visualization of training data reported by football...

Bachelor of Science in Computer ScienceMay 2018

Visualization of training data reportedby football players

Olof ChristenssonAdam Georgsson

Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden

This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology inpartial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science.The thesis is equivalent to 20 weeks of full time studies.

The authors declare that they are the sole authors of this thesis and that they have not usedany sources other than those listed in the bibliography and identified as references. They furtherdeclare that they have not submitted this thesis at any other institution to obtain a degree.

Contact Information:Author(s):Olof ChristenssonE-mail: [email protected]

Adam GeorgssonE-mail: [email protected]

University advisor:Assistant Professor Prashant GoswamiDepartment of Creative Technologies (DIKR)

Faculty of Computing Internet : www.bth.seBlekinge Institute of Technology Phone : +46 455 38 50 00SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57

Abstract

Background. Data from training sessions is gathered by a trainer from the playerswith the goal of analyzing and getting an overview of how the team is performing.The collected data is represented in tabular form, and over time the effort to inter-pret it becomes more demanding.Objectives. This thesis’ goal is to find out if there is a solution where collecting,processing and representing training data from football players can ease and improvethe trainer’s analysis of the team.Methods. A dataset is received from a football trainer, and it contains informa-tion about training sessions for his team of football players. The dataset is used tofind a suitable method and visualize the data. Feedback from the trainer is used todetermine what works and what does not. Furthermore, a survey with examples ofvisualization is given to the players and the trainer to get an understanding of howthe selected charts are interpreted.Results. Representing the attributes of most importance from received datasetrequires a chain of views (usage flow) to be introduced, from primary view to qua-ternary view. Each step in the chain tightens the level of details represented. Boxplot proved to be an appropriate choice to provide an overview of the team’s trainingdata.Conclusions. Visualizing training data gives a significant advantage to the trainerregarding team analysis. With box plotting will the trainer get an overview of theteam and can hereafter dig into more detailed data while interacting with the charts.

Keywords: Visualization, charts, statistics, training-data,

Acknowledgments

Big thanks to:Prashant Goswami: who led us through the thesis processHerman Ottosson: for the idea behind the thesis and the feedbackMartin Söderberg: for useful sport input

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Contents

Abstract i

Acknowledgments iii

1 Introduction 1

2 Related Work 3

3 Method 53.1 Implement web application . . . . . . . . . . . . . . . . . . . . . . . . 73.2 Player survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Results 134.1 Web application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.1.1 Main views . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.1.2 Other views . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5 Analysis and Discussion 23

6 Conclusions and Future Work 25

References 27

A Supplemental Information 29

v

List of Figures

3.1 Dr Abela’s chart chooser [6]. The image was made to help the choiceof the selecting fitting charts depending on what type of data are pre-sented. It is a flowchart that begins in the middle and with the help ofquestions leads to the right chart for the task. . . . . . . . . . . . . . 6

3.2 Borg scale for estimated effort [1]. The perceived exertion in the bodycan be described with a number from a scale 0-10 where 0 is rest and10 is maximal exertion. . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1 Example of stacked bar chart for the attribute effort. The bars on they-axis are only populated with players that are outside the expectedeffort interval. The blue bar is displaying ±1, yellow ±2, and orange<=±3 from expected effort interval. The label displayed on the barsare representing the number of entries inside the bar. . . . . . . . . . 14

4.2 Example of how bubbles in bubble chart are expanding over each otherfor the attribute effort. Each group of colored-bubble represents oneweekday from Monday to Friday with Wednesday removed. All y-axisvalues for the green bubbles are 3,4 and 5 which can be hard to see dueto the width of the bubble. . . . . . . . . . . . . . . . . . . . . . . . . 15

4.3 Example of box plot for the attribute effort. Y-axis displays the effortfor the players. Each colored-box represent one weekday from Mondayto Friday with Wednesday removed. . . . . . . . . . . . . . . . . . . . 15

4.4 The flow between each chart on the web application. Each step presentsa new view with more precise data with which the user can interact.Except for the final view when the period and data type is expandedagain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.5 Box plot combined with table for effort. The box plot belongs to theprimary view and has been interacted with so the secondary view withthe table has been unveiled. . . . . . . . . . . . . . . . . . . . . . . . . 17

4.6 The tertiary view is a pop-up for the effort which discloses when aplayer name is clicked on the secondary view. The chart in the tertiaryview is a line chart with a single line symbolizing the chosen playedand how it relates to the expected effort area that is greyed out. . . . . 18

4.7 An extraction from the quaternary view for effort (the blue line). Thischart is similar to the tertiary view but with an extended time period.It is also combined with freshness (the green line). Injuries and extratraining sessions are marked as red and orange dots. . . . . . . . . . . 18

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4.8 Charts used in the survey. Top left: stacked bar, top right: table form,bottom: box plot combined with a table. Five questions about how easythe charts are to read where asked for each chart. Box plot was alsoasked about individually. . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.1 An unreadable example of line chart representing 15 players’ reportedeffort. The reported effort is for one week. The chart was made duringimplementation of the web application. . . . . . . . . . . . . . . . . . 24

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List of Tables

3.1 Attributes that the players are reporting after every training session. . 8

4.1 Languages, libraries and their version numbers used to implement thewebsite. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Mean values for each question, collected from the result of the surveydone by players. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.3 Answers for each question, collected from the result of the survey doneby the trainer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

