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In this paper, we analyze the field data of NBA star players. We choose 25 of them and download the data from the NBA official website. The data is statistically analyzed from all aspects: the position of the player, the location on the court, the time order of two points and three points and the combination of these elements. We get some results according to the analysis. Fo r example, players of all five positions prefer to make shots right ahead the basketry; attempts of three points reach the climax during the third quarter; over the last five minutes of the game, the rate of the number of two-point attempts is the lowest and that of three-point attempts is the highest at the 46th minute.

Data analysis has permeated into almost all fields, including sports games. It has been for a long time to use data to analyze basketball games.

Wen Wang (2014) mentioned that NBA was also the beneficiary of Age of Big Data and made plenty of money. Recently, CNN reports a person behind the scene of NBA―Kirk Goldsberry, a visiting scholar of Harvard University who is crazy about data. His heat map recording 700 thousand shots caught the attention of Sports VU, the data corporation company of NBA once it was noticed in Sloan Sports Analysis Conference at MIT. Now he is an employee in this company and has finished many refreshing data analysis cases of NBA players [

Yuanyuan Gai (2014) concludes even more cases in his article [

The general manager of Houston Rocket, Morley, graduated from Northwest University and majored in computer science. He had done data analysis of the university players before he joined Celtics. He made a series of decisions about draft, transaction and salary cap using the data measuring system which is created by himself.

In this paper, we analyze the field data of NBA star players. We choose 25 of them and download the data from the NBA official website. The data is statistically analyzed from all aspects: the position of the player, the location on the court, the time order of two points and three points and the combination of these elements.

Our data comes from NBAstat [

POS in

We do data analysis by a free-data base software called MySQL [

Name | POS | Name | POS | Name | POS | Name | POS | Name | POS |
---|---|---|---|---|---|---|---|---|---|

Bogut | c | Green | pf | Irving | pg | Barnes | sf | Thompson | sg |

Howard | c | Love | pf | Lillard | pg | Durant | sf | Ellis | sg |

Jordan | c | Millsap | pf | Lowry | pg | Iguodala | sf | Harden | sg |

Whiteside | c | Nowitzki | pf | Parker | pg | Leonard | sf | Turner | sg |

Kanter | c | Plumlee | pf | Westbrook | pg | James | sf | wade | sg |

POS | Abbreviation | Full name |
---|---|---|

1 | PG | Point guard |

2 | SG | Shooting guard |

3 | SF | Small forward |

4 | PF | Power forward |

5 | C | Center |

The number of shots | Percentage | |
---|---|---|

Pg | 6390 | 25.71% |

Sg | 6066 | 24.41% |

Pf | 5034 | 20.26% |

Sf | 4139 | 16.65% |

C | 3224 | 12.97% |

In

According to the basketry, we can divide the shooting locations into five different kinds: Left Side, Left Side Center, Center, Right Side and Right Side Center.

In

The distributions of shooting data of players of 3 positions at 7 locations are shown in

Besides that, we can also divide to locations where shots are made into 7 kinds: Restricted Area, Mid-Range, Above the Break 3, In The Paint (Non-RA), Right Corner 3, Left Corner 3 and Backcourt. The numbers of shots made by players of five positions at 7 locations are shown in

We are now studying the pattern of shooting distance. 1470 of Harden’s shooting data is made into probability density distribution, shown in

In

Seven Locations | c | pf | pg | sf | sg | Total |
---|---|---|---|---|---|---|

Restricted Area | 1327 | 1789 | 2275 | 1351 | 1866 | 8608 |

Mid-Range | 748 | 1445 | 1710 | 944 | 1590 | 6437 |

Above the Break 3 | 598 | 1023 | 1490 | 1057 | 1576 | 5744 |

In The Paint (Non-RA) | 462 | 629 | 710 | 422 | 715 | 2938 |

Right Corner 3 | 48 | 72 | 115 | 190 | 176 | 601 |

Left Corner 3 | 41 | 73 | 90 | 169 | 139 | 512 |

Backcourt | 0 | 3 | 0 | 6 | 4 | 13 |

Total | 3224 | 5034 | 6390 | 4139 | 6066 | 24,853 |

represents the value of density. The area between the curve and the x-axis is one. There are two climaxes in

It’s obvious that the climax at 0 feet because players of all positions will sometimes attack the basket. Although it’s always crowded and the defense is also tough, the short- distance from the basketry can increase the hit rate by certain extent. 25 feet is where the three-point line is located. Harden is a shooting guard so he’s also a three-point shooter.

The data of other players are basically similar with that of Harden.

The data we collect included 47 kinds of scoring methods. According to the percentage occupied by each scoring method, the data of the number of shots is shown in

The top 10 scoring methods are listed in

Next, let’s analyze the shooting data of two-point and three-point shots. We turn the data of three-point shots of the 25 players into a picture according to the time in the game. We show time series of three-pointer in

We show the distribution of the data of two-point and three-point shots in six quarters (including two quarters of overtime) in

We find that the attempts of two-point shots decrease as time passes and that of three-point shots reach the climax at the third quarter. After half of the game, the players begin to get into the right state and to attempt three-point shots. Also, maybe the team wants to increase the advantage or decrease the disadvantage by three-point shots.

The distribution of the data of two-point shots and three-point shots during the last five minutes in the game is shown in

We discover that the number of two-point shots is the smallest and that of three- point shots is the biggest at the 46^{th} minute. We think that maybe it is the crucial time in a close game and there’s still possibility to cover the gap between points by successful three-point shots. As time goes by, the chance of closing the gap keeps decreasing after the 46^{th} minute and many teams will choose to accept the result without struggling anymore.

In this paper, we first look back at the important events of applying data to analyze the basketball matches. Then we choose 25 star players of all five positions from NBA official website and download their data (25,000 in total). Next, we analyze the data from many aspects: positions of players, time, space, etc. With respect to the positions of the players, we analyze the differences and similarities of the shooting data and find that all of them prefer to make shots right in front of the basketry. Concerning about the space, we divide the court into five and seven locations. We compare the data and use Harden’s data to analyze the relationship between the number of shots and the distance from the basketry. It turns out that there are two climaxes at the location of the basketry and the three-point line. In the aspect of time, we give the time order of two-point and three-point shots and their characteristics during the last five minutes of the game. It shows that the attempts of two-point shots reach the lowest and those of three-point reach the highest.

Through the study of this passage, we find some more detailed results and try to explain them. We believe that with the data analysis in depth, more phenomena will be discovered and may be used in basketball training and games.

Xie, Z.Y. and Gao, J.H. (2016) Data Analysis Instance for NBA Star Shooting. Open Journal of Social Sciences, 4, 1-8. http://dx.doi.org/10.4236/jss.2016.49001