Conference Paper

Pass Evaluation in Women's Olympic Ice Hockey

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Conference Paper
Full-text available
The implementation of a puck and player tracking (PPT) system in the National Hockey League (NHL) provides significant opportunities to utilize high-resolution spatial and temporal data for advanced hockey analytics. In this paper, we develop a technique to classify pass types in the tracking data as either Direct, 1-bank, or Rim passes. We also address two fundamental limitations of our previous model for passing lanes by modeling 1-bank indirect passes and the expected movement of players. We implement our pass classification and extended passing lane models and analyze 198 games of NHL tracking data from the 2021-2022 regular season. We study the types of completed passes and introduce a new passing metric that shows about 59% of completed 1-bank passes have an equal or more open indirect passing lane than the direct lane. Furthermore, we show that our expected movement addition reduces receiver location error in over 94% of completed passes.
Article
Full-text available
Passes are by far football’s (soccer) most frequent event, yet surprisingly little meaningful research has been devoted to quantify them. With the increase in availability of so-called positional data, describing the positioning of players and ball at every moment of the game, our work aims to determine the difficulty of every pass by calculating its success probability based on its surrounding circumstances. As most experts will agree, not all passes are of equal difficulty, however, most traditional metrics count them as such. With our work we can quantify how well players can execute passes, assess their risk profile, and even compute completion probabilities for hypothetical passes by combining physical and machine learning models. Our model uses the first 0.4 seconds of a ball trajectory and the movement vectors of all players to predict the intended target of a pass with an accuracy of $$93.0\%$$ 93.0 % for successful and $$72.0\%$$ 72.0 % for unsuccessful passes much higher than any previously published work. Our extreme gradient boosting model can then quantify the likelihood of a successful pass completion towards the identified target with an area under the curve (AUC) of $$93.4\%$$ 93.4 % . Finally, we discuss several potential applications, like player scouting or evaluating pass decisions.
Article
Full-text available
Introduction: Performance assessment in professional soccer often focusses on notational assessment like assists or pass accuracy. However, rather than statistics, performance is more about making the best possible tactical decision, in the context of aplayer’s positional role and the available options at the time. With the current paper, we aim to construct an improved model for the assessment of pass risk and reward across different positional roles, and validate that model by studying differences in decision-making between players with different positional roles. Methods: To achieve our aim, we collected position tracking data from an entire season of Dutch Eredivisie matches, containing 286.151 passes of 336 players. From that data, we derived several features on risk and reward, both for the pass that has been played, as well as for the pass options that were available at the time of passing. Results: Our findings indicate that we could adequately model risk and reward, outperforming previously published models, and that there were large differences in decision-making between players with different positional roles. Discussion: Our model can be used to assess player performance based on what could have happened, rather than solely based on what did happen in amatch.
Presentation
Full-text available
This presentation introduces a new way to quantify the control that various players exert on different regions of the pitch using spatio-temporal player tracking data from association football.
Conference Paper
Full-text available
Abstract: Many models have been constructed to quantify the quality of shots in soccer. In this paper, we evaluate the quality of off-ball positioning, preceding shots, that could lead to goals. For example, consider a tall unmarked center forward positioned at the far post during a corner kick. Sometimes the cross comes in and the center forward heads it in effortlessly, other times the cross flies over his head. Another example is of a winger, played onside, while making a run in past the defensive line. Sometimes the through-ball arrives; other times the winger must break off their run because a teammate has failed to deliver a timely pass. In both circumstances, the attacking player has created an opportunity even if they never received the ball. In this paper, we construct a probabilistic physics-based model that uses spatio-temporal player tracking data to quantify such off-ball scoring opportunities (OBSO). This model can be used to highlight which, if any, players are likely to score at any point during the match and where on the pitch their scoring is likely to come from. We show how this model can be used in three key ways: 1) to identify and analyze important opportunities during a match 2) to assist opposition analysis by highlighting the regions of the pitch where specific players or teams are more likely to create off-ball scoring opportunities 3) to automate talent identification by finding the players across an entire league that are most proficient at creating off-ball scoring opportunities.
Article
Full-text available
Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.