A.1 Survey result for box plot . . . . . . . . . . . . . . . . . . . . . . . . . 37A.2 Survey result for stacked barchart . . . . . . . . . . . . . . . . . . . . 38A.3 Survey result for table . . . . . . . . . . . . . . . . . . . . . . . . . . 38A.4 Survey result for combination of box plot and table . . . . . . . . . . . 39

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Chapter 1

Introduction

With the help of available technical assistances, the majority of sports clubs andindividual athletes with a serious goal are recording and analyzing their activities tomaximize the resulting outcome. For a single athlete, it is relatively easy to trackher training with the wide range of tools and applications available, but applicationstargeting a whole team are not as many. The majority of existing applications seemto mainly focus on the sport itself (training schedule, exercises, game session, matchresult etc.) and not the athletes within the team [9, 10]. Feedback from the athletescan be a crucial part of how the training is scheduled. A single trainer of a sportsteam usually has an expected view and plan of how each training session should looklike, to in the end reach a specific goal. Therefore it is of importance to the trainerthat each athlete in the team is performing as expected on the sessions. For anathlete to achieve the proper results from each training, they are often requested toeat, sleep and drink (stay hydrated) enough to be able to perform. To manually keeptrack of each athlete status for example, how well they are performing, injuries, sleep,can be both tiresome and time-consuming. Over time it might get harder to keeptrack of the history of each player. Also, it is difficult to keep track of multiple playersand follow each player. If one or more players are not living up the expectations, thetrainer needs to figure out why.

Data presented in charts is easy to grasp, and it is a quick way to familiarize withit, but it is essential to find the right way to present the data [17]. Football teamsconsist of around 20 players, and this could limit what kind of charts that are suitable.There are plenty of ways representing data, and it is often possible to combine themto get a better result. One way to do this is an interactive presentation where it ispossible to show additional information by interacting with each data entry. In anarticle by Albinsson and Andersson, they show how analyzing football statistics canbe improved by combining different datasets [15]. All variables from a dataset mightnot fit in one single chart, but splitting them apart in two and linking them togethercould be a possible option. The second chart behaves differently depending on whatinteraction occurs in the first chart.

Too many variables and details in a chart make it troublesome to read. Dividingthe chart into several charts may help, but a significant amount of charts can beoverwhelming to the user if not presented carefully. When exploring the visualizeddata the user often needs an overview of the charts and a dashboard could help withthat. A dashboard "is a visual display of the most important information needed toachieve one or more objectives consolidated on a single screen so it can be monitoredand understood at a glance" [11].

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2 Chapter 1. Introduction

This thesis’ goal is to find out if there is a solution where collecting, processing andrepresenting training data from football players can ease and improve the trainer’sanalysis of the team. The dataset that will be used is received from a trainer withthe requirement of providing a better and more accessible overview of the content ofthe dataset. The data has been collected using a form, (Google Form) [2], where theplayers have been encouraged to fill in after each training session. The form offersthe possibilities to visualize the answers as bar charts which are processed from theentire dataset. The dataset is continuously growing, and the bar chart ignores thedates which prevent a periodic overview. Furthermore, Google Form offers the optionto export the collected data as a spreadsheet, but analyzing a large spreadsheet canbe both tiresome and time-consuming. To provide a better overview and to ease theanalysis for the trainer, a more robust solution is needed. The research question thisthesis seeks an answer for is: To what extent can visualization ease the analysis oftraining data compared to presenting it in tables?

By developing an application which can gather and represent the data from thetraining sessions for the training, it can ease the analysis process. The applicationshould suitably visualize the data so the trainer easily can get an overview of theteam. With the help of the application, the trainer can locate outliers and morefocus can be put on the players behind the abnormal data. An abnormality couldoriginate from the fact that players have not understood what the trainer expects ofthem on a specific training session. It could also arise that the trainer has put toomany high-intensity sessions after each other and not allowing the players to restenough for the next training. By moving the gathering of data to an application, theapplication could help to solve the problem with missing reports since functionalityto remind the players to fill in the form can be implemented.

Chapter 2

Related Work

Several studies have been done on how to help sports teams continue to improve,both as a team and the individual athletes within the group. The studies stretchfrom targeting individual athletes and how they can improve the team-spirit itself.They also often concern the issue "What can the athlete/team change or improve toget one step closer to the goal?"

One approach is a so-called semi-automated monitoring system by using videosurveillance, i.e., Motion analysis [7, 16]. A number of cameras are used to capture thetraining or game session and can be analyzed afterward. By using a semi-automatedsystem, the dependency on the individual to self-estimate after the training is re-moved. Despite the advantages of these semi-automated systems, they do not pro-vide the big picture. They can only offer what can be seen on the tape and not thereason behind the captured performance for the session. Different surveillance hasdifferent disadvantages, but a few common are cost, need for right lighting condi-tions, the setup of multiple cameras and most crucially understanding the capturedvideo [16]. Similar work has been done as the video surveillance but with GlobalPositioning System (GPS) where the authors tried to find a correlation between theposition of a football player (defender, midfield, forward, etc.) and the distance cov-ered during a football game. With the help of the GPS, they concluded that thedistance traveled during one football game are 9900 ±700 meters [8]. It was alsofound that each position differs by distance traveled. For example, the defender wasdiscovered to have shortest distance traveled and the least amount of short sprints,but furthest distance in the lowest speed range. That seems to be the opposite tothe other position midfield (outer-midfield) which had the longest distance traveledin the highest speed range, but the shortest distance traveled in the lowest range.Another study which also used GPS to find a correlation of distance traveled showssimilar result [12]. The results from both of the GPS-studies indicates that footballtrainers need to adapt the training depending on consideration of what positions theplayers have.

