arrangement of the small multiples works as a time series (by week).

It is also a nice prototype for a box-plot where the ‘inner x-axis’ means something.

Although Points Scored is barely differentiated with the x-axes so skinny, one discerns the correct ordering of the top 4: Patriots, Packers, Colts, Seahawks.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

S(points),S(points-o:points),Replace(team),Replace(week)@team and week<2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22 and season=2014|$1 as Points Scored,$2 as Points Scored – Points Allowed,$3@$4 and 1 as 'Points Differential v. Points Scored NFL 2014'?marker_size=20&polyfit=1&polyfit_linewidth=0.3&polyfit_show=0&columns=20&polyfit_sort=0&xticks=-1&ysymmetric&marker_size=34&polyfit_error_scale=0&width=700&height=500

How Many Outs each Starter got for the Rockies in 2014

How Many Outs each Starter got for the Rockies in 2014
How Many Outs each Starter got for the Rockies in 2014

This set of histograms shows how many outs each starter got for the rockies in 2014.

Belisle and Hernandez started only once.

Chatwood started 4 times and went 5, 6 (twice), and 7 innings.

De La Rosa had the most starts and was often pulled after 6 innings (18 outs).

This matplotlib graphic was made at SportsDatabase.com
with the SDQL

int(3*innings pitched) as Outs,’Number of Games’ @ name and (team=Rockies and season=2014 and order=1) as ‘How Many Outs each Starter for the Rockies got in 2014’?columns=5&height=500