1 OLS REGRESSION

1.1 Inclusion of all predictors

OLS=lm(WP.A.PER~WP.PG.PTS+ WP.PG.AST+ WP.DefensePerformance+ WT.FI.W+ WT.BS.TP.Age+ WT.NBAExperience.Mean+ WT.TimeCleanOrder.SD+ WT.PerGame.GamesStarted.SD+ WT.BS.TS.AST+ WP.NBA.1stYear+ WP.PG.PF+ WP.PG.G+ WP.PG.MP+ BP.Height.Cm+ BP.Weight.Kg+ WP.Age, data=Sdata)
#print_table(OLS)
#tab_model(OLS)
GLM_summary(OLS)
## 
## General Linear Model (OLS Regression)
## 
## Model Fit:
## F(16, 511) = 35.74, p = 9e-73 ***
## R² = 0.52812 (Adjusted R² = 0.51335)
## 
## Unstandardized Coefficients:
## Outcome Variable: WP.A.PER
## N = 528 (127 missing cases deleted)
## ────────────────────────────────────────────────────────────────────────────────────
##                                  b    S.E.       t     p        [95% CI of b]    VIF
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept)                 -0.010 (0.032)  -0.295  .768     [-0.073,  0.054]       
## WP.PG.PTS                    1.040 (0.072)  14.481 <.001 *** [ 0.899,  1.181]  5.583
## WP.PG.AST                    0.129 (0.050)   2.566  .011 *   [ 0.030,  0.227]  2.711
## WP.DefensePerformance        0.433 (0.064)   6.806 <.001 *** [ 0.308,  0.557]  4.341
## WT.FI.W                      0.046 (0.055)   0.846  .398     [-0.061,  0.154]  3.191
## WT.BS.TP.Age                 0.132 (0.055)   2.389  .017 *   [ 0.023,  0.240]  3.157
## WT.NBAExperience.Mean       -0.011 (0.064)  -0.171  .864     [-0.136,  0.115]  4.454
## WT.TimeCleanOrder.SD         0.010 (0.046)   0.213  .831     [-0.081,  0.101]  2.448
## WT.PerGame.GamesStarted.SD   0.017 (0.047)   0.375  .707     [-0.074,  0.109]  2.095
## WT.BS.TS.AST                -0.055 (0.035)  -1.565  .118     [-0.124,  0.014]  1.331
## WP.NBA.1stYear               0.055 (0.035)   1.557  .120     [-0.014,  0.124]  1.297
## WP.PG.PF                     0.040 (0.055)   0.729  .467     [-0.068,  0.149]  3.206
## WP.PG.G                      0.090 (0.043)   2.121  .034 *   [ 0.007,  0.174]  1.922
## WP.PG.MP                    -1.036 (0.098) -10.597 <.001 *** [-1.228, -0.844] 10.123
## BP.Height.Cm                -0.004 (0.061)  -0.061  .951     [-0.124,  0.117]  3.923
## BP.Weight.Kg                 0.038 (0.058)   0.650  .516     [-0.076,  0.152]  3.460
## WP.Age                      -0.021 (0.035)  -0.607  .544     [-0.090,  0.048]  1.281
## ────────────────────────────────────────────────────────────────────────────────────
## 
## Standardized Coefficients (β):
## Outcome Variable: WP.A.PER
## N = 528 (127 missing cases deleted)
## ────────────────────────────────────────────────────────────────────────────────────────────────
##                                  β    S.E.       t     p        [95% CI of β] r(partial) r(part)
## ────────────────────────────────────────────────────────────────────────────────────────────────
## WP.PG.PTS                    1.040 (0.072)  14.481 <.001 *** [ 0.899,  1.181]      0.539   0.440
## WP.PG.AST                    0.128 (0.050)   2.566  .011 *   [ 0.030,  0.227]      0.113   0.078
## WP.DefensePerformance        0.431 (0.063)   6.806 <.001 *** [ 0.307,  0.555]      0.288   0.207
## WT.FI.W                      0.046 (0.054)   0.846  .398     [-0.061,  0.153]      0.037   0.026
## WT.BS.TP.Age                 0.129 (0.054)   2.389  .017 *   [ 0.023,  0.235]      0.105   0.073
## WT.NBAExperience.Mean       -0.011 (0.064)  -0.171  .864     [-0.137,  0.115]     -0.008  -0.005
## WT.TimeCleanOrder.SD         0.010 (0.048)   0.213  .831     [-0.083,  0.104]      0.009   0.006
## WT.PerGame.GamesStarted.SD   0.017 (0.044)   0.375  .707     [-0.070,  0.103]      0.017   0.011
## WT.BS.TS.AST                -0.055 (0.035)  -1.565  .118     [-0.124,  0.014]     -0.069  -0.048
## WP.NBA.1stYear               0.054 (0.035)   1.557  .120     [-0.014,  0.122]      0.069   0.047
## WP.PG.PF                     0.040 (0.054)   0.729  .467     [-0.067,  0.147]      0.032   0.022
## WP.PG.G                      0.089 (0.042)   2.121  .034 *   [ 0.007,  0.172]      0.093   0.064
## WP.PG.MP                    -1.025 (0.097) -10.597 <.001 *** [-1.215, -0.835]     -0.424  -0.322
## BP.Height.Cm                -0.004 (0.060)  -0.061  .951     [-0.122,  0.115]     -0.003  -0.002
## BP.Weight.Kg                 0.037 (0.057)   0.650  .516     [-0.074,  0.148]      0.029   0.020
## WP.Age                      -0.021 (0.034)  -0.607  .544     [-0.088,  0.047]     -0.027  -0.018
## ────────────────────────────────────────────────────────────────────────────────────────────────
#OLS=lm(WP.A.PER~WT.NBAExperience.Mean+ WT.TimeCleanOrder.SD+ WT.PerGame.GamesStarted.SD+ WP.NBA.1stYear+ BP.Height.Cm+ BP.Weight.Kg+ WP.Age, data=Sdata)
#OLS=lm(WP.A.PER~WP.PG.PTS+WT.NBAExperience.Mean+ WT.TimeCleanOrder.SD+ WT.PerGame.GamesStarted.SD+ WP.NBA.1stYear+ BP.Height.Cm+ BP.Weight.Kg+ WP.Age, data=Sdata)
tab_model(OLS,show.se = TRUE, show.p = TRUE, show.df = F, show.r2 = TRUE, show.re.var = TRUE, show.aic = TRUE, show.aicc = TRUE, show.dev = TRUE, show.loglik = TRUE, show.obs = TRUE, show.reflvl = TRUE,digits = 3,title="Table 7. Results of OLS regression for",file = "OLS.doc")
Table 7. Results of OLS regression for
  Job performance
Predictors Estimates std. Error CI p
(Intercept) -0.010 0.032 -0.073 – 0.054 0.768
Self-serving behavior 1.040 0.072 0.899 – 1.181 <0.001
Coworker-serving behavior 0.129 0.050 0.030 – 0.227 0.011
Team-serving behavior 0.433 0.064 0.308 – 0.557 <0.001
Team performance 0.046 0.055 -0.061 – 0.154 0.398
Average age of all
players in a team 		0.132 0.055 0.023 – 0.240 0.017
Average tenure of team
member 		-0.011 0.064 -0.136 – 0.115 0.864
Inequality of team
members’ playing time 		0.010 0.046 -0.081 – 0.101 0.831
Inequality of the number
of starts 		0.017 0.047 -0.074 – 0.109 0.707
Team cooperation -0.055 0.035 -0.124 – 0.014 0.118
Age of joining the team 0.055 0.035 -0.014 – 0.124 0.120
Uncooperative behavior 0.040 0.055 -0.068 – 0.149 0.467
Working time 0.090 0.043 0.007 – 0.174 0.034
Working amount -1.036 0.098 -1.228 – -0.844 <0.001
Height -0.004 0.061 -0.124 – 0.117 0.951
Weight 0.038 0.058 -0.076 – 0.152 0.516
Player age -0.021 0.035 -0.090 – 0.048 0.544
Observations 528
R2 / R2 adjusted 0.528 / 0.513
Deviance 259.954
AIC 1160.264
AICc 1161.607
log-Likelihood -562.132 
#Corr(data)
#GLM_summary(OLS)