An application that can complement motion analysis or used as standalone is aself-reporting system, for example, Trimbite [5]. By allowing each athlete report andprovide their view of the training or game session could help the trainer to understandthe squad better. Trimbite has been used by a Swedish football team to supportthe trainer monitor the squad current status [3]. According to the newspaper, byreporting training load and recovery using the application contributed to a reducednumber of injuries for the team. Analyzing reported training data is often done bysome sort of visualization. It is not always the best option though to visualize data

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4 Chapter 2. Related Work

instead of keeping it in tables. M. McBride et al. are in [13] investigating how tablescompare to graphs when an agent reads network traffic from dark networks. Theagents’ task is to make decisions on how to disrupt a network based on the networktraffic. They conclude that for their particular needs are graphs better to makedecisions quickly, but weakly better disruptions emerge from reading tables. Chartscan also be misunderstood if not constructed correctly, as Dr. Abela writes in [6]

In [14] L. Nuzzo mentions that bar charts can be used for summarizing counts orproportions with categorical data, but might not be the most optional to comparenumeric responses. Furthermore, the bar can be misleading depending on how thelength/width and start point are visualized which were taken into account when thebar charts were drawn. For example, the reader might expect the bar to always startat zero which can cause a stacked bar chart to be hard to understand. The authoralso mentions an alternative chart to be used instead for visualizing measurable data:box plot. Box plot uses statistical summaries (median and interquartile range) thatare more robust than bar chart when there is a lack of data. Compared to a barchart, box plot has been implemented to take outliers into account by highlightingthem. It firstly draws the box plot without the outliers and secondly places theoutlying values as single dots outside the original box, which eases the effort to findthem. The article mentions a few drawbacks of using box plot, and it replaces thepopulation available with percentage making it impossible to see how many entriesthe chart contains. Also, compared to bar chart is box plot not a well-known chart.

Chapter 3

Method

To answer the research question: To what extent can visualization ease the analysisof training data compared to presenting it in tables?, a selection of charts are chosento visualize the dataset. The choice of chart depends on what the data looks like,what the intention to get from it is and feedback from the trainer. Each chart has itslimits on the number of variables and datasets it can contain and which data typesit can represent. Each chart type also has its strengths, and some chart types arebetter at showing the relationship between data for example.

Dataset

The dataset received consists of collected attributes (shown in table 3.1) from 20-25male football players during five months. The players belong to the same team.The age span of involved players are 15-30, and the occupation of the players areunknown. All the players are encouraged to fill in the form created by the trainer,even on a day without training. There is no verification to assure all players areanswering the form and the risk for unrecorded data is high.

Chart capacity

All collected data reported includes a corresponding date of when it occurred andto visualize how information changes from session to session, the date will be usedon the x-axis. To visualize how the data varies between training sessions, a chartwith the capacity of comparing the entries was necessary. Figure 3.1 was used todetermine a suitable chart and the figure proposed to use bar chart or line diagram,the most straightforward chart which for the majority of people find easy to under-stand. Depending on the number of visible lines in a line chart, it could cause therepresented data to be troublesome to understand. Furthermore, there are not manyvariants of the line chart. However, when there are only one or few entries per xand y-axis, the line chart is more suitable. Compared to lines, a bar chart can bedone in multiple different ways. For example wide bars, stacked bars, waterfall bars,negative and positive bars.

Continuing were bubble chart for relationship data tested, the larger the bubblesare, the more entries on the y-axis which provides excellent visualization of reportedeffort and spread. Drawbacks on bubble chart are that multiple different entries onthe y-axis could hide other bubbles when expanding and the scale of the bubblescould vary depending on the number of reported players. The visualized chart needsto be represented similarly each time it is drawn to ease the interpreting of the data

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6 Chapter 3. Method

Figure 3.1: Dr Abela’s chart chooser [6]. The image was made to help the choiceof the selecting fitting charts depending on what type of data are presented. It is aflowchart that begins in the middle and with the help of questions leads to the rightchart for the task.

3.1. Implement web application 7

shown. The variable that causes the same variant of a chart to vary is the numberof participating players and ignoring it could ease the process of understanding thecharts.

One chart which is not present in figure 3.1 but could be placed next to bubblechart is box plot. As mentioned in chapter 2, using box plot removes the possibilityto see the participants for the visualized diagram. The sample for the box plot isthe number of participated players for each training day. For a trainer it might beinteresting to know the sample of the displayed chart and if box plot itself is unableto visualize that, it can be enhanced with an additional chart.

The disadvantage of visualizing with charts is the level of detailed data thatcan be displayed and the risk of misunderstanding the visualized data increases [6].Compared to charts, representing the data in tables can practically present the datain its raw form but in a structured way. With tables comes the possibility to sort andfilter the represented data which makes locating specific data points more obtainable.One drawback with tables is that they can rapidly develop into a significant numberof columns and rows. This could create an overwhelming content of data and gettinga feasible overview becomes challenging.

