Passing and Pressure Metrics in Ice Hockey
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Published in the Artificial Intelligence for Sports Analytics (AISA) Workshop at IJCAI ’21 Montreal, QC, Canada August 21-23, 2021 Passing and Pressure Metrics in Ice Hockey David Radke1 , Daniel Radke2 , Tim Brecht1 and Alex Pawelczyk1 1David R. Cheriton School of Computer Science, University of Waterloo 2Department of Computer Science, Universitat¨ des Saarlandes [email protected], [email protected], brecht, alex.pawelczyk @uwaterloo.ca { } Abstract common than goals, but the 2019-2020 average of only 1.7 SOG per-player per-game also fails to accurately represent Advancements in player tracking technology and performance throughout a game, especially a player’s non- analytics have revolutionized how professional offensive play. In some cases, not including the recorded sports are managed, played, and even consumed. time-on-ice, there may be no indication from current statis- However, these tracking systems have mainly failed tics that a player actually played in a game despite potentially to translate to ice rinks, leaving ice hockey to col- bringing value to their team in other ways such as defense lect relatively coarse and offensively-biased data or play making. This may lead to misaligned incentives be- for player statistics. As a result, current artificial in- tween players trying to improve their statistics and their team telligence (AI) models for player valuation and op- relying on them to play a less offensive role. timal group formation for ice hockey are limited to comparing with offensively-biased data, reinforc- Success in ice hockey relies on possession of the puck and ing these biases in models. The National Hockey passing between teammates. Despite hundreds of passes ev- League (NHL) used a new tracking system in the ery game, no current ice hockey statistic records players’ 2020 Stanley Cup Playoffs, and we design a suite of passing effectiveness. As a result, existing artificial intelli- novel analytics that add new insights into the per- gence (AI) models for player valuation and optimal pairing, formance and behaviour of players, groups of play- or coalition formation have no choice but to compare their ers, and teams. We calculate metrics for passing results or use models that rely on existing offensively-biased lanes and player movement, passing effectiveness, statistics based around goals, assists, and SOG. These statis- and pressure which have not previously been possi- tics which overlook valuable position players who may not ble to compute in hockey. We expect our analytics generate as much offense [Ljung et al., 2018; Luo et al., to support future and more accurate performance 2020]. Utilizing biased data is a known problem in AI and and coalition models, and to be of direct interest to has dramatic impact when deployed. New hockey analytics the ice hockey and AI communities. that capture other critical aspects of the game are necessary for a deeper understanding of player value and improved AI models. 1 Introduction Insights from modern analytics across other sports, many Data analytics in professional sports has revolutionized how of which depend on high-resolution player tracking sys- and what data is collected and used, and is expected to reach tems [Amin, 2018; Baysal and Sahin, 2016; Manafifard et revenues of $4.5 billion by 2024 [BusinessWire, 2018]. In the al., 2017], are used to improve player performance, ros- National Hockey League (NHL), a team can roster 18 skating ter management, and various AI applications [Lewis, 2003; players and two goalies per-game, with five skaters and one Lindstrom¨ et al., 2020]. Unfortunately, the tracking methods goalie in the game at a time. Players interchange during play used in other sports have failed to transfer to ice hockey due with fatigue and according to the situation, meaning play- to technical challenges caused by the fast pace, small puck, ers are often involved in different scenarios all over the ice. white-coloured ice, and other hardware challenges [Walkters However, current hockey statistics consider mainly offensive et al., 2020; Vats et al., 2020; Douglas and Kennedy, 2019]. events like goals, assists, and shots-on-goal (SOG), which oc- This has led to hockey metrics which infer performance based cur relatively infrequently. The 2019-2020 NHL season saw on the aggregation of sparse events instead of tracking actual an average of 3.02 goals scored per-team per-game [NHL, behaviour [Hammond, 2011]. Recent technological advance- 2020]; thus, traditional statistics would suggest a maximum ments have led to the implementation and testing of a new of nine players (45% of the roster) receive a point for a goal or player and puck tracking system during the 2020 NHL Stan- assist (assuming the maximum of two assists for every goal). ley Cup Playoffs for the first time in real gameplay. While In addition, a single player accumulating multiple points on