Introduction

The ranking of a team in the National Basketball Association, also known as the NBA, is quite simple; the more you win the better ranking you will have, and the more you lose the worse your ranking will be. The Atlanta Hawks and Houston Rockets, both teams which we will be analyzing in this report, have seen tremendous fluctuation in their ranking from the 2015-2018 seasons. The Hawks went from being in 7th place in 2015 to 27th place in 2018, whereas the Rockets went from being 17th in 2015 to 1st in 2018. What kind of variables determine how well a team is going to do in a season, and more importantly determine if they are going to win? Well, that is what we are analyzing in this report. Our goal is to understand what kind of things made the Atlanta Hawks become a worse team over the three seasons we are analyzing, and also what kind of variables made the Houston Rockets a better team over the three seasons we are analyzing. To give an understanding of what we will be looking at, here are some of the variables we will be analyzing: salary cap, salary of individual players on each team, three-point percentage for a team as a whole and also individual players, two-point percentage for teams and individuals, minutes played by individual players, distance shot from for individual players, etc. We will analyze the 2015-2016 season stats for each team, the 2016-2017 season stats for each team and also the 2017-2018 season stats for each team. Along with that, we will also analyze the stats of the individual players from the rockets and hawks for the 2015-2016, 2016-2017, and 2017-2018 seasons. This will give us a great understanding of what kind of things changed for the teams as a whole throughout these seasons, and also what kind of things changed for the individual players on these teams throughout these seasons. Note that these teams do not have the same players every year due to the trading of players throughout the NBA.

Ethical considerations

There is much ethical concern when analyzing this type of data in which we are analyzing in this lab. We are analyzing the individual player stats of players on the Rockets and Hawks. We are doing this without consent from the individual players and the organization in which they play. This data could expose some players in showing that they were a reason for their team either doing better or worse in NBA league ranking. This could also put the Atlanta hawks or Houston rockets organization at risk due to us examining why the teams changed so drastically in ranking over three seasons. This analyzation is done out of good intentions and does not work to expose any individual player or the organization in which they play.

Rockets and Hawks team comparison

Analyzing the Graph

This graph is showing the rankings of the Atlanta Hawks and Houston rockets for the 2015-2016, 2016-2017, and 2017-2018 seasons. Note that the y-axis is labeled as rankings would be on an NBA site, or even NBA TV channel, with 27th place being at the top, and 1st place being at the bottom. This means that a downward trend in the graph is a positive thing and an upward trend in the graph is a negative thing. Hence the Rockets having a downward trend going from 17th place to 1st place, and the hawks having an upward trend going from 7th place to 27th place.

Analyzing strength of schedule to points scored

This graph is showing strength of schedule compared to league rank for the Atlanta Hawks and Houston Rockets for the 2015-2018 NBA seasons. Any positive decimal determines a more difficult schedule that the team played, whereas any negative decimal is showing that the team played a easier schedule. As we can see in the graph, as the rockets scheadule got easier, their league rank started to go down (which is a good thing, because as you go down in league rank you are getting closer to being in 1st place). In the 2017-2018 season when the rockets played their easiest schedule, they were the best team in the league ranking in 1st place. Whereas when they were in 17th place in 2015-2016, they played their most difficult schedule. The atlanta hawks are showing almost the same trend as the Houston Rockets in this graph. Except, in 2015-2016 they played one of their easiest schedules and were in 7th place, and in 2017-2018 they played their hardest schedule and were in 27th place. After examining this graph, it is very evident that a teams strength of schedule has strong correlation with their league rank.

