GithubHelp home page GithubHelp logo

neilpaine538 / nhl-player-and-team-ratings Goto Github PK

View Code? Open in Web Editor NEW
20.0 3.0 2.0 396.04 MB

Goals Above Replacement and Elo Rating data for NHL history.

License: MIT License

hockey nhl goals-above-replacement hockey-database nhl-data nhl-database elo elo-rating nhl-elo-ratings nhl-elo nhl-gar-data nhl-history hockey-elo hockey-elo-ratings hockey-data hockey-databank nhl-databank nhl-playoff-odds

nhl-player-and-team-ratings's Introduction

Historical NHL Data

This repo contains my updating NHL player stats, including Position-Relative Game Score above average metrics -- see NHL-GameScore-WAR-data.csv -- Modified Point Shares (MPS) and Goals Above Replacement (GAR) -- see modified-point-shares.csv. Each metric has its own strengths and weaknesses:

GAR and MPS are good for explaining "past-looking" value. MPS has Hockey-Reference's Point Shares as its base, but redistributes league value so that 60 percent goes to forwards, 30 percent to defensemen and 10 percent to goalies (which is in line with the share of league salaries paid to each position group). It also further allocates value such that 40 percent goes to forwards' offense and 10 percent to defensemen's offense (adding up to 50 percent, offense being half the game), while 20 percent goes to forwards' defense, 20 percent to defensemen's defense and 10 percent to goalies (again, adding up to 50 percent on the defense/goaltending side of the game). That gives MPS an internal consistency while still maintaining the simplicity of adding up to roughly match a team's point total for the season (under the old system where the average team had 1 point per game, to avoid MPS inflation for more recent loser-point-marred seasons). Over the 2009-2021 seasons, the game-weighted sum of players' MPS/GP has a 0.99 correlation with their team's goals-per-game differential, which is higher than the equivalent for Evolving Hockey's Goals Above Replacement (0.93), my Game Score-based WAR metric (0.89) or Position-Relative Game Score Above Average per game (0.82).

GAR is identical to MPS in its distributed values, but is denominated in goals (rather than standings points) relative to the replacement level (rather than an absolute floor of zero points). In that sense, it is equivalent to Tom Awad's old Goals Versus Threshold metric, which stopped being publicly available years ago. GAR correlates to past and present team performance with almost exactly the same accuracy as MPS -- if not better -- but differs in the fact that goaltenders have a wider range of possible performance: The best goalies in a season will usually be the most valuable players in the league (not true with MPS) and the worst goalies will almost always be the league's least valuable players. For this reason, if you prefer a concept of goaltending value that regresses outlier performances more to the mean, MPS is the preferrable choice, while GAR is better if you view goalies as "deserving" more of the credit for their observed goaltending performance. (Although, again, this difference has little effect on either metric's ability to "predict" a team's goal differential within a season or in the following season.

That said, MPS performs less well when predicting future team success or failure. The game-weighted sum of players' MPS/GP from the previous season has just a 0.52 correlation with team goal differential per game from the current season, which underscores the need for a metric that focuses more on evaluating persistent performance over time. My research has found that a better metric for this purpose is per-game Game Score -- a statistic originally developed by Dom Luszczyzyn (now of The Athletic) -- with an adjustment for the league's positional average each season. The game-weighted sum of players' Position-Relative Game Score Above Average from the previous season has a correlation of 0.57 with the team's current-season goal differential per-game, which is superior to not just MPS (0.52) but also Game Score WAR (0.55), old-school original Point Shares (0.51) and Evolving-Hockey GAR (0.49). To my mind, this makes Position-Relative Game Score Above Average a good forward-looking complement to a purer value metric like MPS, particularly considering Position-Relative Game Score Above Average contains extra data on 1st vs. 2nd assists, face-offs, shot-blocking, on-ice Corsi and more.

Historical Elo Data and Playoff Odds

This repo used to contain ratings and projections for each team. The Elo model and forecast has been revamped and will now be hosted at FiveThirtyEight.

nhl-player-and-team-ratings's People

Contributors

neil-paine-1 avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

Forkers

japietz typically

nhl-player-and-team-ratings's Issues

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.