Advanced basketball statistics is an R package that allows you to perform different statistics calculations that are used in the world of basketball. In the package we can perform different calculations for the following types of statistics:

• Calculations on a data set representing individual statistics.
• Calculations on a data set representing lineup statistics.
• Calculations on a data set representing play statistics.
• Calculations on a data set representing team statistics.

# Data set

To create our data set we can do it in two different ways:

After deciding how to create our data set we must know what analysis we want to perform, since depending on this we will need different types of data sets:

1. We can use the functions that allow the transformation of a simple data set, that is, without the calculations performed by the data_adjustment() functions. The functions that the transformation allows are the following:
• individuals_data_adjustment. Function that allows transformation of individual stats and defensive stats.
• lineups_data_adjustment. Function that allows the transformation of lineups.
• play_data_adjustment. Function that allows the transformation of plays.
• team_stats. Function that allows you to perform team statistics.
2. We can use Data frames with the format corresponding to the data_adjustment() functions.

Once the two ways of creating the data have been commented, the examples of the data sets for subsequent use are shown:

## Individual Data Set

If we want to perform the analysis of individual statistics, we must have the following data sets:

• Data set that represents individual statistics.
• Data set created the individuals_data_adjustment function:

individual_stats <- data.frame("Name" = c("James","Team"), "G" = c(67,0),
"GS" = c(62,0),"MP" = c(2316,1), "FG" = c(643,0), "FGA" = c(1303,0),
"3P  " = c(148,0),"3PA" = c(425,0),"2P" = c(495,0), "2PA" = c(878,0),
"FT" = c(264,0),"FTA" = c(381,0),"ORB" = c(66,0),  "DRB" = c(459,0),
"AST" = c(684,0),"STL" = c(78,0),  "BLK" = c(36,0),"TOV" = c(261,0),
"PF" = c(118,0),"PTS" = c(1698,0),  "+/-" = c(0,0))

individual_stats
#>    Name  G GS   MP  FG  FGA   FG%  3P 3PA   3P%  2P 2PA   2P%  FT FTA   FT% ORB
#> 1 James 67 62 2316 643 1303 0.493 148 425 0.348 495 878 0.564 264 381 0.693  66
#> 2  Team  0  0    1   0    0 0.000   0   0 0.000   0   0 0.000   0   0 0.000   0
#>   DRB TRB AST STL BLK TOV  PF  PTS +/-
#> 1 459 525 684  78  36 261 118 1698   0
#> 2   0   0   0   0   0   0   0    0   0
• Data set created with the same structure as the one generated by the individuals_data_adjustment function

individual  <- data.frame("name" = c("LeBron James","Team"),"G" = c(67,0),
"GS" = c(62,0),"MP" = c(2316,0),"FG" = c(643,0), "FGA" = c(1303,0),
"Percentage FG" = c(0.493,0),"3P" = c(148,0),"3PA" = c(425,0),
"Percentage 3P" = c(0.348,0),"2P" = c(495,0),"2PA" = c(878,0),
"Percentage 2P" = c(0.564,0),"FT" = c(264,0),"FTA FG" = c(381,0),
"Percentage FT" = c(0.693,0), "ORB" = c(66,0),"DRB" = c(459,0),
"TRB" = c(525,0),"AST" = c(684,0),"STL" = c(78,0),"BLK" = c(36,0),
"TOV" = c(261,0), "PF" = c(118,0),"PTS" = c(1698,0),"+/-" = c(0,0))

individual
#>           name  G GS   MP  FG  FGA Percentage.FG X3P X3PA Percentage.3P X2P
#> 1 LeBron James 67 62 2316 643 1303         0.493 148  425         0.348 495
#> 2         Team  0  0    0   0    0         0.000   0    0         0.000   0
#>   X2PA Percentage.2P  FT FTA.FG Percentage.FT ORB DRB TRB AST STL BLK TOV  PF
#> 1  878         0.564 264    381         0.693  66 459 525 684  78  36 261 118
#> 2    0         0.000   0      0         0.000   0   0   0   0   0   0   0   0
#>    PTS X...
#> 1 1698    0
#> 2    0    0
• Data set representing individual defensive statistics.
• Data set created with the individuals_data_adjustment function:
defensive_stats <- data.frame("Name" = c("Witherspoon ","Team"),
"MP" = c(14,200),"DREB" = c(1,0),"FM" = c(4,0), "BLK" = c(0,0),
"FTO" = c(0,0),"STL" = c(1,1), "FFTA" = c(0,0),  "DFGM" = c(1,0),
"DFTM" = c(0,0))

defensive_stats
#>           Name  MP DRB FM BLK TOTAL FM FTO STL TOTAL FTO FFTA DFGM DFTM
#> 1 Witherspoon   14   1  4   0        4   0   1         1    0    1    0
#> 2         Team 200   0  0   0        0   0   1         1    0    0    0
• Data set created with the same structure as the one generated by the individuals_data_adjustment function
defensive <- data.frame("Name" = c("Witherspoon ","Team"),
"MP" = c(14,200),"DREB" = c(1,0), "FM" = c(4,0),
"BLK" = c(0,0),"TOTAL FM" = c(4,0),"FTO" = c(0,0),
"STL" = c(1,1), "TOTAL FTO " = c(1,0), "FFTA" = c(0,0),
"DFGM" = c(1,0), "DFTM" = c(0,0))

defensive
#>           Name  MP DREB FM BLK TOTAL.FM FTO STL TOTAL.FTO. FFTA DFGM DFTM
#> 1 Witherspoon   14    1  4   0        4   0   1          1    0    1    0
#> 2         Team 200    0  0   0        0   0   1          0    0    0    0
• Data set that represents team statistics.
• Data set created with the team_stats function:
tm_stats <- team_stats(individual_stats)
tm_stats
#>   G   MP  FG  FGA   FG%  3P 3PA   3P%  2P 2PA   2P%  FT FTA   FT% ORB DRB TRB
#> 1 0 2317 643 1303 0.493 148 425 0.348 495 878 0.564 264 381 0.693  66 459 525
#>   AST STL BLK TOV  PF  PTS +/-
#> 1 684  78  36 261 118 1698   0
• Data set created with the same structure as the one generated by the team_stats function
indi_team_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(3006), "FGA" = c(6269),"Percentage FG" = c(0.48),
"3P" = c(782),"3PA" = c(2242), "Percentage 3P" = c(0.349),
"2P" = c(2224), "2PA" = c(4027), "Percentage 2P" = c(0.552),
"FT" = c(1260),"FTA FG" = c(1728),"Percentage FT" = c(0.729),
"ORB" = c(757),"DRB" = c(2490),"TRB" = c(3247),"AST" = c(1803),
"STL" = c(612), "BLK" = c(468),"TOV" = c(1077),"PF" = c(1471),
"PTS" = c(8054),  "+/-" = c(0))

