LA Kings: Three players who exceeded expected production metrics

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LA Kings

LA Kings (Mandatory Credit: Robert Hanashiro-USA TODAY Sports)

Diving into the numbers, these three LA Kings players exceeded their expected production metrics. Two will be restricted free agents.

In today’s NHL, players are evaluated beyond just goals, assists, and their plus-minus ratings. Sources like Natural Stat Trick, Top Down Hockey, and Money Puck, among others, have utilizing predictive modeling to convey advanced metrics that truly define whether a player is meeting their expected production rates.

While CorsiFor percentages are helpful in determining puck possession and therefore scoring chance, expected-goals-for percentage (xGF%) has become a popular advanced analytic to measure an individual’s performance.

Individual expected goals is a lot more useful of a stat than individual corsi is. This is because it shows how many goals a single player was expected to score himself.

Expected goals for percentage is once again excellent at showing how good a player is overall at the game. 50% is the threshold for over a full season that should be used to measure. When a player is above 50% they are helping their team generate higher quality chances than they give up when they are on the ice. When a team is above 50% they, simply, as a whole generated higher quality chances than their opponent. – LWOS/Elieff

As a team, the LA Kings accounted for a 45.3 xGF%, the second-worst mark in the league only ahead of the Chicago Blackhawks. Over the last three seasons, the Kings have committed to the rebuild, accumulating a combined 48.6 xGF%.

For reference, when Los Angeles won the Stanley Cup at the end of the 13-14 season, the Kings had a 55.9 xGF% during the regular season, the second-best in the league. As the organization’s prospects continue to gain more experience combined with some meaningful offseason acquisitions this summer, the team’s xGF% should begin trending in the right direction.

Here were three players who exceeded the predictive model’s numbers.

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