un-xPass: Measuring Soccer Player’s Creativity
Pieter Robberechts, Maaike Van Roy and Jesse Davis
KU Leuven, Dept. of Computer Science; Leuven.AI, B-3000 Leuven, Belgium

Introduction
The best soccer players are often described as being “creative”. For example, Kevin De
Bruyne is widely regarded as an unparalleled genius when it comes to bringing creativity
to the pitch: he sees the options that other players don’t and his sparks of creativity havefrequently turned a closed game around. Hence, media and fans often discuss a player’s
creativity. Moreover, clubs and analysts view this as a valuable trait and thus look for it
when scouting for new players. Yet, it remains unclear how creativity can be concisely
captured and quantified.
The current advanced metrics typically quantify player decisions along two dimensions,
namely their risk and reward. For example, Power et al. [15] and Goes et al. [8] evaluatepasses based on how likely they are to be successfully completed to a teammate (i.e.,risk), and also by how much they would increase the chance of something good
happening (e.g., scoring) if successful (i.e., reward). Neither aligns with what one would
intuitively label as capturing creativity.
Discussions around creativity differ from risk and reward in that being creative entails
going beyond just doing something obvious but useful, to accomplishing something useful
but in a unique or atypical way. Based on these intuitions, a player performs a creative
action when it (1) differs from the typical action that most players would have selected in
the given game state, and (2) has more promising results than this typical choice. We
capture this idea in a single value per action and define the creative decision rating (CDR)
for passes as a composition of three estimates: the likelihood of each possible passdestination, the long-term reward of each passing option and the success probability ofeach passing option. Eventually, the creativity of a pass is quantified by the difference
between the expected values of the chosen pass option and the predicted typical pass.
We use machine learning methods to learn models for each of these components from
StatsBomb 360 event stream data. While each of the three components has previously
been implemented for traditional event stream data and spatio-temporal tracking data,
the hybrid 360 data poses unique challenges. We evaluate two approaches to learning
each of the separate models: a gradient-boosted trees model based on handcraftedfeatures and a deep-learning model based on the SoccerMap architecture [6].

1We compute our creativity metric for the 2021/22 English Premier League season andshow that Kevin De Bruyne is indeed the most creative player, followed by Tariq Lamptey
and Trent Alexander-Arnold. When looking at pairs of players, we found that theinteractions between Mohammed Salah and Sadio Mané exhibited the highest creativity.
Additionally, we found that the average level of creativity is not affected by the gamestate (i.e., time remaining and goal difference), but that the variance of creativity
increases as time progresses.
To summarize, this paper makes the following contributions:
1. We propose a novel metric for capturing the complex notion of creativity in soccer;
2. We compare deep learning and feature-based approaches for estimating (i) the
likelihood of each possible pass destination, (ii) the long-term reward of eachpassing option, and (iii) the success probability of each passing option fromsnapshots of player positioning in StatsBomb 360 data;
3. We provide a number of use cases showcasing our most interesting results andinsights;
The remainder of this paper is organized as follows. Section 2 presents our metric formeasuring the creativity of a player’s pass selection. Next, section 3 presents our
experimental framework for learning the machine learning components that underlie ourmetric. Section 4 provides insights into how our metric performs in practice and presents
our most interesting findings. Finally, section 5 concludes the paper and discusses
directions for future work.

Measuring Creative Passing
In psychology, creativity is commonly defined as “the ability to produce work that is bothnovel (i.e., unexpected, original) and appropriate (i.e., useful)” [12]. Creativity thus goesbeyond just doing something useful, to accomplishing something useful but in a unique oratypical way. For example, a cutback pass almost always puts a player in a good scoring
position. Yet, it is typically not perceived as being creative as it is generally the moststraightforward option when the game state allows it. Hence, creative actions should (1)differ from the typical action that the vast majority of players would select in a given
game state, and (2) have more promising results than this typical choice. In the remainderof this section, we first describe how one can measure an action’s expected usefulness
and originality. Next, we show how both can be combined to measure creativity.
Valuing the usefulness of actions
When considering event stream data, a soccer match can be viewed as a sequence of nconsecutive actions a1, a2, …, an. Each action ai ∈ {pass, dribble, shot, …} with outcome oi ∈{success, fail} moves the game from state Si−1 = {a1, . . . , ai−1} to state Si = {a1, . . . , ai−1, ai}.

