Decision support for Rocket League player improvementJurgen Grotentraast

University of TwenteP.O. Box 217, 7500AE Enschede

The Netherlandsj.grotentraast@student.utwente.nl

ABSTRACTDecision support systems (DSS) are widely used withinsports to make decisions about team formations and tac-tics. Within esports, however, there has only been donelimited research on this topic even though the nature of es-ports shows great potential for the use of DSS. This paperwill focus on the possibilities of DSS in the esports titleRocket League. For this paper, interviews with expertswere conducted. These interviews provided informationabout what decisions teams make and what informationthey base these decisions on. Furthermore, these inter-views gave insights into how information can be extractedfrom the game and what support can actually be delivered.Lastly, a conceptual framework was created and evaluatedas the first design of a decision support system in RocketLeague. The next step would be to create the system andtest its usability.

KeywordsDecision Support Systems, Rocket League, esports

1. INTRODUCTIONIn sports, there is wide use of Decision Support Systems(DSS). Initially designed to help business managers inmaking managerial decisions [3]. DSS interpret given dataand present it in an understandable way to the decisionmaker to aid the decision maker to come to an optimal de-cision [1, 3]. Within sports these uses include, but are notlimited to, tournament scheduling [9]; player performanceevaluation [4] and team selection [16] as well as strategicanalysis and how DSS can lead to a wrong decision [2]. De-spite the potential that DSS shows for business and sportswithin the field of esports, DSS is used to a little degree.The decision, cognitive and data-driven nature of esportshowever reveal great potential for DSS in esports.

According to Juho Hamari and Max Sjoblom [6] esportsis defined as: ”a form of sports where the primary aspectsof the sport are facilitated by electronic systems; the in-put of players and teams, as well as the output of theesports system, are mediated by human-computer inter-faces. In more practical terms, esports commonly refer tocompetitive (pro and amateur) video gaming that is of-

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copiesare not made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copy oth-erwise, or republish, to post on servers or to redistribute to lists, requiresprior specific permission and/or a fee.35th Twente Student Conference on IT July 2nd, 2021, Enschede, TheNetherlands.Copyright 2021, University of Twente, Faculty of Electrical Engineer-ing, Mathematics and Computer Science.

ten coordinated by different leagues, ladders and tourna-ments, and where players customarily belong to teams orother ‘sporting’ organizations who are sponsored by var-ious business organizations.” In this research, the focuswill be on a game called Rocket League. Rocket League iscurrently the number ten in Newzoo’s [15] monthly activeusers rank on PC and even number six on consoles like theSony Playstation. It also ranks number 13th in Newzoo’s[15] viewership statistics on Twitch 1. In 2020 RocketLeague started their tenth competitive season which isthe biggest so far. The two main regions, North Americaand Europe, each will play thirteen multi-day tournamentseach with a prize pool of $100.000 [11] and the smallerregions, Oceania and South America, each play thirteenmulti-day tournaments each with a prize pool of $25.000[11]. In each tournament, there are 20 to 32 participatingteams, each consisting of 3 players and possibly a coachor substitute player [11]. Large soccer teams and automo-bile manufacturer, like FC Barcelona and Renault, evenstarted their own Rocket League teams [10, 12]. In short,Rocket League is soccer with cars. The players control acar that can use boost to fly. With this rocket-poweredcar, they have to try and hit a ball in their opponents net.In the esports scene, the most dominant variant is a 3v3setting. Teams play a best of five or best of seven seriesto determine who wins the matchup.

Decision support in Rocket League could be a real game-changer. Currently, teams have to do hours of replay anal-ysis of themselves and their opponents to see what theydid wrong and what weaknesses from their opponents theycan exploit. Just like in other esports titles, individualdecisions are made within a split second. Everything hap-pens on instinct and it has to happen fast because if aplayer thinks too long, they get scored on. The ability tomake the correct decision in a split second is also whatdistinguishes pro players from everyday players. However,as a team, many tactics can be applied to counter cer-tain playstyles. A decision support system could help inidentifying what tactics work against which playstyles orwhat tactics have worked against certain teams in the past.Therefore, a DSS is most useful before and after a game.Pre-game a DSS can be used to decide upon a strategy,but also to find a strategy that would work best againsttheir opponents. A DSS can also show mistakes the op-ponents make to exploit those or show mistakes the teamitself makes so those can be fixed. Furthermore, it can giveinsights into what the team needs to improve on, this canbe anything ranging from defensive positioning to accu-racy training. Post-game can be interpreted in two ways.First, it can be seen as during a series. In this case, a DSScan be used to adapt the strategy mid-way during a series.

1Twitch is one of the largest streaming platforms on theinternet

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This means that if the opposing team uses a different strat-egy than anticipated, the coach could see what would workbest against this strategy and in between games advise theteam to adapt their current strategy. Second, post-gamecan be seen as after a complete series. In this case, it canbe used to find mistakes in the team’s playstyle. A DSScan also show information about individual players so thatdifferent strategies can be created to support each player’sstrengths.

