“How Was the Match?”: Semantic Similarity between Electronic Media

Commentary and Work Domain Analysis Key Phrases

Gustavo Silva

1 a

, Ricardo Ribeiro

1,2 b

and Rui J. Lopes

1,3 c

Iscte - Instituto Universit

ario de Lisboa, Portugal

INESC-ID Lisboa, Portugal

Instituto de Telecomunicac¸

oes, Lisboa, Portugal

Keywords:

Football, Work Domain Analysis, Match Annotation, Performance Analysis, Semantic Similarity.

Abstract:

Football player’s performance can be measured in an objective way (e. g. Goals scored, assists, interceptions),

this being seldom a method to compare and rank the best players by categories. Over years of study, many other

factors that can inﬂuence the players performance were discovered and studied, considering not only objective

factors, but also subjective factors. Match commentary from different sources (e.g., social and formal media)

also plays an important role on a more subjective performance assessment.

By using semantic similarity analysis, this study aims to contribute to the understanding of the concepts that

are used in this commentaries, notably to each extend key phrases associated to match processes are used in

commentaries published in social and formal media.

1 INTRODUCTION

Team and athlete performance analysis has been

an object of study and usage by practitioners (e.g.

coaches) for several years. Methodologies, metrics,

and studies have been designed to improve the per-

formance of football players and provide a better per-

formance analysis, typically in an objective way us-

ing notational analysis to account for several athletes

actions (e.g., goals, assists, shoots). As illustrated

in Figure 1, these actions are conditioned by many

factors, notably the match context is a main factor

in sports performance. In fact, there are many hu-

man and non-human components working dynami-

cally and constantly changing the environment of a

football match (Mclean et al., 2017).

The combination of human factors and football

complexity makes performance analysis an extremely

challenging task; advances in these studies provide an

increasing number of factors that are considered to

inﬂuence players’ and teams’ performance. On the

other hand, the perceived performance, e.g., by fans

or even specialised media, generally does not follow

these procedures and metrics and is not expressed via

https://orcid.org/0000-0002-4726-4981

https://orcid.org/0000-0002-2058-693X

https://orcid.org/0000-0002-8943-0415

objective metrics.

This study aims to explore the perception of fans

and specialised media of the Work Domain Analysis

(WDA) structure of football, and if it may exist a rela-

tion between objective performance approaches, their

metrics and the subjective performance assessment

expressed by fans in social media and specialised me-

dia on sports websites.

This paper is structured as follows: the next sec-

tion addresses the related work on performance anal-

ysis; Section 3 presents the used information sources;

Section 4 describes the methods used to process the

data and compute the relation between the perceived

performance and the levels of WDA; the achieved re-

sults are presented in Section 5; Final Remarks and

Future Work close the document.

2 LITERATURE REVIEW ON

PERFORMANCE ANALYSIS

There are many factors that can inﬂuence a match re-

sult, over the years, the researchers have been try-

ing to analyse the complexity of this factors. Many

aspects of human behaviour can be analysed, con-

sequently, it is important to determine what will be

analysed and the reason for this. It is important to

144

Silva, G., Ribeiro, R. and Lopes, R.

“How Was the Match?”: Semantic Similarity between Electronic Media Commentary and Work Domain Analysis Key Phrases.

DOI: 10.5220/0010691100003059

In Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2021), pages 144-150

ISBN: 978-989-758-539-5; ISSN: 2184-3201

Figure 1: Player performance analysis in a match.

consider the saying: “not everything that counts can

be counted, and not everything that can be counted

counts” (C. Carling and Reilly, 2005). The sentence

above deﬁnes what happen in a football match. Play-

ers can have a bad performance considering the stats

(e.g., goals, assists, shots, interceptions), but still

make a good match if we consider the match context,

for example, a player who was positioned to avoid

counter-attacks or marked a speciﬁc opponent player

individually and played this role positively, both are

hard aspects to measure, but that we cannot ignore

when analysing the performance of a player.

2.1 Match Annotation

Annotations in football are an important tool to obtain

intelligence in a match, even in a general performance

of the team or an individual classiﬁcation of a player.

