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

Total Points v Total Minutes: all NBA players through Dec 22

scat_nbap_points_minutes_20151222

This scatter plot show the total points scored vs total minutes played for all NBA players through Dec 22.

Curry is well above the curve with 858 points in 941 minutes.

Here are the most efficient scorers thus far in 2015:

Minutes Points Points/Min
941 858.0 0.91 Stephen Curry
780 590.0 0.76 Kevin Durant
1105 834.0 0.75 James Harden
662 490.0 0.74 Demarcus Cousins
958 712.0 0.74 Russell Westbrook
872 635.0 0.73 Lebron James
963 688.0 0.71 Paul George
1011 684.0 0.68 Blake Griffin
1058 712.0 0.67 Damian Lillard
859 556.0 0.65 Anthony Davis
884 573.0 0.65 Reggie Jackson
902 587.0 0.65 Isaiah Thomas
957 609.0 0.64 Carmelo Anthony
900 571.0 0.63 Kawhi Leonard
743 462.0 0.62 Dwyane Wade
1061 654.0 0.62 Demar Derozan
150 92.0 0.61 Tyler Zeller
793 482.0 0.61 Klay Thompson
941 574.0 0.61 Andrew Wiggins
1011 619.0 0.61 Eric Bledsoe
692 411.0 0.59 Zach Lavine
1032 609.0 0.59 Kyle Lowry
778 448.0 0.58 Dirk Nowitzki
939 541.0 0.58 Brook Lopez
787 450.0 0.57 Karl
976 558.0 0.57 Jimmy Butler

Guard v Forward FanDuel Fantasy Points for Each NBA Team

Guard v Forward Fantasy Points
Guard v Forward Fantasy Points

This set of small multiple scatter plots shows the guard’s v forward’s fantasy points for each NBA team through Nov 22, 2015. Each scatter plot show the performance of a team. The horizontal position of each dot gives the total fantasy points for that team’s starting forwards and the vertical position that for the starting guards.

The Hornets in the upper left show the most negative correlation: when the guards get a lot of fantasy points the forwards get few. At the other extreme in the lower right the Nuggets show the most positive correlation: when their guards do well so do their forwards.

Also notable is the relatively tight correlation (seen by the narrow shaded region of normalcy) for the Jazz, Thunder and Grizzlies and that the Wizard’s Forwards are most consistent in their Fantasy output.

How might this inform your Fantasy choices?

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=2015 and team and date|$1,$2@$3 as ‘Guard v Forward Fantasy Points\n all NBA teams in 2015’?polyfit=1&transparency=0.3&polyfit_show=0&marker_size=10&symmetric=1

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

Winning Percentage v Number Played for in one runs games through Aug 19,2015

Winning Percentage v Number of Games\nin one runs games through Aug 19,2015
Winning Percentage v Number of Games in one runs games through Aug 19,2015

This scatter plot shows winning percentage in 1 run games vs number of such games through Aug 19,2015.

The Cardinals have the most one run games at 46. The Pirates and Royals have the best winning precentage at 63%. The Blue Jays have the lowest winning percentage at just 33%.

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

S(1),S(100*(W))/S(1),Replace(team)@team and season=2015 and abs(runs-o:runs)=1|$1 as Games,($2) as Wins,$3@1 as ‘Winning Percentage v Number of Games\nin one runs games through Aug 19,2015′ ?marker_size=40

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