2Consequently, a logical approach to capture the usefulness of actions is by measuring thedifference in quality between the pre-action game state Si−1 and post-action game state
Si. In recent years, several performance metrics have been introduced based on this idea
(e.g., xT, VAEP, g+, PV, OBV). At a high level, they all quantify the usefulness (𝑈) of actions
according to the following equation:
𝑈(𝐴 = 𝑎, 𝑂 = 𝑜 | 𝑆) = 𝑄(𝑆 | 𝐴 = 𝑎, 𝑂 = 𝑜) − 𝑄(𝑆)where 𝑄 captures the value or quality of a particular game state. Generally, this quality isexpressed in terms of the likelihood of scoring or conceding a goal.
Whether or not the action was successful has a large impact on the eventual value of theaction. However, creativity concerns the conception of an action, rather than its
execution. Therefore, creative actions do not necessarily have to succeed. To quantify thecreativity of a player’s decision, we thus abstract away from the actual result of theaction. Therefore, we compute the expected usefulness of an action as the weighted sumof the value of both outcomes:

𝐸[𝑈(𝐴 = 𝑎 | 𝑆)] =∑+𝑃(𝑂 = 𝑜 | 𝐴 = 𝑎, 𝑆) . 𝑈(𝐴 = 𝑎, 𝑂 = 𝑜)−𝑜 ∈ {𝑜 , 𝑜 }+where 𝑃(𝑂 = 𝑜 | 𝐴 = 𝑎, 𝑆) is the probability that action 𝑎 succeeds in game state 𝑆, and−𝑃(𝑂 = 𝑜 | 𝐴 = 𝑎, 𝑆) is the probability that it fails.
Valuing the originality of actions
The second aspect of creativity concerns originality with respect to the choice of actions.
Generally speaking, in soccer, there are three possible high-level actions a player canperform with the ball. A player can try to shoot at the goal, pass it to another teammate,or drive with the ball up the pitch. In this paper, we restrict the space of possible actions 𝐴
to passes, since these are the type of action that is mostly linked to creativity.
More concretely, we characterize a pass ai ∈ 𝐴 by its origin and destination. The origin
corresponds to the current ball location and is included in the current game state. The
destinations can be defined in terms of player identities (i.e., passing to a specific player)
or in terms of locations of the field. To be able to distinguish between simple passes to a
specific player’s location and (often more creative) through balls that a player should run
into, we opt for the second approach in this research. Thus, let 𝐿 be the set of all thepossible locations in a soccer field. Then, we can define Di to be the selected passdestination location of pass ai and 𝑃(𝐷 = 𝑙 | 𝑆) to be a transition probability model forpasses to any location 𝑙 ∈ 𝐿. Finally, the originality of a player’s pass selection decision
can be defined as the complement 1 − 𝑃(𝐷 = 𝑙 | 𝑆).
3

Valuing the creativity of actions
By comparing the expected usefulness of the chosen pass with that of other typicalpasses that the game state allowed, we can now assess whether a player is performing
unexpected actions that lead to more promising results, thereby indicating a notion ofcreativity. Our intuition is that there is seldomly more than one obvious high-value pass
option in any game state and that professional soccer players are all experts at selecting
such options. Therefore, it is generally sufficient to compare the selected pass with thepredicted most likely pass option 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙 = arg max 𝑃(𝐷 = 𝑘 | 𝑆).
𝑘∈𝐿

Combining these insights, we define the creative decision rating (CDR) of a pass to alocation 𝐷 = 𝑙 in a game state S as:𝐶𝐷𝑅(𝐷 = 𝑙 | 𝑆) = 𝐸[𝑈(𝐷 = 𝑙 | 𝑆)] − 𝐸[𝑈(𝐷 = 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙 | 𝑆)]
where 𝐸[𝑈(𝐷 = 𝑙 | 𝑆)] is the pass’ expected usefulness. Since the chosen action and mosttypical action have the same pre-action game state, this reduces to𝐶𝐷𝑅(𝐷 = 𝑙 | 𝑆 )= 𝐸[𝑈(𝐷 = 𝑙 | 𝑆)] − 𝐸[𝑈(𝐷 = 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙 | 𝑆)]= (𝐸[ 𝑄(𝑆 | 𝐷 = 𝑙) ] − 𝐸[ 𝑄(𝑆) ]) − (𝐸[ 𝑄(𝑆 |𝐷 = 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙) ] − 𝐸[ 𝑄(𝑆) ] )= 𝐸[ 𝑄(𝑆 | 𝐷 = 𝑙) ] − 𝐸[ 𝑄(𝑆 | 𝐷 = 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙) ]= ∑ 𝑃(𝑂 = 𝑜 | 𝐷 = 𝑙, 𝑆) * [ 𝑄(𝑆 | 𝐷 = 𝑙, 𝑂 = 𝑜) −𝑜∈𝑂

