finding outstanding performance in handball players based on … · 2020. 4. 20. · keywords:...

Finding outstanding performance in handballplayers based on statistical analysis

Francisco P. Romero1[0000−0002−6560−8645] and EusebioAngulo2,3[0000−0002−2659−3129]

1 Dpt. of Information Systems and Technologies, Univ. of Castilla La Mancha, Spain2 Dpt. of Mathematics, University of Castilla La Mancha, Spain

3 Pozuelo de Calatrava Handball Club

Abstract. This paper investigates how to use several machine learningtechniques to improve the process to select the most valuable players.First, a group decision-making process is carried out to select those in-dicators which better represents the player performance in the match.This work aggregates experts judgments, presented as linguistic labels,into a group opinion with a measure of the group consensus. Then, ourproposal focuses on providing useful visualizations of the values of theseindicators and an outlier detection process which can improve the de-tection of outstanding performance in matches. Our preliminary studycomprehends two very short tournaments, the 2019 Men EHF Final Fourand the 2019 Spanish Women Promotion Playoff. The obtained resultsare promising and suggest continuing and extending this line of research.

Keywords: Handball Player Valuation · Group Decision Making · Out-lier Detection.

1 Introduction

Over the past few years, we have been witnessing an increasing interest in eval-uating the performance of sports teams. Any attempt to assess the process andsuccess of a sports team requires some objective method for valuing players.Sports statistics offer a reasonable and recognizable approach to this issue. Nev-ertheless, the problem is which statistics should be used. There are some popularmeasures like goals or assists, but they fail to capture some essential character-istics.

The evaluation of a player’s performance is one of the most critical aspectsof the application of advanced statistics in sport. It seeks to achieve a moreobjective view of productivity, efficiency, effectiveness, and value of the compo-nents of the game, for example, at the individual level. In many sports, thereis much literature related to the valuation of player performance. One of thefirst attempts to measure the productivity of a player in a team is shown in[1]. In that work, the author proposed an econometric model tailored explicitlyto basketball (NBA players). However, rating players in sports teams is a real

2 Francisco P. Romero et al.

challenge; different sports have different characteristics, and player have differentresponsibilities (offense vs. defense).

Some different approaches have been proposed to rate players for specifictasks, for example, in [2], a Bayesian regression model is put forward to obtainan estimation of the performance of any soccer player in each season with ref-erence to the number of goals scored; and a soccer player ranking system basedcompletely on the value of the passes completed is presented in [3]. The finalvalue is computed based on the relationship between pass locations and shotopportunities generated.

On the other hand, a commonly used approach is to assign a value to aset of actions considered to be of interest and to reward the player taking themwith the associated value [4]. One of the most popular productivity metrics is theplus-minus (PM) rating model. The PM is represented by the difference betweentheir team’s total scoring versus their opponent’s when the player is in the game.Its main advantage is the possibility to capture implicitly a significant amountof information about the actions in the game without an explicitly capture ofthis information. The PM rating and its variations has been used extensively inice-hockey [5] and basketball [6] and soccer [7]. However, there are some doubtsabout its usefulness when it comes to comparing individual players. It is becausethe rating is established by many factors beyond the control of the player beingevaluated. Also, in sports where player-specific functions (NFL or handball)stand out, the effectiveness of this metric is limited and requires attention beyondthis work.

One of the most severe difficulties in solving the problem of outstanding per-formance detection is the sparseness of available data. The use of ArchetypeAnalysis (AA) is proposed to solve this problem[8]. Performance profiles basedon archetypal athletes are not based on averages performances but extreme per-formances , i.e., archetypal athletes (outstandingpositive and/or negativeper-formers) are computed, and performers are related to these archetypal athletes.An alternative, called Archetypoid analysis (ADA), is presented in [9]. ADA isbased on the representation of each observation in a dataset as a mixture ofactual observations, which are a pure type or archetypoids. Unlike archetypeanalysis, archetypoids are real observations, not a combination of samples. ADAhas been extended to deal with sparse functional data and when asymmetricrelations are present in data. ADA extensions have been successfully applied tobasketball and soccer data to identify extreme athletes and teams [10].