1.1 Problem statementCurrently, the research in DSS in esports is limited. Therehave only been two studies that have made a start on deci-sion support within the general esports scene [3, 13]. Onlyone of those two focussed on Rocket League and showedpossibilities for their application to be used as a DSS [13].What both these studies missed, was the input of actualplayers. Both studies focused on the data side of a DSS.They focused on what data could be extracted and howthis data can then be used. In this explorative research,professional players and/or coaches will be interviewed tosee what kind of decisions need to be made and what in-formation is needed for that. These decisions will onlyinclude pre-series decisions; decisions made during a seriesand post-series decisions. The actual in-game decisionswill be left out as these decisions are made in a split sec-ond. Therefore, players cannot evaluate given data in timeto make a decision. Furthermore, this paper will dive intothe possibilities of retrieving this data. Lastly, using thecollected data a theoretical framework for a DSS will bedesigned for Rocket League.

1.1.1 Research questionThe above problem statement leads to the following re-search question: What are the possibilities within RocketLeague for a decision support system aimed at player im-provement? This question can be answered by answeringthe following sub-questions:

1. What decisions need to be made as a team before,during and after a series?

2. What information is needed to make those decisions?

3. How can this information be retrieved from the game?

4. How useful is the proposed Rocket League DSS frame-work?

The structure of the paper is as follows. In section 2, therelated work and background information of this researchwill be discussed. In section 3, the methods used to answerthe research questions will be laid out. In section 4, theresults of the research will be presented. Furthermore, aframework will be described together with its evaluation.In sections 5 the overall conclusion will be given. In sec-tion 6, some improvements and notes will be given on thisresearch as well as suggestions for future research.

2. BACKGROUNDIn this section, related work in the domain of DSS andesports will be discussed. To find related research, Scopus,ACM digital library, Google Scholar and IEEE were used.On these platforms search terms like ’Decision supportsystems’, ’esports’ and ’Rocket League’ were used, eitherseparately or a combination of two of those.

In both decision support systems as well as in esports ingeneral, research has already been done. The results fromthe literature study show that the focus of DSS is mostly

on business decisions. When it comes to DSS in sports,the focus is on decisions like player evaluations [1, 2, 4,9, 16]. These researches show the versatility of DSS. Inesports the research ranges from business models [22, 24];to looking for opportunities in combining physical sportswith esports [17, 23] and looking into mental retardation[7].

There has also been some more data-driven research onspecific esport titles. For example, there have been stud-ies into ranking algorithms [18], as well as a beginningof a decision support system in some sense. Namely, re-search has been conducted in which the movement of play-ers within the esports title Counter Strike Global Offen-sive was tracked and analyzed [21]. Results only showedtwo pieces of research within the field of DSS and esportscombined. One research looked at the development of acommentator support system as well as a player evalua-tion system for StarCraft 2: Wings of Liberty [3]. Onlyone study discussed a DSS concerning Rocket League.

Lastly, results show a total of five pieces of research whicheither are about Rocket League or use Rocket League asa test platform. Firstly, there has been a social study onthe impact of agency in cooperative multiplayer games [19]where Rocket League was one of the test platforms. Otherstudies where Rocket League was used as a test platformwere a study on challenges for casting esport titles [8] aswell as a study on training and performance of severalesports titles [14]. More specific research has also beendone on Rocket League regarding artificial intelligence andthe possibilities of a complete AI team or a team consistingof both humans as well as AI to compete in Rocket League[20].

The last research on Rocket League is closest related tothe research described in the paper. This research looks atmining behavioural patterns to model player skills [13]. Inthis research, an identifier for certain skills was developedand tested on replays. This identifier could see when anadvanced mechanics was pulled off and resulted in a goal.The conclusion of this study showed that the data thatcan be obtained with this identifier is perfect for playerprofiling and thus for analyzing your opponents. It cantherefore be a great fit for a decision support system tosee what are the strengths and weaknesses of certain teamsand adapt the teams own strategy accordingly.

3. METHODOLOGY & APPROACHTo answer the research questions different methodologieswere applied in the current research. In the next section,these research methods and their proposed outcomes willbe discussed

3.1 Decisions within Rocket LeagueThe first research question concerns gathering informa-tion about the decisions that need to be made in RocketLeague. The second research question concerns knowinghow these decisions are made. To find out this informa-tion, an interview with the Rocket League coach of EsportsTeam Twente (ETT) was held. He is not only a coach forthis team, but also for several other teams which play in in-ternational high-level tournaments. During the interview,a player of one of these teams was also present to get moreinsight into what decisions are made by players and whatdecisions are made by coaches. During the interview, thefollowing topics were discussed. Firstly, the preparationof the match was discussed. This included their prepara-tion cycle, where the focus lies in replay analysis and howthe strategy is determined for the next series. Secondly,

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the process during a series was discussed. This part wasabout changes made during a series and how effective cer-tain plays are. Thirdly, their analyses after a series werediscussed. Lastly, the team composition was discussed andhow the team composition alters the strategy of a team.The interview was recorded to be able to analyze the an-swers that were given afterwards. How the answers wereanalysed is described in 3.3.1.

