This small multiple set shows each NBA players cumulative points vs date for the 2016 season. The SDQL is simple:

date,1*S(points) @ team and name and season=2016

Note how `team` in the first conditional position creates the exogenous groups. That is, for each team there is a separate scatter plot. The implicit grouping by `name` maps to the endogenous groups. That is, each curve represents a player’s season-to-date points vs the date.

The league high scorer is on the Thunder with a Rocket close behind.

Suns and Wizards have dominate twins.

Hawks, Timberwolves and Blazers had them.

Cavaliers and Warriors have three.

A significant Celtic, Raptor and Spur recovered.

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

date,1*S(points) @ team and name and season=2016

PyQL is hosting a FantasyData database and they produced this fascinating graphic.

Players to the upper left had a high yards per pass ratio. Brees threw for the most yards in a game.

This scatter plot shows Fan Duel Fantasy Point v Draft King Fantasy Points for the 2015 NBA season through Nov 18th. Each dot represents a players performance in a single game. The vertical axes gives the FanDuel Fantasy Points and the horizontal axis gives the DraftKing Fantasy Points. The relationship is linear with some variations towards higher values for DraftKings.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL FD.FP,DK.FP@(season=2015) as ‘FanDuel Fantasy Points v Draft King Fantasy Points\n2015 NBA games through Nov 18’?transparency=0.2&marker_size=1&polyfit=1&polyfit_show=1

This scatter plot shows Fan Duel Fantasy Points vs Fan Duel Fantasy Points in their previous game for all NBA players in 2015.

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

p:FP as ‘Fan Duel Fantasy Points in Previous Game’,FP as ‘Fan Duel Fantasy Points’@(season=2015) as ‘Fan Duel Fantasy Points vs Previous FD.FP\nfor all NBA Players in 2015’?transparency=0.1

This small multiple set show the total Points Scored v Projected Total for NBA games 1995-2014.

Each dot is a game. The horizontal position is determined by the Vegas-projected total number of points to be scored in that game and the vertical position of our game-dot determined by the actual total number of points scored in that game.

The highest projected total was 246 and the highest actual total was 318 when the Suns beat the Nets 161-157 o December 7, 2006.

This matplotlib graphic was made at SportsDatabase.com
with the SDQL
total as Projected,(points+o:points) as Actual@season and (points>0) as ‘Total Points Scored v Projected Total\nfor NBA games 1995-2014’?polyfit=3&polyfit_show=0&transparency=0.02&polyfit_sort=0 &symmetric=0&polyfit_error_scale=0.000005

This scatter plot shows runs v hits for MLB teams in 2015 through August 18th along with the shaded region on normalcy.

Teams to the upper left, led by the Blue Jays, have the greatest offensive efficiency while teams to the lower right get the most hits with fewest runs.

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

A(hits),A(runs),Replace(team)@team and season=2015|$1 as Hits,$2 as Runs,$3@1 as ‘Hits v Runs\nMLB teams through Aug 18,2015’ ?marker_size=40&polyfit=2&polyfit_error_scale=0.000051

This set of small multiple scatter plots show the runs allowed vs. the run scored by each MLB team through Aug 15,2015. Teams to the right hand side of the graph, led by Toronto score more runs: teams towards the bottom, led by the Cardinals allow few runs.
This matplotlib graphic was made at SportsDatabase.com
with the SDQL

S(runs),S(o:runs),Replace(team)@team and season=2015|$1 as Runs Scored,$2 as Runs Allowed,$3@1 as ‘Runs Allowed v Runs Scored\nMLB teams through Aug 15,2015’ ?marker_size=40&polyfit=2&polyfit_error_scale=0.001&symmetric=1