𝑄(𝑆 | 𝐷 = 𝑙𝑡𝑦𝑝𝑖𝑐𝑎𝑙, 𝑂 = 𝑜)]This leads us to the task of estimating three components that produce a single estimationof pass creativity when combined:-

Pass selection 𝑃(𝐷 = 𝑙 | 𝑆): an estimate of the likelihood of a pass being made to
every other location on the field.
Pass success 𝑃(𝑂 = 𝑜 | 𝐷 = 𝑙, 𝑆): an estimate of pass success probability for apass to every other location on the field.
Pass value 𝑄(𝑆 | 𝐷 = 𝑙, 𝑂 = 𝑜, 𝑆): an estimate of the game state value following a
successful or unsuccessful pass to every other location on the field.

Each component can be estimated utilizing a standard supervised learning pipeline,
where given some input features describing the game state, we can train a classifier toyield a probability between 0 and 1 for each location on the pitch. The next two sections
provide a detailed description of our approach to train these classifiers.

4Experimental Framework
In this section, we describe the dataset used and experimental settings for the inference
and evaluation of each model component.
Data
We build our datasets based on StatsBomb 360 event stream data. This type of data is
extracted from broadcast video and consists of the regular human-annotated event
stream data enhanced with snapshots of player positioning. The event stream describes
on-the-ball actions such as passes, dribbles and shots observed during the match. These
are described by their time of occurrence in the match, the origin and destination
location, the player who performs the action, the outcome of the action, and the bodypart used to execute the action. We work with the SPADL representation of this event
stream.1 The 360 snapshots are recorded at the time of each on-the-ball action andinclude the location and relationship to the ball carrier (i.e., teammate or opponent) of all
players visible in the video.
Following our model design, we focus exclusively on passes. Additionally, we discard
passes not performed by foot, passes from dead-ball situations (i.e., we discard corners,
free-kicks, goal-kicks, kick-offs, and throw-ins), and passes for which the origin or
destination location falls outside the 360 snapshot. Using these criteria, we construct a
training dataset of 118,758 passes from the data of the 2020 European Championship,
and the top-10 teams in the 2020/21 seasons of the English Premier League. A random
sample of 20% of these passes is used as a validation set for model selection. A datasetof 93,631 passes extracted from the top-10 teams in the 2021/22 season of the English
Premier League is set apart for evaluating the models and developing the use cases.
Model settings
For each component, we train two classes of models: an XGBoost model based on
hand-crafted features and a deep-learning SoccerMap model [6]. For all XGBoost models,
we applied the Tree-structured Parzen Estimator (TPE) algorithm for optimizing the max
tree depth ([1, 9]), learning rate ([1e-2, 0.25]), L1 regularization ([1e-8, 100]), L2
regularization ([1e-8, 100]), and the minimum loss reduction required to make a further
partition on a leaf node ([1e-8, 1]). We use early stopping with patience set to 100
boosting rounds.
We train the SoccerMap models using the PyTorch Lightning framework with the adaptive
moment estimation (ADAM) algorithm. We perform a grid search on the learning rate({1e−3, 1e−4, 1e−5, 1e−6}), and batch size parameters ({16, 32, 64}). We use early1https://github.com/ML-KULeuven/socceraction

5

stopping with patience set to 10 epochs and a delta of 1e−3 for the pass successprobability model, and 1e−5 for the pass selection and pass value models.
The SoccerMap-based models naturally produce a 𝑙 × ℎ probability surface covering thefull extent of a soccer field. We use a 104 x 68 grid. To obtain a similar surface for the
XGBoost models, we simulate a pass to each of the 𝑙 × ℎ grid cells and compute thecorresponding feature representation. The prediction for each simulated pass is thenmapped to the corresponding grid cell. For computational reasons, we use a coarser 26 x
17 grid, which we then upscale using bilinear interpolation.

