Category Archives: Uncategorized

MLB Win Percentage vs Number of Games for One Run Games

 mlb_wins_games_for_1rungames_20160826.png August 27, 2016 98 kB 800 × 800 Edit Image Delete Permanently URLTitleCaptionAlt TextDescription ATTACHMENT DISPLAY SETTINGS  Alignment Link To Size                                         1 selected Clear   Insert into post 
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

Cubs Historically Efficient

scat_mlb_rp10h_runs_xseason_20160704

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

Fan Duel Fantasy Point v Draft King Fantasy Points

FanDuel v Draft King Fantasy Points
FanDuel v Draft King Fantasy Points

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

Fan Duel Fantasy Points vs Previous FD.FP for all NBA players through 20151118

Fan Duel Fantasy Points vs Previous FD.FP\nfor all NBA Players in 2015
Fan Duel Fantasy Points vs Previous FD.FP\nfor all NBA Players in 2015

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

Total Points Scored v Projected Total for NBA games 1995-2014

Total Points Scored v Projected Total for NBA games 1995-2014
Total Points Scored v Projected Total for NBA games 1995-2014

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

Guard v Forward Fantasy Points for NBA teams in 2014

scatter_nbap_forwardsFP_guardsFP_2014
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.
scatter_nbap_forwardsFP_guardsFP_xteam_2014

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

Hits v Runs MLB teams through Aug 18,2015

Hits v Runs MLB teams through Aug 18,2015
Hits v Runs MLB teams through Aug 18,2015

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

Runs Allowed v Runs Scored for MLB teams through Aug 15,2015

Runs Allowed v Runs Scored\nMLB teams through Aug 15,2015
Runs Allowed v Runs Scored\nMLB teams through Aug 15,2015

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

How Long Yankees Starters Lasted (2015 season through August 11)

hist_yankee_starter_outs_xname_20150813

This set of small multiples shows how many out each Yankee starter (with 3+ starts) achieved in 2015. Sabathia often goes 7 innings; Tanaka rarely gets pulled mid-inning; Eovaldi averages around 7 and is often pulled mid inning.

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

int(3*innings pitched) as Outs @ name and (team=Yankees and season=2015 and order=1 and name[0] not in ‘BL’) as ‘How Long Yankees Starters Lasted (2015 season through August 11)’?height=540&columns=4