indi_team_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 3006 6269          0.48 782 2242         0.349 2224 4027
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1         0.552 1260   1728         0.729 757 2490 3247 1803 612 468 1077 1471
#>    PTS X...
#> 1 8054    0
• Data set that represents the statistics of the rival team.
• Data set created with the same structure as the one generated by the team_stats function
indi_rival_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(2773), "FGA" = c(6187),"Percentage FG" = c(0.448),
"3P" = c(827),"3PA" = c(2373),"Percentage 3P" = c(0.349),
"2P" = c(1946), "2PA" = c(3814),"Percentage 2P" = c(0.510),
"FT" = c(1270),"FTA FG" = c(1626),"Percentage FT" = c(0.781),
"ORB" = c(668), "DRB" = c(2333),"TRB" = c(3001), "AST" = c(1662),
"STL" = c(585),"BLK" = c(263),"TOV" = c(1130),"PF" = c(1544),
"PTS" = c(7643),  "+/-" = c(0))

indi_rival_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 2773 6187         0.448 827 2373         0.349 1946 3814
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1          0.51 1270   1626         0.781 668 2333 3001 1662 585 263 1130 1544
#>    PTS X...
#> 1 7643    0

## Lineup Data Set

If we want to perform the analysis of lineup statistics, we must have the following data sets:

• Data set that represents basic lineup statistics.

• Data set created with the lineups_data_adjustment function
linp_basic <- data.frame("PG"= c("James","Rondo"),"SG" = c("Green","Caruso"),
"SF" = c("Caldwell","Kuzma"), "PF" = c("Davis","Davis"),
"C" = c("Howard ","Howard"),"MP" = c(7,1), "FG " = c(4,0),
"FGA"  = c(7,0), "X3P" = c(0,0),"X3PA" = c(2,0),"X2P" = c(4,0),
"X2PA" = c(5,0),  "FT" = c(1,0), "FTA" = c(3,0),"ORB" = c(2,0),
"DRB" = c(5,0), "AST " = c(2,0), "STL " = c(1,0), "BLK " = c(0,0),
"TOV " = c(7,2), "PF" = c(1,0),  "PLUS" = c(9,0),"MINUS" = c(17,3))

linp_basic
#>      PG     SG       SF    PF       C MP FG FGA   FG% 3P 3PA 3P% 2P 2PA 2P% FT
#> 1 Rondo Caruso    Kuzma Davis  Howard  1  0   0 0.000  0   0   0  0   0 0.0  0
#> 2 James  Green Caldwell Davis Howard   7  4   7 0.571  0   2   0  4   5 0.8  1
#>   FTA   FT% ORB DRB TRB AST STL BLK TOV PF +  - +/-
#> 1   0 0.000   0   0   0   0   0   0   2  0 0  3  -3
#> 2   3 0.333   2   5   7   2   1   0   7  1 9 17  -8
• Data set created with the same structure as the one generated by the lineups_data_adjustment function
lineup_basic <- data.frame("PG" = c("James","Rondo"),"SG" = c("Green","Caruso"),
"SF" = c("Caldwell","Kuzma"), "PF" = c("Davis","Davis"),
"C" = c("Howard ","Howard"),"MP" = c(7,1), "FG " = c(4,0),
"FGA " = c(7,0),"Percentage FG" = c(0.571,0),
"X3P  " = c(0,0),"X3PA  " = c(2,0),"Percentage 3P" = c(0,0),
"X2P " = c(4,0), "X2PA " = c(5,0), "Percentage 2P" = c(0.8,0),
"FT " = c(1,0), "FTA " = c(3,0), "Percentage FT" = c(0.333,0),
"ORB " = c(2,0), "DRB " = c(5,0),"TRB " = c(7,0), "AST " = c(2,0),
"STL " = c(1,0), "BLK " = c(0,0),"TOV " = c(7,2), "PF" = c(1,0),
"PLUS" = c(9,0),"MINUS" = c(17,3),"P/M" = c(-8,-3))

lineup_basic
#>      PG     SG       SF    PF       C MP FG. FGA. Percentage.FG X3P.. X3PA..
#> 1 James  Green Caldwell Davis Howard   7   4    7         0.571     0      2
#> 2 Rondo Caruso    Kuzma Davis  Howard  1   0    0         0.000     0      0
#>   Percentage.3P X2P. X2PA. Percentage.2P FT. FTA. Percentage.FT ORB. DRB. TRB.
#> 1             0    4     5           0.8   1    3         0.333    2    5    7
#> 2             0    0     0           0.0   0    0         0.000    0    0    0
#>   AST. STL. BLK. TOV. PF.1 PLUS MINUS P.M
#> 1    2    1    0    7    1    9    17  -8
#> 2    0    0    0    2    0    0     3  -3
• Data set that represents extended lineup statistics.