Feedback was received from the trainer to include expected effort range on theoverview chart for effort. Including the span can provide a quicker analyze if theteam is within the expected effort range, which is achievable by complementing boxplot with two lines (upper and lower expected effort).

Two methods will be used to find out how visualization can ease the analysisof training data. One of them is an implementation of a web application aimed toaddress football trainers. As a second method, a survey will be given to the trainerand his team to back up the decision of charts used in the web application.

3.1 Implement web application

To efficiently investigate how different data visualizations performs is web applicationa suitable choice due to the vast amount of available languages and libraries. Nomatter the selection of libraries or languages most devices or platforms can handle aweb application, all that is needed is an internet connection. The data collection canbe done by a simple HTML-form where players report their data which are submittedto a database. Several scripting languages such as JavaScript, Python and PHP aresuitable for web development. Since all chart types often demand their own kind offormatted data, the scripting language will perform the processing. Choosing whichtype to use and how to design it is the crucial part in this thesis. Box plot, bubblechart, pie charts, tables, line charts and several kinds of bar charts have all beentested and used during the development. Feedback from the trainer has been usedon how well the charts convey their underlying data.

The form the players will fill in is the same as the trainer previously has used, butseveral of the questions have been changed slightly to let the answers be convertedto integer values. The conversion into integers eases the processing and comparing ofthe data values. Three of the questions are free text (anything can be written) andwill not be processed and visualized. Instead, the answers will be listed in tables orused as help text for hover events.

8 Chapter 3. Method

Reported attributes

In table 3.1 are the attributes listed that the players are expected to report via aform every day even if no training session has occurred.

Attribute Possible answersTraining date to fill in for Today’s date and 30 days backEffort 0-10Freshness 1-5Self-estimated performanceSleep hours 4-11Number of cooked meals 0-3Number of snacksBreakfast Yes, NoUrine color Dark, medium, light, transparentExtra trainingInjuries Free textOther comments

Table 3.1: Attributes that the players are reporting after every training session.

The collected attributes of most importance for the trainer are effort and fresh-ness. The effort is how exhausting a player experienced the training was on the Borgscale, 0 to 10 where 0 is rest, and 10 is the maximal effort (figure 3.2). Each trainingsession has an expected range on the Borg scale wherein the participating players areexpected to be within. Expected effort varies from day to day, and an effort valueof 3 can be within range one day but not the next day. The trainer is interestedin the number of players that are outside the expected effort-range. Freshness isthe "body feeling" the players have at the end of the training session (or day if notraining occurred). The range of freshness reaches from 1 (very fresh) to 5 (veryworn down). Players can be expected within the upper freshness range (4 or 5) ifthe training session had a high expected effort-range, but a lower range (1 or 2) isnot necessarily bad. Compared to the effort, freshness does not have an expectedrange.

The remaining attributes (sleep, breakfast, meals, self-reported performance andurine color) will be summarized and evaluated together except attributes with freetext. To be able to achieve that, a point system will be created where a singleplayer receives different points depending on reported values. Alarming values willbe evaluated to a low final score and vice versa for non-alarming values. All pointsadded to a total correspond to a final score (Poor, Bad, OK, Good-performing). Thefinal scores could provide an indication of how acceptable values each player hasreported during the week.

Usage flow

When representing the values from the attributes effort and freshness, one require-ment from the trainer was to provide an overview of how the values are divided

3.1. Implement web application 9

Figure 3.2: Borg scale for estimated effort [1]. The perceived exertion in the bodycan be described with a number from a scale 0-10 where 0 is rest and 10 is maximalexertion.

10 Chapter 3. Method

between the players. For effort, it was important to highlight any outliers. Out-liers for the effort is a player who has reported an effort of for example 9 whenthe majority of the other players are within 2-5. Another requirement was to havethe possibility to quickly look into what the reported values were for a chosen dayfrom the overview chart. To meet the wanted requirements a usage flow needs tobe introduced where the trainer uses a series of clicks or hover events (hover overpart of the chart with the mouse to display additional information). The usage flowprovides a path between charts to enable additional information for each step in thechain. With the usage flow, the charts can be made more straightforward and easierto understand.

3.2 Player surveyThe target group for the survey consists of the football team’s players since theyalready are familiar with the term effort. The survey will indicate how easy or hardthe players think various chart types are to interpret. Charts from the implementa-tion were used as models in the investigation. Further on, the same questions aresent to the trainer so a comparison of how conversant individual answers differ fromthe team’s. The result will be used for the application to back up the decisions onwhich chart types to use.

The dataset from the trainer was not used for this survey due to the lack of data,a minimum of 15 players was decided to be needed. Therefore a fake dataset wascreated with 15 players for one week. Each day during the week have at least 12data entries and for every day there exists at least one player who is way off on theeffort scale. Even if the data is simulated, the players will still recognize it as theyhave reported and worked with similar data before. That way futile effort will notbe put to understand what the underlying data contains. Since effort is the primarycategory for the trainer, charts in the survey only included data points from thatcategory.

The question set is about what characteristics the trainer is looking for in hisplayers’ data. The aspects of the items are about finding outliers, number of players(both as a hole and on each value), the spread and the proportion. Before thesurvey is sent out to the players, it will be tested on a few colleagues to detect if thediagrams are hard to understand or if the formulations of the questions are poorly.Each question has the same possible answers, a scale from one to five, where one isvery easy, and five is very difficult or even impossible.