Handball experts like coaches and sports journalists have long estimatedplayer performance. In most of the cases, these valuations have proved theirusefulness in assessing performance during the past few years. However, data-driven approaches to estimate this performance have not yet caught in profes-sional handball. Although the academic and professional attention to quanti-tative analysis of data has exponentially grown past years, there is no muchwork on evaluating a handball player in a game by game scenario [11]. A studyabout the official statistics from an international handball championship andits relation to team performance is presented in [12]. Their results show that

Finding outstanding performance in handball 3

the variables shots saved by the opposing goalkeeper, technical fouls, steals, andgoalkeeper’s saves are the key indicators of team performance.

There also some works focused on physical and anthropometric variables. In[13] an analysis of the performance of Spanish handball players according to theirlevel is presented. Its mains results revealed that anthropometric characteristicsof the top-elite players differ from those of the elite players, mainly in their upperlimbs. On the other hand, [14] analyze different performance factors of jumpthrows in team handball. They show how the measured variables can revealessential performance differences between leagues and playing positions and,taken together, explain most of the variance in ball release height.

In this paper, we look again at earlier attempts to introduce handball playervaluation methods and show how the application of fuzzy logic, in the processof indicators selection, and machine learning techniques, to analyze the perfor-mance results; improve the expert capabilities to evaluate the handball playersperformance. For this purpose, we combine the ideas of [15], and [11], and pro-pose a new method to deal with the detection of outstanding performance inhandball matches.

As a result, we propose a methodology as a solution for the player valuationand outstanding performance detection in handball matches. In this proposal,we define different stages such as the information gathering, manual or fromonline statistics; the definition of a set of relevant performance indicators by theaggregation of the opinion of a panel of experts, and finally, the visualization andoutlier analysis using unsupervised learning techniques. Our final objective is toassist in the decision to chose the most valuable player or to establish a specificman-to-man defense method to players with an outstanding performance duringthe game.

To verify the feasibility of the proposal, we present a preliminary experimen-tal evaluation of the partial and complete application of the proposal to two veryshort tournaments, the 2019 Men EHF Final Four, and the 2019 Women SpanishPromotion Playoff. Our results provide relevant insights into what features maydistinguish an outstanding performance in a very short tournament from therest of the players. Our work distinguishes itself from other work on handballplayer valuation by working with a set of experts to select those most suitableindicators to represent the player performance.

The remainder of the paper is structured as follows. Section 2 presents theproposed methodology, including a graphical abstract of it. In Section 3, wedescribe the indicators selection process using fuzzy aggregation operators tohandle a collection of expert opinions. Section 4 presents the obtained results ofour preliminary experiments, including the first case of study, the 2019 Men EHFFinal Four, based on online statistics; and the second case of research, the 2019Spanish Women Promotion Playoff based on the manual gathering of multipleindicators. Finally, we outline conclusions and future work in Section 5.


2 Methodology

Figure 1 shows a graphical abstract of the proposed methodology.

1. Information Gathering : The necessary information on the statistics of eachmatch is compiled either from the official statistics of the game or throughdirect visualization of it.

2. Indicator Selection: We used an expert-based approach to select those fea-tures that are important for player performance. Then, the experts mustachieve a high level of consensus concerning their preference. Then, the se-lection process obtains the final solution according to the preferences givenby the experts. Finally, a fuzzy aggregation function is applied, and we com-pute the results of the importance of each indicator.

3. 2D/3D Projection: We use Principal Component Analysis (PCA) as a dis-play method to reveal the hidden structure of the indicators dataset[16]. PCAallows us to transform the original set of variables into another set of newnon-correlated variables called the principal components set. PCA is com-monly proposed as a complementary tool to interpret the results obtainedby statistical analysis because the visualization does not allow a completeinterpretation of its components [17].

4. Outlier Detection: The present work proposes to use techniques such as LOF(Local Outlier Factor) [18], an algorithm based on the density, explicitlycreated to detect outliers and results in a value of an object p representingthe degree to which p is an outlier. Thus, we can identify those players whoseperformance is different from others.

5. Expert Interpretation: Handball expert’s interpretation of obtained results iscrucial, which enable to characterize the used automatic method and theirresults more concretely.