Analyzing the boxplot

Boxplot analyzation

Wins ggpairs

Atlanta

The first ggpairs graph examined factors that affected wins. There is a negative correlation(-0.77) of wins compared with percentage three points(X3P) and three points attempts(X3PA). Consistent with our findings before, as players’ shooting accuracy decreased, less wins were accompanied with 3 points shots. Rather, since shooters have left the team, they are getting points through 2-point shots, which showed positive relation with the wins. (X2P=0.881,X2PA=0.675). The changes for Atlanta Hawks’ scoring strategy might due to the lack of shooters in the team. The second graph examined the relationship of 3 points shots, minute played, and age directly with the team rank. As rank declined, less three point shots are made and players are playing more minutes on the court. Also, the average age in the team had decreased, meaning that the Hawks was losing experienced players as their rank went down. The reason why the “minute played” factor having a small impact on the rank might due to the fact that our dataset only contained 3 seasons, which is not enough to show strong correlations. However, given this relationship, we can conclude that as the Hawks was losing its place, players were playing more time on the court than before though the relationship was not significant.

Houston

For Houston Rockets, instead of shifting strategy as the Hawks did, they did better on both three point shots and two point shots because the result is showing strong correlation for 3 point shots(0.996) and 2 point shots(0.995) with their wins. Also, when we compare the percentage free throws(FT) of Houston with Atlanta, Houston is showing a strong positive relation with wins but Atlanta showed a negative correlation(-0.348) instead. As the rank went up, we found the Rockets players’ age also went up with weak correlation with the rank, and the ‘minute played’ was also weakly related to the rank as it decreased so that more players are taking turns to play on court. The efficiency, which measures a player’s ability to score in one minute, also went up with the Rockets’ rank. Admittedly, though our interpretations are based on the weak correlations and may not reflect significant differences or have a good p-value, the change is actually significant when we saw subtle changes of player’s performance and team strategy as a result of rapid lift or drop of rank in only three seasons.

Rockets and Hawks Top player comparison

Year to minutes played for top players analyzation

Year to total points played for top players analyzation

Change in top scorers over the three seasons

ATLtop3<- filter(Atlanta, Player=="Paul Millsap"|Player=="Jeff Teague"|Player=="Dennis Schroder")
atable<-ATLtop3 %>% 
  group_by(Year,Player) %>% 
  summarise_at(vars(Total.Points,efficiency),funs(mean))
kable(atable,caption = "Table0: Change in top scorers in Atlanta Hawks over three seasons, efficiency defined as points/minute played")
Table0: Change in top scorers in Atlanta Hawks over three seasons, efficiency defined as points/minute played
Year Player Total.Points efficiency
2015-2016 Dennis Schroder 879 0.5422579
2015-2016 Jeff Teague 1239 0.5494457
2015-2016 Paul Millsap 1385 0.5232338
2016-2017 Dennis Schroder 1414 0.5690141
2016-2017 Paul Millsap 1246 0.5317968
2017-2018 Dennis Schroder 1301 0.6260828 
Houston<- Playerstats %>% 
  filter(Team=="HOU")

HOUtop3<- filter(Houston, Player=="James Harden"|Player=="Eric Gordon"|Player=="Chris Paul")
atable<-HOUtop3 %>% 
  group_by(Year,Player) %>% 
  summarise_at(vars(Total.Points,efficiency),funs(mean))
kable(atable,caption = "Table 0: Change in top scorers in Atlanta Hawks over three seasons, efficiency defined as points/minute played")
Table 0: Change in top scorers in Atlanta Hawks over three seasons, efficiency defined as points/minute played
Year Player Total.Points efficiency
2015-2016 James Harden 2376 0.7603200
2016-2017 Eric Gordon 1217 0.5238915
2016-2017 James Harden 2356 0.7994571
2017-2018 Chris Paul 1081 0.5852734
2017-2018 Eric Gordon 1243 0.5770659
2017-2018 James Harden 2191 0.8588789

Analyzation

Top players for rockets and hawks total points comparison

Total points analyzation for Houston

This is a pie chart showing Houston’s top players total points percentage from three seasons. Top players are labeled as players who scored more than 1000 points in a season. As you can see in this pie chart, James Harden has been the leading scorer for the Rockets the three seasons we analyzed. We can see that Eric Gordon was the second highest scorer on the team for two seasons, and the rest were among the highest scorers for one season. A tentative conclusion we can come to from this is that without James Harden, the Rockets would not have been able to become the best team in the NBA.