Inference of model components
In this section, we provide a detailed description and evaluation of the approaches
followed for estimating each of the components required to estimate the creative decision
rating of an action.
Pass selection
To estimate the most typical pass in a given game situation, our model requires acomponent that produces a selection probability distribution over all possible passdestinations. Learning the full pass selection surface is not straightforward, though. For
each pass, one typically only has ground truth selection information about one location on
the pitch (i.e., the pass end location). In order to learn a calibrated probability surface
over all pitch locations, Fernandez et al. [6] proposed the SoccerMap deep learning
architecture which can propagate this sparse information to the entire pitch. Therefore,
our approach uses a similar SoccerMap-based deep learning model to learn the passselection surface.
As the target output, we use a sparse matrix where a value of 1 is given for every
observed pass in its corresponding destination location. As the input, we use thefollowing nine channels:● Channel A & B: Two sparse matrices with the locations of the players in theattacking and defending team, respectively.
● Channel C & D: Two dense matrices with the distance to the ball and the goal forevery location.
● Channel E & F: Two dense matrices with the sine and cosine of the angle between
every location and the ball location.
● Channel G: One dense matrix with the angle between every location and the goal.
● Channel H & I: Two sparse matrices with the two components of the velocity vectorof the ball, derived from the timestamps and ball location in the event data during
the two preceding actions. These channels give an indication of the direction inwhich the ball is moving.

6

Figure 1: Pass selection probability surface on a logarithmic scale for two example game situations
where purple colors represent more likely pass destinations. Blue and red circles represent theplayer’s location of the attacking and defending team respectively. The blue team is in possession
and plays left to right. The white arrow represents the selected pass.

Due to the lack of other approaches for estimating the full pass selection surface, we
compare our results against approaches that predict the most likely receiver. First, weestablish a naive baseline that consistently predicts the nearest teammate as the most
likely receiver. Second, we train an XGBoost ranking classifier predicting the likelihoodthat a given player is effectively the receiver of a possible pass based on the following setof handcrafted features for each pass option in the 360 snapshot: origin and destination
location, pass distance, pass angle, angle to goal at origin and destination, distance to thenearest defender at the destination, and distance of the nearest defender to a straightline between origin and destination. Per pass, one data point receives a positive label, theeffective receiver, and all the other data points are labeled zero.
Table 1 presents the results for the baseline models and the SoccerMap model on the test
data. To allow for a comparison between the location-based SoccerMap model and thereceiver-based baseline models, we compute the most likely receiver from the prediction
surface of the SoccerMap model as the teammate closest to the most likely passdestination and compute the accuracy as the percentage of passes for which the receiver
was predicted correctly. The SoccerMap model is only slightly less accurate than thefeature-based model. Although, it should be noted that mapping the location-based
predictions to players introduces an additional error. In particular for passes that are
played at a distance from the player’s current location. Hence, the difference in accuracy
between the XGBoost and SoccerMap models is likely smaller in reality than what the
metrics reflect.

7

Table 1: The average loss and accuracy of three alternative implementations for the pass selection
component: (1) a naive baseline predicting the nearest teammate as the most likely receiver, (2) an
XGBoost model based on handcrafted features and (3) a deep-learning model based on the
SoccerMap architecture.
LogLoss

Accuracy

Closest teammate

-

0.395

XGBoost-0.538

SoccerMap

6.277

0.513

Model / Feature set

Pass success probability
For estimating pass success, we define a binomially distributed outcome, according to thedefinition of success used by StatsBomb. That is, passes that reach a teammate on-side
are labeled as “successful”. All passes that go out of bounds or that are intercepted arelabeled as “failed”.
One important caveat with respect to modeling pass success is that the intended target
location of a pass is only known for successful passes. The end location of failed passes
is recorded as the location where the ball was intercepted or went out of bounds. Hence,
it is impossible to construct an accurate feature representation for attempted passes.
Previous work has addressed this issue in two ways: (1) by ignoring the problem andassuming that most passes will be intercepted near their intended destination [6], and (2)by estimating the intended receiver based on the direction of the pass and the positionsof potential receivers [1,15]. Approaches to estimate the intended receiver range from
simple distance-based rules to advanced physics-based approaches that model the ball
trajectory and player movement [1]. As the 360 snapshots lack information about thevelocity of players and the ball, we follow the approach of Power et al. [15] and estimate
the intended receiver as the one closest to where the ball was intercepted and with thesmallest angle to the line of the pass:
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑟 =𝑀𝑖𝑛 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒×

𝑀𝑖𝑛 𝐴𝑛𝑔𝑙𝑒
𝐴𝑛𝑔𝑙𝑒

If the predicted receiver is positioned outside the field’s boundaries, we clip its
coordinates to the field’s nearest boundary. If no player is within 20° of the pass line, we
assume that the intended receiver is not included in the snapshot and we proceed withthe observed end location. Figure 2 illustrates this procedure.

