## 1 Introduction  
In team handball, the individual match performance of each player is essential for winning games; however, measuring this individual match performance is a complex problem in sport science. In team handball training and match practice, the subjective coach's judgment is often used to analyze individual performance. However, the method of subjective rating of performance outcomes could contain a certain outcome bias that can negatively influence differing players' specific roles, varying players' involvements and the effects of their actions on the game, thus falsifying the data obtained and having negative effects on the scientific quality of the data [1, 2]. It is known that coaches typically focus their attention on the critical aspects of the game, so most peripheral game actions are either ignored or remembered poorly [3, 4]. There is always a danger of concentrating on useless or irrelevant performance indicators, which can lead to the formulation of trivial training recommendations or the derivation of insane training goals. The feedback derived from this is often inadequate for players and therefore not effective in terms of optimal training and performance control. For this reason, providing non-biased and objective feedback to players and coaches seems to be more efficient in order to be successful [5]. Depending on the major purposes of the match analysis (e.g., time and movement analysis, tactical analysis, technical analysis, collection of statistical data), the concept of performance indicators in the context of game analysis has proven its worth so far [6; 7; 8], whereas the integration of all dimensions of performance is important [9]. Relevant performance indicators are typically based on valid measurement methods. There have been numerous studies in team ball sports using different approaches to match analysis, and a great variety of performance indicators and calculation methods [10]. In team handball, match analysis can be classified into four different scopes: (i) descriptive analysis [11; 12; 13], (ii) comparative analysis, (defensive analysis, playing position, game results) [14; 15; 16], (iii) contextual variables (game location, time outs) [17; 18; 19; 20], and (iv) predictive analysis (goal scoring, game results, goal keeper substitution, players' exclusion or additional field player) [21; 22; 23; 24; 25; 26; 27]. Most of these studies used game-related tournament statistics provided by the European Handball Federation (EHF) and International Handball Federation (IHF), or their own observational approaches, to measure performance indicators. Those studies using the match statistics provided by the EHF and IHF lose control of their own data, as the EHF and IHF do not provide detailed information on how they determine these statistics. Consequently, in scientific studies, self-performed match analysis should be preferred instead of match statistics provided by the EHF and IHF [28]. However, to the best of our knowledge, there are no scientific studies (i) which analyze individual player performances, exploiting the full range of situational technical actions in offensive and defensive sequences during play, (ii) which create an individual score system for an individual player based on the positive and negative effects of the single game actions made, and (iii) which mathematically take the factors of time and goal difference of a single game action into account, based on the current scientific state of research. To counteract this lack of knowledge, the PlayerScore was developed to determine the individual player performance of each field player during the 26 th Men's Handball World Championship in Denmark and Germany in 2019 [29]. The aim of the PlayerScore was to calculate only one significant variable regarding player performance, instead of numerous variables, as is common in a match analysis. The authors developed a ranking procedure based on the recorded actions per player in the game.