Situation Modeling and Visual Analytics for Decision Support in Sports

Anders Dahlbom and Maria Riveiro

Informatics Research Centre, University of Sk¨ovde, P.O. Box 408, SE-541 28 Sk¨ovde, Sweden

Keywords:

Sports, Decision Support, Situation Modeling, Visual Analytics, Information Fusion.

Abstract:

High performance is a goal in most sporting activities, for elite athletes as well as for recreational practitioners,

and the process of measuring, evaluating and improving performance is one fundamental aspect to why people

engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous

data in order to apply actions that change important properties for improved outcome. The number of computer

based decision support systems in the context of data analysis for performance improvement is scarce. In

this position paper we brieﬂy review the literature, and we propose the use of information fusion, situation

modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to

elite, in the process of performance evaluation.

1 INTRODUCTION

Sports have in various forms engaged people through-

out history. In its essence, sports is about objectively

determining a winner in some sporting event using

measures such as time, length, height and score. The

goal and motivation for an athlete is thus to achieve

high performance with respect to these measures, in

order to perform better than the opponents. The sub-

jective experience of carrying out sports is however

also of high importance in order to keep athletes, and

recreational practitioners, motivated and engaged.

The development of sports technology has histor-

ically been driven by research in e.g. biomechanics

and physiology aimed at improving the performance

of elite athletes. This involves efforts with respect to

e.g. physiological performance, technical skill, car-

diovascular capacity, muscle strength, tactical deci-

sion making and mental focus. To this end, many

tools and systems have been developed, e.g. video

analysis and motion capture technologies, heart rate

monitors, and systems for monitoring oxygen uptake.

However, only a few of these systems are mobile and

usable outdoors. Moreover, the interest in sports is

also high among novice from recreational and health

perspectives, and many of the traditional systems are

generally not accessible from these perspectives.

Technology and tools related to sports, recre-

ational activities and healthcare represent rapidly

growing areas. There are many applications for

smartphones, e.g. Runkeeper, Nike+, as well as

equipment such as sports watches with GPS and heart

rate monitors. These tools are used by numerous prac-

titioners around the world and are often connected to

social media. Although there is great potential for

data analysis and decision support, there seems to be

a lack of tools which includes advanced data analy-

sis capabilities, for supporting performance evalua-

tion, for both elite and recreational activities. There

is a need for rich models that can be personalized and

used for aiding performance improvement.

We believe that techniques used in information fu-

sion, situation modeling and visual analytics can be

used as a suitable foundation for constructing deci-

sion support tools for (1) capturing the complete per-

formance evaluation process, (2) understanding, con-

structing and maintaining the underlying models and

(3) for interpreting and understanding data and infor-

mation. In itself, interaction with models and e.g. vir-

tual coaching may also lead to enhanced experience.

The rest of this position paper is organized as fol-

lows. Section 2 brieﬂy reviews literature regarding

the analysis of sports data. Section 3 presents and

motivates our future research path towards decision

support within sports. Finally, section 4 discusses the

this path and outlines our future work.

2 SPORTS AND IT

Although computer science and sports havemore than

50 years together, initially focusing on sports infor-

539

Dahlbom A. and Riveiro M..

Situation Modeling and Visual Analytics for Decision Support in Sports.

DOI: 10.5220/0004973105390544

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 539-544

ISBN: 978-989-758-027-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

mation (documentation) and later on sports informat-

ics (processing), it was not until a few decades ago

that computer science emerged as an important inter-

disciplinary partner to sports science (Baca, 2006).

(Perl, 2006) observes a change about 20 years ago,

where the spectrum of computer science in sports

changed. Perl attributes this change to advances

in computer science coupled to hardware, software,

communication and multimedia. Baca also roughly

identiﬁes these four aspects as the main areas of re-

search related to computer science in sports: (1) data

acquisition, processing and analysis, (2) modeling

and simulation, (3) data bases and expert systems, and

(4) multimedia, presentation and virtual reality. From

a data analysis perspective, we distinguish four dif-

ferent themes in the following: (1) classiﬁcation and

prediction, (2), event and activity detection in sports

video data (3) collection and fusion of sensor data,

and (4) performance improvement guidance.