tracking itself is a technological accomplishment, the data multiple goals means the number of possible players that re- does not provide general managers, coaches, players, agents, ceive statistical updates begins to decrease. SOG are more fans, or AI models with meaningful information. We propose a suite of novel player analytics in hockey de- develop advanced analytics for team and individual perfor- signed to extract and aggregate insightful information from mance. Several studies leverage player and ball tracking data the raw tracking data. To the best of our knowledge, we are to analyze passing, quantifying a pass’s disruption to defen- the first to propose new ice hockey analytics using the re- sive formations [Goes et al., 2019] and the number of out- cently collected tracking data. Our goal is to represent situ- played opponents [Steiner et al., 2019]. Other work, with ational player strengths, weaknesses, and trends throughout sufficient tracking data, has used deep learning to evaluate the the ice surface to offer new perspectives into player perfor- behaviour of players in soccer by predicting the performance mance and value. We make the following contributions: of “league average” players and teams in simulated scenar- • We develop new hockey analytics designed to improve ios [Meerhoff et al., 2019; Lindstrom¨ et al., 2020]. [Kempe et our understanding of passing, player movement, trends, al., 2018] evaluate tracking data to show that scoring events and how players respond under pressure. and player performance typically highlighted by human an- notators can be determined and assessed automatically using • We propose a new passing lane metric and a metric to de- ridge regression. Similarly in [Fernandez,´ 2019], tracking termine the degree to which potential pass receivers are data, deep learning-based models and stochastic processes are open (or available). These metrics can can be adapted to used to calculate the likelihood that a soccer possession ends other sports that involve passing. in a goal and assign value to passes. • We utilize real tracking data from the 2020 Stanley Cup Playoffs to compute our new metrics and discuss initial 3.2 Analytical Insights in Ice Hockey insights that our analytics provide. Existing research in ice hockey player valuation and an- The aim of our work is two fold. First, we urge the AI and alytics has mainly utilized event data, typically using the hockey communities to recognize the need for higher reso- SPORTLOGiQ NHL dataset which records the location, in- lution hockey analytics that provide insight into how players volved players, and time of events, such as shots, hits, and perform apart from sparse offensive events. Second, we hope passes [Liu et al., 2018; Silva et al., 2018; Yu et al., 2019]. our work provides new initial benchmarks for player valua- These have been utilized to extract representations of play- tion and coalition formation AI models in hockey, helping to ers’ abilities through clustering and Markov Decision Pro- provide a more balanced view of player performance. Es- cesses [Schulte and Zhao, 2017], and deep learning [Liu et tablishing new metrics are necessary for AI to mature in ice al., 2020; Mehrasa et al., 2018; Guo et al., 2020]. These hockey; thus, we view our contributions as the first steps in models typically benchmark their results with the offensively- the new frontier of ice hockey analytics supported by track- biased currently recorded statistics due to a lack of recorded ing data and towards a large corpus of future work involving ground truth statistics based around other aspects of the game. game theory [Yan et al., 2020], multi-team systems [Zaccaro Current advanced statistics used by the NHL, such as Corsi et al., 2020], and cooperative AI [Dafoe et al., 2021]. and Fenwick, have been shown to be good performance indi- cators for teams [Macdonald, 2012]. Corsi is a plus/minus 2 Background rating of shot attempts (shots for minus shots against) dur- Ice hockey in the NHL is played on an ice surface that is ing even strength play. Shot attempts include blocked shots, 200 feet long and 85 feet wide (imperial units are used in missed shots, and SOG, which have traditionally been tracked the NHL). A game consists of two teams competing for three by human annotators [Hammond, 2011]. Fenwick is similar 20-minute periods. Overtime rules vary between the regular to Corsi, but omits blocked shots since this could be a player’s season and playoffs. A maximum of six players of any com- positive skill. Positive Corsi and Fenwick scores imply the bination of defense, forwards, and one goalie are allowed on player’s team produced more offense than their opponent dur- the ice at any time for each team. Penalties remove a player ing the even strength time the player was on the ice. from the ice surface for two or five minutes depending on the severity of the infraction, so that the penalized team temporar- 4 Dataset ily has fewer players in the game.