With the sports evolution on the last decades the need

of more researches about the complexity of the eval-

uation of a player performance was found (Barros

et al., 2018). In football scenario, even it being a sport

very complex, it is possible to analyse the participa-

tion of a player in a match through predeﬁned stats

(e.g. shots, interceptions, assists, goals).

The match annotations are alternatives to analyse

a player performance in a less empiric way, and to

compare a player with other player based in a com-

mon stat. This form of analysis is used by sports TV

channels, sports journalists, and bet sites. According

to the Figure 2, the player participation in a match

is converted in stats by analysis tools and used by

coaches and clubs to assist the decision making.

2.2 Work Domain Analysis

Work Domain Analysis (WDA) is a system analy-

sis method that aims to, in a structured mode, as-

sociate actors, their fundamental functions and re-

sources used by themselves in a context based in the

functional environment that establishes the purposes

to be achieved.

In the football scenario, the whole squad has sev-

eral common functions (e.g., positioning, connect

passes, etc.), but each position has speciﬁc roles on

a football match (e.g., a striker is the responsible for

scoring the goals, the central back is the responsible

to intercept the opponents and avoid opponents effec-

tive attacks). Based on a preview study, the author

classiﬁed hierarchically a conceptual method to link

the functions and purposes of players in a football

match (Berber et al., 2020).

The structure is designed from speciﬁc compo-

nents to general components, Each level is linked with

the adjacent level based on the relation of the purpose

and functions of the player position in a match:

Functional Purpose. The main functions of a player

in a match (Prevent goals scored, Score goals,

Relieve pressure, Create chances). Example: a

striker has as main function to score goals.

Values and Priority Measures. Criteria used to

analyse the progress of a player to achieve the

functional purposes(Positioning, Goal Conceded,

Saves made, Goals scored). Example: the

quantity of goals a striker scored in a match.

Purpose-related Functions. Functions that need

to be done to achieve the functional pur-

poses(Defend, Attack, Leadership, Adaptability,

Communication). Example: a striker has to

“How Was the Match?”: Semantic Similarity between Electronic Media Commentary and Work Domain Analysis Key Phrases

145

Figure 2: Typical analyse of extraction of Match Annotations in the professional football teams (Stein et al., 2016).

establish communication with the teammates to

ﬁnd the best way to score goals.

Object-related Processes. The process used by

players to achieve a purpose-related func-

tion(Dive, Shooting, Break Lines, Free Kicks,

Vision). Example: a striker has to pass, tackle,

and kick to perform the purpose-related functions.

3 MATERIALS

Three different sources were used in this project as

represented in Figure 3. Each source is a different

perception of the same match, based on the context

of each platform. Reddit comments are in an in-

formal language, where essentially the author has an

open space to talk anything he wants about a football

match. In Formal Media, there are two sources: Live

Match commentary and Player Ratings. The ﬁrst one,

Live Match, is the update, in real time, of the events

in a match and the comment about the events as they

happen. The Player Ratings comments are analyses

about the general participation of a player in a match

and the respective rating of the performance. Figure 3

illustrates the relationship between the used informa-

tion sources and shows their number of items.

Figure 3: Commentary text sources.

In this present study Reddit was used for obtain-

ing the social media content data. This platform

aims connect users by grouping them in communi-

ties through the creation of rooms about topics, where

users can comment and react to other’s comments. In

2020, Reddit had over 52 million daily active users,

nearly 303 million posts and two billion comments.

Reddit is organised around the following concepts:

Users. who interact in Reddit. A user can comment

and react in threads, follow other users and join

communities.

Communities. Group of Users with common inter-

ests about a topic.

Threads. A room where users can interact about a

given topic, for example, in a Football Match

Thread, the principal objective is talk about foot-

ball and related subjects.

Comments. A space designed for users to interact

with other users or just to express opinion. Com-

ments must respect the platform policies, but, the

user is free to express his opinion using any lan-

guage, including slang, emojis and hashtags.