Total points analyzation for Atlanta

This is a pie chart showing Atlanta’s top players total points percentage from three seasons. Top players are labeled as players who scored more than 1000 points in a season. There are no outliers for who has scored the highest percentage of points the past three seasons for the Hawks as there was for the Rockets. It seems that over the three seasons we have analyzed, the top scorers have been distributed among the team.

Distance shot at for top players comparison

Analyzation of graphs

These are graphs showing the aaverage

Distance shot compared with total points with hawks and rockets

Rockets analyzation

This graph is showing the change of total points when the average distance shot from the basket has increased for the Houston Rockets in three seasons, and the numbers on top of the graph are showing their rank in the different seasons. As we can see from the fitted lines, the Houston Rockets have a uprising trend in distance shot from, and also,in total points scored. The gradient of the fitted line increased the most when the Rockets climbed up from 17th place to 3rd place, and they still maintained their long-distance shooting accuracy when they finally reached first place. As a NBA superstar for the Rockets, James Harden stood out in total points scored which could have a large impact on the slope of the lines. However, he cannot affect the accuracy of the team alone. His teammates improved a lot on accuracy in 2016-2017 season, which might be due to their successful change of training strategy made by the Rockets’ head coach.

Hawks analyzation

This graph is showing the change of total points when the average distance shot from the basket has increased for the Atlanta Hawks in three seasons, and the numbers on top of the graph are showing their rank in the different seasons. For the Atlanta Hawks, as their average distance shot increased, their total points decreased. As time went bon, in the season 2016-2017, the performance of players was starting to decrease with Dwight Howard as a boundary line, which means players with total points less than 500 points were losing their accuracy as shooting distance increased. When we proceed to the 2017-2018 season, though the stratification problem was solved, the data points, as a whole, were showing a decreasing accuracy for most players. Closer distance means less 3-point shots were made. As a contrast, Houston’s improvement is wholesome, and each individuals in the team was improving to make more scores with higher accuracy.

Salary distribution for top players of the hawks and rockets

Pie Chart analyzation

This is a pie chart showing Houston’s top players salary percentage from three seasons. Top players are labeled as players who scored more than 1000 points in a season. We can see in this that the top two highest paid players are James Harden and Eric Gordon. They are the highest paid, therefore they are labeled as the team’s most efficient players. We can see this on the pie chart with top scorers. James harden is the highest paid and is the top scorer on the rockets, whereas Eric Gordon is the second highest paid and is the second highest scorer on the team. This would help us come to the assumption that for the Rockets organization, players are based on their ability to score points.

Analyzing the Pie Chart

This is a pie chart showing Atlanta’s top players salary percentage from three seasons. Top players are labeled as players who scored more than 1000 points in a season. This pie chart relates to the Rockets pie chart up above. The two highest scorers on the team are Paul Millsap and Dennis Schroder, whereas the two highest paid players on the team are Paul Millsap and Dennis Schroder. We can see that the Hawks also pay their players based on how efficient they are and how many points they score.

Top scorers for Houston and Atlanta over three seasons

Houstons top players efficency pie chart

This is a pie chart showing Houston’s top players scoring efficiency from three seasons. Top players are labeled as players who scored more than 1000 points in a season. Scoring efficiency is how many points a player has per minute over a season. We can see that the highest paid players and top scorers James Harden and Eric Gordon also have the highest scoring efficiency. This is showing us that without Eric Gordon and James Harden, the rockets would not have improved to be one of the best teams in the NBA.

Atlantas top players efficency pie chart

This is a pie chart showing Atlanta’s top players scoring efficiency from three seasons. We can see that the highest paid players and top scorers Paul Millsap and Dennis Schroder also have the highest scoring efficiency. A thing that can be said from this is that the Hawks do not have a top player as good as James Harden. This could be a reason that the Hawks went from 7th to 27th place in the league rankings.

