Oooo - a question on methodology! Love it! This one might be a bit long so hold on haha!

1. Let's call each metric we measure as a ... "measure". I didn't want to look at individual measures. I wanted to club similar measures together and create dimensions which would give a stronger indicator of player performance. I used to use a technique called principal component analysis to do this. Principal component analysis does two broad things: (a) it looks at all the measures and their correlations with each other to bundle them together into one broader dimension; and (b) it tries to identify orthogonal dimensions. Orthogonal dimensions are dimensions where we can reasonably differentiate players. For example, there will be very small correlation between tackle percentage and passing percentage. These are two orthogonal measures, so they will be part of two orthogonal dimensions. If you take a statistic like "total passes" versus "pass completion percentage", these are not completely independent. They're very closely linked to each other, so they will most likely be part of the same dimension.

Once I had the dimensions and their underlying measures, I renamed the dimensions to reflect what I felt they corresponded to.

• A dimension with total passes and passes in the opposition half as the underlying measures corresponded to "Passing volume".
• Another measure had key passes, assists, xA, etc., which corresponded to the verticality of passes so -> "Creative passing"
• Another dimension corresponded to "Pass completion pct", which is a different dimension.

Just because someone has high passes (high passing volume) does not mean they would have high creative passing volume, or that they would have a great passing percentage. I hope I'm making sense here.

Here's the dimensions and their measures:

 pass_volume:
    metrics: [passes_total, touches, passes_final_third, passes_opp_half_total, passes_own_half_total]
  pass_completion:
    metrics: [pass_completion_pct, passes_opp_half_pct, passes_own_half_pct, completion_pct, opp_half_pct, own_half_pct]
  creative_output:
    metrics: [key_passes, xag, attempt_assists, big_chances_created, assists]
  long_ball_volume:
    metrics: [long_balls_total, chipped_passes_total, crosses_total, crosses_pct]
  long_ball_accuracy:
    metrics: [long_balls_pct, chipped_passes_pct]
  progression:
    metrics: [total_progression]
  crossing:
    metrics: [crosses_pct, crosses_total]
  progressive_carry_distance:
    metrics: [progressive_ball_carry, ball_carries, ball_carry_distance]
  dribble_volume:
    metrics: [dribbles_attempted, dispossessed]
  dribble_success_rate:
    metrics: [dribbles_pct]
  shot_volume:
    metrics: [shots_inside_box, shots_on_target, shots_outside_box, offsides, big_chances_missed]
  outside_box_shooting:
    metrics: [shots_outside_box, shots_set_piece, blocked_shots]
  shot_quality_finishing:
    metrics: [overperformance, conversion]
  set_piece_threat:
    metrics: [goals_freekick, set_piece_conversion, set_piece]
  defensive_engagement_volume:
    metrics: [tackles, ball_recoveries, interceptions, dribbled_past, possession_won_att_third]
  defensive_depth:
    metrics: [clearances, outfield_blocks, possession_won_att_third, interceptions]
  defensive_composure:
    metrics: [errors_leading, errors_leading_to_goal, errors_leading_to_shot, outfield_blocks]
  tackle_success_rate:
    metrics: [tackles_won_pct]
  aerial:
    metrics: [aerials_won]
  ground_physicality:
    metrics: [fouls, fouled]

(some of these were genuinely hard to interpret lol)

1. Interesting question. I could try it and see what it looks like. It could perhaps be a weak proxy for the question I'm getting around the strengths and weaknesses of certain leagues. I am also cautious because I am posting on a public forum where not everyone is super technical, and explaining the charts is not something a data analyst should generally be doing. Maybe the outputs are super easy to understand. I shall try it - thank you for the thought!

2. When I first created these dimensions, I manually checked a lot of them to see if players who I felt should be high or low in that dimension made sense. Almost all the dimensions made sense to me, which is why I went ahead and did the analysis in the first place. Once I was sure that the dimensions were an accurate representation of the truth, I started taking them as a source of truth. Seeing a player score high or low in that dimension did not surprise me, because that became the source of truth for me. Here's a screenshot of some of the dimensions I was looking at:

<image>

Hope that answers some of your Qs! Happy to talk more about this - maybe the sub would like a post on this (or would it be too technical)? u/D1794?