3.2 Available data from the gameThe third research question concerns retrieving relevantdata from the game. Therefore, an interview was con-ducted with a data analyst from ETT who specializes inRocket League analysis. He developed his own libraryin Python to work with the data obtained from replaysand has already done multiple analysis projects on RocketLeague including on positioning. During the interview,the following topics were discussed. Firstly, some of hisearlier work was discussed to get an idea of what he al-ready accomplished. Next, different possible parts of theDSS, like for example positional mistakes on offence anddefence and whether or not it was the right decision tochallenge the ball, were discussed to find out whether ornot this could be extracted from the game. Furthermore,the type of data that could be used for all possible partswas also discussed throughout the interview. Lastly, someexamples of his work were shown and discussed to see howeverything comes together in visual form. The interviewwas recorded to be able to analyze the answers that weregiven afterwards. How the answers were analysed is de-scribed in 3.3.1.

3.3 Creation and evaluation of the concep-tual framework

3.3.1 Framework developmentAfter the interviews, the results from research questionsone, two and three were used to create the framework.First, all interviews were transcribed. Next, categorieswere distinguished in the results from research questionsone and two. Then, the results from research questionthree were also categorised in the same categories. Fi-nally, this categorisation led to three different parts to beincluded in the framework. A statistical overview of play-ers and team, positional mistakes and challenges. For cre-ating the first part, the statistical website ballchasing.com[5] was used. For the description of parts two and three,the answers from the data analyst were used.

3.3.2 EvaluationFor the evaluation of the framework, the coach of ETTwas asked once again, as well as the captain and a playerof the Rocket League team from Delft Student EsportsAssociation (DSEA). The latter two have been playing to-gether for a long time and are also playing in high-leveltournaments just like the teams coached by the coach ofETT. During the evaluation, the three different parts ofthe framework were discussed. These different parts area statistical overview of players and team, positional mis-takes and challenges. For each topic, the goal was to findout whether it would be useful to have this in a systemand what could be improved. Lastly, they were asked ifanything was missing from the proposed solution.

4. RESULTSIn this section, the results obtained from the different partsof my research will be discussed. In the first part, theresults for research question 1 and 2 will be discussed asthey were obtained from the same interview. In the second

part, the results for research question 3 will be discussedbased on the interview with the data analyst. The thirdpart covers the explanation of the framework based uponthe results from the first two parts. The last part will beabout the evaluation of the framework.

4.1 Decisions within Rocket League4.1.1 Preparation

In preparation for a match, the team watches replays andplays practice matches against other teams. During replayanalysis, the coach will focus on the positions of his teamand on the decisions that they make during the game.For example, if a player goes towards the ball while theyshould not have, the coach will tell them. Furthermore,if a player is too far forward or too far to one side, thecoach will also mention this. If a goal is scored againstthe team, the coach tries to point out what went wrongand how this could have been prevented. Players focusmore on their own decision. Their decisions are based ontwo main things, instinct and what is going on on thefield. The players look at where all the players are on thefield and where the ball is and instinctively decide whereto move their car. Even the slightest change in positionfrom a single player can change this decision. When theywatch a replay, they look at their own decisions that theymade and look for any flaws and wrongdoings. The play-ers try to remember why something did not work to trysomething different in a similar situation next time. Forexample, if a player notices he is always late to the ball onthe left wall because he does not take the optimal path,he can adjust his decision in the next game by either tak-ing a better path or by rotating back. The replays thatare analysed are often from recent matches, as these cangive the players a lot of insights into what is going wrongat the moment, but sometimes these are also some olderreplays if they lost their previous match against this oppo-nent to see why they lost last time. When watching theseolder replays, the coach will look also look for weaknessesin the opponents playstyle to exploit. For example, usingthe website ballchasing.com [5], the amount of boost usedper second of each player can be seen. When this is higherthan average, it means that this team uses a lot of boost.Therefore, it can be useful to actively try to steal moreboost to make it more difficult for the opponent. It canbe concluded that for preparation, teams mainly focus ontheir own mistakes which are identified by looking at re-plays, but they can also focus on weaknesses from otherplayers.

