Points as a function of Assists for Each NBA Team

Points as a function of Assists for each NBA team through Nov 15'
Points as a function of Assists for each NBA team through Nov 15'

 

This scatter plot show points on the vertical axis and assists on the horizontal axies for each NBA team in 2014 (through Nov 15).

Each dot represents a game.

The least squares fit is show and the slope is given above each of the small multiples.

The Knicks have the lowest slope at about three assist per point.

At the other extreme, the Blazers are getting about three points for one assist.

 

Blazers Set New First Half Scoring Mark with 84

Blazers Set New First Half Scoring Mark with 84
Blazers Set New First Half Scoring Mark with 84

 

The Portland Trailblazers set a new mark for first half scoring last night with 84.

Their previous high was 76 set in 1999.

Improve this graph! Start here:
http://sportsdatabase.com/nba/query?output=matplotlib.histogram&sdql=S2%2Cdate+as+%27Number+of+Games%27%40%28team%3DBlazers+and+S2+is+not+None%29+as+%27Blazers+Set+New+First+Half+Scoring+Mark+with+84%27%3Frwidth%3D0.7

Consider the SDQL: S2@(team=Blazers) as ‘Blazers Set New First Half Scoring Mark with 84’ and season

Blazer first half points grouped by season
Blazer first half points grouped by season

Note that grouping by season causes the rendering of a stacked histogram colored by season.

 

Number of Plays vs Points for each NFL team in 2013

Number of Plays vs PointsnFor each NFL team in 2013
Number of Plays vs Points for each NFL team in 2013

The small multiple of scatter plots shows the number of offensive plays vs. points for each NFL team in 2013, sorted by the slope of the least squares fit.

Most teams score more points when they have more offensive plays.

Contrarily, rhe Rams, Bengals, Eagles, Bears and Chiefs scored more when they had fewer plays.

Why is that?

The SDQL used to make this plot is: plays,points@team and (season=2013) as ‘Number of Plays vs PointsnFor each NFL team in 2013’?polyfit=1

 

Points vs Opponent Points for each NFL Team in 2013

Points vs Opponent Points for each NFL Team in 2013
Points vs Opponent Points for each NFL Team in 2013

 

This set of small multiples shows the team’s points vs. their opponent’s points for each team in 2013.

The scatter plots are sorted by Pearson Correlation.

The Rams’ final score is most negatively correlated with their opponent’s score: The more points they score the fewer their opponent scores.

The Steelers score was most positively correlated with their opponents: The more their opponents score the more the Steelers score.

NBA Points vs Opp Points for each Team in 2013

Scatter Plot of NBA Points vs Opponent Points for each team on 2013.
Scatter Plot of NBA Points vs Opponent Points for each team on 2013.

 

This small multiple set of scatter plots shows the points vs opponent points for each NBA team in 2013.

The small multiples are sorted by Pearson Correlation, (shown on the top right),

The Knicks final score is least dependent on what their opponent is doing.

On the other end of the spectrum, the Raptor are happy to score high or low, depending on the opponent.

The motivated viewer sees that the highest scoring game was 145-130, Rockets over Lakers.

The Bulls favor low scoring games, except for two.

 

Scatter Plot of NBA Points vs Opponent Points in 2013

Scatter Plot of Points vs Opponent Points for all NBA games in 2013
Scatter Plot of Points vs Opponent Points for all NBA games in 2013

This scatter plot shows the team points vs opponent points for all NBA games in 2013.

One sees quickly that the highest scoring game was 145 to 130ish.

The strange brain shaped pattern shows that NBA teams avoid very close scores: in fact a margin of 7 was most common in 2013.

If the teams’ scores were independent we would expect a circle rather than a brain.

I like the gigantic transparent icons because the viewer observers the gray scale directly.

Below is a cleaner version which avoids overlapping icons.

One sees more clearly that there are no ties in the NBA and although, ‘we knew that’, mapping this knowledge to the graphic roots the viewer and gives confidence for further insights.

It seems not inappropriate to spread out these final scores visually – what if that last shot went in ?

What do you think?

Points vs Opponent Points for all NFL Ga

 

Points vs Opponent Points for all NFL Games 1989 - 2013
Points vs Opponent Points for all NFL Games 1989 - 2013

The final score (points vs opponent points) for every NFL game 1989 – 2013.

This scatter plot works as a ‘heat map.’

We see quickly that:

  • A total of 1 never happens (it is impossible by NFL scoring rules)
  • A total of 2 is rare (a safety)
  • A total of 4, while possible, has never happened.
  • The most common score is around 23, or somewhere in there.
  • Ties happen and are rare.

It could probably be improved with a better gradation scheme.