• Data set created with the lineups_data_adjustment function
linp_extended <-  data.frame("PG" = c("James","Rondo"),"SG"= c("Green","Caruso"),
"SF" = c("Caldwell","Kuzma"), "PF" = c("Davis","Davis"),
"C" = c("Howard ","Howard"),"MP" = c(7,1), "FG " = c(6,0),
"OppFG " = c(6,0), "FGA " = c(10,0),"OppFGA " = c(9,0),
"X3P  " = c(2,0),"Opp3P  " = c(1,0),"X3PA " = c(4,0),
"Opp3PA " = c(3,0),"X2P" = c(4,0),"Opp2P" = c(5,0),"X2PA " = c(6,0),
"Opp2PA" = c(8,0) , "FT " = c(0,0),"OppFT " = c(1,0), "FTA " = c(0,0),
"OppFTA" = c(1,0),"OppRB" = c(2,0),"OppOppRB" = c(1,0),"DRB" = c(4,0),
"OppDRB" = c(1,0),"AST " = c(5,0),"OppAST " = c(4,0),"STL" = c(1,0),
"OppSTL" = c(3,0),"BLK" = c(0,0),"OppBLK" = c(1,0),"TOppV" = c(5,2),
"OppTOppV" = c(3,2),"PF" = c(1,0),"OppPF" = c(3,0),"PLUS" = c(15,0),
"MINUS" = c(14,3))

linp_extended
#>      PG     SG       SF    PF       C MP FG oFG FGA oFGA 3P o3P 3PA o3PA 2P o2P
#> 1 Rondo Caruso    Kuzma Davis  Howard  1  0   0   0    0  0   0   0    0  0   0
#> 2 James  Green Caldwell Davis Howard   7  6   6  10    9  2   1   4    3  4   5
#>   2PA o2PA FT oFT FTA oFTA ORB oORB DRB oDRB TRB oTRB AST oAST STL oSTL BLK
#> 1   0    0  0   0   0    0   0    0   0    0   0    0   0    0   0    0   0
#> 2   6    8  0   1   0    1   2    1   4    1   6    2   5    4   1    3   0
#>   oBLK TOV oTOV PF oPF  +  - +/-
#> 1    0   2    2  0   0  0  3  -3
#> 2    1   5    3  1   3 15 14   1
• Data set created with the same structure as the one generated by the lineups_data_adjustment function
lineup_extended <-  data.frame("PG" = c("James","Rondo"),"SG" = c("Green","Caruso"),
"SF" = c("Caldwell","Kuzma"), "PF" = c("Davis","Davis"),
"C" = c("Howard ","Howard"),"MP" = c(7,1), "FG " = c(6,0),
"OppFG " = c(6,0), "FGA " = c(10,0),"OppFGA " = c(9,0),
"X3P  " = c(2,0),"Opp3P" = c(1,0),"X3PA" = c(4,0),"Opp3PA" = c(3,0),
"X2P" = c(4,0),"Opp2P " = c(5,0), "X2PA " = c(6,0),"Opp2PA " = c(8,0) ,
"FT " = c(0,0),"OppFT " = c(1,0), "FTA " = c(0,0),"OppFTA " = c(1,0),
"OppRB " = c(2,0),"OppOppRB " = c(1,0), "DRB" = c(4,0),"OppDRB" = c(1,0),
"TRB" = c(6,0),"OppTRB" = c(2,0), "AST " = c(5,0),"OppAST " = c(4,0),
"STL " = c(1,0),"OppSTL " = c(3,0), "BLK " = c(0,0),  "OppBLK " = c(1,0),
"TOppV " = c(5,2), "OppTOppV " = c(3,2),"PF" = c(1,0),"OppPF" = c(3,0),
"PLUS" = c(15,0),"MINUS" = c(14,3),"P/M" = c(1,-3))

lineup_extended
#>      PG     SG       SF    PF       C MP FG. OppFG. FGA. OppFGA. X3P.. Opp3P
#> 1 James  Green Caldwell Davis Howard   7   6      6   10       9     2     1
#> 2 Rondo Caruso    Kuzma Davis  Howard  1   0      0    0       0     0     0
#>   X3PA Opp3PA X2P Opp2P. X2PA. Opp2PA. FT. OppFT. FTA. OppFTA. OppRB. OppOppRB.
#> 1    4      3   4      5     6       8   0      1    0       1      2         1
#> 2    0      0   0      0     0       0   0      0    0       0      0         0
#>   DRB OppDRB TRB OppTRB AST. OppAST. STL. OppSTL. BLK. OppBLK. TOppV. OppTOppV.
#> 1   4      1   6      2    5       4    1       3    0       1      5         3
#> 2   0      0   0      0    0       0    0       0    0       0      2         2
#>   PF.1 OppPF PLUS MINUS P.M
#> 1    1     3   15    14   1
#> 2    0     0    0     3  -3
• Data set that represents team statistics.

• Data set created with the team_stats function:
tm_stats <- team_stats(individual_stats)
tm_stats
#>   G   MP  FG  FGA   FG%  3P 3PA   3P%  2P 2PA   2P%  FT FTA   FT% ORB DRB TRB
#> 1 0 2317 643 1303 0.493 148 425 0.348 495 878 0.564 264 381 0.693  66 459 525
#>   AST STL BLK TOV  PF  PTS +/-
#> 1 684  78  36 261 118 1698   0
• Data set created with the same structure as the one generated by the team_stats function
lineup_team_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(3006), "FGA" = c(6269),"Percentage FG" = c(0.48),
"3P" = c(782),"3PA" = c(2242), "Percentage 3P" = c(0.349),
"2P" = c(2224), "2PA" = c(4027), "Percentage 2P" = c(0.552),
"FT" = c(1260),"FTA FG" = c(1728),  "Percentage FT" = c(0.729),
"ORB" = c(757), "DRB" = c(2490),"TRB" = c(3247),"AST" = c(1803),
"STL" = c(612),  "BLK" = c(468),"TOV" = c(1077),"PF" = c(1471),
"PTS" = c(8054),  "+/-" = c(0))

lineup_team_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 3006 6269          0.48 782 2242         0.349 2224 4027
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1         0.552 1260   1728         0.729 757 2490 3247 1803 612 468 1077 1471
#>    PTS X...
#> 1 8054    0
• Data set that represents the statistics of the rival team.

lineup_rival_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(2773), "FGA" = c(6187),"Percentage FG" = c(0.448),
"3P" = c(827),"3PA" = c(2373),"Percentage 3P" = c(0.349),
"2P" = c(1946), "2PA" = c(3814),"Percentage 2P" = c(0.510),
"FT" = c(1270),"FTA FG" = c(1626),  "Percentage FT" = c(0.781),
"ORB" = c(668), "DRB" = c(2333),"TRB" = c(3001), "AST" = c(1662),
"STL" = c(585),"BLK" = c(263),"TOV" = c(1130),"PF" = c(1544),
"PTS" = c(7643),  "+/-" = c(0))

lineup_rival_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 2773 6187         0.448 827 2373         0.349 1946 3814
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1          0.51 1270   1626         0.781 668 2333 3001 1662 585 263 1130 1544
#>    PTS X...
#> 1 7643    0