4.1.2 During the seriesDuring a series of games against a team, the coach againfocuses on positional mistakes and decisions from playersto go for the ball similarly as is done during the prepara-tion. The players are not as much involved in the processduring the series, as they need to focus more on playingthe game itself. Next to positional mistakes, the coachtries to look for weaknesses in the opponent’s playstyle toexploit. During the series itself, there is no statistical in-formation that the coach uses to find these mistakes andweaknesses. The coach watches along with the match andtells the players what he notices. A coach often has a lotof his own experience and can therefore point out theseweaknesses. For example, the only resource to managein the game is boost, some teams use more boost thanothers. Therefore, if the coach notices that a team usesa lot of boost, he will tell the players to steal the oppo-nents boost more often to slow them down. The playerswill then adapt their decisions by looking for more oppor-

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tunities to steal the boost from their opponent. Anotherexample could be that the coach notices that the opponentis very aggressive and moves up very far. The coach willthen instruct his players to look out for this. If a playernow notices this, he might decide to hit the ball downfieldimmediately instead of passing or dribbling to catch theopponent off guard. It can be concluded that during aseries, the coach again looks for mistakes that are beingmade and, if necessary, adjust their strategy according tohow their opponent plays.

4.1.3 After the seriesAfter a series is done, the cycle already starts over. Theyanalyse the replays of this series to prepare for the nextone. The focus is again on the positional mistakes thatwere made and the decisions to challenge the ball. Themost common mistake, and reason a goal is scored againsta team, is that all three players move up the pitch toofar. This is caused by the last player deciding to moveup as well, while he should have stayed further back. Theball is thrown over his head and the goal is wide open.Another common mistake is that players decide to go forthe ball while they have no chance of getting to it on timeor they come from the wrong angle. Both scenarios willmost likely result in the ball flying towards their side ofthe field or even towards the goal.

4.1.4 Team compositionUsually, a team has a certain playstyle that they try toplay in every match. Sometimes this playstyle is adjustedaccordingly depending on which team they play or it isadjusted halfway through the series. This initial strategyis all based on the players that are part of the team. Someplayers are more defensive while others are more offensive-oriented. A team consisting of three offensive players wholeave the backfield completely open will not work. There-fore, there needs to be a proper variety in players. Duringthe interview, the coach mentioned that the team from theplayer who was present was actually built around him. Hestated that usually when he is building a team, he firstlooks for a player who is capable of easily transitioningfrom defence to offence and who is capable of creatingscoring chances, as this is an important part of the game.He continued that the rest of the team would play a moresupportive role for this so-called star player. For the play-ers, this means that they have to adjust their decisionmaking accordingly. They know that this one player istheir key to transition, so if they have to choose betweendribbling themselves, or passing to this player, the latteris probably a better decision. For this star player, thismeans his decisions become more focused on getting theball to the other side of the field, preferably in front ofthe goal. This means he will be looking for the easiestway out of their own side. This can be either by passingor by dribbling it himself either on the ground or in theair. So, a team’s strategy is based on the players on theteam. A team is often built around a star player withgreat mechanical ability to transition the ball.

4.2 Available data from the gameWhile discussing the different topics mentioned in the sec-tion 3.2 with the data analyst it became clear that RocketLeague provides data that can be used for improving de-cision making during a match. This is because of the wayreplays are saved within Rocket League. Therefore, re-plays are a valuable source of data for a DSS.

4.2.1 ReplaysInstead of saving the replay as a recording, a replay is ac-

Figure 1. A heatmap 15 seconds before a goal was scoredon the top net

tually saved as a sequence of game states. Each game stateshows information about all the players, the ball and theboost pads. For players it shows their position in threedimensions, their velocity in three dimensions and theirrotation in three dimensions, but also their boost leveland whether or not they can still jump for a second time.Everything is measured in the measurement unit from thegame engine, called an unreal unit. So the velocity wouldbe measured in unreal unit per second. Mathonat, Bouli-caut and Kaytoue gave an example of part of the replayfile in their paper on recognizing skill plays [13] whichshows the positions of each actor (players and ball) on thefield: ”{{time : 1.256, Px : 578, Py : 5768, Pz : 2245, Pvx :22425, Pvy : 15848, Pvz : 354, Prx : 0, Pry : 589, Prz :23, Ballx : 5588, Bally : 789, Ballz : 22, . . . , }, {time :1.298, Px : 7578, Py : 254, Pz : 4678, Pvx : 511, Pvy : 555, Pvz :7863, Prx : 6365, Pry : 5665, Prz : 6, Ballx : 568, Bally :8663, Ballz : 665...}, {. . . }, . . . }, with Pi being a positionof the car in dimension i, Psi its velocity, Pri its rotation,etc.” So these replay files already contain all the infor-mation needed for analysing positional mistakes and chal-lenges.