Fig. 1. Proposal Graphical Abstract.


3 Indicators Selection

Performance indicators selection becomes complicated when applied to subjec-tive or vague areas of a domain like players valuation. This section presents theapplication of a group decision-making process to obtain a list of indicators toevaluate handball player performance and its relevancy evaluation according toa set of experts.

A collection of basic indicators is selected as a baseline of the decision processto know which indicators have to be considered to model the player/performance..Table 1 shown those indicators that can be obtained from online statistics ofhandball games.

Table 1. Basic Indicators Set.

Indicator Description Indicator Description

7MGoals 7-metre Goals 7mMS 7-metre Missed Shots6MGoals 6-metre Goals 6mMS 6-metre Missed Shots9MGoals 9-metre Goals 6mMS 9-metre Missed ShotsWingGoals Wing Goals 6mMS Wing Missed ShotsFBGoals Fast Break Goals FBMS FastBreak Missed ShotsAdditional Positive actions (steals, etc.) Punish Exclusions and YC

GKSaves Goalkeeper Saves (total) GKRGoals Goalkeeper Goals Received

This basic set is extended using the criteria of a National Handball Coach andthe indicators used for the evaluation of junior players by the Spanish HandballFederation. This extension achieves a higher level of granularity than the BasicIndicators Set Table 2 shows the selected collection of indicators distinguishingthe positive from the negative.

Table 2. Extended Indicators Set.

Positive Negative

9mGoal 9mMissedWingGoal WingMissed6mGoal 6mMissedFBGoal, 7mGoal FBMissed, 7mMissedAssist, Steal, Block Turnover HandlingReceived 7m Fouls Committed 7m Fouls, ExclusionsDefensive Actions Uncompleted Defensive Withdrawal

Goalkeeper Saves (total) Goalkeeper Goals Received

The obtained indicators set is given to the 15 experts (Spanish HandballCoaches). Every expert, depending on his own experiences, must express anopinion regarding the chosen indicators. In this case, each indicator is evaluatedby linguistic labels belonging to the following label set: High (H), Medium (M)


and Low (L). Each linguistic label is formally represented as a fuzzy set [19] toaggregate these opinions later.

Once the expert opinions are known, a fuzzy aggregation operator is appliedto aggregate the set of linguistic labels representing the evaluations of all in-dicators. An Ordered Weighted Averaging (OWA) [20] operator of dimensionn is defined as a mapping F : Rn → R which has an associated weightingvector W = [w1, w2, . . . , wn] in which wj ∈ [0, 1] and

∑nj=1 wj = 1 and where

F (a1, a2, . . . , an) =∑n

j=1 wjbi, with bj being the j th largest of the collection ofthe aggregated objects ai.

The OWA Operator are used to aggregate the crisp/real values. Inspiredby Yager’s OWA operator, and Zadeh’s extension principle, the type-1 OWAoperator was defined in [21]. This operator provide us with a new technique fordirectly aggregating opinions expressed linguistically and represented as (type-1)fuzzy sets.

In this case, the aggregated output for each opinion will be the fuzzy setrepresenting how relevant each indicator would be to measure the player per-formance. Then, the fuzzy aggregation function is applied, Table 3 shows theresults of the importance of each chosen indicator.

Table 3. Indicators Relevance.

Valuation Indicators

Positive

High 9mGoal, FBGoal, Steals, Assists, Goalkeeper SavesMedium Defensive Actions, 7mGoal, 6mGoal, Received 7m Fouls

Low WingGoal,Block

Negative

High Exclusions,7mMissed, 6mMissed, FBMissed,Uncompleted Defensive Withdrawal, Goalkeeper Goals Received

Medium Committed 7m Fouls, 9mMissedLow WingMissed, Attack Foul, Turnover Handling

These results allow us to define a weighting scheme to evaluate the perfor-mance of a handball player in a specific match. To compute this weight we first,we assign a value to each action using its statistical impact in the results, then weaggregate this value for each group of indicators (High positive, High negative,and so on) and, finally we round the final value to a 0.05 (see Eq 1).

