NBA analysis - Basketball Essay Example

  1. Data Description

The data for the National Basketball Association (NBA) teams that played in the playoff from 1994-1995 to 2002-2003 was downloaded from (NBA Reference 2007) - NBA analysis introduction. The data contained team names, percentage of games won during normal season, total points scored during normal season, total points scored by opponent during normal season, and number of games won during the playoffs.

 

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1.1 Data variables

The following variables are used in this study:

PCT : Winning Percentage of Team during Normal Season
PTS : Average of points scored by team during Normal Season
DPTS : Average of points scored by opponent teams during Normal Season
POWINS : Number of wins in the Playoffs

1.2 Descriptive Statistics of Data

During the period of the study from 1994 to 2003, the data for 259 playoff game is described in Table 1. Table 1 show that the mean winning percentage during normal season for teams that played in the playoffs is 0.5 with a standard deviation of 0.16. The number of points scored is equal to number of points scored by opponents which makes perfect sense. The following table will analyze the data for teams that played the final, quarter final, and others teams that only in the playoffs.


Table 1: Descriptive Statistics of Wining Percentage (PCT), Points Scored (PTS),

Points Scored by Opponents (DPTS), Playoff Wins (POWINS).

Variable Number Mean Mode Std. Deviation
PCT 259 0.5 0.6097 0.16196
PTS 259 96.384 95.85 4.87160
DPTS 259 96.384 97.93 5.05113
POWINS 259 1.36 0 3.027

 

NBA teams are classified in Table 2 according to their achievement. Four groups are created as follows: NBA winners, NBA runner ups, NBA quarterfinalists, and rest of teams played in NBA playoff. Comparison between NBA winners and their runner ups in Table 2 shows that Mean PCT of NBA winner is higher than runner up teams indicating the relationship between winning the NBA and season winning percentage. NBA winners also have higher Mean PTS and lower Mean DPTS than runner ups.

Table 2: Analysis of Data according to NBA Position in Playoffs

Team N Mean PCT Mean PTS Mean DPTS
NBA Winner 9 .7478 99.9900 92.7895
NBA Runner up 9 .6833 99.2667 93.7712
NBA quarter final 18 .6944 99.5251 94.0087
Rest of Playoffs 223 .4672 95.8690 96.8267
Total 259      


  1. Regression Analysis

The relationship between offense represented by PTS, defense represented by DPTS, normal season winning percentage represented by PCT, and Winning playoff games  represented by NOWINS, is tested using the Ordinary Least Squares (OLS) regression analysis.

2.1 Regression Analysis I: Winning NBA vs. Offense

Regression analysis was applied with the independent variable average points scored during normal season (PTS) and the dependent variable number of playoff wins (POWINS). Table 3 summarizes the results of the regression analysis.

Table 3: Regression results for POWINS as a function of PTS

Independent Variable

Normal Season Points Scored (PTS)

Dependent variable:

Number of Playoff games Won (POWINS)

Constant -15.2773
Coefficient PTS 0.172564
T Stat 4.635
R2 0.077151
Adjust R2 0.07356
Standard Error 2.913131
Observations 259

Table 3 shows adjusted R-squared of .074 which means the independent variable the average points scored during normal season predicts 7.4% of winning games in the playoffs and winning the NBA Championship. T-stat for this variable is 4.635 so it can be seen as statistically significant however it doesn’t seem to explain much. Winning NBA Championship can be estimated using the equation:

Playoff Wins(POWINS) = -15.2773 + 0.1725(PTS) + E

2.2 Regression Analysis II: Winning NBA vs. Defense

Regression analysis was applied with the independent variable average points scored from opponents during normal season (DPTS) and the dependent variable number of playoff wins (POWINS). Table 4 summarizes the results of the regression analysis.

Table 4: Regression results for POWINS as a function of DPTS

Independent Variable

Normal Season Opponent Points Scored (DPTS)

Dependent variable:

Number of Playoff games Won (POWINS)

Constant 15.38339
Coefficient DPTS -0.14554
T Stat -4.01425
R2 0.059002
Adjust R2 0.05534
Standard Error 2.941638
Observations 259

Table 4 shows adjusted R-squared of .059 which means the independent variable the average points scored from opponents during normal season predicts 5.9% of winning games in the playoffs and winning the NBA Championship.  T-stat for this variable is -4.014 so it is statistically significant however it has a limited ability to predict winning the NBA championship. Winning NBA Championship can be estimated using the equation:

Playoff Wins (POWINS) = 15.38339 + 0.14554(DPTS) + E

 


2.3 Regression Analysis III: Winning NBA vs. Offense and Defense

Regression analysis was applied with the independent variables average points scored (PTS) and average points scored by opponents during normal season (DPTS) and the dependent variable number of playoff wins (POWINS). Table 6 summarizes the results of the regression analysis.