## Play Data Set

If we want to perform the analysis of play statistics, we must have the following data sets:

• Data set that represents play statistics.
• Data set created with the play_data_adjustment function
play_stats <- data.frame("Name" = c("Sabonis ","Team"),
"GP" = c(62,71),"PTS" = c(387,0), "FG" = c(155,1),
"FGA" = c(281,1),"3P" = c(6,1),"3PA" = c(18,1),
"FT" = c(39,1),  "FTA" = c(53,1),"ANDONE" = c(12,1),
"AST" = c(0,1), "TOV" = c(27,1))

play_stats
#>       Name GP PTS  FG FGA   FG% 3P 3PA   3P%  2P 2PA   2P% FT FTA   FT% ANDONE
#> 1 Sabonis  62 387 155 281 0.552  6  18 0.333 149 263 0.567 39  53 0.736     12
#> 2     Team 71   0   1   1 1.000  1   1 1.000   0   0 0.000  1   1 1.000      1
#>   AST TOV
#> 1   0  27
#> 2   1   1
• Data set created with the same structure as the one generated by the play_data_adjustment function
play <- data.frame("Name" = c("Sabonis ","Team"),
"GP" = c(62,71),"PTS" = c(387,0), "FG" = c(155,1),
"FGA" = c(281,1),"3P" = c(6,1),"3PA" = c(18,1),
"FT" = c(39,1), "FTA" = c(53,1),"ANDONE" = c(12,1),
"AST" = c(0,1), "TOV" = c(27,1))

play
#>       Name GP PTS  FG FGA X3P X3PA FT FTA ANDONE AST TOV
#> 1 Sabonis  62 387 155 281   6   18 39  53     12   0  27
#> 2     Team 71   0   1   1   1    1  1   1      1   1   1
• Data set that represents team statistics.
• Data set created with the team_stats function:
tm_stats <- team_stats(individual_stats)
tm_stats
#>   G   MP  FG  FGA   FG%  3P 3PA   3P%  2P 2PA   2P%  FT FTA   FT% ORB DRB TRB
#> 1 0 2317 643 1303 0.493 148 425 0.348 495 878 0.564 264 381 0.693  66 459 525
#>   AST STL BLK TOV  PF  PTS +/-
#> 1 684  78  36 261 118 1698   0
• Data set created with the same structure as the one generated by the team_stats function
play_team_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(3006), "FGA" = c(6269),"Percentage FG" = c(0.48),
"3P" = c(782),"3PA" = c(2242), "Percentage 3P" = c(0.349),
"2P" = c(2224), "2PA" = c(4027), "Percentage 2P" = c(0.552),
"FT" = c(1260),"FTA FG" = c(1728),  "Percentage FT" = c(0.729),
"ORB" = c(757),"DRB" = c(2490),"TRB" = c(3247),"AST" = c(1803),
"STL" = c(612), "BLK" = c(468),"TOV" = c(1077),"PF" = c(1471),
"PTS" = c(8054),  "+/-" = c(0))

play_team_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 3006 6269          0.48 782 2242         0.349 2224 4027
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1         0.552 1260   1728         0.729 757 2490 3247 1803 612 468 1077 1471
#>    PTS X...
#> 1 8054    0
• Data set that represents the statistics of the rival team.
play_rival_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(2773), "FGA" = c(6187),"Percentage FG" = c(0.448),
"3P" = c(827),"3PA" = c(2373),"Percentage 3P" = c(0.349),
"2P" = c(1946), "2PA" = c(3814),"Percentage 2P" = c(0.510),
"FT" = c(1270),"FTA FG" = c(1626),"Percentage FT" = c(0.781),
"ORB" = c(668),"DRB" = c(2333),"TRB" = c(3001),"AST" = c(1662),
"STL" = c(585),"BLK" = c(263),"TOV" = c(1130),"PF" = c(1544),
"PTS" = c(7643),  "+/-" = c(0))

play_rival_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 2773 6187         0.448 827 2373         0.349 1946 3814
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1          0.51 1270   1626         0.781 668 2333 3001 1662 585 263 1130 1544
#>    PTS X...
#> 1 7643    0

## Team Data Set

If we want to perform the analysis of team statistics, we must have the following data sets:

• Data set that represents the statistics of the team.

• Data set created with the team_stats function:
tm_stats <- team_stats(individual_stats)
tm_stats
#>   G   MP  FG  FGA   FG%  3P 3PA   3P%  2P 2PA   2P%  FT FTA   FT% ORB DRB TRB
#> 1 0 2317 643 1303 0.493 148 425 0.348 495 878 0.564 264 381 0.693  66 459 525
#>   AST STL BLK TOV  PF  PTS +/-
#> 1 684  78  36 261 118 1698   0
• Data set created with the same structure as the one generated by the team_stats function
team_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(3006), "FGA" = c(6269),"Percentage FG" = c(0.48),
"3P" = c(782),"3PA" = c(2242), "Percentage 3P" = c(0.349),
"2P" = c(2224), "2PA" = c(4027),"Percentage 2P" = c(0.552),
"FT" = c(1260),"FTA FG" = c(1728),"Percentage FT" = c(0.729),
"ORB" = c(757),"DRB" = c(2490),"TRB" = c(3247),"AST" = c(1803),
"STL" = c(612),"BLK" = c(468),"TOV" = c(1077),"PF" = c(1471),
"PTS" = c(8054),  "+/-" = c(0))

team_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 3006 6269          0.48 782 2242         0.349 2224 4027
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1         0.552 1260   1728         0.729 757 2490 3247 1803 612 468 1077 1471
#>    PTS X...
#> 1 8054    0
• Data set that represents the statistics of the rival team.