2.1 Classiﬁcation and Prediction

Ofﬂine classiﬁcation and prediction can typically be

approached using two perspectives: data-driven and

knowledge-driven. The former approach is closely

tied to data mining and machine learning, where tech-

niques such as artiﬁcial neural networks and decision

trees are constructed from historical data and then

used as models for classifying new data samples. The

latter approach is coupled with knowledge engineer-

ing and expert systems, where models are created by

eliciting knowledge from experts.

Within sports, e.g. artiﬁcial neural networks have

been applied to the talent identiﬁcation problem in

team sports, where the aim is to identify promising

players/athletes that can be drafted for a team (Mc-

Cullagh, 2010). In the study, a number of parameters,

e.g. physiological, medical, psychological and an-

thropometric, are used to classify and predict promis-

ing talents within the Australian football league. Ex-

pert systems have also been applied on the problem

of identifying potential sport talents. For example,

(Papi´c et al., 2011) have used a fuzzy logic approach

to identify potential talent among children with re-

spect to e.g. basketball, gymnastics, swimming, and

tennis, based on knowledge from kinesiology experts

such as functional, motorical and morphological fea-

tures.

Machine learning learning techniques have also

been applied for predicting the performance of indi-

vidual athletes using e.g. artiﬁcial neural networks

for predicting performance in swimming (Edelmann-

Nusser et al., 2002), C4.5, na¨ıve Bayes and ran-

dom forests for predicting skill in table tennis (Maeda

et al., 2012), and artiﬁcial neural networks and sup-

port vector machines for classifying subjects in run-

ning/walking (Fischer et al., 2011). Moreover, pre-

dictive modeling has also been applied in team sports,

e.g. (O’Donoghue, 2012) uses multiple linear regres-

sion for predicting the outcome of rugby matches.

2.2 Event and Activity Detection

Event and activity detection in sports video data is an-

other area which has received much focus, where a

common theme is discovery and recognition of pat-

terns. Much work has been carried out coupled to

the analysis and identiﬁcation of distinct events in

sports video data, e.g. the use of dynamic Bayesian

networks for play-break detection in soccer games

(Wang et al., 2005) and the use of tracking techniques

and semantic extraction for editorial content creation

(Xu et al., 2009). These have the common theme of

supporting media creation. Similar techniques have

however also been used for supporting coaches dur-

ing actual events, e.g. the use of probabilistic tech-

niques for capturing the key notions of games (Beetz

et al., 2009), and the use of simple statistical measures

on time-series data for analyzing player performance

in team sports to detect key turning points in player

performance (L¨uhr and Lazarescu, 2007).

2.3 Sensor Fusion

Sensor fusion and collection is another interesting

application of computer science in sports. This

is closely connected to information fusion and the

combination of data and information from multi-

ple sources for improved estimation and prediction.

(Flanagan, 2009) states that a signiﬁcant challenge for

e.g. coaches working with alpine skiing is to accu-

rately detect speed and trajectory of athletes through-

out courses during training and competition. Re-

cently, fusion and technologies such as GPS, inertial

sensors, high-speed cameras and fusion have been ap-

plied to this task. (Brodie et al., 2008) investigates a

fusion motion capture system for optimizing ski rac-

ing, where information from individual runs is fused

and collected and used for e.g. post action analy-

sis. Brodie et al. argue that the usage of such fusion

systems is more suitable than classical optical sys-

tems which are limited to analyzing portions of indi-

vidual runs, compared with analyzing complete runs.

(Kruger and Edelmann-Nusser, 2010) compares a full

body inertial system with optical video based systems

and concludes that full-body inertial systems are ben-

eﬁcial since they are not restricted to analyzing ac-

tions within speciﬁc volumes. Besides winter sports,

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540

wearable systems have also been investigated in con-

nection to running (Cheng et al., 2010). Similarly,

advantages include easy deployment and the possi-

bility of collecting data from multiple runs and over

long distances, compared to classical optical based

systems which are more difﬁcult to deploy and which

have limited viewing volumes.