Pscore =

0.95 ∗ {High positive} − 0.80 ∗ {High negative}0.85 ∗ {Medium positive} − 0.70 ∗ {Medium negative}0.65{Low positive} − 0.60{Low negative}

(1)


4 Results

Our preliminary study comprehends two very short tournaments, the 2019 MenEHF Final Four and the 2019 Spanish Women Promotion Playoff.

4.1 2019 Men EHF Final Four

The first approach to validate we propose to analyze the player performance ofthe last Men EHF Final Four held in Cologne past 1st and 2nd of June. Wegather the information from the Online Statistics1 provided by the EHF after averification process with the visualization of every game.

We generated four datasets, one for each match as well as one for all thematches together. The list of considered indicators for this analysis is the BasicIndicators Set previously seen in Table 1. These data-sets are analyzed accordingto the proposed methodology by using Python and Scikit-learn library2.

The first step is to obtain a good visualization of the five multidimensionaldatasets. For this purpose, Principal Component Analysis (PCA) is used toproject the data captured to a lower dimensional space (2D or 3D) to obtaina good visualization of the data with mininum loss. Previous to the applica-tion of PCA each dataset is normalized using the StandardScaler, this processstandardize the values by removing the mean and scaling to unit variance.

After the visualization process, we hypothesize that players with outstandingperformance will be outliers concerning the performance of the rest of the play-ers. In this way, using a outlier detection technique we can identify the playerswith the most extreme performance, i.e, those whose dissimilarity is for posi-tive reasons will be the candidates to be the most valuable. For this purpose,we use the Local Outlier Factor (LOF) algorithm , which is an unsupervisedanomaly detection method which considers as outliers the samples that have asubstantially lower density than their neighbors. The anomaly score of each sam-ple measures depends on how isolated the object is concerning the surroundingneighborhood.

Figure 2 shows the 2D representation obtained by PCA in addition to theelements marked as outlier by LOF and its valuation using the basic indicators(Table 1). It can be seen that except in the final and in the complete competitionthe ”Player of the Match” (SF1: Nenadic, SF2: Kristopan, 3y4: Mem, FI/Full:Karacic) is identified as an outlier with a very high anomaly value.

However, there are several shortcomings to deal with. First, the statisticsinformation provided online has poor quality. Some of the primary indicatorsare not correctly computed (for example, Assists) and others and not even con-sidered. As a result, we can detect outstanding performance, but not a wholepicture of the player performance in the tournament. The reason why the MostValuable Player of the competition (Igor Karacic) are not correctly detected isthat the contribution of different players based on assists, defensive actions, etc.,

1 http://www.ehfcl.com/en/men/matches2 http://scikitlearn.org


is not accurately reflected in the statistics. The inclusion of a more granular setof indicators could be a solution for these problems as can be seen in the follow-ing section. Another factor to consider when you chose a most valuable playeris the momentum when the player makes these valuable actions. For example,Karacic scores three goals without error in the second part of the first match,the last one with 2 minutes remainder; and in the final he scores when after 6-1partial against.

Fig. 2. 2D representation of the player performance in the 2019 Men EHF Final Four

4.2 2019 Spanish Women Promotion Playoff

Data was collected from the 2019 Spanish Women Promotion Playoff to LigaGuerreras Iberdrola. A single round-robin tournament in which the four teams(Logrono Sporting La Rioja, Handbol Sant Quirze, Salud Tenerife and VinoDona Berenguela Bolanos) classified since the previous qualifiers fought for onlyone place that gave access to participate next season in the top category ofSpanish women’s handball3.

3 See http://shorturl.at/dgsLT


The statistical information, composed by the full set of indicators shown inTable 3, has been captured manually by direct viewing of matches. Subsequently,the aggregation formula presented in Eq. 1 has been used to obtain the valuationof each player in each game as can be seen in Table 4. Each ”Player of the Match”has been highlighted in bold.

Table 4. Top Players Complete Valuation.

Player ValuationPlayer Team Match1 Match2 Match3 Match4 Match5 Match6 Acc.