4.2.2 Positional mistakes using heatmapsAs can be seen in the example piece of replay file, the po-sition of every player is logged multiple times a second.Since Rocket League is such a fast-paced game, playersare constantly on the move, because being idle on a spotoften leads to a goal for the other team. Therefore, it isnot possible to use the exact locations of players to see ifthey are out of position. Instead, a heatmap can be used.A heatmap shows spots on the map where a lot of activitytakes place. In figure 1 a heatmap is shown fifteen secondsbefore a goal is scored on the top goal. The red colour in-dicates a high amount of activity while blue is less activity.

A heatmap can also be used to identify positional mis-takes. Results from the interview show that you can usea heatmap to find the optimal positions in certain sit-uations. Using the information from the replay files, itis possible to gather only data from situations where theball is shot on target and a certain player has a certainamount of boost and momentum. From this data, oneheatmap from all the shots that are saved is made andone heatmap from all the shots that went in. These twoheatmaps can be subtracted from each other which shouldcreate a heatmap that shows one or two red spots wherethe defence could be positioned best. Now if a shot isnot saved, it is possible to compare the positioning of the

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player who attempted the save to the heatmap showingthe optimal position. This way a player will be able tosee where they should have positioned to have a betterchance at saving the ball. The same can be done on theoffensive side, by looking at optimal positioning as well.These heatmaps can be used very broadly, by looking atan overall positioning, or they can be used for very spe-cific situations. The first is more useful in offence, as this iswhere the instinctive decisions from players are also moreimportant. So an overall positioning is more relevant thanthe positioning in very specific situations. For example,the data analyst from ETT discovered, using heatmaps,the optimal positioning for every player when this teamis on offence. The latter is more useful on the defensiveside, as these can show the players what to do in thoseexact situations instead of giving a very broad position-ing. With this insight, players can change the positionaldecisions they make, as they have more information aboutwhere they should be.

4.2.3 Challenges on the ballIn Rocket League players constantly challenge the ball,since giving possession to the opponent is not desirable.Therefore, when a player is dribbling, a player from theother team will often challenge him as soon as possible.These challenges play a key part in the game as the re-sults impact who has possession and which team will playoffence and which team plays defence. Unfortunately, itis not possible to analyse what the best timing for thesechallenges are according to the data analyst. However,it is possible to give insights into how challenges are wonand what causes a challenge loss. If players know whatcauses a challenge win or loss, their decision to challengethe ball can be improved. For example, by knowing whyhe loses his challenges, he can decide to pull off the chal-lenge earlier to try and cause a bad hit by the other player.Or he might change the way he approaches the challengeby slightly turning his care more in a certain direction orhitting the ball higher or lower.

4.3 Framework developmentWith the results from sections 4.1 and 4.2, the frameworkcan be laid out. The results show potential for a DSS inthe form of three different parts. A statistical overview ofthe players and the team, insights into positional mistakesand insights into challenges. These parts foster the deci-sion making for strategies and training, improvements inpositional and rotational decisions and improvements inchallenge decisions respectively. The complete conceptualframework can be seen in figure 2. The following partsexplain each branch of the framework in more detail.

Figure 2. The conceptual framework for a Rocket LeagueDSS

4.3.1 Statistical overviewA statistics overview can be used by the players to seewhat their strengths and weaknesses are as players and asa team. Using the statistics that can be gathered fromballchasing.com [5] and the implementation of recognitionof skill shots from Mathonat, Boulicaut and Kaytoue [13]a list of statistics for each player and a list of statistic forthe team was created. The result can be seen in table 1.

Table 1. List of player statistics and team statisticsPlayer statistics Team statistics

Accuracy Win rateGoals/game Goals/gameShots/game Shots/game

Assists/game Assists/gameSaves/game Saves/game

Demos/game Demos/gameBumps/game Bumps/gameBoost/second Boost/second

Big/small boost pickups Big/small boost pickupsBig/small boost stolen Big/small boost stolen

Ceiling shots/game Goals conceded/gamePower shots/gameWave dash/gameMusty flick/gameFront flick/gameAir dribble/gameFlip reset/game

With these statistics, the players can look for aspects ofthe game they might have to work on individually and asa team. For example, if a player has high accuracy anda large number of goals per game, this player’s strengthis scoring, but maybe his saves per game are less high sohis defence is something he can work on. This is also ap-plicable to the team’s statistics. For example, if the goalsconceded per game is very high, their defence is somethingthey have to work on, or while their shots/game might bevery high, meaning they have a lot of pressure, but thegoals per game and accuracy are very low they have towork on their shooting. Furthermore, these statistics canbe used in the process of creating a team to see what play-ers are capable of and what roles they would fill best. Aplayer that scores a lot of Ceiling shots, flip resets andother mechanical shots is more offensive orientated, whilea player who has a lot of saves per game is more defensiveorientated. In a well-rounded team, you need solid play-ers on both sides of the field. Lastly, these statistics canalso be used for new teams to create their strategy, alsoby looking at the strengths and weaknesses of the playersin the same way as mentioned before.