2 PLOT

plot_model(OLS, sort.est = TRUE)

3 REPORT

For statistical modeling, we applied OLS regression to examine all predictors of job performance in ML. While the model’s overall performance of explainability does not surpass that ofSAFL, it exceeds ALMMo and several ML (e.g., LASSO, BRR, RF), with R² valued at 0.528 and an adjusted R² of 0.513. This indicates the model based on OLS regression, boasting an R² of 0.528 and an adjusted R² of 0.513, significantly elucidates more than half the variability in job performance. On the significance front, both self-serving and team-serving behaviors were strongly linked to job performance improvements, evidenced by estimates of 1.040 (p < 0.001) and 0.433 (p < 0.001), respectively. Moreover, coworker-serving behavior and the average age of all team players were positively correlated with job performance, as demonstrated by estimates of 0.129 (p = 0.011) and 0.132 (p = 0.017). Noteworthy is the finding that workload Working amount was inversely related to job performance, with an estimate of -1.036 (p < 0.001), suggesting a detrimental effect of increased workload on performance. Other variables did not exhibit statistical significance in this context. This outcome underscores that XML slightly outperforms ordinary least squares (OLS) regression in performance of explainability. The enhanced data fit provided by XML can be ascribed to its methodology of establishing multiple local models, thereby capturing the nuances within the dataset more effectively.

#report(OLS)