Data-driven evaluation of soccer players performance

Data-driven

evaluation of soccer players performance

Paolo Cintia - University of Pisa

{'eventName': 8,

'eventSec': 8.221464, 'id': 217097515,

'matchId': 2576132, 'matchPeriod': '1H', 'playerId': 8306,

'positions': [{'x': 42, 'y': 14}, {'x': 74, 'y': 33}],

'subEventName': 83,

'tags': [{'id': 1801}], 'teamId': 3158}

pass

high pass

accurate

1700 events per match (in average)

identifiers

Ranking soccer players

● Multidimensional and flexible

existing metrics are based just on passes or shots (e.g., FS, PSV)

Characteristics of the method

PlayeRank

● Dynamic and role-aware

compare apples to apples

● Validated

existing metrics are validated just against goals or assists (!)

soccer logs

Feature weighting Role detector

learning

Player Rating

Players Rankings Individual performance

extraction

feature weights

Role detector

Learning Rating

batch online

𝜇 (a)

(c) (b)

b1

b2 c1

(d) Ranking

d2 c2

Feature weighting

Role classification

Rating computation

performance rating of u in game g

taking into account

the number of goals

18 competitions

30 million events

20K matches

21K players

Experiments

Best players in the dataset

Evaluation of PlayeRank

1. We randomly extract pairs of players from the rankings 2. Ask professional scouts who’s the best for each pair 3. Ask PlayeRank who’s the best for each pair

4. Compute the agreement between the answers Compare with state-of-the-art:

● FC → Duch et al., Quantifying the Performance of Individual Players in a Team Activity, PLoS One

● PSV → Brooks et al., Developing a Data-Driven Player Ranking in Soccer using Predictive Model Weights, SIGKDD

Evaluation of PlayeRank

Evaluation of PlayeRank

Evaluation of PlayeRank

Evolution of players

Evolution of players

Patterns of performance

Versatility of players

Versatility of players

https://arxiv.org/abs/1802.04987

Player ranking

The ranking of players (by role) can be computed by

aggregating over all ratings of the players