NHL Goals v. Penalty Minutes

NHL Goals v Penalty Minutes
NHL Goals v Penalty Minutes

This matplotlib scatter plot shows Goals v Penalty Minutes for each NHL team through Nov 28, 2014.

Also shown in the second order polynomial fit and shaded region of normalcy.

The Sabres are outstanding in their low number of goals in relation to their high number of penalty minutes.

At the other extreme, the Lightning score a lot of goals with few penalty minutes.

The sports data graphic was made at SportsDatabase.om with the SDQL: S(penalty minutes),S(goals),R(team),R(name)@team and season=2014|$1 as Penalty Minutes,$2 as Goals,$3@1 and ($3) as ‘NHL Goals v Penalty Minutes’?marker_size=50&polyfit=2&polyfit_error_scale=0.01

Superbowl XLVIII (2014)

Fantasy Scoring in the 2014 Superbowl
Fantasy Scoring in the 2014 Superbowl

This Plot.ly box plot summarizes the fantasy points scoring in Superbowl 48 (2014).

Every dot represents a players performance in a game during the 2014 season and the Superbowl is the big one.

Players are sorted by left to right by their deviation from their performance leading up to the Superbowl.

Manning underperformed by 1.7 standard deviations and the Seahawks defense exceeded their average performance by an amazing 2.8 standard deviations.

This plot.ly box plot was build on SportsDatabase.com

Points Scored v Points Allowed NFL week 12, 2014

'Points Scored v Points Allowed NFL week 12, 2014'
'Points Scored v Points Allowed NFL week 12, 2014'

This matplotlib scatter plot show the points scored verses points allowed for each NFL team in week 12, 2014.

One sees quickly that the Eagles scored the most points with 43, followed by the Broncos, Bills and Dolphins.

The Cardinals, Jaguars and Jets all scored only 3 points.

Symmetry along the diagonal makes it easy to pick out match ups with winning teams above the diagonal and losers below.

The Browns and Falcons had the closest scoring games while the Bills and Jets had the widest margin.

This MatPlotLib scatter plot was made at SportsDatabase.com with the SDQL o:points as ‘Points Allowed’,points as ‘Points Scored’,team@(week=12 and season=2014) as ‘Points Scored v Points AllowednNFL week 12, 2014’?marker_size=40

Points Differential v. Points Scored for each CFL team through week 22, 2014

Points Differential v. Points Scored for each CFL team through week 22, 2014
Points Differential v. Points Scored for each CFL team through week 22, 2014

 

This matplotlib small multiple scatter plot shows the points differential v. points Scored for each CFL team through week 22, 2014.

The Stampeders have been on top all year while the Tiger Cats got off to a slow start and then have won a lot of close games.

The data visualization was made at http://SportsDatabase.com with the SDQL: S(points),S(points-o:points),Replace(team),Replace(week)@team and week<2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23 and season=2014

Average Points v Season NFL 1989 – 2014

Average Points v Season NFL 1989 - 2014
Average Points v Season NFL 1989 - 2014

This scatter plot show the average points scored by a team in NFL games from 1989 – 2014.

Also shown is the second order polynomial fit.

Scores have been increasing.

This Matplotlib scatter plot was made at SportsDatabase.com with the SDQL:
R(season),A(points)@season|$1 as Season,$2 as Points@1 as ‘Average Points v SeasonnNFL 1989 – 2014’?marker_size=200&polyfit=2&polyfit_error_scale=0.000000001&ymin=18&ymax=24

Can you improve this graph?

Add explanatory parameters? 

Runs Allowed v. Runs Scored for MLB teams in the 2014 regular season

Runs Allowed v. Runs Scored for MLB teams in the 2014 regular season
Runs Allowed v. Runs Scored for MLB teams in the 2014 regular season

 

This MLB scatter plot show runs allowed v. runs scored for MLB teams in the 2014 regular season.

The 2nd degree polynomial fit is also shown.

The Rockies standout in allowing the most runs and scoring a lot, also.

Washington scored more runs than average and allowed the fewest.

