WNBA Statistical Analysis Report

1 Introduction

This is the space where i’ll describe the data set and my team and the years.

Table discussion

2 Tables

#Table for entire league stats
df %>%
  kbl(digits = 2, caption="2025 League stats") %>%
  kable_classic_2(full_width=F)

2025 League stats

team_name	mean_score	sd_score	mean_fgp	sd_fgp	mean_reb	sd_reb	mean_tpfgp	sd_tpfgp	mean_steals	sd_steals
Aces	85.52	9.56	45.27	5.82	33.78	5.88	35.27	7.08	6.80	2.67
Dream	76.93	10.59	41.28	6.78	35.95	4.41	30.83	9.32	7.14	2.82
Fever	84.50	10.17	45.56	5.38	35.10	5.49	35.00	8.99	5.88	2.29
Liberty	84.98	9.92	44.53	5.61	36.90	5.77	35.38	10.06	7.75	2.19
Lynx	82.36	11.39	45.21	6.34	33.15	5.06	37.80	9.43	8.36	3.17
Mercury	81.93	12.60	44.28	7.34	32.26	5.39	32.97	10.34	6.55	2.12
Mystics	79.30	8.69	43.36	4.82	31.85	4.66	36.64	8.69	7.28	2.24
Sky	77.40	9.62	42.44	5.22	36.60	5.57	31.74	11.62	7.00	3.30
Sparks	78.40	10.57	42.63	6.15	32.67	5.52	32.09	11.00	7.30	2.78
Storm	82.67	9.65	43.43	5.39	34.67	6.02	28.35	9.03	9.24	3.27
Sun	80.36	9.89	44.30	5.28	33.43	4.62	32.84	11.67	7.89	3.29
Wings	84.20	11.47	44.47	5.24	34.75	4.65	32.06	11.75	7.12	2.95

#table for 
aces_summary %>%
  kbl(digits = 2, caption="2025 Las Vegas Aces") %>%
  kable_classic(full_width=F)

2025 Las Vegas Aces

result	mean_win	sd_win	mean_loss	sd_loss
Loss	79.81	9.95	79.81	9.95
Win	88.57	7.93	88.57	7.93

3 Histgrams

Histogram discussion

p = my_team %>%
  ggplot( aes(x=team_score, fill=result)) +
  geom_histogram( color="#e9ecef", alpha=0.6, position = 'identity') +
  scale_fill_manual(values=c("#69b3a2", "#404080")) 



boxplot(team_score ~ result, data = my_team, 
        col = c("#a7a8aa", "#a7a8aa"), 
        main = "Points score by game result",
        xlab = "Result", ylab = "Points")

4 Models

\(~\)

My final model is team_score= -35.9722989 + 2.2275506 * field_goal_pct + 0.4512189 * total_rebounds + 1.5823741 * 3-pt % + -0.0312377 * 3-pt% * fg%.

This model significantly predicts team score, F(4, 4) = 22.0357486, p<.0001, adjusted R^2=0.651549.

See table below for reslts…

	Dependent variable:
	team_score
field_goal_pct	2.228***
	(0.658)
total_rebounds	0.451***
	(0.145)
three_point_field_goal_pct	1.582*
	(0.813)
field_goal_pct:three_point_field_goal_pct	-0.031*
	(0.018)
Constant	-35.972
	(29.316)
Observations	46
R2	0.683
Adjusted R2	0.652
Residual Std. Error	5.642 (df = 41)
F Statistic	22.036*** (df = 4; 41)
Note:	p<0.1; p<0.05; p<0.01

hist(model4$residuals)

plot(model4$fitted.values, model4$residuals)

ols_plot_cooksd_bar(model4)

ols_plot_resid_lev(model4, threshold=3)

I built the model to predct my tema’s points for a game in which they achieve the median value for each variable. The predicted team score is 86.4104953, with 95% confidence interval (84.6215024,88.1994882).