rival_stats <- data.frame("G" = c(71), "MP" = c(17090),
"FG" = c(2773), "FGA" = c(6187),"Percentage FG" = c(0.448),
"3P" = c(827),"3PA" = c(2373),"Percentage 3P" = c(0.349),
"2P" = c(1946), "2PA" = c(3814),"Percentage 2P" = c(0.510),
"FT" = c(1270),"FTA FG" = c(1626),"Percentage FT" = c(0.781),
"ORB" = c(668),"DRB" = c(2333),"TRB" = c(3001),"AST" = c(1662),
"STL" = c(585),"BLK" = c(263),"TOV" = c(1130),"PF" = c(1544),
"PTS" = c(7643),  "+/-" = c(0))

rival_stats
#>    G    MP   FG  FGA Percentage.FG X3P X3PA Percentage.3P  X2P X2PA
#> 1 71 17090 2773 6187         0.448 827 2373         0.349 1946 3814
#>   Percentage.2P   FT FTA.FG Percentage.FT ORB  DRB  TRB  AST STL BLK  TOV   PF
#> 1          0.51 1270   1626         0.781 668 2333 3001 1662 585 263 1130 1544
#>    PTS X...
#> 1 7643    0

Once the data sets have been created, we must choose which function or functions we want to use. Below are the different functions that the package has:

# Functions for individual statistics

For individual statistics there are the following functions:

• individuals_defensive_actual_floor_stats. Function that allows the calculation of the current defensive statistics.
• individuals_defensive_estimated_floor_stats. Function that allows the calculation of the estimated defensive statistics.
• individuals_games_adder. Function that allows the sum of two different individual statistics.
• individuals_ofensive_floor_stats. Function that allows the calculation of offensive statistics.
• individuals_stats_per_game. Function that allows the calculation of statistics per game.
• individuals_stats_per_minutes. Function that allows the calculation of statistics per minutes.
• individuals_stats_per_possesion. Function that allows the calculation of statistics per possessions.

Below is an example of how each function works and what the input parameters should be for these:

For this function we will have to enter individual statistics, team statistics and rival team statistics:

advanced_stats <- individuals_advance_stats(individual,indi_team_stats,indi_rival_stats)
#>           Name  G GS   MP   PER eFG%   TS%  3PAr   FTr ORB%  DRB% TRB%  AST%
#> 1 LeBron James 67 62 2316 28.68 0.55 0.577 0.326 0.292 3.15 21.45 12.4 49.07
#>   STL% BLK%  TOV%  USG%
#> 1  1.6 1.39 15.07 31.53

## individuals_defensive_actual_floor_stats

For this function we will have to enter defensive individual statistics, team statistics and rival team statistics:

defensive_actual_stats <- individuals_defensive_actual_floor_stats(defensive,indi_team_stats,indi_rival_stats)
defensive_actual_stats
#>           Name  MP DStops DScPoss DPoss Stop% TMDPoss%   DRtg
#> 1 Witherspoon   14   3.62       1  4.62 0.784    0.002 106.22
#> 2         Team 200   1.00       0  1.00 1.000    0.000 106.32

## individuals_defensive_estimated_floor_stats

For this function we will have to enter individual statistics, team statistics and rival team statistics:

defensive_estimated_stats <- individuals_defensive_estimated_floor_stats(individual,indi_team_stats,indi_rival_stats)
defensive_estimated_stats
#>           Name  G Stops Stop%   DRtg
#> 1 LeBron James 67  7.73 0.532 106.23

For this function we will have to enter the two individual statistics previously calculated which we want to add the statistics:

games_adder <- individuals_games_adder(individual,individual)
#>           Name   G  GS   MP   FG  FGA   FG%  3P 3PA   3P%  2P  2PA   2P%  FT
#> 1 LeBron James 134 124 4632 1286 2606 0.493 296 850 0.348 990 1756 0.564 528
#> 2         Team   0   0    0    0    0 0.000   0   0 0.000   0    0 0.000   0
#>   FTA   FT% ORB DRB  TRB  AST STL BLK TOV  PF  PTS +/-
#> 1 762 0.693 132 918 1050 1368 156  72 522 236 3396   0
#> 2   0 0.000   0   0    0    0   0   0   0   0    0   0

## individuals_ofensive_floor_stats.

For this function we will have to enter individual statistics, team statistics and rival team statistics:

ofensive_stats <- individuals_ofensive_floor_stats(individual,indi_team_stats,indi_rival_stats)
ofensive_stats
#>           Name  G Sc. Poss  Poss Floor%   ORtg Pts Prod/G
#> 1 LeBron James 67    12.74 24.12  0.528 115.67       27.9

## individuals_stats_per_game

For this function we will have to enter individual statistics:

per_game_stats <- individuals_stats_per_game(individual)
per_game_stats
#>           Name  G GS    MP  FG   FGA   FG%   3P  3PA   3P%   2P  2PA   2P%   FT
#> 1 LeBron James 67 62 34.57 9.6 19.45 0.493 2.21 6.34 0.348 7.39 13.1 0.564 3.94
#>    FTA   FT%  ORB  DRB  TRB   AST  STL  BLK TOV   PF   PTS +/-
#> 1 5.69 0.693 0.99 6.85 7.84 10.21 1.16 0.54 3.9 1.76 25.34   0

## individuals_stats_per_minutes

For this function we will have to enter the individual statistics and the number of minutes to which we want to project the statistics:

per_minutes_stats <- individuals_stats_per_minutes(individual,36)
per_minutes_stats
#>           Name  G GS   MP   FG   FGA   FG%  3P  3PA   3P%   2P   2PA   2P%  FT
#> 1 LeBron James 67 62 2316 9.99 20.25 0.493 2.3 6.61 0.348 7.69 13.65 0.564 4.1
#>    FTA   FT%  ORB  DRB  TRB   AST  STL  BLK  TOV   PF   PTS +/-
#> 1 5.92 0.693 1.03 7.13 8.16 10.63 1.21 0.56 4.06 1.83 26.39   0

## individuals_stats_per_possesion

For this function we will have to enter the individual statistics, the team statistics, the rival team statistics, the number of minutes that a single game lasts and the number of possessions to which we want to project the statistics:

per_poss_stats <- individuals_stats_per_possesion(individual,indi_team_stats,indi_rival_stats,100,48)
per_poss_stats
#>           Name  G GS   MP   FG   FGA   FG%   3P  3PA   3P%    2P   2PA   2P%
#> 1 LeBron James 67 62 2316 13.2 26.75 0.493 3.04 8.73 0.348 10.16 18.03 0.564
#>     FT  FTA   FT%  ORB  DRB   TRB   AST STL  BLK  TOV   PF   PTS +/-
#> 1 5.42 7.82 0.693 1.36 9.42 10.78 14.04 1.6 0.74 5.36 2.42 34.86   0