8

Figure 2: The intended receiver (green circle) is identified as the teammate closest to where theball was intercepted or went out of bounds (red circle) and has the smallest angle to the line of thepass. Only players within 20° of the pass line (gray area) are considered as potential intended
receivers.

Estimating the intended receiver has some obvious limitations though. First, the estimate
will be inaccurate when the intended receiver is not included in the 360 snapshot, when
the ball is blocked early in its flight path, when the ball is deflected, or when two players
are close to each other. In addition, the end location of attempted passes is replaced bythe (x,y) coordinates of the expected receiver at the time of the pass. In reality, a pass isoften played in front of the receiver to run onto.
We implemented three simple location-based gradient-boosted trees models to evaluate
whether the advantages of identifying the intended receiver outweigh the limitations: i)
receiver-agnostic: a baseline model in which we use only the origin of the pass; ii)
observed end location: we use both the observed origin and end location of the pass; andiii) intended end location: we replace the end location of the pass by the coordinates ofthe most likely receiver. Instead of the raw coordinates, we use the distance and angle to
the goal and the distance to the sideline as features in each of these models. Additionally,
we describe the relationship between the start and end location of a pass by the passdistance (total and along both axes) and angle.
Our results show that using the intended end location provides little to no advantage.
First, we can observe that using the observed end location is more accurate (Table 2).
Although, we hypothesize this is because the observed end location often gives away theoutcome of a pass. For example, passes with an observed end location on the sideline orwithin a 1 meter radius of the origin are most likely failed passes. More importantly, using
the intended end location produces inaccurate estimates for through balls, because theseare mapped to player locations when they fail. For example in Figure 3, the model using

9the intended end location predicts the prior probability for a large part of the opponent’s
penalty box, since there are no examples with a potential receiver on that location in the
training data. The main downside of using the observed end location is that locations near
the pass origin are assigned very low success probabilities, which is probably due toblocked passes.

Figure 3: Pass success probability surface for a pass starting at the location of the blue circle toall other locations using a receiver-agnostic model, a model trained on the observed end locationof each pass, and a model trained on the estimated intended end locations.

Given these results, we build further upon the observed end location and extend thelocation-based feature set with attributes available in the event stream:
1) The ball height (ground, shoulder level, above shoulder level);
2) The speed (distance covered / time) during the two preceding actions in thepossession sequence;3) The time the passer was in ball possession before attempting the pass.
Furthermore, we craft features from the 360 snapshots that describe the situation aroundthe passer, receiver and ball trajectory:4) The distance of the nearest defender to the passer and receiver;5) The distance of the nearest defender to a straight line connecting the passlocation and the receiver’s location;6) The number of opposing players in the passing path, where the path is defined asa triangular corridor between the pass origin and the receiver’s location with abase of 1 meter at the receiver’s location.
These features are inspired by recent work of Szczepański and McHale [19], Anzer andBauer [1], Goes et al. [8] and Power et al. [15].
Next to the feature-based models, we again learn a SoccerMap deep learning model. As
the input, we use channels A through G as defined in the previous section. Finally, weestablish a naive baseline that assigns the average pass completion to all passes. As
shown in Table 2, the XGBoost model using the full set of handcrafted features generally
performs best.

10Table 2: The performance of three alternative implementations for the pass success probability
component: (1) a naive baseline predicting the average pass completion, (2) an XGBoost model
based on handcrafted features and (3) a deep-learning model based on the SoccerMap
architecture.
Precision