Atlanta and Houston total points t.test

Season.Comparasion Team t.test.p.value
2015-2016/2016-2017 ATL 0.4099
2016-2017/2017-2018 ATL 0.8155
2015-2016/2017-2018 ATL 0.5513
2015-2016/2016-2017 HOU 0.8703
2016-2017/2017-2018 HOU 0.9312
2015-2016/2017-2018 HOU 0.3714 
## 
##  Welch Two Sample t-test
## 
## data:  Atlanta$Total.Points[Atlanta$Year == "2015-2016"] and Atlanta$Total.Points[Atlanta$Year == "2016-2017"]
## t = 0.47897, df = 33.444, p-value = 0.6351
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -237.2803  383.4979
## sample estimates:
## mean of x mean of y 
##  496.0588  422.9500
## 
##  Welch Two Sample t-test
## 
## data:  Atlanta$Total.Points[Atlanta$Year == "2016-2017"] and Atlanta$Total.Points[Atlanta$Year == "2017-2018"]
## t = 0.29465, df = 36.932, p-value = 0.7699
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -221.6976  297.1430
## sample estimates:
## mean of x mean of y 
##  422.9500  385.2273
## 
##  Welch Two Sample t-test
## 
## data:  Atlanta$Total.Points[Atlanta$Year == "2015-2016"] and Atlanta$Total.Points[Atlanta$Year == "2017-2018"]
## t = 0.79572, df = 29.7, p-value = 0.4325
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -173.7452  395.4083
## sample estimates:
## mean of x mean of y 
##  496.0588  385.2273
## 
##  Welch Two Sample t-test
## 
## data:  Houston$Total.Points[Houston$Year == "2015-2016"] and Houston$Total.Points[Atlanta$Year == "2016-2017"]
## t = -0.45768, df = 34.996, p-value = 0.65
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -508.8241  321.6065
## sample estimates:
## mean of x mean of y 
##  513.9412  607.5500
## 
##  Welch Two Sample t-test
## 
## data:  Houston$Total.Points[Houston$Year == "2016-2017"] and Houston$Total.Points[Atlanta$Year == "2017-2018"]
## t = 1.2882, df = 29.423, p-value = 0.2077
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -126.1216  556.0581
## sample estimates:
## mean of x mean of y 
##  525.4444  310.4762
## 
##  Welch Two Sample t-test
## 
## data:  Houston$Total.Points[Houston$Year == "2015-2016"] and Houston$Total.Points[Atlanta$Year == "2017-2018"]
## t = 1.2401, df = 28.356, p-value = 0.2251
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -132.4379  539.3679
## sample estimates:
## mean of x mean of y 
##  513.9412  310.4762

Atlanta and Houston top players total points t.test

Season.Comparasion Team t.test.p.value
2015-2016/2016-2017 ATL 0.4099
2016-2017/2017-2018 ATL 0.8155
2015-2016/2017-2018 ATL 0.5513
2015-2016/2016-2017 HOU 0.8703
2016-2017/2017-2018 HOU 0.9312
2015-2016/2017-2018 HOU 0.3714 
## 
##  Welch Two Sample t-test
## 
## data:  ATLtop$Total.Points[ATLtop$Year == "2015-2016"] and ATLtop$Total.Points[ATLtop$Year == "2016-2017"]
## t = 0.90906, df = 4.4484, p-value = 0.4099
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -173.8032  353.3032
## sample estimates:
## mean of x mean of y 
##   1291.00   1201.25
## 
##  Welch Two Sample t-test
## 
## data:  ATLtop$Total.Points[ATLtop$Year == "2016-2017"] and ATLtop$Total.Points[ATLtop$Year == "2017-2018"]
## t = -0.25125, df = 3.5489, p-value = 0.8155
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -356.716  300.216
## sample estimates:
## mean of x mean of y 
##   1201.25   1229.50
## 
##  Welch Two Sample t-test
## 
## data:  ATLtop$Total.Points[ATLtop$Year == "2015-2016"] and ATLtop$Total.Points[ATLtop$Year == "2017-2018"]
## t = 0.71835, df = 1.8789, p-value = 0.5513
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -330.5868  453.5868
## sample estimates:
## mean of x mean of y 
##    1291.0    1229.5
## 
##  Welch Two Sample t-test
## 
## data:  HOUtop$Total.Points[ATLtop$Year == "2015-2016"] and HOUtop$Total.Points[HOUtop$Year == "2016-2017"]
## t = 0.18301, df = 2.1953, p-value = 0.8703
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2731.579  2996.579
## sample estimates:
## mean of x mean of y 
##    1919.0    1786.5
## 
##  Welch Two Sample t-test
## 
## data:  HOUtop$Total.Points[ATLtop$Year == "2016-2017"] and HOUtop$Total.Points[HOUtop$Year == "2017-2018"]
## t = 0.089981, df = 5.9903, p-value = 0.9312
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -890.9448  958.9448
## sample estimates:
## mean of x mean of y 
##   1419.25   1385.25
## 
##  Welch Two Sample t-test
## 
## data:  HOUtop$Total.Points[ATLtop$Year == "2015-2016"] and HOUtop$Total.Points[HOUtop$Year == "2017-2018"]
## t = 1.0195, df = 3.4453, p-value = 0.3741
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1016.955  2084.455
## sample estimates:
## mean of x mean of y 
##   1919.00   1385.25