# Functions for lineup statistics

For individual statistics there are the following functions:

• lineups_backcourt. Function that allows searching for backcourt players.
• lineups_comparator_stats. Function that allows comparison between the statistics of the lineup and the rival lineup
• lineups_games_adder. Function that allows the sum of two different lineup statistics.
• lineups_paint. Function that allows searching for paint players.
• lineups_players. Function that allows searching for players by position.
• lineups_searcher. Function that allows searching for players within the data frame.
• lineups_separator. Function that allows you to obtain the statistics of the lineups or the rivals against those lineups.
• lineups_stats_per_possesion. Function that allows the calculation of statistics per possessions

Below is an example of how each function works and what the input parameters should be for these:

For this function we will have to enter extended lineup statistics and the number of minutes that a single game lasts:

lnp_advanced_stats <- lineups_advance_stats(lineup_extended,48)
#>      PG     SG       SF    PF       C MP TEAM%   PACE   ORtg   DRtg Net Rtg
#> 1 James  Green Caldwell Davis Howard   7 1.000 81.959 123.49 119.07    4.42
#> 2 Rondo Caruso    Kuzma Davis  Howard  1 0.125  0.000   0.00   0.00    0.00
#>   3Par FTr  TS% eFG%  AST%  ORB% DRB% TRB%  TOV%
#> 1  0.4   0 0.75  0.7 0.833 0.667  0.8 0.75 0.333
#> 2  0.0   0 0.00  0.0 0.000 0.000  0.0 0.00 1.000

## lineups_backcourt

For this function we will have to enter basic lineup statistics or extended lineup statistics:

lnp_bs_backcourt <- lineups_backcourt(lineup_basic)
lnp_bs_backcourt
#>      PG     SG       SF MP FG FGA Percentage FG 3P 3PA Percentage 3P 2P 2PA
#> 1 James  Green Caldwell  7  4   7         0.571  0   2             0  4   5
#> 2 Rondo Caruso    Kuzma  1  0   0         0.000  0   0             0  0   0
#>   Percentage 2P FT FTA Percentage FT ORB DRB TRB AST STL BLK TOV PF +  - +/-
#> 1           0.8  1   3         0.333   2   5   7   2   1   0   7  1 9 17  -8
#> 2           0.0  0   0         0.000   0   0   0   0   0   0   2  0 0  3  -3
lnp_ex_backcourt <- lineups_backcourt(lineup_extended)
lnp_ex_backcourt
#>      PG     SG       SF MP FG oFG FGA oFGA 3P o3P 3PA o3PA 2P o2P 2PA o2PA FT
#> 1 James  Green Caldwell  7  6   6  10    9  2   1   4    3  4   5   6    8  0
#> 2 Rondo Caruso    Kuzma  1  0   0   0    0  0   0   0    0  0   0   0    0  0
#>   oFT FTA oFTA ORB oORB DRB oDRB TRB oTRB AST oAST STL oSTL BLK oBLK TOV oTOV
#> 1   1   0    1   2    1   4    1   6    2   5    4   1    3   0    1   5    3
#> 2   0   0    0   0    0   0    0   0    0   0    0   0    0   0    0   2    2
#>   PF oPF  +  - +/-
#> 1  1   3 15 14   1
#> 2  0   0  0  3  -3

## lineups_comparator_stats

For this function we will have to enter extended lineup statistics:

lnp_comparator <- lineups_comparator_stats(lineup_extended,48)
lnp_comparator
#>      PG     SG       SF    PF       C MP Team%  Pace FG FGA    FG% 3P 3PA   3P%
#> 1 James  Green Caldwell Davis Howard   7 1.000 81.96  0   1 -0.067  1   1 0.167
#> 2 Rondo Caruso    Kuzma Davis  Howard  1 0.125  0.00  0   0  0.000  0   0 0.000
#>   2P 2PA   2P%   eFG%   TS% FT FTA FT% +/-
#> 1 -1  -2 0.042 -0.022 0.008 -1  -1  -1   1
#> 2  0   0 0.000  0.000  -Inf  0   0   0  -3

For this function we will have to enter basic lineup statistics or extended lineup statistics:

lnp_games_adder <- lineups_games_adder(lineup_basic,lineup_basic)
#>      PG     SG       SF    PF       C MP FG FGA   FG% 3P 3PA 3P% 2P 2PA 2P% FT
#> 1 Rondo Caruso    Kuzma Davis  Howard  2  0   0 0.000  0   0   0  0   0 0.0  0
#> 2 James  Green Caldwell Davis Howard  14  8  14 0.571  0   4   0  8  10 0.8  2
#>   FTA   FT% ORB DRB TRB AST STL BLK TOV PF  +  - +/-
#> 1   0 0.000   0   0   0   0   0   0   4  0  0  6  -6
#> 2   6 0.333   4  10  14   4   2   0  14  2 18 34 -16

## lineups_paint

For this function we will have to enter basic lineup statistics or extended lineup statistics:

lnp_bc_paint <- lineups_paint(lineup_basic)
lnp_bc_paint
#>      PF       C MP FG FGA   FG% 3P 3PA 3P% 2P 2PA 2P% FT FTA   FT% ORB DRB TRB
#> 1 Davis  Howard  1  0   0 0.000  0   0   0  0   0 0.0  0   0 0.000   0   0   0
#> 2 Davis Howard   7  4   7 0.571  0   2   0  4   5 0.8  1   3 0.333   2   5   7
#>   AST STL BLK TOV PF +  - +/-
#> 1   0   0   0   2  0 0  3  -3
#> 2   2   1   0   7  1 9 17  -8
lnp_ex_paint <- lineups_paint(lineup_extended)
lnp_ex_paint
#>      PF       C MP FG oFG FGA oFGA 3P o3P 3PA o3PA 2P o2P 2PA o2PA FT oFT FTA
#> 1 Davis  Howard  1  0   0   0    0  0   0   0    0  0   0   0    0  0   0   0
#> 2 Davis Howard   7  6   6  10    9  2   1   4    3  4   5   6    8  0   1   0
#>   oFTA ORB oORB DRB oDRB TRB oTRB AST oAST STL oSTL BLK oBLK TOV oTOV PF oPF  +
#> 1    0   0    0   0    0   0    0   0    0   0    0   0    0   2    2  0   0  0
#> 2    1   2    1   4    1   6    2   5    4   1    3   0    1   5    3  1   3 15
#>    - +/-
#> 1  3  -3
#> 2 14   1