1. How easy/difficult do you think it is to see how many players attended theFriday training?

2. How easy/difficult do you think it is to see which day was most exhausting forthe team?

3. How easy/difficult do you think it is to see how broad the spread among theplayers is?

4. How easy/difficult do you think it is to detect an outlier? Outliers are playerswho differ from the rest of the team.

3.2. Player survey 11

5. How easy/difficult do you think it is to detect how many had an effort of 4 onthe Friday training?

Chapter 4

Results

4.1 Web application

Implementation of a web application was successfully achieved. On the site, playerscan report their data, and the trainer can obtain the result from different chartsdivided into parts of a specific usage flow. To visualize the charts on the website theJavaScript library Plotly JS was used due to the well-documented reference page [4].Remaining parts of the site was implemented using PHP, HTML/CSS, BootStrap,JavaScript and MySQL as the database.

Library/language Version numberPlotly JS ^v4.0PHP ^v5.4HTML ^v5.0MySQL ^v4.xBootStrap ^v4.0

Table 4.1: Languages, libraries and their version numbers used to implement thewebsite.

Selection of charts

It was expeditiously discovered that the tested comparison charts (line chart andseveral combinations of the bar chart) were more suited for describing a single playercompared to the whole team. The comparison charts rapidly became skewed andchallenging to understand. Several variations of bar charts were tested with the y-axis representing either amount of players or attribute level. Color coding and labelswere added to complement the bars further. When a chart visualizes effort for theteam and y-axis represent the number of players, the bar colors indicate on whateffort level the bar symbolizes. In a worst-case scenario could the number of bars fora single day exceed what is considered readable, but stacking the bars on top of eachother solves that. Therefore did stacked bar chart turn out to be the best candidateof the bar charts for the provided dataset. In figure 4.1 is the y-axis representingthe total amount of players while the labels inside the bars are indicating how manyplayers reported that specific value. The colors show how much the players differfrom the expected effort and bars that goes below the baseline represents a negativedifference. This chart succeeds to express what is sought, but the reader is exposed

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14 Chapter 4. Results

to too many details for the chart to be a viable option when it comes to giving anoverview that is quick to grasp.

Figure 4.1: Example of stacked bar chart for the attribute effort. The bars on the y-axis are only populated with players that are outside the expected effort interval. Theblue bar is displaying ±1, yellow ±2, and orange <=±3 from expected effort interval.The label displayed on the bars are representing the number of entries inside the bar.

Bubble chart and box plot are better suited for relationships as partly seen in Dr.Abela’s chart chooser in figure 3.1. For the used dataset, bubble chart was shownnot suitable due to it occurred too often that the drawn bubbles expanded over eachother (see figure 4.2). A smooth transition for the bubble between the scale of for oneplayer and the scale for multiple players (10-20) was not found, due to an irregularnumber of players for each day. A large scale could cause the circle to expand overthe others, and a small scale could make the outliers hard to identify due to its size.Therefore is bubble chart not a good choice when the number of participated playersvaries. The chart was also proven to be unsuited for the usage flow due to problemof identifying which bubble the click event was triggered on.

A chart which ignores the population was needed and box plot proved to dothat (figure 4.3). Regardless how ranging in size the datasets are, box plot keeps itslayout. Also, feedback from the trainer revealed that box plot was a desirable wayto represent the different attributes.

4.1. Web application 15

Figure 4.2: Example of how bubbles in bubble chart are expanding over each otherfor the attribute effort. Each group of colored-bubble represents one weekday fromMonday to Friday with Wednesday removed. All y-axis values for the green bubblesare 3,4 and 5 which can be hard to see due to the width of the bubble.

Figure 4.3: Example of box plot for the attribute effort. Y-axis displays the effortfor the players. Each colored-box represent one weekday from Monday to Friday withWednesday removed.


Usage flow

Each step in the usage flow, called view, will have its chart starting from primary viewdown to quaternary view as shown in figure 4.4. Each chart will provide a weeklyoverview with the possibility to jump between weeks. From the primary view, thetrainer can continue further down the usage flow using a series of clicks. For eachclick, another view is displayed, and for each view, the displayed data becomes morespecific and more detailed until the user has reached the quaternary view. As theinitial goal of the web application was to provide an understandable overview of theessential attributes (effort and freshness), the primary charts will contain those two.

Figure 4.4: The flow between each chart on the web application. Each step presentsa new view with more precise data with which the user can interact. Except for thefinal view when the period and data type is expanded again.

4.1.1 Main views

Primary view

Box plot was selected to act as the start point in the usage flow for both of theattributes (effort and freshness) due to its ability to provide a good overview andhighlight outliers. The box plot for the effort was enhanced with two lines displayingthe interval which the players are expected to be within. The area between the linesis colored to indicate where the box is supposed to lie. Each drawn box have a clickevent attached, and will trigger the secondary view to show when clicked. There isno static expected freshness for the players. There is, however, an anticipated trendof how the freshness will change over the week. After an exhausting training session,it is not uncommon that several of the players are reporting high (4 or 5) freshness.


Figure 4.5: Box plot combined with table for effort. The box plot belongs to theprimary view and has been interacted with so the secondary view with the table hasbeen unveiled.