Recall

F1

AUC

Brier

0.858

1.000

0.924

0.500

0.122

Receiver agnostic

0.871

0.988

0.926

0.704

0.110

Intended end location

0.901

0.980

0.939

0.849

0.131

Observed end location

0.916

0.975

0.945

0.886

0.075

+ event data attributes

0.925

0.970

0.947

0.916

0.068

+ 360-based features

0.936

0.966

0.951

0.939

0.062

0.926

0.975

0.950

0.930

0.065

Model / Feature set
Avg pass completion
XGBoostSoccerMap

Pass value
The final two components correspond to the expected usefulness of completed and failed
passes, which reduces to estimating the value of a game state. Several approaches havebeen introduced to capture the value of game states, but generally they estimate thelikelihood of scoring (i.e., the offensive value) or conceding (i.e., the defensive value) agoal in the near future. Unlike some other approaches [7,10], we train two separate
models for these offensive and defensive components of game state value; however, we
use an equivalent architecture for both cases. This allows us to inspect the model’s
predictions at a higher level of granularity. The total game state value is then obtained as
the difference between both model’s estimates.
We define the offensive value as the probability of scoring a goal in the next 10 actions;and correspondingly the defensive value as the probability of conceding a goal in the next
10 actions [5]. However, goals are rare and provide a weak learning singal. As a solution,
previous work has proposed the use of expected goals (xG) values to train possession
value models [10,18]. Therefore, we make use of StatsBomb’s xG values to determine thelikelihood of scoring from any shot in the next 10 actions. If this sequence of 10 actions
contains multiple shots, we combine their xG value as
𝑥𝐺𝑠𝑒𝑞 = 1 −∏𝑠ℎ𝑜𝑡 ∈ 𝑠𝑒𝑞

1 − 𝑥𝐺𝑠ℎ𝑜𝑡
11This corresponds to taking the complement of the probability that the defending teamdoes not allow a goal from a sequence of shots.
The key difference between various game state value models is the amount of contextual
information they take into account. The most basic approaches only consider the locationof the ball [16,17,22], while intermediate approaches leverage more contextual informationthat can be extracted from traditional event stream data [5,10,18,21], and the mostadvanced approaches extract a detailed spatial representation from tracking dataaccounting for the locations of all players [7]. However, to the best of our knowledge, nopublic models exist for estimating the game state value based on 360 data,2 which can beseen as a limited version of the full spatio-temporal tracking data as it provides only apartial view of the player’s locations and lacks information about the velocity andacceleration of players and the ball. Again, we experiment with a set of XGBoost models
based on handcrafted features and a deep-learning model based on the SoccerMap
architecture. As baselines, we consider the location-based Expected Threat (xT)3 [17] andevent data-based VAEP [5] frameworks.
For the XGBoost, we extend VAEP’s game state representation with features extracted
from the 360 data that capture the number of outplayed players and the ballinterceptability. The number of outplayed players is computed using a simplified version
of Impect’s Packing Rate: a defender is considered to be packed if he is positioned
between the ball and the goal before a forward pass, but would be further from the goal
than the ball after the pass. For interceptability, we use the number of defenders in a 3
meter and 5 meter radius around the pass’ end location. For the SoccerMap models we
use the seven input channels described earlier (A- G), as well as two dense matrices with
the number of players of the team in possession and the opposing team that would bepacked after a pass to each location.
We need the expected value of both completed and failed passes. Since the outcome ofthe pass is a feature in VAEP’s game state representation, a value for completed andfailed passes can be obtained by modifying the feature value. For the SoccerMap models,
we learn separate models on completed and failed passes. For simplicity we only report
the performance of all offensive game state value models on completed passes in Table
3. Also, we evaluate the performance on a binary goal / no goal label rather than the xG
values to allow comparison with the xT and VAEP baselines.

2
3

It is unclear whether StatsBomb’s own On Ball Value (OBV) metric uses the 360 snapshots.
We use the xT grid provided at https://karun.in/blog/data/open_xt_12x8_v1.json

12Table 3: The performance of five alternative implementations for the offensive pass value
component: (1) the location-based expected threat (xT) framework, (2) the event data-based VAEP
framework trained on binary goal/no-goal labels, (3) the event data-based framework trained onxG values, (4) the VAEP framework with features extracted from 360 snapshots trained on xG
values, and (3) a deep-learning model based on the SoccerMap architecture. All models areevaluated on completed passes only using binary goal/no-goal labels.
AUC

Brier

LogLoss

xT

0.741

0.012

0.062

VAEP

0.757

0.011

0.056

VAEP-xG

0.766

0.011

0.056

VAEP360

0.776

0.011

0.055

SoccerMap

0.743

0.011

0.064

Model / Feature set

Use Cases
We now present a number of observations that result from computing the Creative
Decision Rating metric for the 2021/22 Premier League season. First, we present a rankingof the most creative players and quantify how their creativity pairs with their technicalabilities. Second, we look at the creativity of interactions between pairs of players. Third,
we look for a relationship between a player’s position and creativity. Fourth, we look at theeffect of the game state on creativity.
Most creative players
Table 4 shows the top-10 players in terms of Creative Decision Rating per 90 minutes(CDR90) in our dataset. To obtain a robust ranking, we only include players who
performed at least 250 passes according to the criteria defined in section 3. Kevin De
Bruyne tops the ranking, closely followed by Tariq Lamptey. Liverpool’s right-back Trent
Alexander-Arnold completes the top three.