## lineups_players

For this function we will have to enter basic lineup statistics or extended lineup statistics and the number of position that we want to find:

lnp_bc_players <- lineups_players(lineup_basic,5)
lnp_bc_players
#>         C MP FG FGA Percentage FG 3P 3PA Percentage 3P 2P 2PA Percentage 2P FT
#> 1  Howard  1  0   0         0.000  0   0             0  0   0           0.0  0
#> 2 Howard   7  4   7         0.571  0   2             0  4   5           0.8  1
#>   FTA Percentage FT ORB DRB TRB AST STL BLK TOV PF +  - +/-
#> 1   0         0.000   0   0   0   0   0   0   2  0 0  3  -3
#> 2   3         0.333   2   5   7   2   1   0   7  1 9 17  -8
lnp_ex_players <- lineups_players(lineup_extended,5)
lnp_ex_players
#>         C MP FG oFG FGA oFGA 3P o3P 3PA o3PA 2P o2P 2PA o2PA FT oFT FTA oFTA
#> 1  Howard  1  0   0   0    0  0   0   0    0  0   0   0    0  0   0   0    0
#> 2 Howard   7  6   6  10    9  2   1   4    3  4   5   6    8  0   1   0    1
#>   ORB oORB DRB oDRB TRB oTRB AST oAST STL oSTL BLK oBLK TOV oTOV PF oPF  +  -
#> 1   0    0   0    0   0    0   0    0   0    0   0    0   2    2  0   0  0  3
#> 2   2    1   4    1   6    2   5    4   1    3   0    1   5    3  1   3 15 14
#>   +/-
#> 1  -3
#> 2   1

## lineups_searcher

For this function we will have to enter basic lineup statistics or extended lineup statistics, the name of the players that we want to find and the number of players that we want to find:

lnp_bc_searcher <- lineups_searcher(lineup_basic,1,"James","","","")
lnp_bc_searcher
#>      PG    SG       SF    PF       C MP FG. FGA. Percentage.FG X3P.. X3PA..
#> 1 James Green Caldwell Davis Howard   7   4    7         0.571     0      2
#>   Percentage.3P X2P. X2PA. Percentage.2P FT. FTA. Percentage.FT ORB. DRB. TRB.
#> 1             0    4     5           0.8   1    3         0.333    2    5    7
#>   AST. STL. BLK. TOV. PF.1 PLUS MINUS P.M
#> 1    2    1    0    7    1    9    17  -8
lnp_ex_searcher <- lineups_searcher(lineup_extended,1,"James","","","")
lnp_ex_searcher
#>      PG    SG       SF    PF       C MP FG. OppFG. FGA. OppFGA. X3P.. Opp3P
#> 1 James Green Caldwell Davis Howard   7   6      6   10       9     2     1
#>   X3PA Opp3PA X2P Opp2P. X2PA. Opp2PA. FT. OppFT. FTA. OppFTA. OppRB. OppOppRB.
#> 1    4      3   4      5     6       8   0      1    0       1      2         1
#>   DRB OppDRB TRB OppTRB AST. OppAST. STL. OppSTL. BLK. OppBLK. TOppV. OppTOppV.
#> 1   4      1   6      2    5       4    1       3    0       1      5         3
#>   PF.1 OppPF PLUS MINUS P.M
#> 1    1     3   15    14   1

## lineups_separator

For this function we will have to enter extended lineup statistics and the indicator of what type of separation we want to make:

lnp_ex_sep_one <- lineups_separator(lineup_extended,1)
lnp_ex_sep_one
#>      PG     SG       SF    PF       C MP FG FGA FG% 3P 3PA 3P% 2P 2PA   2P% FT
#> 1 James  Green Caldwell Davis Howard   7  6  10 0.6  2   4 0.5  4   6 0.667  0
#> 2 Rondo Caruso    Kuzma Davis  Howard  1  0   0 0.0  0   0 0.0  0   0 0.000  0
#>   FTA FT% ORB DRB TRB AST STL BLK TOV PF  +  - +/-
#> 1   0   0   2   4   6   5   1   0   5  1 15 14   1
#> 2   0   0   0   0   0   0   0   0   2  0  0  3  -3
lnp_ex_sep_two <- lineups_separator(lineup_extended,2)
lnp_ex_sep_two
#>      PG     SG       SF    PF       C MP FG FGA   FG% 3P 3PA   3P% 2P 2PA   2P%
#> 1 James  Green Caldwell Davis Howard   7  6   9 0.667  1   3 0.333  5   8 0.625
#> 2 Rondo Caruso    Kuzma Davis  Howard  1  0   0 0.000  0   0 0.000  0   0 0.000
#>   FT FTA FT% ORB DRB TRB AST STL BLK TOV PF  +  - +/-
#> 1  1   1   1   1   1   2   4   3   1   3  3 15 14   1
#> 2  0   0   0   0   0   0   0   0   0   2  0  0  3  -3

## lineups_stats_per_possesion

For this function we will have to enter basic lineup statistics, team statistics, rival team statistics, the number of minutes that a single game lasts and the number of possessions to which we want to project the statistics:

lnp_per_poss_stats <- lineups_stats_per_possesion(lineup_basic,lineup_team_stats,lineup_rival_stats,100,48)
lnp_per_poss_stats
#>      PG     SG       SF    PF       C MP    FG   FGA   FG% 3P   3PA 3P%    2P
#> 1 James  Green Caldwell Davis Howard   7 27.17 47.55 0.571  0 13.59   0 27.17
#> 2 Rondo Caruso    Kuzma Davis  Howard  1  0.00  0.00 0.000  0  0.00   0  0.00
#>     2PA 2P%   FT   FTA   FT%   ORB   DRB   TRB   AST  STL BLK   TOV   PF     +
#> 1 33.96 0.8 6.79 20.38 0.333 13.59 33.96 47.55 13.59 6.79   0 47.55 6.79 61.14
#> 2  0.00 0.0 0.00  0.00 0.000  0.00  0.00  0.00  0.00 0.00   0 95.10 0.00  0.00
#>        -     +/-
#> 1 115.48  -54.34
#> 2 142.65 -142.65

# Functions for play statistics

For individual statistics there are the following functions:

• play_games_adder. Function that allows the sum of two different play statistics.
• play_stats_per_game. Function that allows the calculation of statistics per game.
• play_stats_per_possesion. Function that allows the calculation of statistics per possessions.
• play_team_stats. Function that allows to carry out the statistics of the team.