Reddit offers an Application Programming Inter-

face (API). By using it, we can retrieve the data

through authenticated requests. Then, this data can

be ﬁltered and organised. In the end, it can be used to

study topics or the sentiment associated to this kind

of contents. Also, through Reddit API it is possible to

make ﬁltered searches about Users, Communities and

Threads. Using the Reddit API, a dataset containing

fans’ comments and opinions on that match was cre-

ated.

To obtain formal media contents, it was used web

scraping, that consists in collecting data from web

pages to obtain data, from different, in this case,

sports sites.

As case subject, we used the match between

Manchester City F.C. and Chelsea F.C. on the ﬁnal of

the UEFA Champions League 2021, that took place

on May 29, 2021. In this match, Chelsea F.C. beat

Manchester City F.C. by 1-0, winning the tournament.

icSPORTS 2021 - 9th International Conference on Sport Sciences Research and Technology Support

146

4 METHOD

To understand the relation between the perceived per-

formance by fans and specialised media, we com-

pute the semantic similarity between the Reddit’s

posts, live comments, and players’ assessments and

the levels of WDA. We experimented different ap-

proaches and the best results were achieved by using

BERT (Devlin et al., 2019) to generate computational

representations of the textual data from fans and for-

mal media and of the key phrases corresponding to

several levels of WDA and the cosine (Eq. 1) to com-

pute the similarity between these vectorial represen-

tations.

sim

cos

(x, y) =

x · y

kxkkyk

∑

i=1

∑

i=1

∑

i=1

(1)

BERT stands for Bidirectional Encoder Represen-

tations from Transformers, which hints about its na-

ture. BERT is a language representational model

which uses context, left and right, to generate rep-

resentations for raw text. This model is based on

the concept of transformer, which is a neural net-

work architecture that follows the encoder-decoder

structure using stacked self-attention and point-wise,

fully connected layers for both the encoder and de-

coder (Vaswani et al., 2017). In this work, we used

DistilBERT (Sanh et al., 2019), a more efﬁcient ver-

sion of BERT, that achieves comparable results. As

implementation, we used the Python Sent2Vec

pack-

age.

5 RESULTS

The method described in Section 4 was applied to the

collected datasets described in Section 3. Speciﬁcally,

we computed the semantic similarity, sim

i j

between

each entry (i.e., sentence), s

and key phrase, k

de-

ﬁned via WDA (here s

corresponds to the i

sen-

tence of dataset S

m=1,...,3

, and k

corresponds to the

WDA key phrase at level L

n=1,...,4

. This resulted

in 12 matrices (three datasets and four levels), with

values between 0 and 1, and presented as heat maps

in Figure 4, where rows and columns correspond to

entries (sentence) and WDA key phrases respectively.

These results show a great dispersion of the similarity

score across all domains: i.e., between sources, lev-

els, and between entries from the same source at the

same WDA level.

https://github.com/pdrm83/sent2vec

In order to assess how the similarity varied be-

tween levels and datasets we computed the simi-

larity mean and standard variation for all the 12

level/dataset combinations. According to Table 1, the

general content of all the data sources is more similar

with the key phrases from the WDA level L3. Value

& Priority Measures Level, which means that both,

informal and formal media, tend to describe matches

using an objective perspective more based on players

stats and less based in their participation in more ab-

stract processes (described in level L4). In contrast

to this, the WDA level that has less similarity with

the content of each information source is L1. Object-

related processes, which means that in a general con-

text, the comments are not about the secondary (i.e.

“means-to-an-end”) functions of a player in a match

but about the objective performance and the princi-

pal functions (e.g., a striker has to score goals). On

the other dimension, at all four levels, Reddit en-

tries present the higher similarity values while Rat-

ings present the smaller values. This is contrary to

what was expected, that is, that formal media live

commentary and player ratings would be more se-

mantically similar to WDA key phrases than fan’s

comments on social media.

Table 1: Similarity of the different information sources with

the WDA levels (mean and standard deviation).