Here are the same data, this time shown with run differential on the vertical axis:

'Runs Differential v. Runs Scored for MLB teams in 2014'
'Runs Differential v. Runs Scored for MLB teams in 2014'

 

Finally, here is the same graphic for the post season.

scatter_mlb_runs_diff_2014_playoffs
scatter_mlb_runs_diff_2014_playoffs

Total Points Differential v. Total Points Scored for NFL teams after each week in 2013

Total Points Differential v. Total Points Scored for NFL teams after each week in 2013
Total Points Differential v. Total Points Scored for NFL teams after each week in 2013

This small multiple shows the total points differential verses total points scored for NFL teams after each week in 2013.

I like this because these small multiples work as a time-series and it looks like a race.

The story of the lowly Jaguars is apparent: they lost several games badly to start the season and then had a little win streak after their bye week.

The Giants started nearly as badly and recovered sooner.

The Broncos were always on top EXCEPT after they played the Seahawks.

This small multiple scatter plot was made at http://SportsDatabase.com with the SDQL: S(points),S(points)-S(o:points),Replace(team),Replace(week)@team and week

Make your own and improve it!

Here is how things look through week 11, 2014.

Total Points Differential v. Total Points Scored for NFL teams after each week in 2014
Total Points Differential v. Total Points Scored for NFL teams after each week in 2014

Average Points Margin v. Average Number of Assist for NBA teams through Nov 19, 2014

Average Points Margin v. Average Number of Assist for NBA teams through Nov 19, 2014
Average Points Margin v. Average Number of Assist for NBA teams through Nov 19, 2014

This NBA scatter plot shows the average margin verses the average number of assist for NBA teams through Nov 19, 2014 and was built with the SDQL:
A(assists),A(points-o:points),Replace(team)@team and season=2014|$1 as Assists,$2 as Margin,$3@1 as ‘Average Points Margin v. Average Number of Assistn for NBA teams through Nov 19, 2014’?polyfit=2&polyfit_show=0&ymax=17&ymin=-17&xmin=17&xmax=27&marker_size=50&polyfit_error_scale=0.002

The second order polynomial fit line is also show with a shaded region of confidence.

The general trend is for teams with more assists to have a higher average margin.

The Raptors are exceptional in that they have a high margin with few assists.

The 76ers stick out the other end with an exceptionally low average margin.

Here is what this curve looked like at the end of 2013:

Average Points Margin v. Average Number of Assist for NBA teams in 2013
Average Points Margin v. Average Number of Assist for NBA teams in 2013

Points Allowed v. Points Scored for each NFL team through week 11, 2014

Points Allowed v. Points Scored for each NFL team through week 11, 2014
Points Allowed v. Points Scored for each NFL team through week 11, 2014

This scatter plot shows the points scored and allowed for each NFL team through week 11, 2014.

Also shown in the least squares fit line and the shaded region on confidence.

The negative slope confirms that the best defense is a good offense.

Bucking this trend, and outside of the shaded area, we see that the Lions have allowed the fewest points at 156 and have scored around 7th fewest at around 190.

The Packers have scored the most, followed closely by the Patriots and then the Colts. Eagles and Broncos.

The Packers, Patriots, and Bronco have allowed about the average number of points while the other high scoring teams have also allowed a lot of points.

What else do you see?

This scatter plot was made with the SDQL:

S(points),S(o:points),Replace(team)@team and season=2014|$1 as Points Scored,$2 as Points Allowed,$3@1 as ‘Points Allowed v. Points Scorednfor each NFL team through week 11, 2014′ ?marker_size=80&polyfit=1&polyfit_linewidth=0.3&polyfit_show=0

Is it better to show points differential on the y-axis?

Points Differential v. Points Scored for each NFL team through week 11, 2014
Points Differential v. Points Scored for each NFL team through week 11, 2014

 

This scatter plot was made at http://SportsDatabase.com with the SDQL: S(points),S(points)-S(o:points),Replace(team)@team and season=2014|$1 as Points Scored,$2 as Points Scored – Points Allowed,$3@1 as ‘Points Differential v. Points Scorednfor each NFL team through week 11, 2014′ ?marker_size=80&polyfit=1&polyfit_linewidth=0.3&polyfit_show=0