2.4 Performance Improvement

Performance improvement is perhaps the least inves-

tigated area of computer science within sports. Al-

ready in 2006, Bartlett argued for the potential of us-

ing expert systems and machine learning techniques

in sports biomechanics analysis for improvement of

performance. (Bartlett, 2006) however concluded that

the uses so far were few. (Owusu, 2007) presents

a general model, recognize - critique - recommend,

which can be used for performance improvement in

sports. Owusu discusses the use of neural networks

and expert systems for recognition and critique, but

concludes that very little has been investigated on

these topics and especially on the ﬁnal step of pro-

viding recommendations for improving performance.

With the recent wide spread use of mobile plat-

forms, excellent opportunities for feedback arise,

which can be closely connected to individual train-

ing sessions. (Kranz et al., 2013) investigates the

use of smart phones for automatic recognition, as-

sessment and feedback during indoor exercises. (No-

vatchkov et al., 2011) presents a framework for mo-

bile coaching based on a client-server architecture,

where coaches in near real-time can analyze data col-

lected from athletes offsite and still be able to provide

feedback. Similarly, (Tampier et al., 2012) also in-

vestigates a framework based on the client-server ar-

chitecture for providing marathon runners with feed-

back within the context of mobile coaching. Tampier

et al. however also investigates the use of a simple

model to simulate load and performance, which can

be used for automatic feedback to e.g. avoid overload

and underperformance. Moreover, the potential ben-

eﬁts of real-time feedback is highlighted by (Kirby,

2009), who argues that an athlete that receives online

feedback can make immediate corrections, compared

to post-action review with corrections applied at the

next training session.

3 MODELING AND SUPPORT

As pointed out earlier, measuring and improving per-

formance, and enhancing experience, are two fun-

damental concept related to why people engage in

sports. Measuring and improving performance, also

referred to as performance evaluation, is an essen-

tial part of sports and it is concerned with domain

modeling and evaluation with respect to such mod-

els (Owusu, 2007). Owusu suggests the process of

recognize, critique and recommend, separating the

linear process of performance evaluation from the

speciﬁc domain dependent model. The recognize

step involves identifying the particulars of the ac-

tions that are carried out, the critique, or diagnosis,

step aims at identifying faults with respect to ex-

pected or optimal performance, and the recommend

step involves suggesting actions for improving per-

formance. With respect to automation and decision

support, a fair amount of work has been carried out

related to the critique step, while work on recognition

so far mainly has focused on low-level recognition

(data level), leaving high-level recognition largely un-

explored. Moreover, work on the recommend step is

also unexplored within the sports domain. Although

work has been carried out on each of these steps, in-

dividually, there is a general lack of work on automa-

tion and decision support that captures the complete

process of performance evaluation (Owusu, 2007).

3.1 Information Fusion

Techniques for data and information fusion (IF) have

been identied as key enablers for providing decision

support when large amounts of heterogeneous data

need to be analyzed (Liggins et al., 2009). In IF,

functions for building and using models and analyz-

ing data are typically separated into six levels (Lig-

gins et al., 2009): (level 0) feature assessment (esti-

mating and predicting feature states), (level 1) object

assessment (fusing sensor data to obtain accurate es-

timations of an entities attributes), (level 2) situation

assessment (estimating entity-to-entity and entity-to-

environment relations), (level 3) impact assessment

(estimating utility/cost of feature, object, or situation

states, including future effects of planned or predicted

actions), (level 4) process reﬁnement (meta-processes

concerned with monitoring and optimizing the over-

all fusion process) and (level 5) cognitive renement

(cognitive aids and human computer interaction).