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Table 4: The top-10 players who completed at least 250 passes in the 2021/22 English Premier
League season in terms of our Creative Decision Rating (CDR), normalized for minutes played.
Player

Team

CDR90

1

Kevin De Bruyne

Manchester City

0.0834

2

Tariq Lamptey

Brighton & Hove Albion

0.08173

Trent Alexander-Arnold

Liverpool

0.0584

4

Raphinha

Leeds United

0.0563

5

Hakim Ziyech

Chelsea

0.0527

6

Martin Ødegaard

Arsenal0.04807

Lucas Moura

Tottenham Hotspur

0.04738

Harry Kane

Tottenham Hotspur

0.0472

9

Bukayo Saka

Arsenal0.041510

Mason Mount

Chelsea

0.0413

There is a natural tension between the creativity and quantity of a player’s passes. This is
illustrated in Figure 3, which shows the number of passes that players execute on average
per 90 minutes (quantity) in function of the average creativity of these actions. The
reason is twofold. First, players who perform more passes generally play in a moredefensive position, from which it requires less creativity to progress the ball. For example,
Harry Kane has a very high average CDR, but attempts relatively few passes as a striker.
Second, if a player performs a high number of passes, then it is harder for each pass tohave a high value. However, as shown by the dotted isoline, players like De Bruyne,
Lamptey, Alexander-Arnorld, Raphina and Ziyech pair a high creativity with a large
quantity of passes.

14

Figure 3: Scatter plot contrasting a player’s average Creative Decision Rating (CDR) with theaverage number of passes performed per 90 minutes. The dotted isoline shows the gap between
the top-ranked players and the rest. Only players who completed at least 250 passes in the2021/22 English Premier League season are included.

Combining creativity and technical skills
Our creativity metric rewards players who attempt atypical passes, regardless of theresult of the pass. While this enables a fine-grained evaluation of a player’s creativity, itomits an important piece of a player’s performance evaluation. To assess whether a player
pairs vision with the technical abilities to successfully complete the passes he attempts,
we apply the execution rating metric proposed by Bransen et al. [3]. Their metricmeasures the technical execution quality of a pass as the difference between theobserved outcome of the pass (e.g., did the cross reach a teammate) and the predictedprobability that the pass would be successful based on the context under which it was
performed. Formally, the Execution Rating (ER) is defined as+

𝐸𝑅(𝑂 = 𝑜, 𝐷 = 𝑙 | 𝑆) = [𝑜 ] − 𝑃(𝑂 = 𝑜 | 𝐷 = 𝑙, 𝑆)+

where [𝑜 ] takes the value of one if the pass succeeds and is zero otherwise, and𝑃(𝑂 = 𝑜 | 𝐷 = 𝑙, 𝑆) is given by the action success predictor from the previous section.
Intuitively, the metric rewards players who successfully perform difficult passes andpunishes players who flub an easy pass.
Figure 4 compares the average CDR and ER of passes, grouped by player. In general, we
found no strong relationship between the creative and technical abilities of players. The
execution rating is mainly determined by a player’s position, with defensive midfielders
scoring the highest and strikers scoring the lowest. We assume this is because ​theconsequences of losing possession are less detrimental for attacking players. Hence, they
can afford more mistakes.

15

PlayerER

1

Kevin De Bruyne

0.0054

2

Tariq Lamptey

0.00073

Trent Alexander-Arnold

0.00384

Raphinha-0.0203

5

Hakim Ziyech

0.0028Figure 4: The relation between average creative decision rating and execution rating for eachplayer in the 2021/22 English Premier League season (left) and average execution rating for the
top-5 most creative players (right). Only players who completed at least 250 passes are included.