Secondary view

A tabular form was selected for secondary view due to its ability to provide usefulinsight into specific data entries and to complement the primary view. The drawntable will contain values which box plot from the primary view was unable to provide,i.e., the total number of players and data values for the clicked box that day. Playerswho are within the expected effort are by default hidden under a label (an HTMLtag). The hidden athletes can be displayed by manually clicking the blue markedlabel Click here to display x more players inside the expected effort range. Eachplayer label will have another click event connected to them and when clicked willtrigger the tertiary view to be displayed.

Tertiary view

Line diagram was selected for the tertiary view to display attributes for one singleplayer over time. The presented data for the line diagram is for the chosen week, butit has the feature to scroll back and forth in time quickly. Similar to primary view,the tertiary view was enhanced with two lines displaying the interval of expectedeffort. The tertiary view is presented as a pop-up window (using modal in HTML),removing the need of navigating to a new page. The pop-up will hover above theprimary and secondary view when triggered, see figure 4.6. The tertiary view forfreshness is similar except the expected interval lines. From the pop-up, the trainercan choose to go back to the secondary view or continue to the quaternary view.

Quaternary view

To provide the possibility to view all data categories for one single athlete a quater-nary view was created. Here are all data entries represented in different charts. Fora single player, there will only be one entry per type of data. Line chart and barchart is used to describe the entries over time and the free text fields are listed with


associated date. See figure 4.7 for an example.

Figure 4.6: The tertiary view is a pop-up for the effort which discloses when a playername is clicked on the secondary view. The chart in the tertiary view is a line chartwith a single line symbolizing the chosen played and how it relates to the expectedeffort area that is greyed out.

Figure 4.7: An extraction from the quaternary view for effort (the blue line). Thischart is similar to the tertiary view but with an extended time period. It is alsocombined with freshness (the green line). Injuries and extra training sessions aremarked as red and orange dots.


4.1.2 Other views

In addition to the main views, there are also other views which do not have the samepriority for the trainer but are still important.

Comments and injuries

Since the free text attributes entries are hard to interpret were they not visualized.Instead the written text received from the form will be represented in tabular formwith weekly intervals, ordered by weekdays. Each entry in the table will contain thedate when the free text was submitted, the content itself and name of the author(player). Clicking on a player’s name leads directly to the quaternary view for thatplayer.

Players overview and latest freshness/injury

The trainer asked for a page where the possibility to see the latest reported statusfor all players. After further discussion, it was concluded that only latest freshnessand injury was wanted. As a result, an overview-page of all players present in theteam was created. On the page, it is possible to view most recent freshness andinjury. Since it is only one entry point per player, a table form was the best way torepresent this kind of data.

Point system

To provide a better overview of the collected data from the players, including alldata entries, a point system was developed. The system summarizes all the recordedattributes for one day (except the free text) and calculates points depending on thereported value for the categories. The number of reported training sessions thereafterdivides the computed value. Four different intervals are used to determine a resultwhere the summarized value lies and how good the value is, displayed in the bulletpoint list below. The calculated result presents an overview for the trainer, describingif the outcome for each player is negative or positive. The trainer decides what valuesthere are to be considered positive contra negative. Negative values on several playersor a single player should not be reviewed as a grade or final score but act as a flagor indicator for the trainer. The indicator should hint the trainer that one or moreplayers are not reporting the expected values, for example, less than six hours ofsleep or only one meal that day.

The point system is visualized in tabular form combined with a progress bardisplaying how the summarized value relates to the maximum amount (100). Fromthe point system, there is a navigation link to the individual player views provided.The four different intervals are:

• Above 90: Excellent performing

• Between 90 and 70: Good performing

• Between 70 and 50: OK performing.

• Below 50: Bad performing.


4.2 Survey

The resulting charts from the implementation were used in the survey and are shownin figure 4.8. Three different charts are represented in the survey: boxplot, stacked barchart and a table (all charts used the same dataset). There was also a combinationof two charts to see if it brings complementing advantages. In the combination is thebox plot used again next to a simplified version of the table. It simulates that a boxhas been selected and the table only displays the data belonging to that box.

Figure 4.8: Charts used in the survey. Top left: stacked bar, top right: table form,bottom: box plot combined with a table. Five questions about how easy the charts areto read where asked for each chart. Box plot was also asked about individually.

A trainer and 18 players from his team answered the survey. The values in Table4.2 are the mean rounded to two decimals from what the players answered. Onemeans they thought Question X was very easy to answer and five is for very hard orimpossible. If ten players would have answered a ’1’ on Question 1 for Box plot andeight players answered a ’2’, the mean is calculated as shown in equation (4.1) and

4.2. Survey 21

put in the corresponding table cell. The questions are referred to same numbers asin section 3.2

10 ∗ 1 + 8 ∗ 218

= 1, 44.. (4.1)

Question Box plot Stacked barchart Table Box plot + TableQuestion 1 3,27 2,33 1,28 2,50Question 2 1,44 2,16 1,78 1,72Question 3 1,72 1,77 2,00 2,00Question 4 2,06 2,39 2,11 1,72Question 5 2,89 3,06 1,17 1,94Mean Total 2,28 2.34 1,67 1,97

Table 4.2: Mean values for each question, collected from the result of the survey doneby players.

Question Box plot Stacked barchart Table Box plot + TableQuestion 1 5 5 1 1Question 2 2 5 1 1Question 3 3 3 1 1Question 4 2 1 1 1Question 5 5 5 1 1Mean Total 3,4 3,8 1 1

Table 4.3: Answers for each question, collected from the result of the survey done bythe trainer.