Conference Paper
Full-text available
In this paper, we present a model for ball control in soccer based on the concepts of how long it takes a player to reach the ball (time-to-intercept) and how long it takes a player to control the ball (time-to-control). We use this model to quantify the likelihood that a given pass will succeed we determine the free parameters of the model using tracking and event data from the 2015-2016 Premier League season. On a reserved test set, the model correctly predicts the receiving team with an accuracy of 81% and the specific receiving player with an accuracy of 68%. Though based on simple mathematical concepts, various phenomena are emergent such as the effect of pressure on receiving a pass. Using the pass probability model, we derive a number of innovative new metrics around passing that can be used to quantify the value of passes and the skill of receivers and defenders. Computed per-team over a 38-game dataset, these metrics are found to correlate strongly with league standing at the end of the season. We believe that this model and derived metrics will be useful for both post-match analysis and player scouting. Lastly, we apply the approach used to compute passing probabilities to calculate a pitch control function that can be used to quantify and visualize regions of the pitch controlled by each team.
Article
Full-text available
This paper develops a simulator for matches in the National Hockey League with the intent of assessing strategies for pulling the goaltender. Aspects of the approach that are novel include breaking the game down into ner and more realistic situations, introducing the eect of penalties and including the home-ice advantage. Parameter estimates used in the simulator are obtained through the analysis of an extensive data set using constrained Bayesian estimation via Markov chain methods. Some surprising strategies are obtained which do not appear to be used in practice.
Article
When I watch films, TV shows or sports I often find myself thinking about the physics of the situation. Here in North America, we’re approaching the end of ice hockey season.
Article
A hockey player's plus-minus measures the difference between goals scored by and against that player's team while the player was on the ice. This measures only a marginal effect, failing to account for the influence of the others he is playing with and against. A better approach would be to jointly model the effects of all players, and any other confounding information, in order to infer a partial effect for this individual: his influence on the box score regardless of who else is on the ice. This chapter describes and illustrates a simple algorithm for recovering such partial effects. There are two main ingredients. First, we provide a logistic regression model that can predict which team has scored a given goal as a function of who was on the ice, what teams were playing, and details of the game situation (e.g. full-strength or power-play). Since the resulting model is so high dimensional that standard maximum likelihood estimation techniques fail, our second ingredient is a scheme for regularized estimation. This adds a penalty to the objective that favors parsimonious models and stabilizes estimation. Such techniques have proven useful in fields from genetics to finance over the past two decades, and have demonstrated an impressive ability to gracefully handle large and highly imbalanced data sets. The latest software packages accompanying this new methodology -- which exploit parallel computing environments, sparse matrices, and other features of modern data structures -- are widely available and make it straightforward for interested analysts to explore their own models of player contribution.
Article
A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. We investigate the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. Furthermore, the learned classifiers are analysed to determine if there is a relationship between the complexity of the algorithm that computed the predictor variable and the importance of the variable to the classifier. Experimental results show that we are able to produce a classifier with 85.8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers. Finally, we show that the inter-rater agreement on pass classification between the machine classifier and a human observer is of similar magnitude to the agreement between two observers.
Article
This is a note regarding the excellent article “Misapplications Reviews: Pulling the Goalie Revisited” by Morrison and Wheat [Morrison, D. G., R. D. Wheat. 1986. Misapplications reviews: Pulling the Goalie revisited. Interfaces 16(6) 28–34.]. Being a hockey fan, I thoroughly enjoyed the article. I think it is an interesting example of modeling and optimization, well suited for teaching in a NHL city. However, I teach in Edmonton, the recently acclaimed hockey capital of North America, and my students felt uneasy about the first assumption made by the authors, “when both teams have their goalies on the ice, the two teams are of equal ability and each scores goals at a constant rate of L per minute.”
Article
Not Available Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
Expected passes: Determining the difficulty of a pass in football (soccer) using spatio-temporal data
  • Gabriel Anzer
  • Pascal Bauer
  • Anzer Gabriel
Modelling the Collective Movement of Football Players. Master's thesis
  • Francisco José Peralta Alguacil
  • Peralta Alguacil Francisco José
Expected Completed Pass Model
  • Marc Richards
  • Richards Marc
SportMEDIA Technology tracking system technical information
  • Smt
  • SMT
Catapult-Sports. 2022. Catapult sports CLEARSKY local position system
  • Catapult-Sports
From Grapes and Prunes to Apples and Apples: Using Matched Methods to Estimate Optimal Zone Entry Decision-Making in the National Hockey League
  • Asmae Toumi
  • Michael Lopez