Most creative duos
A limitation of our creativity metric is that it gives all credit to the player who gives thepass. Yet, it is often the player on the receiving end who enables the pass by slipping intoa pocket of space. Therefore, Table 5 looks at the pairs of players who exhibited thehighest creativity with mutual passes. Unsurprisingly, the pairs that rank highest havespent many minutes together on the pitch and are fully attuned to each other. Mohamed
Salah and Sadio Mane have led Liverpool’s attack for five seasons, while Harry Kane andHeung-Min Son are a sterling duo at Tottenham Hotspur.
Table 5: Player pairs with the highest Creative Decision Rating (CDR) per interaction in the2021/22 English Premier League season. Only pairs with at least 50 interactions are included.
Player

Team

CDR

1

Mohamed Salah
Sadio Mané

Liverpool

0.03872

Harry Kane
Heung-Min Son

Tottenham Hotspur

0.020216

3

Leandro Trossard
Neal Maupay

Brighton & Hove Albion

0.0132

4

Bernardo Silva
Kevin De Bruyne

Manchester City

0.0126

5

Kevin De Bruyne
Philip Foden

Manchester City

0.0125

6

Marc Cucurella
Neal Maupay

Brighton & Hove Albion

0.0114

7

Raphinha
RodrigoLeeds United

0.01148João Canceloİlkay Gündoğan

Manchester City

0.0096

9

Bruno Fernandes
Cristiano Ronaldo

Manchester United

0.0095

10

Jordan Henderson
Sadio Mané

Liverpool

0.0079The link between creativity and position
Being creative is most often associated with the role of a playmaker or “number 10”, whotypically operates from the position of an attacking midfielder. Figure 5 shows that players
who operate from this position indeed score high on our creativity metric. However, wefound that wingers are on average the most creative players in the 2021/22 Premier
League. This position has become more common for offensive playmakers to carry out inrecent years. For example, Messi typically operates from a wide offensive position.
Another trend is to use a wingback as a playmaker. This is illustrated by Tariq Lamptey
and Trent Alexander-Arnold, who both rank among the top creative players according toour metric.

17

Figure 5: The average creative decision rating per pass, grouped by a player’s position on the field.
Darker green colors reflect higher creativity. Not enough data was available for the CM and SS
positions. For a definition of each position, we refer to StatsBomb’s data specification guide.

Interestingly, we found a large imbalance between the left and right wings, with the rightwing being significantly more creative. Further research on other leagues should be
carried out to determine whether this could be generalized or should be attributed to theplayer selection in the 2021/22 Premier League season.
The link between creativity and game state
Both the time remaining and the goal difference have no clear effect on the average CDR(Figure 6). However, the variance in creativity increases as time progresses, particularly ininjury time. Possibly, this could be attributed to fatigue and teams taking more defensive
risks near the end of the game. This creates extra space, which could be exploited withcreative passing. However, as players get more tired, this also gives rise to more "missed"
creative opportunities.

18

Figure 6: The distribution of the creative decision rating grouped by 15-minute time intervals (left)
and goal difference (right). A negative goal difference corresponds to the team in possession being
behind.

Related WorkIn team sports, creativity has been emphasized as an important factor of success by bothresearchers [12] and practitioners [2]. Specifically for soccer, Kempe and Memmert [9]showed that successful teams use more highly creative actions to score goals. However,
the evaluation of creativity in these studies is mostly conducted via psychologicalassessments [14,20] or experts rating each action of a player [11,13]. Obviously, thisapproach does not scale.
Despite being a highly desired attribute, no previous research has looked at statisticalmodels to objectively quantify creativity. That is because creativity is generally seen as anintangible quality, something which cannot be analyzed through statistics. Yet, creativity
can be seen as an aspect of decision-making, which is typically evaluated as a trade-off
between an action’s risks and rewards [4,8,15]. Our creativity metric builds on theserisk-reward frameworks, adding a notion of originality and making an abstraction of theactual result of the action [3].

Conclusions
The above-outlined metrics are a first step towards capturing the complex notion of thecreative abilities of soccer players. In conclusion, with these metrics, it will be possible tocompare different players on their creative abilities in general as well as in various
different scenarios. This can provide clubs and analysts with valuable information during
the scouting process.

19

In the future, we plan to extend our creativity metric to other action types, in particular
shots and dribbles. Currently, our metric generally undervalues the creativity of passes ingame states where a shot or dribble would be the most typical action instead of a pass.
Generalizing the pass selection model to an action selection model would solve thisproblem. Also, it would allow valuing the creativity of other action types. Second, we aim
to experiment with alternative approaches for evaluating the value of typical actions.
While taking the most likely pass works well in practice, this approach would provide aninaccurate result when multiple passes are equally likely. Finally, we aim to incorporate
spatio-temporal tracking data to obtain more accurate estimates for our pass selection,pass success and pass value components.
Acknowledgments
This work was supported by the KU Leuven Research Fund (C14/17/070, C14/18/062). We
thank StatsBomb for providing the data used in this research.

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