Prediction for a variables that affect Houston being an 82 win team

##       1 
## 81.8457

Analysis of the perdict model

This is a model that perdicts what variables need to be improved, or not improved, in order for the rockets to have about a 100 percent win percentage. That would be winning 82 games out of the 82 played. To give a quick analysis of what needs to be improved for this to happen,the rockets need to shoot about 80 more three pointers a season than they did in 2017 (the season in which they had the most wins) and they also need to shoot 54 more two pointers and 39 more freethrows. The teams two point percentage also needs to go up by 0.018 percent while the teams free throw percentage needs to increase by 0.007. With this being said, the teams three point percentage which is how many three pointers the team makes out of what they shoot, can stay the same or even dcrease by a little bit which was surprising. In essence, if all of the things listed above are improved, the rockets are likley to have a 100 percent win percentage every season.

##       1 
## 81.8457

Analyzation of three point percentage to wins

This graph is showing three-point percentage compared to wins for the Houston rockets and Atlanta hawks. There is an evident trend showing in both of these graphs. For the rockets, we can see that as the teams three-point percentage increases, the amount of wins they have also increased. We can see their three-point percentage was highest in the 2017-2018 season when they got 1st place, and their three-point percentage is lowest in the 2015-2016 season in which they got 17th place. For the Hawks on the other hand, as their three-point percentage increased their wins decreased. This is the opposite of what we saw for the rockets. One thing we can take from these graphs is that an increase in three-point percentage for the rockets was a major contributor to their increase in league rank.