4.3.2 Positional mistakesAs mentioned before, heatmaps can give an insight intopositional mistakes players make. Using the data fromthe replay files (see section 4.2.1) it is possible to createthese heatmaps. For this framework, it is proposed to usethese to highlight mistakes from players after each game.It will show where the player is positioned and where theplayer could have positioned better. On defence, this canbe useful when a goal is scored. On offence, this can beuseful when either a goal is not scored because the ballwas not reached in time, or when the ball is going overthe head of the last player causing the switch from offenceto defence. So the system will only point out mistakesand suggest possible positioned which, according to dataanalysis, is better suitable in that situation. In figure 3

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an example of how this indication of a mistake would looklike. The circled blue cross indicates the player that needsto make the save but fails. The green dot is the ball that ismoving towards the goal, indicated by the arrow. The twored spots on either side of the goal show the positions thatthe circled player should have roughly been at to save theball. Next to this, some information about boost levelsand speed of the ball and players can be shown, but thiswould merely give more insights into the type of situation,not in the optimal positioning. Now the player can usethis to alter his decision making when a similar situationoccurs the next time. He can now for example decide toturn back to goal a little bit earlier in order to be on eitherof the two red spots, giving him a better chance at savingthe ball.

Figure 3. An example of how the DSS will show the posi-tional mistake

4.3.3 ChallengesThe final part of the framework is about challenges. Theproposed system will show the challenges made during agame and shows what was the reason the player won orlost the challenge. This insight can then be used by playersto make a more informed decision when challenging theball. For example, sometimes it might be more favourableto ”lose” a challenge, as it may end up with a teammatethat way. When the players have more information aboutwhat happens during a challenge, if they rotate their carin a certain way, for example, they can use this in theirdecisions on how to approach the challenge.

4.4 EvaluationThe final research question considers how useful the pro-posed framework actually is. Overall, the participants ofthe evaluation were very positive regarding the proposedframework. They would definitively use such a systemonce it is available, also to know for sure what impactit will actually have, as this is difficult to predict from a

conceptual framework alone. For every part of the frame-work, aspects were discussed that were already up to paras well as improvements that can be made to get evenmore useful information out of the DSS. In the followingsections, these different on par aspects and improvementsare discussed for each part of the framework. Overall, theproposed parts were considered to be must-have featuresin a DSS. The improvements were mostly nice-to-have fea-ture which would make the system even better.

4.4.1 Statistical overviewThe proposed statistical overview as discussed in section4.3.1 could be very useful, according to the participants.Especially the overview of the different mechanical abili-ties of players can give proper insights into how effectiveplayers are with certain mechanics. The first nice-to-havefeature they would like to be added is to not only showstatistics of mechanical plays that resulted in a goal, buta more detailed overview. So these plays could be dividedinto different categories, for example, goals, outplays, as-sisted and conceded possession. Where the first exampleis an actual goal is scored with the mechanic, the secondone or more players from the opposite team are passed,the third resulted in an assist for the player who used themechanic and the last is a total fail and the ball endedup with a player of the opposing team. Furthermore, theywould like to be able to see trends in the gameplay of play-ers. This could be used for example in a players accuracyto see how well this player improves over the span of a year.A possibility could be to show the accuracy for each seriesover the last year, showing how well a player is shootingcurrently while this can also be compared to how well thisplayer was shooting half a year or a year ago. A differentkind of trend that would be very useful is statistical in-sights into what loses a game. For example, showing thepercentage of time in front of the ball can be an indicatorof why you lost a series. If this percentage is very high,so players are in front of the ball a lot, it means that thebackfield is more open which can cause more goals to bescored by the opposing team. When showing this over thetime span of a year, players and coaches can see whetheror not the team or a specific player is improving in thisregards. Some extra stats that can be included as well, arethe percentage of possession, insights into speed of playersand a percentage showing for each side how much timethe ball spends on that side. With these statistics, a teamcan see if they need to work on breaking out of their ownhalf, or maintaining possession or if they need to work onhow fast they are playing. Lastly, a must regarding thesestatistics is that the statistics need to be limited to com-petitive play only, so these should not include everydayranked play with friends. In conclusion, the participantsbelieve this statistical overview to be useful for decisionsregarding training, what the team or a player needs towork on, and strategies.

4.4.2 Positional mistakesAfter explaining the positional mistakes part of the frame-work to the participants, they were immediately enthusias-tic, but also a little sceptical. They were concerned aboutthe accuracy of such a system, as a small deviation in theposition of the ball or a single player, can alter the posi-tioning of everything else. They first needed to see such asystem before knowing if it is useful and if it could work.However, if it works the way it was explained they saw verymuch potential. They did wonder if it would be possible toalso look at the positioning on the backboard during thedefence as the backboard plays a key part in the game forcreating offensive chances. Therefore, insights into back-

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board positioning are would be greatly appreciated as well,however, it is not sure if this is possible, but it seems likely.Since heatmaps do not work in three-dimensional space,since there would be too much data with a value of zero.For the heatmaps already discussed in this paper whichcan be used for the positional mistakes, the height is nottaken into consideration. However, when looking at thebackboard it should be possible to only take the heightand width of the field into consideration and set the lengthas a fixed position at either the blue or the orange back-board. In conclusion, this part of the system would be avery useful addition for aiding players in improving theirpositional decisions, according to the participants.