This conceptual division of a complex fusion pro-

cess in low and high level functions matches the tasks

that need to be resolved to design and build computer

based systems that provide performance evaluation

support. For example, in a running context techniques

used in level 0 and 1 are needed to establish biomet-

ric attributes and tracking objects, while the relation

of these objects to the environment (e.g. terrain char-

acteristics and weather) or to other objects is a situ-

SituationModelingandVisualAnalyticsforDecisionSupportinSports

541

ation assessment problem. The inferences done for

establishing the consequences or effects of certain ac-

tions, e.g. increasing the rhythm, are based on the ap-

plication of impact assessment techniques, while pro-

cess reﬁnement involves multi-objective optimization

methods that monitor the overall performance.

IF provides a unifying framework that can be used

for approaching the issues involved in constructing

models of performance for athletes and recreational

practitioners. The framework, as well as IF meth-

ods and techniques have been successfully applied in

many different areas, such as military command and

control, computer security, transportation, etc. How-

ever, they have not been used extensively in the area

of sports and performance evaluation.

3.2 Situation Modeling

The domain speciﬁc models employed in perfor-

mance evaluation should capture relations between

situations and performance, or more speciﬁcally, be-

tween situation types and performance, where situa-

tions are instances of situation types. Depending on

the level of abstraction, e.g. from technique to tactic,

situations and situation types may be represented us-

ing feature state, object state or distinct actions/tasks

that were carried out. To provide rich support, the

models should preferably surpass the use of simple

and individual measures (e.g. positions, directions,

speeds at individual points in time) to instead model

more complex behaviors and causal relations that can

be used for increasing performance. They may also

require the combination of data not only from indi-

vidual athletes, but also from the environment and

from other athletes. This would address the need for

new methods and tools that reﬂect the complexity and

holistic nature of performance in sport identiﬁed by

(Balagu´e and Torrents, 2005).

Models of situations and situation types can be

built using both knowledge- and data-driven ap-

proaches. Optimally, these approaches are combined

in such a way that domain knowledge is used to cre-

ate a priori models which can be reﬁned in relation to

analyzes of available data. The reverse is also possi-

ble, discovering novel information in data, which can

be exploited or reﬁned by experts. A hybrid approach

would combine the strengths of information fusion,

data mining and knowledge engineering, and promote

the use of models and systems that can be adapted.

In order to cover all three steps of the performance

evaluation process, from mapping the present situa-

tion to its type, to suggesting corrections for reach-

ing situations that are instances of optimal situation

types, it is important that distinct pairs of situation

types/performance are related to each other. It is not

enough to only be able to classify situations (e.g.

novices and experts), but also to understand which

parameters to inﬂuence in order to change behavior

(improving performance). It is also important that the

models allow for prediction of future outcomes based

on the current state and future actions. Ideally, the

models should, when possible, be used in real-time

by athletes, who would experience adequate feedback

about how to change the current behavior. This puts

the research at the state of the art:

A well-known problem and most popular in

training preparation is ﬁnding an accurate

stimulus in an appropriate time for each ath-

lete. In efﬁciency sports solving this problem

is the key to success. [...] It is almost im-

possible to ﬁnd studies that combine computer

science and pin point this topic. [...] Unfor-

tunately none of [the suggested approaches]

works, and even try to adopt machine learn-

ing algorithms to support trainers decisions.

(Mezyk and Unold, 2010, p. 1499)

3.3 Visual Analytics

In order to provide interactive, personalized and im-

mersive interfaces to deal with both data and models,

visual analytics can be used as a design framework.

Visual analytics (VA) is deﬁned as analytical reason-

ing supported by highly interactive visual interfaces

(Thomas and Cook, 2005) and it strives to facilitate

the analytical reasoning process by creating software

that maximizes the human capacity to perceive, un-

derstand, and learn from large, complex and dynamic

data and situations.

VA advocates not only for an interactive presen-

tation of data and outcomes, but also for the design

of transparent user modeling and adaptation modules

guided by the user, in our case, coaches and experts.

Thus, future virtual coaching software can reach the

ﬂexibility that (Owusu, 2007) claims necessary in this

domain. VA also provides techniques that support in-

teractive and user-friendly environments that present

relevant information such as summaries, actionable

steps, performance enhancement processes, statistics

about outcomes, etc.; allowing engaging experiences

that leads to more active participation by the users.