Chapter 5

Analysis and Discussion

After trying a different combination of charts for the categories effort and freshness,and receiving feedback from our supervisor and the trainer, we concluded that havingall needed variables present in one chart is not an acceptable solution. To represent alldata variables in one single chart is too much information for the user to comprehendat a glance. Therefore the solution was to divide the variables into multiple stepsof different views. After each step, the reader narrows down deeper into a singleplayer’s data.

To satisfy the need of getting an overview of the team status that is easy tounderstand, was box plot selected. The reason behind it was that the chart’s layoutdoes not change if there are few data points contra multiple data points. A bubblechart would, for example, get huge bubbles that overflow into other bubbles if therewere a high number of players. Scaling down the bubbles is not an acceptable solutiondue to that a significant gap in the player amount would lead to the smaller bubblesbeing downscaled to a point where they are not visible anymore. Box plot remainsintact despite variances in size of the datasets, and it will still be able to show howthe players’ effort are spread. It will also show where the mean and outliers are.With the help of the greyed area of expected effort, the primary view makes clearhow the team relates to the trainer’s expectations. Since expected effort can varyfrom day to day line charts are suitable.

Even if box plot is great at showing how the players are relating to each otherin effort, it will not show specific distribution and to whom the data points belong.That is why a secondary view was created with the role of "zooming in" on a selectedday in the box plot. For the secondary view, a table was chosen to represent thedata. A table is good at showing detailed data, and therefore it contemplates thebox plot’s weaknesses as excellent. When the primary and secondary view is putside by side with each other, it becomes a powerful combination of the chart types’strengths. Since players who are within the expected effort area are not the mostinteresting to view, a decision was made to hide those players in the table by default.

When the user has come to the tertiary view she has already narrowed down thedata to a single player and box plot is no longer a valid option since it needs morethan one player to be rewarding. Line chart was not a valid option for representingmultiple players since the associated lines would make it too cluttered, as shown infig 5.1, but for a single player line chart is excellent in how it shows change over time.

Since effort and freshness are the attributes that the trainer is most interestedin, the first chart on the quaternary view is a line chart populated with mentionedattributes and upper/lower markers for expected effort. Request from the trainer

23

24 Chapter 5. Analysis and Discussion

Figure 5.1: An unreadable example of line chart representing 15 players’ reported ef-fort. The reported effort is for one week. The chart was made during implementationof the web application.

was to also include other training, injuries, and comments in the first charts. Inthat way, it was easier to see the date the entries originated of. The remainingtraining attributes do not need as much focus and will therefore not need to beeasily attainable. Introducing them in the quaternary view will not draw attentionfrom the user until they are wanted.

The table got the lowest score in the player survey which means it was the easiestto read data from considering the questions asked. This table result must not bemistaken for the table form the trainer previously had been using which was theunprocessed raw format. Tables can be very informative but only when excludingunnecessary data and when the dataset is small or sorted. Surprisingly was box plotcombined with table harder to understand than the table alone. It is likely becausebox plot is not commonly used and looks foreign to most people. In the combination,it might confuse more than it helps if the reader is not used to look at box plots.As long as the user of the website is familiar with box plots the combination of boxplot and table should be better than the separative alternatives. There was a morescattered answer for the box plot and stacked bar chart which supports the thoughtthat they are harder to understand for an untrained eye. Some questions in thesurvey are not even possible to answer for some charts but still have some low-valueanswers. Either the players have not understood the question/chart or have notresponded truthfully. The trainer’s answers are more like how we think about thecharts. One noteworthy difference is that he thought the table was very easy to readin all cases, which he also answered for the combined chart. It could mean that thecombined version is redundant and unnecessary. Not to forget is that the table inthe survey does not have considerably many rows and columns and is not that hardto read. If the table consisted of data from 30-40 players instead of 15, it would havebeen much harder to read. Visualization also adds aesthetic value and catches theeye better than a table does.

Chapter 6Conclusions and Future Work

Conclusions

Of all the charts that we have tried visualizing with the dataset received from thetrainer, box plot has proven to be the most suitable for effort and freshness. Box plotcan give the trainer a quick and understandable overview for all his players, whichthe trainer was unable to do before using his method (reading from a spreadsheet).Remaining attributes were visualized for a single player with bar and line charts.

When representing all variables from a dataset in a single chart are not achievable,dividing them into multiple charts that are chained together could be one acceptablesolution. Adding the possibility to interact with the chart, the user can choose tolook at a more detailed view if wanted.

Compared to reading the data in the raw table form, processing, sorting andvisualizing the data has proven to ease the understanding and analysis of given data.Tables are still useful, but the reader needs to know what she is looking for, for themto be viable.

Future work

Correlations between data types can be experimented with and visualized to improvethe analysis further. With the help of a correlation values could harmful behaviorbe detected at an early stage and automated warnings be sent out to the trainerand affected player. Extending the collection of training data with more attributessuch as heartbeats per minute, GPS data, and video material could provide a morecomprehensive analysis.

The trainer has had some problems with motivating the players to report theirtraining data. Simplifying or even automate parts of the reporting process with cloudsyncing with external applications might improve the reporting frequency. Externalapplications could be used, for example, the software used for collecting data fromactivity bracelets.

A study could be performed to conclude if data gathered and analyzed fromtraining sessions help the trainer plan the sessions. Also, if it helps the players toevolve and perform better on game sessions, compared to not collect and analyze.