4.4.3 ChallengesThe support in challenges as described in section 4.3.3could be very useful according to the participants. See-ing what wins or loses a challenge is valuable data forthem as players and coach to see how the challenges canbe improved. However, they did see a problem with thedefinition of a challenge win, as what is considered a wincan be very different given the circumstances. Therefore,a must-have feature is not to know why a challenge waswon or lost, but instead the system must give insights intowhat happens with the ball in general. So instead of givinginsights into why you win or lost the challenge, the systemshould give insight into what happens with the challengeand what would have happened if the player hits the ballslightly higher, or lower, or if the player rotates the carslightly more to one side. So instead of only looking atwhat happens in that specific challenge, also show whatwould happen if the challenge would be done differently.In conclusion, insights into what happens in a challengecan help players in deciding how to approach the ball.

4.4.4 ExtrasDuring the evaluation, the participants also came up withsome extra nice-to-have features which they would like tosee in a DSS. There were two main functions they wouldlike to see added to the system. The first is a way ofseeing the effectiveness of touches in general. As men-tioned before, it is possible to recognize mechanical playsand see what the result of those plays are, but the partic-ipants mentioned that not every player will go for thosemechanical plays. Therefore, it would be even more use-ful to get insights into certain trends in a player’s overalltouches. The system could for example identify that acertain player misses the ball very often when the ball isat a certain height, or when this player approaches theball from a certain angle. In this part of the system, itwould then also be useful to see what happens after aplayer hits the ball, so how effective was the touch. If thetouch ends up giving away possession to the enemy, thistouch was not very effective, but if the touch allows theteam to move onto offence, it was a very effective touch.This information again aids players and teams in decisionson training. Furthermore, this information can also helpplayers to decide what kind of touch to get in differentsituations. If they know a certain type of touch is difficultfor them, they can opt to go for a different touch.

The second added feature they would love to have is puttingthe communications next to the replay. During a matchthe players are in a voice call together, telling each otherevery piece of information about their position, boost leveland where they place the ball, but also if another playerhas the time or is being chased. Sometimes, information isnot communicated or not communicated well enough. If itis possible to put the communications next to the replay in

such a way that the calls that were made during the actualmatch are synced with the replay, a coach or the playerscould see what they did wrong in their communication.For example, it would be possible to notice that a playerdid not communicate something properly which was veryimportant information for his teammates. Especially thecoach mentioned that if he could see trends in certain in-formation not being communicated, the gameplay of histeam could be improved greatly since this missing pieceof information can lead to a different decision by someoneelse which can, in turn, be more favourable. It also givesthe coach to ask players why they decided not to com-municate something. This can then be used to improvecommunications in future matches.

5. CONCLUSIONTo conclude, there is definitively possibilities for decisionsupport systems in Rocket League which can help playersand teams improve. By interviewing a coach and playerinsights was gained into what kind of decisions are madeduring a match and what information they use for this.In preparation for matches, the coach and players watchreplays and try to find the mistakes the team makes. Themost important decisions, as mentioned by the partici-pants, are positional decisions and when to go for the ball.The most important aspect is the mistakes in these de-cisions, as this is where support can be given. Thesemistakes are identified by looking at the positioning ofthe team in replays when goals are scored against themor when their overall position during the match could bebetter. Next to those decisions, there are also decisionsmade when deciding upon a strategy. This is mostly de-pendent on the team composition. The team compositionis based on the capabilities of each player, which can beseen in the statistics available to the coach and players.Next, insights were gained into the information that canbe retrieved from the game. By interviewing an analyst,insight was gained into how replay files can be used to an-alyze positional mistakes and challenges. The positionalmistakes can be analyzed by using heatmaps, which can beused to identify positions on the map which give a playera better chance at saving the ball, or which show where aplayer could have positioned on offence to avoid the ballfrom going over his head. It turned out it was not possibleto gather insights into when to go for a ball, but instead,challenges could be analyzed to improve a players abilityto get a favourable outcome when they do decide to gofor the ball. Afterwards, the information from both inter-views was combined to develop a conceptual framework.This framework consists of three parts. First, a statisticaloverview of team and player statistics. This can be usedto see what a player or the team needs to work on, orwhat strengths of certain players are to develop a strat-egy. Second, support in positional mistakes can be given.By showing possible improvements to the players whenthey were unable to save a shot, the players can improvetheir positioning for the next match. The final part of theframework is about challenges. This part of the systemwould give insights into what causes a player to lose orwin a challenge. This can then be used to replicate wonchallenges and improve the player’s way of challenging theball overall. Lastly, this conceptual framework was evalu-ated. During the evaluation, each part of the frameworkwas discussed and improvements and concerns were men-tioned by the participants which resulted in some must-have functionalities and some nice-to-have features. Themust-have features included the proposed statistics in thestatistical overview and the proposed feature of showing