S1. Reddit

L4.Functional purposes 0.437±0.078

L3.Value & priority measures 0.468±0.078

L2.Purpose-related functions 0.411±0.078

L1.Object-related processes 0.326±0.075

S2. Live Commentary

L4.Functional purposes 0.399±0.104

L3.Value & priority measures 0.427±0.107

L2.Purpose-related functions 0.378±0.099

L1.Object-related processes 0.300±0.089

S3. Ratings

L4.Functional purposes 0.351±0.081

L3.Value & priority measures 0.378±0.083

L2.Purpose-related functions 0.330±0.077

L1.Object-related processes 0.253±0.067

We also investigated if the key phrases at each

level would or not maintain their similarity rank

across the different data sources. According to Table

2, the ranking of most similar key phrases is very sim-

ilar in the three data sources, i.e., informal and formal

media comments typically tend to comment the match

based on the same key phrases. (Due to space limita-

tions Table 2 only shows the top ﬁve and bottom two

key phrases for each level.)

“How Was the Match?”: Semantic Similarity between Electronic Media Commentary and Work Domain Analysis Key Phrases

147

(a) L4 × S1. Reddit

(b) L4. × S2. Live Commentary

(d) L3. × S1. Reddit

(e) L3. × S2. Live Commentary

(f) L3. × S3. Ratings

(g) L2. × S1.Reddit

(h) L2. × S2. Live Commentary

(i) L2. × S3. Ratings

(j) L1. × S1. Reddit

(k) L1. × S2. Live Commentary

(l) L1. × S1. Ratings

Figure 4: Similarity score between entry and key sentence at different levels (L4.Functional purposes, L3.Value & priority

measures, L2.Purpose-related functions, L1.Object-related processes).

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Table 2: Comparison of rank and mean across layers and entity sources.

Live Live

Reddit Reddit Commentary Commentary Ratings Ratings

Key ID Key phrase Rank Mean Rank Mean Rank Mean

L4.22 Assist in goal scoring 1 0.470 3 0.426 5 0.376

L4.8 Create goal scoring op-

portunities

2 0.470 1 0.430 1 0.381

L4.20 Bring others into offen-

sive situations

3 0.468 2 0.429 2 0.378

L4.7 Break up opposition at-

tacks

4 0.461 5 0.423 4 0.376

L4.3 Provide a safe passing

option

5 0.461 6 0.423 6 0.375

...

L4.2 Initiate build-up 21 0.394 22 0.359 22 0.311

L4.10 Stretch opposition 22 0.393 20 0.364 21 0.318

L3.32 Goals scored 1 0.493 1 0.446 1 0.398

L3.21 Runs without the ball 2 0.492 2 0.446 3 0.394

L3.13 Effective defensive

clearances

3 0.487 5 0.441 5 0.391

L3.3 Goals conceded 4 0.484 7 0.438 7 0.389

L3.10 Effective contests 5 0.484 3 0.445 2 0.397

...

L3.18 Block shots and crosses 31 0.436 32 0.394 32 0.346

L3.7 Interceptions 32 0.432 30 0.399 30 0.352

L2.7 Maintain position in

team structure

1 0.456 1 0.413 1 0.365

L2.15 Play in line with coach

ethos

2 0.455 2 0.411 2 0.363

L2.13 Appropriate decision-

making

3 0.444 3 0.407 3 0.361

L2.18 Manage own ﬁtness

physical condition

4 0.443 5 0.406 5 0.358

L2.12 Maintain resilience 5 0.442 4 0.406 4 0.360

...

L2.5 Communication 17 0.370 17 0.343 17 0.296

L2.1 Defend 18 0.344 18 0.320 18 0.271

L1.62 Recognise when and

how to support team

members

1 0.410 1 0.374 1 0.330

L1.30 Recognise/anticipate

team member actions

2 0.390 2 0.355 2 0.311

L1.15 Initial distribution of the

ball

3 0.385 3 0.353 3 0.303

L1.26 Organise team members

at opposition set pieces

4 0.379 5 0.342 4 0.294

L1.21 Provide protection from

injury

5 0.373 4 0.343 5 0.293

...

