Soccer Statistics Across Leagues

Skillful Pikachu
Henry Friedberg, Pranathi Adhikari, John Aclufi, Shara Shanmugasundaram

5/5/23

Topic and Motivation

Topic: Analyze offensive stats for soccer players in various leagues during 2021 season.

Motivation: Find relationships between soccer players, leagues, and their stats.

Research Questions:

• What factors influence booking frequency of players?
• Does aggressiveness differ between European and non-European leagues?

Data Introduction

Player Name	Player Age	Goals Scored
Son Heung-Min	31	23
Mohamed Salah	31	23
Cristiano Ronaldo	38	18
H. Kane	30	17
S. Mané	31	16

• Dataset contains the top 20 scorers from the top 27 football leagues for the 2021 league season.

  • Rows: Individual Players

  • Columns: Attributes (Ex: Bookings, Height, Rating, League)

• The data is used by football fans, sports bettors, and researchers

Highlights from EDA Pt. 1

Analysis Pt. 1

# A tibble: 5 × 5
  term             estimate std.error statistic p.value
  <chr>               <dbl>     <dbl>     <dbl>   <dbl>
1 (Intercept)        9.18      5.62       1.63   0.103 
2 player_height_cm   0.0344    0.0299     1.15   0.251 
3 player_weight_lb  -0.0168    0.0128    -1.31   0.190 
4 player_age        -0.0235    0.0336    -0.698  0.485 
5 games_rating      -1.13      0.524     -2.15   0.0321

Used two-sided hypothesis test to determine whether or not the difference in true population means of highly rated players and non-highly rated players is significant

• \(H_0 = \mu_{high} - \mu_{non-high} = 0\)

• \(H_A = \mu_{high} - \mu_{non-high} \not= 0\)

• Reject the null hypothesis (p-value = 0.02)

Conclusions Pt.1

• Found that player rankings influence the bookings of players

  • We are 95% confident that the difference between the true population means of bookings for non-highly ranked and highly ranked players is between 0.14 and 1.2 (i.e. positive), implying that non-highly ranked players are booked more than highly ranked players, on average

Highlights from EDA Pt. 2

• Part A
• Part B

Analysis Pt. 2

# A tibble: 5 × 5
  term                 estimate std.error statistic     p.value
  <chr>                   <dbl>     <dbl>     <dbl>       <dbl>
1 (Intercept)          -1.98      0.489      -4.04  0.0000528  
2 avg_penalty_by_game  -2.76      1.51       -1.82  0.0686     
3 avg_goals_per_min   305.       65.9         4.62  0.00000387 
4 avg_shots_per_game   -0.196     0.210      -0.932 0.351      
5 duels_total           0.00564   0.00106     5.29  0.000000120

Used two-sided hypothesis test to determine whether or not the difference in true population means of goals per minute/duels for European leagues and non-European leagues is significant

• \(H_0 = \mu_{EU} - \mu_{non-EU} = 0\)

• \(H_A = \mu_{EU} - \mu_{non-EU} \not= 0\)

• Goals per minute: fail to reject the null hypothesis (p-value = 0.196)

• Duels: reject the null hypothesis (p-value = 0)

Conclusion Pt. 2

• Average goals per minute (a proxy for aggressiveness) does not differ between European and non-European leagues.

• Total number of duels (a proxy for aggressiveness) differs between European and non-European leagues.

  • We are 95% confident that the difference between the true population means of duels for European and non-European leagues is between 30.63766 and 86.54825 (i.e. positive), implying that European leagues duel more than non-European leagues, on average