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positional improvements. However, for this last feature,there were some concerns regarding the accuracy of sucha system. Furthermore, a must-have feature would be togive insight into why the ball goes the direction it goes af-ter a challenge and how certain aspects, like the rotationof the car or how high the ball is hit, influence this direc-tion of the ball, instead of looking at what wins or losesa challenge. This is due to the fact that a win for oneperson can be considered a loss for another, as it dependson the intention behind the challenge. Next, there weresome extra nice-to-have features on top of the proposedfeatures. These included, extra statistics in the statisticaloverview and next to positional mistakes on the ground,the participants would like to get insights into backboardpositioning. Lastly, the participants also gave some nice-to-have functionalities outside of the proposed functional-ities. These included the effectiveness of touches overalland the ability to put their communications over a replayfile to see what is missing in their communication. Theparticipants of the evaluation definitively saw potential ina system such as explained in the framework, as this couldreduce the time they need to spend analyzing replays bya great portion. They believed it would be very useful tohave and they would definitively try it out if they got thechance.

6. DISCUSSIONGiven the circumstances, the final proposed framework isa significant step in the right direction. It definitively givesperspective for an actual DSS within Rocket League to bedeveloped. As can also be seen in the evaluation, the par-ticipants’ reactions were quite positive and they would usesuch a product once it is developed. However, room forimprovement is also there. In the following sections, somesmall improvements and shortcomings of this research willbe discussed, followed by the future work and some diffi-culties to overcome in this future work.

6.1 Improvements & shortcomingsThe biggest improvement can be achieved in the reliabilityof this research since there has only been conducted oneinterview with a coach to find out what kind of decisionsare made. Due to the fact that a very specific type of play-ers was required it was difficult to find these. The playersneeded to compete at a high enough level that they alsoprepared for their matches by looking at replays. Teamsthat just played a lot together and then join tournamentsfor fun were not considered as they could not give insightsinto a preparation cycle for higher-level teams. Therefore,the research could have been more reliable by interview-ing more than one coach. However, the one interview thatwas conducted was with a coach from multiple teams whohad a lot of knowledge on the subject, so this interviewstill gave plenty of insights into the preparation of teamsand the decisions they make. Furthermore, there was alsoa player from one of the teams present who could give in-sights into the decisions that players make and what thedifference is between their decisions and those made by thecoach. Overall, there was enough information to create aproper framework, but the information could have beenverified with more interviewees.

Furthermore, even though the participants of the evalua-tion were positive regarding the framework, they did men-tion that they could not predict how useful this systemwould actually be until they have used it. They had highexpectations, but this can only be known for sure once asystem is developed and tested.

6.2 Continuation for this researchSince the result of this research is a framework, the nextstep would be to actually develop the DSS. Some addi-tional research could be conducted on additions to the DSSfirst, but implementation is the biggest prospect. With theideas proposed in this paper, some difficulties immediatelyarise when implementation would start. The biggest issueis the fact that there is no proper way of working with thereplay data. Even though everything is stored properly inthe file, this is all stored in a format which can not be usedfor data analysis. The data analyst from ETT who wasinterviewed has created his own library to transform theraw binary game data into Pythonic data. This library isnot yet available to the public, so there is no proper wayof analysing the data for any developer willing to take thisup. The analyst was busy documenting everything so itis probable that this will be made available soon, but itis not there yet. Furthermore, when developing the posi-tional mistakes implementation, an aspect that came upduring the evaluation was the positioning on the back-board of the goal. As heatmaps are nearly impossible tocreate in three dimensions as a lot of the data points willhave a value of zero, the idea came up to fold open theentire field and take the positioning on the wall and back-board as if the players were positioned on an extension ofthe actual field. It is not guaranteed that this can work,but there is potential.

6.3 Future workIn a more general sense, the methods applied in this re-search are also suitable to be applied in other research re-garding decision support. These methods can be appliedto research on DSS within esports or on DSS in general.Conducting interviews to see what kind of decisions needto be made and what information is used for them can beused for any research regarding DSS. Furthermore, inter-views are also a suitable way of gather information on whatdata is available. For data analysis, some general trendscan also be identified. First data needs to be gatheredand relevant data needs to be filtered out. Next, the dataneeds to be transformed into a useable format. Finally,the actual analysis can be performed.

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