Money Puck: The Effectiveness of Statistical Analysis in Building an NHL Team

The 2013 Collective Bargaining Agreement in the National Hockey League limits contracts offered to free agents in terms of length and variance in yearly salary. These changes have made finding undervalued free agents even more important to teams’ general managers. The purpose of this study is to evaluate players and teams with both traditional and advanced metrics to determine how players are valued in comparison to their impact on their team’s performance. A team’s winning percentage is hypothesized to be a function of shooting percentage and save percentage, as well as proxies for puck possession time, such as shots on goal per game, shots against per game, blocked shots, missed shots, and face-off percentage. It is also hypothesized that players with higher puck possession attributes will impact a team’s winning percentage to a greater extent than those with lower metrics, and so should be a key factor in determining how general managers use available salary money to improve their team. Based on data from NHL.com and stats.hockeyanalysis.com, we estimate team performance of all 30 NHL teams for each of the six previous seasons of play (2007-2013) as a function of puck possession proxies. We find that puck possession proxies significantly impact a team’s winning percentage and that free agents with higher performance metrics have a significantly greater impact on team performance as much as several less expensive players in cases that a team lacks depth

An Econometric Analysis of Collegiate Player Performance to Create a Model for Forecasting Contributions to Team Success

At the conclusion of each basketball season, each conference selects 1st, 2nd, and sometimes 3rd all-conference teams based on player performance for that season. Often, these all-conference teams reflect biases in the media rather than evaluations based on player performance alone. The baseball statistic Wins Above Replacement, WAR, is useful in quantifying the impact of each player through the number of wins contributed to his respective team by comparing each player to a designated replacement level player. This statistic can also be applied to basketball analysis to perform a similar function as in baseball, despite a vastly different formulation. However, the WAR statistic has limitations in its player analysis in basketball, particularly through failing to include defensive statistics and having no established definition of a replacement player. In this paper, I utilize the Wins Above Replacement statistic along with other key statistics, particularly in the defensive aspect of the game, to create an econometric model to better determine which players contributed the most to their team’s success. These statistics determine which players should be selected to the all-conference team at the end of the collegiate basketball season

Projecting the Future Individual Contributions of NHL Players

Professional sports are a multibillion-dollar industry with millions of people invested in the outcomes of games and seasons. Owners, management, and fans sit on the edges of their seats wondering what will happen next. Lots of work has been done forecasting success at the team level across a variety of sports, but player level predictions are less common. Predictive work related to the NHL is even rarer. This thesis explores the ability to predict NHL player performance in a given season using publicly available information via statistical learning methods. Data featured in the analysis includes play-by-play and shift information, box score statistics, a variety of composite and catch-all statistics, injury information, and player biographical information. Data was compiled and analyzed to find meaningful relationships between past and future performance. The results of the analysis found the most predictive values in . season’s raw numbers can be supplemented with more information to improve predictive power

Estimating an NBA player's impact on his team's chances of winning

Traditional NBA player evaluation metrics are based on scoring differential or some pace-adjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game still in question (e.g. tie score with five minutes left) in exactly the same way as they treat performances with the outcome virtually decided (e.g. when one team leads by 30 points with one minute left). Because they ignore the context in which players perform, these measures can result in misleading estimates of how players help their teams win. We instead use a win probability framework for evaluating the impact NBA players have on their teams' chances of winning. We propose a Bayesian linear regression model to estimate an individual player's impact, after controlling for the other players on the court. We introduce several posterior summaries to derive rank-orderings of players within their team and across the league. This allows us to identify highly paid players with low impact relative to their teammates, as well as players whose high impact is not captured by existing metrics.Comment: To appear in the Journal of Quantitative Analysis of Spor

Testing The Utility Of The Pythagorean Expectation Formula On Division One College Football: An Examination And Comparison To The Morey Model

The Pythagorean Expectation Formula was the impetus for the statistical revolution of Major League Baseball. The formula, introduced by Bill James, has been used by baseball statisticians to forecast the number of wins a team should have given the total number of runs scored versus those allowed. Since its use in baseball, the formula has been applied to the NFL, the NBA, and the NHL. This study examines if the original formula, as introduced by James, can be fitted for and used to retrospectively predict winning percentage for NCAA Division I football teams. Residual analysis helps the authors conclude that the Pythagorean Expectation Formula provides an accurate prediction of the expected winning percentage for a team given its scoring offense and scoring defense production. Given the formulas predictive ability, coaches and athletic directors can now examine the achievement of their teams and make decisions about filling potential vacancies at college football programs

Measuring the Technical Efficiency of Hockey Players: Empirical Evidence from Czech Hockey Competition

Ice hockey is a very popular sport in the Czech Republic. Nowadays, hockey player efficiency analysis is a useful tool that helps sports managers with player selection, team composition and team performance evaluation. The literature offers only a limited number of scientific studies that deal with the evaluation of the efficiency of hockey players or clubs. The aim of this research is to use data envelopment analysis to help Czech hockey clubs, managers and coaches to evaluate the efficiency of their players. This research evaluates the technical efficiency of Czech hockey players using three data envelopment analysis models, ranks the best players based on their super-efficiency scores, and then tries to uncover the main sources of player inefficiency. The models are empirically applied to players playing in the Tipsport extraliga in the 2021/22 season. The evaluation used in this paper attempts to incorporate greater objectivity into decision making and thus may be an important step in developing a systematic methodology for evaluating hockey players

Understanding how random chance affects the outcome of an ice hockey game

Estimating the effect of random chance (’luck’) has long been a question of particular interest in various team sports. In this thesis, we aim to determine the role of luck in a single icehockey game by building a model to predict the outcome based on the course of events in a game. The obtained prediction accuracy should also to some extent reveal the effect of random chance. Using the course of events from over 10,000 games, we train feedforward and convolutional neural networks to predict the outcome and final goal differential, which has been proposed as a more informative proxy for outcome. Interestingly, we are not able to obtain distinctively higher accuracy than previous studies, which have focused on predicting the outcome with infomation available before the game. The results suggest that there might exist an upper bound for prediction accuracy even if we knew ’everything’ that went on in a game. This further implies that random chance could affect the outcome of a game, although assessing this is difficult, as we do not have a good quantitative metric for luck in the case of single ice hockey game prediction

How Often Does the Best Team Win? A Unified Approach to Understanding Randomness in North American Sport

Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport comparisons has been impeded by the difficulty of translating luck from one sport to another. In this manuscript, we develop Bayesian state-space models using betting market data that can be uniformly applied across sporting organizations to better understand the role of randomness in game outcomes. These models can be used to extract estimates of team strength, the between-season, within-season, and game-to-game variability of team strengths, as well each team’s home advantage. We implement our approach across a decade of play in each of the National Football League (NFL), National Hockey League (NHL), National Basketball Association (NBA), and Major League Baseball (MLB), finding that the NBA demonstrates both the largest dispersion in talent and the largest home advantage, while the NHL and MLB stand out for their relative randomness in game outcomes. We conclude by proposing new metrics for judging competitiveness across sports leagues, both within the regular season and using traditional postseason tournament formats. Although we focus on sports, we discuss a number of other situations in which our generalizable models might be usefully applied