25

References

[1] Borg scale. http://exercise.trekeducation.org/assessment/borg-scale-rpe/.

[2] Google form. https://www.google.com/forms/about. Accessed: 2018-05-10.

[3] Minskade antalet anmälda skador i damallsvenskan. http://www.efd.se/minskade-antalet-anmalda-skador-i-damallsvenskan/. Accessed: 2018-05-10.

[4] Plotly js. https://plot.ly/javascript/. Accessed: 2018-05-10.

[5] Trimbite. http://www.trimbite.com/. Accessed: 2018-05-10.

[6] A. Abela. Advanced Presentations by Design : Creating Communication ThatDrives Action. Center for Creative Leadership, 2nd edition, 2013.

[7] L. Nelsen et al C. Carling, J. Bloomfield. The role of motion analysis in elitesoccer: contemporary performance measurement techniques and work rate data.Sports Medicine, Vol 38, Issue 10:839–862, 2008.

[8] S. Landin C. Hedlund. Gps som tekniskt hjälpmedel inom fotbollen. Master’sthesis, Högskolan Dalarna, 2005.

[9] FNF Coaches. https://fnfcoaches.com/best-apps-for-football-coaches/. https://fnfcoaches.com/best-apps-for-football-coaches/. Accessed: 2018-05-10.

[10] M. Cox. Top 6 effective football coaching apps. https://www.thesoccerstore.co.uk/blog/football-coaching/top-6-effective-football-coaching-apps/. Accessed: 2018-05-10.

[11] A. Few. The effective visual communication of data. Information dashboarddesign, Vol 7, Issue 2:12, 2006.

[12] A. Pio D. Tore G. Raiola G. Altavilla, L. Riela. The physical effort required fromprofessional football players in different playing positions. Journal of PhysicalEducation and Sports, Vol 17, No 3, 2017.

[13] M. Caldara M. McBride. The efficacy of tables versus graphs in disrupting darknetworks: An experimental study. Social Networks, Vol 35, Issue 3:406–422,2013.

[14] R. L. Nuzzo. The box plots alternative for visualizing quantitative data. PM &R, Vol 8, Issue 3:268–272, 2016.

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28 References

[15] D. Andersson P. A. Albinsson. Extending the attribute explorer to supportprofessional team-sport analysis. Information Visualization, Vol 7, Issue 2:163–169, 2008.

[16] C. Button S Barris. A review of vision-based motion analysis in sport. SportsMedicine, Vol 38, Issue 12:1025–1043, 2008.

[17] S. J. Kistler T. Azzam, A. A. Germuth. Data visualization and evaluation. NewDirections for Evaluation, Volume 2013 Issue 139:7–32, 2013.

Appendix ASupplemental Information

Survey

29

37

Result from survey

Surveyee Q1 Q2 Q3 Q4 Q5Player 1 2 2 2 3 3Player 2 5 1 1 4 3Player 3 3 3 1 1 2Player 4 5 1 1 3 2Player 5 2 3 4 5 3Player 6 1 1 1 1 1Player 7 5 1 4 3 2Player 8 4 1 1 2 4Player 9 1 1 1 4 1Player 10 1 1 1 1 5Player 11 5 1 1 1 3Player 12 4 2 3 1 3Player 13 3 1 1 1 2Player 14 5 1 1 1 5Player 15 3 1 1 1 3Player 16 3 2 3 1 2Player 17 3 1 2 2 3Player 18 4 2 2 2 5Trainer 5 2 3 2 5

Table A.1: Survey result for box plot

38 Appendix A. Supplemental Information


Table A.2: Survey result for stacked barchart


Table A.3: Survey result for table

39


Table A.4: Survey result for combination of box plot and table

Dataset used to generate charts for survey

{" t r a i n e r " : "Herman" ," team " :" Karlskrona " ," p l aye r s " : [{"name" :" Spe la re A" ," data " : [ { " date ":"2018−03−27" ," e f f o r t " :{"

l e v e l " :"7" ," d i f f " :3}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " : −1}}]} ,

{"name" :" Spe la re B" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re C" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re D" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " : 1 } } ] } ,

{"name" :" Spe la re E" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " :1}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " : 0 } } ] } ,

40 Appendix A. Supplemental Information

{"name" :" Spe la re F " ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"3" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " : 1 } } ] } ,

{"name" :" Spe la re G" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " : −1}}]} ,

{"name" :" Spe la re H" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re I " ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"3" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re K" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :1}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re M" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"9" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re N" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :2}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re O" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"6" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"4" ," d i f f " :0}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " : 0 } } ] } ,

{"name" :" Spe la re P" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :3}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"7" ," d i f f " :0}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"5" ," d i f f " : 0 } } ] } ,

{"name" :" Player Q" ," data " : [ { " date ":"2018−03−26" ," e f f o r t " :{"l e v e l " :"9" ," d i f f " :2}} ,{" date ":"2018−03−27" ," e f f o r t " :{"l e v e l " :"8" ," d i f f " :4}} ,{" date ":"2018−03−29" ," e f f o r t " :{"l e v e l " :"3" ," d i f f ":−3}} ,{" date ":"2018−03−30" ," e f f o r t " :{"l e v e l " :"1" ," d i f f " : −4}}]} ]}

Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden

visualization of training data reported by football...

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