MEASURING SINGLE GAME PRODUCTIVITY
The underlying idea behind it is a great one: based on everything a player did during a game, how good was his performance.
The stats I used are goals, primary assists, secondary assists, shots on goal, blocked shots, penalty differential, faceoffs, 5-on-5 corsi differential, 5-on-5 goal differential.
Player Game Score = (0.75 * G) + (0.7 * A1) + (0.55 * A2) + (0.075 * SOG) + (0.05 * BLK) + (0.15 * PD) – (0.15 * PT) + (0.01 * FOW) – (0.01 * FOL) + (0.05 * CF) – (0.05 * CA) + (0.15 * GF) – (0.15* GA)
Goalie Game Score = (-0.75 * GA) + (0.1 * SV)
Game Score for goalies is just to place them on a level playing field with players and to see whether a goalie stole a game, lost a game, or if he was just solid. There are better ways to evaluate how he played, this just puts it in relation with the rest of his team.
I opted for this route instead for simplicity and to cater more towards the individual stats versus the on-ice ones (using Cane’s weights, corsi ended up explaining more of Game Score at the game level than individual points did). Realistically, there’s many ways to go about this and I don’t doubt there’s a better way, this is simply mine.
Overall, I’d say the stat does a reasonably good job at the season level which bodes well for its ability at the game level. It’s also fairly repeatable from year-to-year, although considering the stats that go into it, that’s probably not a huge surprise (the only stat included that can suffer from a lot of random year-to-year variation is 5-on-5 goal difference).
Weaknesses:
Context is key and Game Score is completely devoid of it.
Teammates, competition, score, pace, zone starts, playing time, special teams time, team effects – none of it is accounted for. All those things will affect a player’s stats in differing ways, but Game Score ignores it.
At the full season level something more complicated is worth the added accuracy (relative score/zone/venue adjustments) and could serve reasonably well as a relatively simple all-in-one stat; but at the game level simplicity is perhaps more important.
Game Score is very much driven by offensive contributions which means some defensive players will be very underrated.
That’s Game Score in a nutshell. It’s not a perfect stat – it’s meant to be a rough measure after-all – but I do think it works well for its intended purpose and is effective at what it does. It’s meant to answer “who had the best game” by adding proper perspective to a combination of a player’s total contributions and into an easily understood all-in-one stat.
Consistency, streakiness, clutchiness; whether they’re real or random is a question a stat like Game Score can help answer and one that we perhaps couldn’t answer properly beforehand.
GAME SCORE
Game Score is a catch-all statistic created by Dom Luszczyszyn of The Athletic that quantifies the total value of a player’s productivity from a single game.
Game Score incorporates the following stats in an attempt to quantify the overall performance of a player: goals, primary assists, secondary assists, shots on goal, blocked shots, penalty differential, faceoffs, 5v5 Corsi differential and 5v5 goal differential. Obviously not all stats carry the same importance, so Dom assigned weights to each of the metrics to come up with the following formula for Game Score:
Skater Game Score = (0.75 * G) + (0.7 * A1) + (0.55 * A2) + (0.075 * SOG) + (0.05 * BLK) + (0.15 * PD) – (0.15 * PT) + (0.01 * FOW) – (0.01 * FOL) + (0.05 * CF) – (0.05 * CA) + (0.15 * GF) – (0.15* GA)
Goalie Game Score = (-0.75 * GA) + (0.1 * SV)
Game Score can be used in raw counting terms, or it can be depicted as a per-60 minutes of ice time stat.
It can also be used as a season long indicator of performance. Game Score quantifies a player’s total productivity across each individual game, so in theory it should serve as a strong barometer for who has had the best statistical season.
And if we want to adjust for uneven playing time or games played, we can also view the Game Score as a rate statistic at both 5v5 and across all situations.
That said, Game Score still is a valuable tool for helping to analyze players that belong in the discussion for the Hart.
Dom also created another stat based off of Game Score, which he calls Game Score Value Added (GSVA). GSVA is a three-year version of Game Score that is translated to its value in wins. In other words, GSVA is similar to WAR, GAR and wPAR, in that it communicates player value in terms of wins, as opposed to points or any other production metric. One particularly useful application of GSVA is Dom’s use of the model in the pre-season to project individual player performance, and then aggregating these performances by team to project team performance.
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