Table 5: Regression results for POWINS as a function of PTS & DPTS

Independent Variable

Normal Season Points Scored (PTS) &

Normal Season Opponent Points Scored (DPTS)

Dependent variable:

Number of Playoff games Won (POWINS)

Constant -1.17317
Coefficient PTS 0.355392
T Stat PTS 9.187244
Coefficient DPTS -0.32916
T Stat DPTS -8.82269
R2 0.292327
Adjust R2 0.286799
Standard Error 2.55598
Observations 259

Table 5 shows adjusted R-squared of .292 which means the independent variable the average points scored and average point scored by opponents during normal season predicts 29.2% of winning games in the playoffs and winning the NBA Championship.  T-stat for this variable is 9.1872 and -8.823 which means both variables are statistically significant and PTS is a little more significant than DPTS. This is significantly better than the first two regressions.  However, it is still rather low. Winning NBA Championship can be estimated using the equation:

Playoff Wins (POWINS) = -1.17317 +0.35539(PTS) – 0.32916(DPTS) + E

Second, the relationship between winning games is analyzed in relation to offense and defense.

2.4 Regression Analysis IV: Winning Games vs. Offense

Regression analysis was applied with the independent variable average points scored during normal season (PTS) and the dependent variable number of season wins (PCT). Table 6 summarizes the results of the regression analysis.

Table 6: Regression results for PCT as a function of PTS

Independent Variable

Normal Season Points Scored (PTS)

Dependent variable:

Percentage of Winning (PCT)

Constant -0.89017
Coefficient PTS 0.014423
T Stat 7.721161
R2 0.188292
Adjust R2 0.185134
Standard Error 0.146171
Observations 259

Table 6 shows adjusted R-squared of .188 which means that offense represented by the independent variable the average points scored during normal season predicts 18.8% of winning games. T-stat for this variable is 7.721 so it is statistically significant. Winning games can be predicted using the equation:

Game Win Percentage(PCT) = -0.89017 + 0.14423(PTS) + E

 

The following is the scatter diagram of the PTS relating to PCT. The line representing the equation developed in the previous section is also shown in figure 2.

Figure 1: Scatter Plot of PCT vs PTS with Line Fit

 

2.5 Regression Analysis V: Winning Games vs. Defense

Regression analysis was applied with the independent variable average points scored during normal season (DPTS) and the dependent variable number of season wins (PCT). Table 7 summarizes the results of the regression analysis.

Table 7: Regression results for PCT as a function of DPTS

Independent Variable

Normal Season Points Scored by Opponent (DPTS)

Dependent variable:

Percentage of Winning (PCT)

Constant 2.055452
Coefficient DPTS -0.01614
T Stat -9.34003
R2 0.253419
Adjust R2 0.250514
Standard Error 0.140185
Observations 259

Table 7 shows adjusted R-squared of .253 which means that defense represented by the independent variable the average points scored by opponent during normal season predicts 25.3% of winning games. T-stat for this variable is -9.34 so it is statistically significant. Winning games can be predicted using the equation:

Game Win Percentage (PCT) = 2.055452 + 0.01614(DPTS) + E

The following is the scatter diagram of the DPTS relating to PCT.

Figure 1: Scatter Plot of DPTS vs. PTS

2.6 Regression Analysis VI: Winning Games vs. Offense & Defense

Regression analysis was applied with the independent variable average points scored during normal season (PTS) and average points scored by opponents (DPTS) and the dependent variable number of season wins (PCT). Table 8 summarizes the results of the regression analysis.

Table 8: Regression results for PCT as a function of PTS & DPTS

Independent Variable

Normal Season Points Scored (PTS) & Normal Season Points Scored by Opponent (DPTS)

Dependent variable:

Percentage of Winning (PCT)

Constant 0.527437
Coefficient PTS 0.032799
T Stat PTS 58.304
Coefficient DPTS -0.03308
T Stat DPTS -60.9773
R2 0.947714
Adjust R2 0.947305
Standard Error 0.037171
Observations 259

Table 8 shows adjusted R-squared of .948 which means that offense represented by average points scored and defense represented by average points scored by opponent predicts 94.8% of winning games. T-stats for these variables are 58.304 and -60.9773 so they are both statistically significant with defense being little more significant than offense. Winning games can be predicted using the equation:

Game Win Percentage (PCT) = 0.527437 + 0.032799(PTS) – 0.03308(DPTS) + E

 

  1. Conclusion:

In predicting the winning of NBA playoff games in relation to offense and defense, three regression analyses were completed. The results of the first regression show that offense predicts 7.4% of winning playoff games and the NBA Championship. The results of the second regression show that defense predicts 5.9% of winning the NBA Championship. Offense edge over defense in predicting winning NBA championship is negligible and can be neglected. Results from third regression shows that offense and defense together predict 29.2% of winning the NBA championship. Therefore championships are not won by defense but they are won by both defense and offense.

In predicting the winning of basketball games in relation to offense and defense, another three regression analyses were completed. The results of the fourth regression show that offense predicts 18.8% of winning the NBA Championship. The results of the fifth regression show that defense predicts 25.3% of winning the NBA Championship. Defense is little better at predicting winning of games than offense, but the difference is still negligible and can be neglected. Results from sixth regression shows that offense and defense together predict 94.8% of winning basketball games. Games are not won by defense but they are won by combination of both defense and offense.

It is concluded that “Offense and defense wins (94.8%) games and offers a fair chance (29.2%) in winning Championships.”

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