## Start:  AIC=149.87
## Rank ~ X3P. + MP + Avg..Distance.Shot + Age + X2P. + FT.
## 
##                      Df Sum of Sq    RSS    AIC
## - FT.                 1      1.42 1850.9 147.90
## - Avg..Distance.Shot  1      4.58 1854.0 147.96
## - X2P.                1      8.49 1857.9 148.03
## - MP                  1     48.45 1897.9 148.75
## - X3P.                1     66.27 1915.7 149.07
## <none>                            1849.5 149.87
## - Age                 1    319.98 2169.4 153.30
## 
## Step:  AIC=147.9
## Rank ~ X3P. + MP + Avg..Distance.Shot + Age + X2P.
## 
##                      Df Sum of Sq    RSS    AIC
## - Avg..Distance.Shot  1      5.83 1856.7 146.01
## - X2P.                1      9.07 1859.9 146.07
## - MP                  1     47.09 1898.0 146.75
## - X3P.                1     68.22 1919.1 147.13
## <none>                            1850.9 147.90
## + FT.                 1      1.42 1849.5 149.87
## - Age                 1    320.25 2171.1 151.33
## 
## Step:  AIC=146.01
## Rank ~ X3P. + MP + Age + X2P.
## 
##                      Df Sum of Sq    RSS    AIC
## - X2P.                1      8.77 1865.5 144.17
## - MP                  1     41.41 1898.1 144.76
## - X3P.                1     66.70 1923.4 145.21
## <none>                            1856.7 146.01
## + Avg..Distance.Shot  1      5.83 1850.9 147.90
## + FT.                 1      2.67 1854.0 147.96
## - Age                 1    359.42 2216.1 150.02
## 
## Step:  AIC=144.17
## Rank ~ X3P. + MP + Age
## 
##                      Df Sum of Sq    RSS    AIC
## - MP                  1     34.71 1900.2 142.79
## - X3P.                1     61.81 1927.3 143.28
## <none>                            1865.5 144.17
## + X2P.                1      8.77 1856.7 146.01
## + Avg..Distance.Shot  1      5.53 1859.9 146.07
## + FT.                 1      3.38 1862.1 146.10
## - Age                 1    354.50 2220.0 148.08
## 
## Step:  AIC=142.79
## Rank ~ X3P. + Age
## 
##                      Df Sum of Sq    RSS    AIC
## - X3P.                1     77.64 1977.8 142.16
## <none>                            1900.2 142.79
## + MP                  1     34.71 1865.5 144.17
## + X2P.                1      2.06 1898.1 144.76
## + FT.                 1      0.25 1899.9 144.79
## + Avg..Distance.Shot  1      0.22 1900.0 144.79
## - Age                 1    368.46 2268.6 146.82
## 
## Step:  AIC=142.16
## Rank ~ Age
## 
##                      Df Sum of Sq    RSS    AIC
## <none>                            1977.8 142.16
## + X3P.                1     77.64 1900.2 142.79
## + MP                  1     50.54 1927.3 143.28
## + FT.                 1     23.05 1954.8 143.76
## + Avg..Distance.Shot  1     22.64 1955.2 143.76
## + X2P.                1     15.10 1962.7 143.90
## - Age                 1    447.12 2424.9 147.09
## Stepwise Model Path 
## Analysis of Deviance Table
## 
## Initial Model:
## Rank ~ X3P. + MP + Avg..Distance.Shot + Age + X2P. + FT.
## 
## Final Model:
## Rank ~ Age
## 
## 
##                   Step Df  Deviance Resid. Df Resid. Dev      AIC
## 1                                          27   1849.451 149.8736
## 2                - FT.  1  1.420159        28   1850.871 147.8997
## 3 - Avg..Distance.Shot  1  5.834177        29   1856.705 146.0067
## 4               - X2P.  1  8.766044        30   1865.471 144.1669
## 5                 - MP  1 34.709275        31   1900.180 142.7937
## 6               - X3P.  1 77.639508        32   1977.820 142.1553
## 
## Call:
## lm(formula = Rank ~ Age, data = Atlanta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.373  -5.867  -1.219   7.627  14.133 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  39.8228     8.7296   4.562 5.98e-05 ***
## Age          -0.9295     0.3190  -2.914  0.00618 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.018 on 35 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1953, Adjusted R-squared:  0.1723 
## F-statistic: 8.492 on 1 and 35 DF,  p-value: 0.00618

## 
##  Shapiro-Wilk normality test
## 
## data:  model7$residuals
## W = 0.9369, p-value = 0.03681

Analyzation

Multiple factors considered to affect the ranking of Atlanta Hawks were plugged into the stepAIC model and the best model was selected. It appeared that age is the best model. The p-value is shown to be 0.0061, which suggests that relationship exists between the Hawks’ team rank and the age of players in their team. The multiple R-squared value is 19.53% which means that the variance of the model explained 19.53% of the total variance. Though it’s not big enough to cover all the variance, the residuals are actually following a homoscedastic pattern( shapiro test, p-value =0.03681). Also, the points on the qq plot are sticking close to the gray dotted line, and only three points are skewed on the top. To test the scattering pattern of the residuals, a histogram was also made to test whether it follows normal distribution. It turns out that points are normally distributed except the residuals from 0 to 5, the normal distribution is a little bit skewed to the right but in general, it’s fairly a good model. According to the result of linear regression model, as the Hawks’ team rank declined, younger players are dominating the team. Since experienced shooter were transferred to other teams, younger players do not have enough experience to carry up the team, and decreased shooting accuracy is also important for the chief coach to consider.

Conclusion