Below is an example of how each function works and what the input parameters should be for these:

For this function we will have to enter play statistics:

play_adv_stats <- play_advance_stats(play_stats)
#>       Name  G Poss Freq  PPP PTS  FG FGA   FG% 3P 3PA   3P%  2P 2PA   2P%  eFG%
#> 1 Sabonis  62  329    1 1.18 387 155 281 0.552  6  18 0.333 149 263 0.567 0.562
#>     FT% AST%  TOV% And One% Score%
#> 1 0.161    0 0.082    0.036  0.519

For this function we will have to enter play statistics:

pl_game_adder <- play_games_adder(play_stats,play_stats)
#>       Name  GP PTS  FG FGA   FG% 3P 3PA   3P%  2P 2PA   2P% FT FTA   FT% ANDONE
#> 1 Sabonis  124 774 310 562 0.552 12  36 0.333 298 526 0.567 78 106 0.736     24
#> 2     Team 142   0   2   2 1.000  2   2 1.000   0   0 0.000  2   2 1.000      2
#>   AST TOV
#> 1   0  54
#> 2   2   2

## play_stats_per_game

For this function we will have to enter play statistics:

play_per_game_stat <- play_stats_per_game(play_stats)
play_per_game_stat
#>       Name GP  PTS  FG  FGA   FG%  3P  3PA   3P%  2P  2PA   2P%   FT  FTA   FT%
#> 1 Sabonis  62 6.24 2.5 4.53 0.552 0.1 0.29 0.333 2.4 4.24 0.567 0.63 0.85 0.736
#>   And One AST  TOV
#> 1    0.19   0 0.44

## play_stats_per_possesion

For this function we will have to enter the play statistics, the team statistics, the rival team statistics, the number of minutes that a single game lasts and the number of possessions to which we want to project the statistics:

play_per_poss_stats <- play_stats_per_possesion(play_stats,play_team_stats,play_rival_stats,100,48)
play_per_poss_stats
#>       Name GP      PTS       FG      FGA   FG%     3P     3PA   3P%       2P
#> 1 Sabonis  62 33336.42 13351.80 24205.51 0.552 516.84 1550.53 0.333 12834.95
#> 2     Team 71     0.00    47.55    47.55 1.000  47.55   47.55 1.000     0.00
#>        2PA   2P%      FT     FTA   FT% And One   AST     TOV
#> 1 22654.98 0.567 3359.48 4565.45 0.736 1033.69  0.00 2325.80
#> 2     0.00 0.000   47.55   47.55 1.000   47.55 47.55   47.55

## play_team_stats

For this function we will have to enter play statistics:

play_team_stats <- play_team_stats(play_stats)
play_team_stats
#>   GP PTS  FG FGA   FG% 3P 3PA   3P%  2P 2PA   2P% FT FTA   FT% And One AST TOV
#> 1 71 387 156 282 0.553  7  19 0.368 149 263 0.567 40  54 0.741      13   1  28

# Functions for team statistics

For individual statistics there are the following functions:

• team_stats_per_game. Function that allows the calculation of statistics per game.
• team_stats_per_minutes. Function that allows the calculation of statistics per minutes.
• team_stats_per_possesion. Function that allows the calculation of statistics per possessions.

Below is an example of how each function works and what the input parameters should be for these:

For this function we will have to enter the team statistics and the rival team statistics:

team_adv_stats  <- team_advanced_stats(team_stats,rival_stats,48)
#>    G    MP   ORtg   DRtg NetRtg   Pace  3PAr   FTr   TS%  eFG% AST% AST/TO
#> 1 71 17090 112.14 106.23   5.91 100.95 0.358 0.276 0.573 0.542  0.6   1.67
#>   ASTRATIO%  ORB%  TOV% FT/FGA Opp eFG% Opp TOV%  DRB% Opp FTr
#> 1      25.1 0.245 0.133  0.201    0.515    0.141 0.788   0.205

## team_stats_per_game

For this function we will have to enter the team statistics:

team_per_game_stat <- team_stats_per_game(team_stats)
team_per_game_stat
#>    G    MP    FG  FGA  FG%    3P   3PA   3P%    2P   2PA   2P%    FT   FTA
#> 1 71 17090 42.34 88.3 0.48 11.01 31.58 0.349 31.32 56.72 0.552 17.75 24.34
#>     FT%   ORB   DRB   TRB   AST  STL  BLK   TOV    PF    PTS +/-
#> 1 0.729 10.66 35.07 45.73 25.39 8.62 6.59 15.17 20.72 113.44   0

## team_stats_per_minutes

For this function we will have to enter the team statistics and the number of minutes to which we want to project the statistics:

team_per_minutes_stat <- team_stats_per_minutes(team_stats,36)
team_per_minutes_stat
#>    G    MP    FG   FGA  FG%   3P   3PA   3P%    2P   2PA   2P%    FT  FTA   FT%
#> 1 71 17090 31.66 66.03 0.48 8.24 23.61 0.349 23.42 42.41 0.552 13.27 18.2 0.729
#>    ORB   DRB  TRB   AST  STL  BLK   TOV    PF   PTS +/-
#> 1 7.97 26.23 34.2 18.99 6.45 4.93 11.34 15.49 84.83   0

## team_stats_per_possesion

For this function we will have to enter the team statistics, the rival team statistics and the number of possessions to which we want to project the statistics::

team_per_poss_stat <- team_stats_per_possesion(team_stats,rival_stats,100)
team_per_poss_stat
#>    G    MP    FG   FGA  FG%    3P   3PA   3P%    2P   2PA   2P%    FT   FTA
#> 1 71 17090 41.86 87.29 0.48 10.89 31.22 0.349 30.97 56.07 0.552 17.54 24.06
#>     FT%   ORB   DRB   TRB  AST  STL  BLK TOV    PF    PTS +/-
#> 1 0.729 10.54 34.67 45.21 25.1 8.52 6.52  15 20.48 112.14   0