This scatter plot shows the profit and loss for each MLB team in 2017. The all star break as well as the playoffs are seen as breaks in the curves.
This matplotlib graphic was made at SportsDatabase.com
with the SDQL
date,1*S((WD and line) or (W and 100) or (LD and -100) or (L and line) ) as ‘Loss | Profit’,team@(season=2017) as ‘Profitability of Each MLB Team in 2017’ and team?marker_size=14&ysymmetric=1&width=1200
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

mlb_wins_games_for_1rungames_20160826.png
August 27, 2016
This scatter plot show winning percentage (vertical axis) vs number of game (of the horizontal axis) for each MLB team in 2016 through August 26.
The Mariners have the most (47);
The Rangers the highest win rate (77.78%)
This matplotlib graphic was made at SportsDatabase.com
with the SDQL
S(1) as Number of One Run Games, A(W) as Winning Percentage of One Run Games, R(team) @ team and (math.fabs(runs-o:runs)=1 and season=2016) as ‘Winning Percentage of One Run Games v Number of One Run Games\nfor MLB teams through Aug 26, 2016’ ?ymin=0.2 &ymax=0.8 &polyfit=1
These scatter plots show the average runs per 10 hits vs average runs for MLB teams. Each of the small multiples represents a season starting with 2005 in the upper left through 2016 (through July 4) in the lower right.
The highest average runs per game was achieved by the Yankees in 2007.
The most outstanding team over this time period was the 2015 Blue Jays.
The scatter plot for 2016 at the lower right shows that the Cubs are on pace to set the highest runs per hit efficiency in more than 10 years and that Atlanta and the Royals are near the historic futility of the 2013 Marlins and the 2010 Mariners.
This matplotlib graphic was made at SportsDatabase.com
with the SDQL
A(runs),A(10*runs)/A(hits),Replace(team),R(season)@team and season|$1 as Runs,$2 as Runs Per 10 Hits,$3@$4 and ($4>2004) as ‘Runs per 10 Hits vs Runs\nMLB Teams since 2005’?marker_size=38&width=1600
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
These scatter plots show the combined fantasy points of the starting guards v combined fantasy points of the starting forward for each NBA player-game in 2014: that is, each dot represents player performance in a single game.
This matplotlib graphic was made at SportsDatabase.com
with the SDQL
S(FP@position=F and date and team) as Forwards,S(FP@position=G and date and team) as Guards,R(team) @ season =2014 and team and date|$1,$2@1 as ‘Guard v Forward Fantasy Points\n all NBA teams in 2014’?polyfit=3&transparency=0.3&polyfit_show=0
A small change of the SDQL breaks this relationship down by team.
This matplotlib graphic was made at SportsDatabase.com
with the SDQL
S(FP@position=F and date and team) as Forwards,S(FP@position=G and date and team) as Guards,R(team) @ season =2014 and team and date|$0001,$2@$3 as ‘Guard v Forward Fantasy Points\n all NBA teams in 2014’?polyfit=2&transparency=0.3
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