An example of an interactive visual analytics frame-

work for the analysis of sport data is presented by

(Chung et al., 2013). This application sorts events

from rugby match videos and supports the incorpora-

tion of domain knowledge.

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542

3.4 A Combined View

In this position paper, we argue that situation model-

ing and visual analytics should be used when design-

ing decision support applications, since together,they

can capture the whole performance evaluation pro-

cess, they can build and maintain underlaying behav-

ioral models, and they support comprehensibility, in-

terpretations and interaction with users.

Figure 1 presents an illustration of how visual an-

alytics and situation modeling can be combined for

providing decision support in the performance eval-

uation process. The users depicted in the model

(coaches, athletes, recreational practitioners) assess

and modify the data, models and goals through an

interactive visual interface. This also includes and

combines the knowledge discovery and knowledge

engineering processes. On the left, the characteriza-

tion of the performance evaluation process outlined

by (Owusu, 2007) uses the data, the models and the

goals as input for providing recommendations. We

have omitted the distinct information fusion levels in

the model to not obscure the view. If included they

would be mapped onto the left and center, i.e. directly

onto the performance evaluation process (including

the data, domain speciﬁc models and goals). Simi-

larly, knowledge engineering and data mining would

be mapped onto the right and center of the model.

Performance evaluaon

Data

Model(s)

Goals

HCI

Crique

Recommend

Recognize

feedback

Figure 1: A combined view of situation modeling and visual

analytics for performance evaluation.

The model (ﬁgure 1) can be used in a forensic

fashion, to identify relations between different factors

and the outcome, for increased performance of indi-

vidual athletes. It can also be used in real-time for

comparing the present behavior of individual athletes

with patterns of other athletes, or with some optimum

behavior, in order to directly give correctional feed-

back. Finally, the model could also be used for ana-

lyzing future impacts of the present behavior (e.g., for

predicting the outcome of different actions).

As an example, an application using the frame-

work described in ﬁgure 1 in the area of running

would allow individual users to analyze their own

behavior to e.g. identify causal relations connected

to their performance. It would also allow individual

users to compare their own behavior and performance

to that of others. Moreover, it would be possible to

provide recommendations based not only on the indi-

vidual model, but also based on the models of others.

4 DISCUSSION

Modern technical solutions make it possible to equip

people and objects in the environment with sensors

that can transmit data to the cloud. This allows for

large amounts of data to be collected, which could be

used in forming a large number of e-services. More-

over, data can be also be shared between devices and

between services, as well as be connected to social

media enabling for individuals to share data, mod-

els and experiences. By exploiting modern infras-

tructure, more extensive analyzes and decision sup-

port (including immersive visualizations and feed-

back mechanisms) can be provided directly through

mobiles devices, e.g. smart phones. The opportuni-

ties for providing both elite athletes as well as recre-

ational practitioners with advanced support, e.g. feed-

back on physiology, technique, tactic and strategy, are

enormous and also provide many research challenges.

In this position paper we have brieﬂy reviewed the

present situation with respect to data analysis and per-

formance evaluation in sports. We have outlined how

future computer based decision support systems can

be built using information fusion, situation modeling

and visual analytics. Although the model presented in

this paper (ﬁgure 1) is general in its nature and nec-

essarily needs to be specialized for individual sports

(e.g. identifying and mapping features, objects, re-

lations, measures, feedback and goals), it highlights

some key components, and their relations, that are im-

portant for constructing decision support tools for per-

formance evaluation. Furthermore, the model stresses

the importance of including the user in the many pro-

cesses related to performance evaluation. Our present

work focuses on investigating these concepts in the

area of golf and one of our goals is to investigate the

suggested model for providing decision support. Our

long-term goals is to carry out investigations related

to other sports, as well as in other domains.

ACKNOWLEDGMENTS

We are grateful to the Swedish Winter Sports Re-

search Centre for partly supporting this work.

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543

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