L1.34 Understand coach’s in-

tent

68 0.243 68 0.229 66 0.202

L1.65 Risk-taking 69 0.200 69 0.188 69 0.144

“How Was the Match?”: Semantic Similarity between Electronic Media Commentary and Work Domain Analysis Key Phrases

149

6 FINAL REMARKS AND

FUTURE WORK

In the work described in this paper it was possible to

explore how key phrases associated to different lev-

els of Work Domain Analysis are used in football

matches commentary published electronically by dif-

ferent sources. From this exploratory work the fol-

lowing conclusions could be obtained:

• The similarity score between commentary entries

and WDA key phrases shows a great dispersion

across all domains (sources, levels, and entries);

• The higher similarity values are obtained at the

WDA level L3. Values & priority measures. It is

worth of note that the key phrases identiﬁed at this

level have usually a closely related match annota-

tion item (e.g., Goals scored);

• Contrary to what may be expected, comments

from users in social media show, for all WDA

levels, higher semantic similarity values that com-

mentary entries in formal media.

Concerning future work, we foresee six main

ideas on how to increase the potential of this project:

• The informal and formal media have close simi-

larity scores, with higher similarity values being

achieved by fans comments - it is important to un-

derstand how this conclusion generalise to other

matches;

• Perform a more comprehensive study of the dif-

ferent key phrases, notably their relative ranking

and their potentially hierarchical structure (e.g.,

Goals and Goals scored/conceded or Runs and

Runs with/without the ball).

• Sentiment polarity of fans perspective can pro-

vide unanticipated insights concerning perfor-

mance analysis of football players. Sentiment

analysis captures the subjective part of perfor-

mance, and analysis based on metrics (stats about

passes, goals, and assists, for example) its objec-

tive part;

• Apply our method to other social media platforms

and sources of formal media commentary notably,

comparing how users behaviour in different plat-

forms;

• Try to adapt the used methods to other sports;

• The creation of a speciﬁc platform to connect

football fans and the Data Department of the Foot-

ball Teams could lead to an integrated (qualita-

tive and quantitative) perspective on performance

analysis.

ACKNOWLEDGEMENTS

Rui J. Lopes was partly supported by the Fundac¸

para a Ci

encia e Tecnologia, under Grant Num-

ber UIDB/50008/2020 attributed to Instituto de

Telecomunicac¸

oes. Ricardo Ribeiro was partly sup-

ported by national funds through Fundac¸

ao para

a Ci

encia e a Tecnologia (FCT) with reference

UIDB/50021/2020.

REFERENCES

Barros, B., Serr

ao, C., and Lopes, R. J. (2018). Distributed

crowd-based annotation of soccer games using mo-

bile devices. In Proceedings of the 6th International

Congress on Sport Sciences Research and Technology

Support - Volume 1: icSPORTS,, pages 40–48.

Berber, E., McLean, S., Beanland, V., Read, G. J. M., and

Salmon, P. M. (2020). Deﬁning the attributes for spe-

ciﬁc playing positions in football match-play: A com-

plex systems approach. Journal of Sports Sciences,

38(11–12):1248–1258.

C. Carling, A. M. W. and Reilly, T. (2005). Handbook of

soccer match analysis: A systematic approach to im-

proving performance. Routledge, 1st edition.

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.

(2019). BERT: Pre-training of deep bidirectional

transformers for language understanding. In Proc. of

the 2019 Conference of the North American Chapter

of the Association for Computational Linguistics: Hu-

man Language Technologies, pages 4171–4186.

Mclean, S., Salmon, P., Gorman, A., Read, G., and

Solomon, C. (2017). What’s in a game? A systems

approach to enhancing performance analysis in foot-

ball. PLOS ONE, 12:e0172565.

Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019).

Distilbert, a distilled version of BERT: smaller, faster,

cheaper and lighter. CoRR, abs/1910.01108.

Stein, M., Janetzko, H., Breitkreutz, T., Seebacher, D.,

Schreck, T., Grossniklaus, M., Couzin, I., and Keim,

D. A. (2016). Director’s cut: Analysis and annota-

tion of soccer matches. IEEE Computer Graphics and

Applications, 36(5):50–60.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,

L., Gomez, A. N., Kaiser, u., and Polosukhin, I.

(2017). Attention is all you need. In Proceedings of

the 31st International Conference on Neural Informa-

tion Processing Systems, page 6000–6010.

icSPORTS 2021 - 9th International Conference on Sport Sciences Research and Technology Support

150