A. Gonzalez (GK) Tenerife - 6.5 - -3.5 - -2.5 0.50S. Oliveira Tenerife - 4.7 - 1.0 - -0.1 5.60A. Chelaru Tenerife - 4.1 - 7.8 - 4.8 16.70A. Isic Tenerife - 5.3 - -1.2 - 10.2 14.10A. Cioca Tenerife - -0.7 - 6.0 - 6.3 11.60K. Bojicic Tenerife - 1.2 - -0.5 - 7.1 7.8N. Gracia Bolanos - 1.8 2.8 - 0.9 - 5.5O. Scalona Bolanos - 1.4 3.7 - 3.3 - 8.4G. B. Menendez Bolanos - 0.6 2.9 - 9.7 - 13.2M. D. Aranda Bolanos - -1.6 4.0 - 6.9 - 9.3M. Mera (GK) S.Quirze 14.5 - 0.5 - - -34.5 -19.05C. Robles S.Quirze 2.3 - 3.7 - - 1.0 7.00E. Torregrosa S.Quirze 2.2 - -2.9 - - -0.1 -0.8G. las Carreras S.Quirze 4.5 - 3.5 - - -0.6 7.5P. Plana S.Quirze 0.2 - 4.6 - - 0.0 4.8D. de Sousa La Rioja 2.9 - - 9.2 17.2 - 29.3C. Lopez La Rioja 3.8 - - 4.0 3.1 - 10.9S. Edreira La Rioja 1.8 - - 7.0 5.3 - 14.1C. Rivas La Rioja -2.0 - - 4.0 0.9 - 2.9

The valuations of the matches are adjusted to what happened in the games.In five out six games, the chosen ”Player of the Match” by the coaches andthe sports journalists are the players with more points in the valuation system.In the match that does not coincide, the chosen player is the second that hasmore points. Moreover, the detection of an outstanding player during a gameusing this valuation method could generate changes in the defensive system (onedefender practices man-to-man defense, the others practice a zone system).

On the other hand, using the proposed methodology to visualize the resultsand identify outstanding performance, we identify five out of six Players of thematch using their high anomaly score. In the remaining two is in the top - 3 mostsignificant anomaly values. There are also some high anomaly scores related toplayers with poor performance (for example Goalkeepers).

The main concern to solve in this approach is related to the information-gathering step. The process to capture the necessary information to calculate thefull set of indicators with a minimal loss of data is very complicated (between 2and 3 hours per match).


5 Conclusions and Future Work

In this work, we have presented a methodology to evaluate handball player per-formance and detect outstanding performances in matches. Visualization, outlieranalysis, and group decision-making techniques provide us a collection of toolsto solve some of the issues to deal to evaluate handball players performance.

Preliminary experiments have been carried out to verify the feasibility of theproposal. Two very short tournaments (2019 Men EHF Final Four and 2019Spanish Women Promotion Playoff) have been intensely studied to extract andanalyze the gather performance indicators. Considering our results, we can con-clude that the proposal is relevant and provide useful insights about the playerperformances in different matches.

These preliminary results are promising but minimal. As future work, weneed to extend the study with a more massive amount of data. Due to thedifficulty to manually obtain and label massive datasets, we will explore the useof Deep Learning techniques (like in [22]) to get play-by-play handball dataset.Using this dataset could be possible to make the information gathering processmore reliable and easier applying a machine learning-based automatic featureselection method. Thus, the data collection could be limited to the selectedfeature, making the information gathering process more reliable and manageable.

Moreover, there are some possibilities of improvement in the process to assignweights to the performance features. Although the application of OWA operatorsoffers us promising results, their combination with results provided by machinelearning techniques could improve the accuracy of our results. The use of machinelearning techniques to automate the process of weighting performance featureshas been tested successfully in several works to evaluate soccer players [23] [24].

Finally, some limitations of our proposal should be noted and studied. First,the provided player valuations reward little to the great defenders. It would begood to establish criteria that allow valuing better the collective defensive gameand the players with positive actions in this sense. Furthermore, it is very com-plicated to consider so many aspects of the game in real-time; then, it would berecommendable to simplify the process of capturing data from each match, i.e.,to find a balance between information quality and gathering effort. Third, theuse of other techniques such as Kernel or Incremental PCA for data transforma-tion and Isolation Forests to outlier detection could generate improvements inthe application of the methodology.

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